Managing a Process to Meet Demand

Ideally, the capacity of a process will be such that its output just matches demand. Excess capacity is wasteful and costly; too little capacity means dissatisfied customers and lost
page 14revenue. Having the right capacity requires having accurate forecasts of demand, the ability to translate forecasts into capacity requirements, and a process in place capable of meeting expected demand. Even so, process variation and demand variability can make the achievement of a match between process output and demand difficult. Therefore, to be effective, it is also necessary for managers to be able to deal with variation.

Process Variation

Variation occurs in all business processes. It can be due to variety or variability. For example, random variability is inherent in every process; it is always present. In addition, variation can occur as the result of deliberate management choices to offer customers variety.

There are four basic sources of variation:

1. The variety of goods or services being offered. The greater the variety of goods and services, the greater the variation in production or service requirements.

2. Structural variation in demand. These variations, which include trends and seasonal variations, are generally predictable. They are particularly important for capacity planning.

3. Random variation. This natural variability is present to some extent in all processes, as well as in demand for services and products, and it cannot generally be influenced by managers.

4. Assignable variation. These variations are caused by defective inputs, incorrect work methods, out-of-adjustment equipment, and so on. This type of variation can be reduced or eliminated by analysis and corrective action.

Variations can be disruptive to operations and supply chain processes, interfering with optimal functioning. Variations result in additional cost, delays and shortages, poor quality, and inefficient work systems. Poor quality and product shortages or service delays can lead to dissatisfied customers and can damage an organization’s reputation and image. It is not surprising, then, that the ability to deal with variability is absolutely necessary for managers.

Throughout this book, you will learn about some of the tools managers use to deal with variation. An important aspect of being able to deal with variation is to use metrics to describe it. Two widely used metrics are the
mean (average) and the
standard deviation. The standard deviation quantifies variation around the mean. The mean and standard deviation are used throughout this book in conjunction with variation. So, too, is the normal distribution. Because you will come across many examples of how the normal distribution is used, you may find the overview on working with the normal distribution in the appendix at the end of the book helpful.

Managing the Supply Chain to Achieve Schedule, Cost, and Quality Goals

Consider a bicycle factory. This might be primarily an
assembly operation: buying components such as frames, tires, wheels, gears, and other items from suppliers, and then assembling bicycles. The factory also might do some of the
fabrication work itself, forming frames and making the gears and chains, and it might buy mainly raw materials and a few parts and materials such as paint, nuts and bolts, and tires. Among the key management tasks in either case are scheduling production, deciding which components to make and which to buy, ordering parts and materials, deciding on the style of bicycle to produce and how many, purchasing new equipment to replace old or worn-out equipment, maintaining equipment, motivating workers, and ensuring that quality standards are met.

Obviously, an airline company and a bicycle factory are completely different types of operations. One is primarily a service operation, the other a producer of goods. Nonetheless, these two operations have much in common. Both involve scheduling activities, motivating employees, ordering and managing supplies, selecting and maintaining equipment, satisfying quality standards, and—above all—satisfying customers. Also, in both businesses, the success of the business depends on short- and long-term planning.

page 16 

A primary function of an operations manager is to guide the system by decision making. Certain decisions affect the
design of the system, and others affect the
operation of the system.

System design involves decisions that relate to system capacity, the geographic location of facilities, the arrangement of departments and the placement of equipment within physical structures, product and service planning, and the acquisition of equipment. These decisions usually, but not always, require long-term commitments. Moreover, they are typically
strategic decisions.
System operation involves management of personnel, inventory planning and control, scheduling, project management, and quality assurance. These are generally
tactical and
operational decisions. Feedback on these decisions involves
measurement and
control. In many instances, the operations manager is more involved in day-to-day operating decisions than with decisions relating to system design. However, the operations manager has a vital stake in system design because
system design essentially determines many of the parameters of system operation. For example, costs, space, capacities, and quality are directly affected by design decisions. Even though the operations manager is not responsible for making all design decisions, he or she can provide those decision makers with a wide range of information that will have a bearing on their decisions.

A number of other areas are part of, or support, the operations function. They include purchasing, industrial engineering, distribution, and maintenance.

Purchasing is responsible for the procurement of materials, supplies, and equipment. Close contact with operations is necessary to ensure correct quantities and timing of purchases. The purchasing department is often called on to evaluate vendors for quality, reliability, service, price, and ability to adjust to changing demand. Purchasing is also involved in receiving and inspecting the purchased goods.

Industrial engineering is often concerned with scheduling, performance standards, work methods, quality control, and material handling.

Distribution involves the shipping of goods to warehouses, retail outlets, or final customers.

Maintenance is responsible for general upkeep and the repair of equipment, the buildings and grounds, heating and air-conditioning, parking, removing toxic wastes, and perhaps security.

The operations manager is the key figure in the system. He or she has the ultimate responsibility for the creation of goods or provision of services.

page 17 

The kinds of jobs that operations managers oversee vary tremendously from organization to organization, largely because of the different products or services involved. Thus, managing a banking operation obviously requires a different kind of expertise than managing a steelmaking operation. However, in a very important respect, the
jobs are the same: They are both essentially
managerial. The same thing can be said for the job of any operations manager regardless of the kinds of goods or services being created.

The service sector and the manufacturing sector are both important to the economy. The service sector now accounts for more than 70 percent of jobs in the United States, and it is growing in other countries as well. Moreover, the number of people working in services is increasing, while the number of people working in manufacturing is not. The reason for the decline in manufacturing jobs is twofold: As the operations function in manufacturing companies finds more productive ways of producing goods, the companies are able to maintain or even increase their output using fewer workers. Furthermore, some manufacturing work has been
outsourced to more productive companies, many in other countries, that are able to produce goods at lower costs. Outsourcing and productivity will be discussed in more detail in this and other chapters.

Many of the concepts presented in this book apply equally to manufacturing and service. Consequently, whether your interest at this time is on manufacturing or on service, these concepts will be important, regardless of whether a manufacturing example or service example is used to illustrate the concept.

The Why Manufacturing Matters reading gives another reason for the importance of manufacturing jobs.

Models

A

model
is an abstraction of reality, a simplified representation of something. For example, a toy car is a model of a real automobile. It has many of the same visual features (shape, relative proportions, wheels) that make it suitable for the child’s learning and playing. But the toy does not have a real engine, it cannot transport people, and it does not weigh 3,000 pounds.

Other examples of models include automobile test tracks and crash tests; formulas, graphs, and charts; balance sheets and income statements; and financial ratios. Common statistical models include descriptive statistics such as the mean, median, mode, range, and standard deviation, as well as random sampling, the normal distribution, and regression equations.

Models are sometimes classified as physical, schematic, or mathematical.

Physical models look like their real-life counterparts. Examples include miniature cars, trucks, airplanes, toy animals and trains, and scale-model buildings. The advantage of these models is their visual correspondence with reality. 3-D printers (explained in
Chapter 6) are often used to prepare scale models.

Schematic models are more abstract than their physical counterparts; that is, they have less resemblance to the physical reality. Examples include graphs and charts, blueprints, pictures, and drawings. The advantage of schematic models is that they are often relatively simple to construct and change. Moreover, they have some degree of visual correspondence.

Mathematical models are the most abstract: They do not look at all like their real-life counterparts. Examples include numbers, formulas, and symbols. These models are usually the easiest to manipulate, and they are important forms of inputs for computers and calculators.

The variety of models in use is enormous. Nonetheless, all have certain common features: They are all decision-making aids and simplifications of more complex real-life phenomena. Real life involves an overwhelming amount of detail, much of which is irrelevant for any particular problem. Models omit unimportant details so that attention can be concentrated on the most important aspects of a situation.

page 19 

Because models play a significant role in operations management decision making, they are heavily integrated into the material of this text. For each model, try to learn (1) its purpose, (2) how it is used to generate results, (3) how these results are interpreted and used, and (4) what assumptions and limitations apply.

The last point is particularly important because virtually every model has an associated set of assumptions or conditions under which the model is valid. Failure to satisfy all of the assumptions will make the results suspect. Attempts to apply the results to a problem under such circumstances can lead to disastrous consequences.

Managers use models in a variety of ways and for a variety of reasons. Models are beneficial because they:

1. Are generally easy to use and less expensive than dealing directly with the actual situation.

2. Require users to organize and sometimes quantify information and, in the process, often indicate areas where additional information is needed.

3. Increase understanding of the problem.

4. Enable managers to analyze what-if questions.

5. Serve as a consistent tool for evaluation and provide a standardized format for analyzing a problem.

6. Enable users to bring the power of mathematics to bear on a problem.

This impressive list of benefits notwithstanding, models have certain limitations of which you should be aware. The following are three of the more important limitations.

1. Quantitative information may be emphasized at the expense of qualitative information.

2. Models may be incorrectly applied and the results misinterpreted. The widespread use of computerized models adds to this risk because highly sophisticated models may be placed in the hands of users who are not sufficiently knowledgeable to appreciate the subtleties of a particular model; thus, they are unable to fully comprehend the circumstances under which the model can be successfully employed.

3. The use of models does not guarantee good decisions.

Quantitative Approaches

Quantitative approaches to problem solving often embody an attempt to obtain mathematically optimal solutions to managerial problems.
Q
uantitative approaches to decision making in operations management (and in other functional business areas) have been accepted because of calculators and computers capable of handling the required calculations. Computers have had a major impact on operations management. Moreover, the growing availability of software packages for quantitative techniques has greatly increased management’s use of those techniques.

Although quantitative approaches are widely used in operations management decision making, it is important to note that managers typically use a combination of qualitative and quantitative approaches, and many important decisions are based on qualitative approaches.

Performance Metrics

Managers use metrics to manage and control operations. There are many metrics in use, including those related to profits, costs, quality, productivity, flexibility, assets, inventories, schedules, and forecast accuracy. As you read each chapter, note the metrics being used and how they are applied to manage operations.

Analysis of Trade-Offs

Operations personnel frequently encounter decisions that can be described as
trade-off decisions. For example, in deciding on the amount of inventory to stock, the decision maker must take into account the trade-off between the increased level of customer service that the additional inventory would yield and the increased costs required to stock that inventory.

Decision makers sometimes deal with these decisions by listing the advantages and disadvantages—the pros and cons—of a course of action to better understand the consequences
page 20of the decisions they must make. In some instances, decision makers add weights to the items on their list that reflect the relative importance of various factors. This can help them “net out” the potential impacts of the trade-offs on their decision.

Degree of Customization

A major influence on the entire organization is the degree of customization of products or services being offered to its customers. Providing highly customized products or services such as home remodeling, plastic surgery, and legal counseling tends to be more labor intensive than providing standardized products such as those you would buy “off the shelf” at a mall store or a supermarket or standardized services such as public utilities and internet services. Furthermore, production of customized products or provision of customized services is generally more time consuming, requires more highly skilled people, and involves more flexible equipment than what is needed for standardized products or services. Customized processes tend to have a much lower volume of output than standardized processes, and customized output carries a higher price tag. The degree of customization has important implications for process selection and job requirements. The impact goes beyond operations and supply chains. It affects marketing, sales, accounting, finance, and information systems.

A Systems Perspective

A systems perspective is almost always beneficial in decision making. Think of it as a “big picture” view. A

system
can be defined as a set of interrelated parts that must work together. In a business organization, the organization can be thought of as a system composed of subsystems (e.g., marketing subsystem, operations subsystem, finance subsystem), which in turn are composed of lower subsystems. The systems approach emphasizes interrelationships among subsystems, but its main theme is that
the whole is greater than the sum of its individual parts. Hence, from a systems viewpoint, the output and objectives of the organization as a whole take precedence over those of any one subsystem.

A systems perspective is essential whenever something is being designed, redesigned, implemented, improved, or otherwise changed. It is important to take into account the impact on all parts of the system. For example, if the upcoming model of an automobile will add forward collision braking, a designer must take into account how customers will view the change, the cost of producing the new system, installation procedures, and repair procedures. In addition, workers will need training to make and/or assemble the new system, production scheduling may change, inventory procedures may have to change, quality standards will have to be established, advertising must be informed of the new features, and parts suppliers must be selected.

Establishing Priorities

In virtually every situation, managers discover that certain issues or items are more important than others. Recognizing this enables the managers to direct their efforts to where they will do the most good.

Typically, a relatively few issues or items are very important, so that dealing with those factors will generally have a disproportionately large impact on the results achieved. This well-known effect is referred to as the

Pareto phenomenon
. This is one of the most important and pervasive concepts in operations management. In fact, this concept can be applied at all levels of management and to every aspect of decision making, both professional and personal.

The Industrial Revolution

The Industrial Revolution began in the 1770s in England and spread to the rest of Europe and to the United States during the 19th century. Prior to that time, goods were produced in small shops by craftsmen and their apprentices. Under that system, it was common for one person to be responsible for making a product, such as a horse-drawn wagon or a piece of furniture, from start to finish. Only simple tools were available; the machines in use today had not been invented.

Then, a number of innovations in the 18th century changed the face of production forever by substituting machine power for human power. Perhaps the most significant of these was the steam engine, because it provided a source of power to operate machines in factories. Ample supplies of coal and iron ore provided materials for generating power and making machinery. The new machines, made of iron, were much stronger and more durable than the simple wooden machines they replaced.

In the earliest days of manufacturing, goods were produced using

craft production
: Highly skilled workers using simple, flexible tools produced goods according to customer specifications.

Craft production had major shortcomings. Because products were made by skilled craftsmen who custom-fitted parts, production was slow and costly. And when parts failed, the replacements also had to be custom made, which was also slow and costly. Another shortcoming was that production costs did not decrease as volume increased; there were no
economies of scale, which would have provided a major incentive for companies to expand. Instead, many small companies emerged, each with its own set of standards.

A major change occurred that gave the Industrial Revolution a boost: the development of standard gauging systems. This greatly reduced the need for custom-made goods. Factories began to spring up and grow rapidly, providing jobs for countless people who were attracted in large numbers from rural areas.

Despite the major changes that were taking place, management theory and practice had not progressed much from early days. What was needed was an enlightened and more systematic approach to management.

Scientific Management

The scientific management era brought widespread changes to the management of factories. The movement was spearheaded by the efficiency engineer and inventor Frederick Winslow Taylor, who is often referred to as the father of scientific management. Taylor believed in a “science of management” based on observation, measurement, analysis and improvement of work methods, and economic incentives. He studied work methods in great detail to identify the best method for doing each job. Taylor also believed that management should be responsible for planning, carefully selecting and training workers, finding the best way to perform each job, achieving cooperation between management and workers, and separating management activities from work activities.

Taylor’s methods emphasized maximizing output. They were not always popular with workers, who sometimes thought the methods were used to unfairly increase output without a corresponding increase in compensation. Certainly, some companies did abuse workers in their quest for efficiency. Eventually, the public outcry reached the halls of Congress, and hearings were held on the matter. Taylor himself was called to testify in 1911, the same year in which his classic book,
The Principles of Scientific Management, was published. The publicity from those hearings actually helped scientific management principles to achieve wide acceptance in industry.

page 22 

A number of other pioneers also contributed heavily to this movement, including the following:

Frank Gilbreth was an industrial engineer who is often referred to as the father of motion study. He developed principles of motion economy that could be applied to incredibly small portions of a task.

Henry Gantt recognized the value of nonmonetary rewards to motivate workers, and developed a widely used system for scheduling, called Gantt charts.

Harrington Emerson applied Taylor’s ideas to organization structure and encouraged the use of experts to improve organizational efficiency. He testified in a congressional hearing that railroads could save a million dollars a day by applying principles of scientific management.

Henry Ford, the great industrialist, employed scientific management techniques in his factories.

During the early part of the 20th century, automobiles were just coming into vogue in the United States. Ford’s Model T was such a success that the company had trouble keeping up with orders for the cars. In an effort to improve the efficiency of operations, Ford adopted the scientific management principles espoused by Frederick Winslow Taylor. He also introduced the
moving assembly line, which had a tremendous impact on production methods in many industries.

Among Ford’s many contributions was the introduction of

mass production
to the automotive industry, a system of production in which large volumes of standardized goods are produced by low-skilled or semiskilled workers using highly specialized, and often costly, equipment. Ford was able to do this by taking advantage of a number of important concepts. Perhaps the key concept that launched mass production was

interchangeable parts
, sometimes attributed to Eli Whitney, an American inventor who applied the concept to assembling muskets in the late 1700s. The basis for interchangeable parts was to standardize parts so that any part in a batch of parts would fit any automobile coming down the assembly line. This meant that parts did not have to be custom fitted, as they were in craft production. The standardized parts could also be used for replacement parts. The result was a tremendous decrease in assembly time and cost. Ford accomplished this by standardizing the gauges used to measure parts during production and by using newly developed processes to produce uniform parts.

page 23 

A second concept used by Ford was the

division of labor
, which Adam Smith wrote about in
The Wealth of Nations (1776). Division of labor means that an operation, such as assembling an automobile, is divided up into a series of many small tasks, and individual workers are assigned to one of those tasks. Unlike craft production, where each worker was responsible for doing many tasks, and thus required skill, with division of labor the tasks were so narrow that virtually no skill was required.

Together, these concepts enabled Ford to tremendously increase the production rate at his factories using readily available inexpensive labor. Both Taylor and Ford were despised by many workers, because they held workers in such low regard, expecting them to perform like robots. This paved the way for the human relations movement.

The Human Relations Movement

Whereas the scientific management movement heavily emphasized the technical aspects of work design, the human relations movement emphasized the importance of the human element in job design. Lillian Gilbreth, a psychologist and the wife of Frank Gilbreth, worked with her husband, focusing on the human factor in work. (The Gilbreths were the subject of a classic film,
Cheaper by the Dozen.) Many of her studies dealt with worker fatigue. In the following decades, there was much emphasis on motivation. Elton Mayo conducted studies at the Hawthorne division of Western Electric. His studies revealed that in addition to the physical and technical aspects of work, worker motivation is critical for improving productivity. Abraham Maslow developed motivational theories, which Frederick Hertzberg refined. Douglas McGregor added Theory X and Theory Y. These theories represented the two ends of the spectrum of how employees view work. Theory X, on the negative end, assumed that workers do not like to work, and have to be controlled—rewarded and punished—to get them to do good work. This attitude was quite common in the automobile industry and in some other industries, until the threat of global competition forced them to rethink that approach. Theory Y, on the other end of the spectrum, assumed that workers enjoy the physical and mental aspects of work and become committed to work. The Theory X approach resulted in an adversarial environment, whereas the Theory Y approach resulted in empowered workers and a more cooperative spirit. William Ouchi added Theory Z, which combined the Japanese approach with such features as lifetime employment, employee problem solving, and consensus building, and the traditional Western approach that features short-term employment, specialists, and individual decision making and responsibility.

Decision Models and Management Science

The factory movement was accompanied by the development of several quantitative techniques. F. W. Harris developed one of the first models in 1915: a mathematical model for inventory order size. In the 1930s, three coworkers at Bell Telephone Labs—H. F. Dodge, H. G. Romig, and W. Shewhart—developed statistical procedures for sampling and quality control. In 1935, L.H.C. Tippett conducted studies that provided the groundwork for statistical sampling theory.

At first, these quantitative models were not widely used in industry. However, the onset of World War II changed that. The war generated tremendous pressures on manufacturing output, and specialists from many disciplines combined efforts to achieve advancements in the military and in manufacturing. After the war, efforts to develop and refine quantitative tools for decision making continued, resulting in decision models for forecasting, inventory management, project management, and other areas of operations management.

During the 1960s and 1970s, management science techniques were highly regarded; in the 1980s, they lost some favor. However, the widespread use of personal computers and user-friendly software in the workplace contributed to a resurgence in the popularity of these techniques.

The Influence of Japanese Manufacturers

A number of Japanese manufacturers developed or refined management practices that increased the productivity of their operations and the quality of their products, due in part to the influence of Americans W. Edwards Deming and Joseph Juran. This made them
page 24very competitive, sparking interest in their approaches by companies outside Japan. Their approaches emphasized quality and continual improvement, worker teams and empowerment, and achieving customer satisfaction. The Japanese can be credited with spawning the “quality revolution” that occurred in industrialized countries, and with generating widespread interest in lean production.

The influence of the Japanese on U.S. manufacturing and service companies has been enormous and promises to continue for the foreseeable future. Because of that influence, this book will provide considerable information about Japanese methods and successes.

Table 1.5 provides a chronological summary of some of the key developments in the evolution of operations management.

TABLE 1.5

Historical summary of operations management

Environmental Concerns

Concern about global warming and pollution has had an increasing effect on how businesses operate.

Stricter environmental regulations, particularly in developed nations, are being imposed. Furthermore, business organizations are coming under increasing pressure to reduce their carbon footprint (the amount of carbon dioxide generated by their operations and their supply chains) and to generally operate sustainable processes.

Sustainability
refers to service
page 28and production processes that use resources in ways that do not harm ecological systems that support both current and future human existence. Sustainability measures often go beyond traditional environmental and economic measures to include measures that incorporate social criteria in decision making.

All areas of business will be affected by this. Areas that will be most affected include product and service design, consumer education programs, disaster preparation and response, supply chain
page 29waste management, and outsourcing decisions. Note that outsourcing of goods production increases not only transportation costs, but also fuel consumption and carbon released into the atmosphere. Consequently, sustainability thinking may have implications for outsourcing decisions.

Because they all fall within the realm of operations, operations management is central to dealing with these issues. Sometimes referred to as “green initiatives,” the possibilities include reducing packaging, materials, water and energy use, and the environmental impact of the supply chain, including buying locally. Other possibilities include reconditioning used equipment (e.g., printers and copiers) for resale, and recycling.

The reading above suggests that even our choice of diet can affect the environment.

Ethical Conduct

The need for ethical conduct in business is becoming increasingly obvious, given numerous examples of questionable actions in recent history. In making decisions, managers must consider how their decisions will affect shareholders, management, employees, customers, the community at large, and the environment. Finding solutions that will be in the best interests of all of these stakeholders is not always easy, but it is a goal that all managers should strive to achieve. Furthermore, even managers with the best intentions will sometimes make mistakes. If mistakes do occur, managers should act responsibly to correct those mistakes as quickly as possible, and to address any negative consequences.

Many organizations have developed
codes of ethics to guide employees’ or members’ conduct.

Ethics
is a standard of behavior that guides how one should act in various situations. The Markula Center for Applied Ethics at Santa Clara University identifies five principles for thinking ethically:

• The
  Utilitarian Principle: The good done by an action or inaction should outweigh any harm it causes or might cause. An example is not allowing a person who has had too much to drink to drive.

• The
  Rights Principle: Actions should respect and protect the moral rights of others. An example is not taking advantage of a vulnerable person.

• The
  Fairness Principle: Equals should be held to, or evaluated by, the same standards. An example is equal pay for equal work.

• The
  Common Good Principle: Actions should contribute to the common good of the community. An example is an ordinance on noise abatement.

• The
  Virtue Principle: Actions should be consistent with certain ideal virtues. Examples include honesty, compassion, generosity, tolerance, fidelity, integrity, and self-control.

The center expands these principles to create a framework for ethical conduct. An

ethical framework
is a sequence of steps intended to guide thinking and subsequent decisions or actions.
page 30Here is the one developed by the Markula Center for Applied Ethics:

1. Recognize an ethical issue by asking if an action could be damaging to a group or an individual. Is there more to it than just what is legal?

2. Make sure the pertinent facts are known, such as who will be impacted, and what options are available.

3. Evaluate the options by referring to the appropriate preceding ethical principle.

4. Identify the “best” option and then further examine it by asking how someone you respect would view it.

5. In retrospect, consider the effect your decision had and what you can learn from it.

More detail is available at the Center’s website:
http://www.scu.edu/ethics/practicing/decision/framework.html.

Operations managers, like all managers, have the responsibility to make ethical decisions. Ethical issues arise in many aspects of operations management, including:

• Financial statements: accurately representing the organization’s financial condition.

• Worker safety: providing adequate training, maintaining equipment in good working condition, maintaining a safe working environment.

• Product safety: providing products that minimize the risk of injury to users or damage to property or the environment.

• Quality: honoring warranties, avoiding hidden defects.

• The environment: not doing things that will harm the environment.

• The community: being a good neighbor.

• Hiring and firing workers: avoiding false pretenses (e.g., promising a long-term job when that is not what is intended).

• Closing facilities: taking into account the impact on a community, and honoring commitments that have been made.

• Workers’ rights: respecting workers’ rights, dealing with workers’ problems quickly and fairly.

The Ethisphere Institute recognizes companies worldwide for their ethical leadership. Here are some samples from their list:

Apparel: Gap

Automotive: Ford Motor Company

Business services: Paychex

Café: Starbucks

Computer hardware: Intel

Computer software: Adobe Systems, Microsoft

Consumer electronics: Texas Instruments, Xerox

page 31 

E-commerce: eBay

General retail: Costco, Target

Groceries: Safeway, Wegmans, Whole Foods

Health and beauty: L’Oreal

Logistics: UPS

You can see a complete list of recent recipients and the selection criteria at Ethisphere.com.

The Need to Manage the Supply Chain

Supply chain management is being given increasing attention as business organizations face mounting pressure to improve management of their supply chains. In the past, most organizations did little to manage their supply chains. Instead, they tended to concentrate on their own operations and on their immediate suppliers. Moreover, the planning, marketing, production and inventory management functions in organizations in supply chains have often operated independently of each other. As a result, supply chains experienced a range of problems that were seemingly beyond the control of individual organizations. The problems included large oscillations of inventories, inventory stockouts, late deliveries, and quality problems. These and other issues now make it clear that management of supply chains is essential to business success. The other issues include the following:

1. The need to improve operations. Efforts on cost and time reduction, and productivity and quality improvement, have expanded in recent years to include the supply chain. Opportunity now lies largely with procurement, distribution, and logistics—the supply chain.

2. Increasing levels of outsourcing. Organizations are increasing their levels of
   
   outsourcing
   , buying goods or services instead of producing or providing them themselves. As outsourcing increases, some organizations are spending increasing amounts on supply-related activities (wrapping, packaging, moving, loading and unloading, and sorting). A significant amount of the cost and time spent on these and other related activities may be unnecessary. Issues with imported products, including tainted food products, toothpaste, and pet foods, as well as unsafe tires and toys, have led to questions of liability and the need for companies to take responsibility for monitoring the safety of outsourced goods.

1. Increasing transportation costs. Transportation costs are increasing, and they need to be more carefully managed.

2. Competitive pressures. Competitive pressures have led to an increasing number of new products, shorter product development cycles, and increased demand for customization. And in some industries, most notably consumer electronics, product life cycles are relatively short. Added to this are the adoption of quick-response strategies and efforts to reduce lead times.

3. Increasing globalization. Increasing globalization has expanded the physical length of supply chains. A global supply chain increases the challenges of managing a supply chain. Having far-flung customers and/or suppliers means longer lead times and greater opportunities for disruption of deliveries. Often, currency
   page 32differences and monetary fluctuations are factors, as well as language and cultural differences. Also, tightened border security in some instances has slowed shipments of goods.

4. Increasing importance of e-business. The increasing importance of e-business has added new dimensions to business buying and selling and has presented new challenges.

5. The complexity of supply chains. Supply chains are complex; they are dynamic, and they have many inherent uncertainties that can adversely affect them, such as inaccurate forecasts, late deliveries, substandard quality, equipment breakdowns, and canceled or changed orders.

6. The need to manage inventories. Inventories play a major role in the success or failure of a supply chain, so it is important to coordinate inventory levels throughout a supply chain. Shortages can severely disrupt the timely flow of work and have far-reaching impacts, while excess inventories add unnecessary costs. It would not be unusual to find inventory shortages in some parts of a supply chain and excess inventories in other parts of the same supply chain.

7. The need to deal with trade wars. Trade wars can occur if a country objects to its trade imbalance with another country. This can result in tariffs and retaliatory tariffs, causing changes in cost structures. Uncertainty about how long and to what degree tariffs will be in place can greatly increase pressure on companies that have global supply chains.

Elements of Supply Chain Management

Supply chain management involves coordinating activities across the supply chain. Central to this is taking customer demand and translating it into corresponding activities at each level of the supply chain.

The key elements of supply chain management are listed in
Table 1.6. The first element, customers, is the driving element. Typically, marketing is responsible for determining what customers want, as well as forecasting the quantities and timing of customer demand. Product and service design must match customer wants with operations capabilities.

TABLE 1.6

Elements of supply chain management

Processing occurs in each component of the supply chain: It is the core of each organization. The major portion of processing occurs in the organization that produces the product or service for the final customer (the organization that assembles the computer, services the car, etc.). A major aspect of this for both the internal and external portions of a supply chain is scheduling.

Inventory is a staple in most supply chains. Balance is the main objective; too little causes delays and disrupts schedules, but too much adds unnecessary costs and limits flexibility.

page 33 

Purchasing is the link between an organization and its suppliers. It is responsible for obtaining goods and/or services that will be used to produce products or provide services for the organization’s customers. Purchasing selects suppliers, negotiates contracts, establishes alliances, and acts as a liaison between suppliers and various internal departments.

The supply portion of a value chain is made up of one or more suppliers, all links in the chain, and each one capable of having an impact on the effectiveness—or the ineffectiveness—of the supply chain. Moreover, it is essential that the planning and execution be carefully coordinated between suppliers and all members of the demand portion of their chains.

Location can be a factor in a number of ways. Where suppliers are located can be important, as can the location of processing facilities. Nearness to market, nearness to sources of supply, or nearness to both may be critical. Also, delivery time and cost are usually affected by location.

Two types of decisions are relevant to supply chain management—strategic and operational. The strategic decisions are the design and policy decisions. The operational decisions relate to day-to-day activities: managing the flow of material and product and other aspects of the supply chain in accordance with strategic decisions.

The major decision areas in supply chain management are location, production, distribution, and inventory. The
location decision relates to the choice of locations for both production and distribution facilities. Production and transportation costs and delivery lead times are important.
Production and
distribution decisions focus on what customers want, when they want it, and how much is needed. Outsourcing can be a consideration. Distribution decisions are strongly influenced by transportation cost and delivery times, because transportation costs often represent a significant portion of total cost. Moreover, shipping alternatives are closely tied to production and inventory decisions. For example, using air transport means higher costs but faster deliveries and less inventory in transit than sea, rail, or trucking options. Distribution decisions must also take into account capacity and quality issues. Operational decisions focus on scheduling, maintaining equipment, and meeting customer demand. Quality control and workload balancing are also important considerations.
Inventory decisions relate to determining inventory needs and coordinating production and stocking decisions throughout the supply chain. Logistics management plays the key role in inventory decisions.

Enterprise Resource Planning (ERP) is being increasingly used to provide information sharing in real time among organizations and their major supply chain partners. This important topic is discussed in more detail in
Chapter 13.

Operations Tours

Throughout the book you will discover operations tours that describe operations in all sorts of companies. The tour below is of Wegmans Food Markets, a major regional supermarket chain. Wegmans has been consistently ranked high on
Fortune magazine’s list of the 100 Best Companies to Work For since the inception of the survey a decade ago.

Why Some Organizations Fail

Organizations fail, or perform poorly, for a variety of reasons. Being aware of those reasons can help managers avoid making similar mistakes. Among the chief reasons are the following:

1. Neglecting operations strategy.

2. Failing to take advantage of strengths and opportunities, and/or failing to recognize competitive threats.

3. Putting too much emphasis on short-term financial performance at the expense of research and development.

4. Placing too much emphasis on product and service design and not enough on process design and improvement.

   page 44 

5. Neglecting investments in capital and human resources.

6. Failing to establish good internal communications and cooperation among different functional areas.

7. Failing to consider customer wants and needs.

The key to successfully competing is to determine what customers want and then directing efforts toward meeting (or even exceeding) customer expectations. Two basic issues must be addressed. First: What do the customers want? (Which items on the preceding list of the ways business organizations compete are important to customers?) Second: What is the best way to satisfy those wants?

Operations must work with marketing to obtain information on the relative importance of the various items to each major customer or target market.

Understanding competitive issues can help managers develop successful strategies.

Strategies and Tactics

If you think of goals as destinations, then strategies are the roadmaps for reaching those destinations. Strategies provide
focus for decision making. Generally speaking, organizations have overall strategies called
organizational strategies, which relate to the entire organization. They also have
functional strategies, which relate to each of the functional areas of the organization. The functional strategies should support the overall strategies of the organization, just as the organizational strategies should support the goals and mission of the organization.

Tactics
are the methods and actions used to accomplish strategies. They are more specific than strategies, and they provide guidance and direction for carrying out actual
operations, which need the most specific and detailed plans and decision making in an organization. You might think of tactics as the “how to” part of the process (e.g., how to reach the destination, following the strategy roadmap), and operations as the actual “doing” part of the process. Much of this book deals with tactical operations.

It should be apparent that the overall relationship that exists from the mission down to actual operations is
hierarchical. This is illustrated in
Figure 2.1.

A simple example may help to put this hierarchy into perspective.

page 46 

Here are some examples of different strategies an organization might choose from:

Low cost. Outsource operations to third-world countries that have low labor costs.

Scale-based strategies. Use capital-intensive methods to achieve high output volume and low unit costs.

Specialization. Focus on narrow product lines or limited service to achieve higher quality.

Newness. Focus on innovation to create new products or services.

Flexible operations. Focus on quick response and/or customization.

High quality. Focus on achieving higher quality than competitors.

Service. Focus on various aspects of service (e.g., helpful, courteous, reliable, etc.).

Sustainability. Focus on environmental-friendly and energy-efficient operations.

A wide range of business organizations are beginning to recognize the strategic advantages of sustainability, not only in economic terms, but also through promotional benefits by publicizing their sustainability efforts and achievements.

Sometimes, organizations will combine two or more of these, or other approaches, into their strategy. However, unless they are careful, they risk losing focus and not achieving advantage in any category. Generally speaking, strategy formulation takes into account the way organizations compete and a particular organization’s assessment of its own strengths and weaknesses in order to take advantage of its

core competencies
—those special attributes or abilities possessed by an organization that give it a
competitive edge.

The most effective organizations use an approach that develops core competencies based on customer needs, as well as on what the competition is doing. Marketing and operations work closely to match customer needs with operations capabilities. Competitor competencies are important for several reasons. For example, if a competitor is able to supply high-quality products, it may be necessary to meet that high quality as a baseline. However, merely
matching a competitor is usually not sufficient to gain market share. It may be necessary to exceed the quality level of the competitor or gain an edge by excelling in one or more other dimensions, such as rapid delivery or service after the sale. Walmart, for example, has been very successful in managing its supply chain, which has contributed to its competitive advantage.

page 47 

To be effective, strategies and core competencies need to be aligned.
Table 2.2 lists examples of strategies and companies that have successfully employed those strategies.

TABLE 2.2

Examples of operations strategies

Strategy Formulation

Strategy formulation is almost always critical to the success of a strategy. Walmart discovered this when it opened stores in Japan. Although Walmart thrived in many countries on its reputation for low-cost items, Japanese consumers associated low cost with low quality, causing Walmart to rethink its strategy in the Japanese market. And many felt that Hewlett-Packard (HP) committed a strategic error when it acquired Compaq Computers at a cost of $19 billion. HP’s share of the computer market was less after the merger than the sum of the shares of the separate companies before the merger. In another example, U.S. automakers adopted a strategy in the early 2000s of offering discounts and rebates on a range of cars and SUVs, many of which were on low-margin vehicles. The strategy put a strain on profits, but customers began to expect those incentives, and the companies maintained them to keep from losing additional market share.

On the other hand, Coach, the maker of leather handbags and purses, successfully changed its longtime strategy to grow its market by creating new products. Long known for its highly durable leather goods in a market where women typically owned few handbags, Coach created a new market for itself by changing women’s view of handbags by promoting “different handbags for different occasions” such as party bags, totes, clutches, wristlets, overnight bags, purses, and day bags. And Coach introduced many fashion styles and colors.

To formulate an effective strategy, senior managers must take into account the core competencies of the organizations, and they must
scan the environment. They must determine
page 48what competitors are doing, or planning to do, and take that into account. They must critically examine other factors that could have either positive or negative effects. This is sometimes referred to as the

SWOT
analysis (strengths, weaknesses, opportunities, and threats). Strengths and weaknesses have an internal focus and are typically evaluated by operations people. Threats and opportunities have an external focus and are typically evaluated by marketing people. SWOT is often regarded as the link between organizational strategy and operations strategy.

An alternative to SWOT analysis is Michael Porter’s five forces model,

¹
which takes into account the threat of new competition, the threat of substitute products or services, the bargaining power of customers, the bargaining power of suppliers, and the intensity of competition.

In formulating a successful strategy, organizations must take into account both order qualifiers and order winners.

Order qualifiers
are those characteristics that potential customers perceive as minimum standards of acceptability for a product to be considered for purchase. However, that may not be sufficient to get a potential customer to purchase from the organization.

Order winners
are those characteristics of an organization’s goods or services that cause them to be perceived as better than the competition.

Characteristics such as price, delivery reliability, delivery speed, and quality can be order qualifiers or order winners. Thus, quality may be an order winner in some situations, but in others only an order qualifier. Over time, a characteristic that was once an order winner may become an order qualifier.

Obviously, it is important to determine the set of order qualifier characteristics and the set of order winner characteristics. It is also necessary to decide on the relative importance of each characteristic so that appropriate attention can be given to the various characteristics. Marketing must make that determination and communicate it to operations.

Environmental scanning
is the monitoring of events and trends that present either threats or opportunities for the organization. Generally, these include competitors’ activities; changing consumer needs; legal, economic, political, and environmental issues; the potential for new markets; and the like.

Another key factor to consider when developing strategies is technological change, which can present real opportunities and threats to an organization. Technological changes occur in products (high-definition TV, improved computer chips, improved cellular telephone systems, and improved designs for earthquake-proof structures); in services (faster order processing, faster delivery); and in processes (robotics, automation, computer-assisted processing, point-of-sale scanners, and flexible manufacturing systems). The obvious benefit is a competitive edge; the risk is that incorrect choices, poor execution, and higher-than-expected operating costs will create competitive
disadvantages.

Important factors may be internal or external. The following are key external factors:

1. Economic conditions. These include the general health and direction of the economy, inflation and deflation, interest rates, tax laws, and tariffs.

2. Political conditions. These include favorable or unfavorable attitudes toward business, political stability or instability, and wars.

3. Legal environment. This includes antitrust laws, government regulations, trade restrictions, minimum wage laws, product liability laws and recent court experience, labor laws, and patents.

4. Technology. This can include the rate at which product innovations are occurring, current and future process technology (equipment, materials handling), and design technology.

5. Competition. This includes the number and strength of competitors, the basis of competition (price, quality, special features), and the ease of market entry.

6. Customers. Loyalty, existing relationships, and understanding of wants and needs are important.

   page 49 

7. Suppliers. Supplier relationships, dependability of suppliers, quality, flexibility, and service are typical considerations.

8. Markets. This includes size, location, brand loyalties, ease of entry, potential for growth, long-term stability, and demographics.

The organization also must take into account various
internal factors that relate to possible strengths or weaknesses. Among the key internal factors are the following:

1. Human resources. These include the skills and abilities of managers and workers, special talents (creativity, designing, problem solving), loyalty to the organization, expertise, dedication, and experience.

2. Facilities and equipment. Capacities, location, age, and cost to maintain or replace can have a significant impact on operations.

3. Financial resources. Cash flow, access to additional funding, existing debt burden, and cost of capital are important considerations.

4. Products and services. These include existing products and services, and the potential for new products and services.

5. Technology. This includes existing technology, the ability to integrate new technology, and the probable impact of technology on current and future operations.

6. Other. Other factors include patents, labor relations, company or product image, distribution channels, relationships with distributors, maintenance of facilities and equipment, access to resources, and access to markets.

After assessing internal and external factors and an organization’s distinctive competence, a strategy or strategies must be formulated that will give the organization the best chance of success. Among the types of questions that may need to be addressed are the following:

What role, if any, will the internet play?

Will the organization have a global presence?

To what extent will
outsourcing be used?

What will the supply chain management strategy be?

To what extent will new products or services be introduced?

What rate of growth is desirable and
sustainable?

What emphasis, if any, should be placed on lean production?

How will the organization differentiate its products and/or services from competitors’?

The organization may decide to have a single, dominant strategy (e.g., be the price leader) or have multiple strategies. A single strategy would allow the organization to concentrate on one particular strength or market condition. On the other hand, multiple strategies may be needed to address a particular set of conditions.

Many companies are increasing their use of outsourcing to reduce overhead, gain flexibility, and take advantage of suppliers’ expertise. Amazon provides a great example of some of the potential benefits of outsourcing as part of a business strategy.

Growth is often a component of strategy, especially for new companies. A key aspect of this strategy is the need to seek a growth rate that is sustainable. In the 1990s, fast-food company Boston Market dazzled investors and fast-food consumers alike. Fueled by its success, it undertook rapid expansion. By the end of the decade, the company was nearly bankrupt; it had overexpanded. In 2000, it was absorbed by fast-food giant McDonald’s.

Companies increase their risk of failure not only by missing or incomplete strategies; they also fail due to poor execution of strategies. And sometimes they fail due to factors beyond their control, such as natural or man-made disasters, major political or economic changes, or competitors that have an overwhelming advantage (e.g., deep pockets, very low labor costs, less rigorous environmental requirements).

A useful resource on successful business strategies is the Profit Impact of Market Strategy (PIMS) database (
www.pimsonline.com). The database contains profiles of over 3,000 businesses located primarily in the United States, Canada, and western Europe. It is used by companies and academic institutions to guide strategic thinking. It allows subscribers to answer strategy questions about their business. Moreover, they can use it to generate benchmarks and develop successful strategies.

page 50 

According to the PIMS website,

The
database is a collection of statistically documented experiences drawn from thousands of businesses, designed to help understand what kinds of strategies (e.g., quality, pricing, vertical integration, innovation, advertising) work best in what kinds of business environments. The data constitute a key resource for such critical management tasks as evaluating business performance, analyzing new business opportunities, evaluating and reality testing new strategies, and screening business portfolios.
The primary role of the PIMS Program of the Strategic Planning Institute is to help managers understand and react to their business environment. PIMS does this by assisting managers as they develop and test strategies that will achieve an acceptable level of winning as defined by various strategies and financial measures.

Source:
https://www.inc.com/encyclopedia/profit-impact-of-market-strategies-pims.html

Supply Chain Strategy

A supply chain strategy specifies how the supply chain should function to achieve supply chain goals. The supply chain strategy should be aligned with the business strategy. If it is well executed, it can create value for the organization. It establishes how the organization should work with suppliers and policies relating to customer relationships and sustainability. Supply chain strategy is covered in more detail in a later chapter.

Sustainability Strategy

Society is placing increasing emphasis on corporate sustainability practices in the form of governmental regulations and interest groups. For these and other reasons, business organizations are or should be devoting attention to sustainability goals. To be successful, they will need a sustainability strategy. That requires elevating sustainability to the level of organizational
page 51governance; formulating goals for products and services, for processes, and for the entire supply chain; measuring achievements and striving for improvements; and possibly linking executive compensation to the achievement of sustainability goals.

Global Strategy

Global strategies have two different aspects. One relates to where parts or products are made, or where services such as customer support are performed. The other relates to where products or service are sold. With wages and standards of living increases in countries such as China and India, new market opportunities present themselves, requiring well-thought out strategies to take advantage of those potential opportunities while minimizing any associated risks.

As globalization increased, many companies realized that strategic decisions with respect to globalization had to be made. One issue companies face today is that what works in one country or region does not necessarily work in another, and strategies must be carefully crafted to take these variabilities into account. Another issue is the threat of political or social upheaval. Still another issue is the difficulty of coordinating and managing far-flung operations. Indeed, “In today’s global markets, you don’t have to go abroad to experience international competition. Sooner or later the world comes to you.”

²

Strategic Operations Management Decision Areas

Operations management people play a strategic role in many strategic decisions in a business organization.
Table 2.4 highlights some key decision areas. Notice that most of the decision areas have cost implications.

TABLE 2.4

Strategic operations management decisions

Two factors that tend to have universal strategic operations importance relate to quality and time. The following section discusses quality and time strategies.

Quality and Time Strategies

Traditional strategies of business organizations have tended to emphasize cost minimization or product differentiation. While not abandoning those strategies, many organizations have embraced strategies based on
quality and/or
time.

Quality-based strategies
focus on maintaining or improving the quality of an organization’s products or services. Quality is generally a factor in both attracting and retaining customers.
page 53Quality-based strategies may be motivated by a variety of factors. They may reflect an effort to overcome an image of poor quality, a desire to catch up with the competition, a desire to maintain an existing image of high quality, or some combination of these and other factors. Interestingly enough, quality-based strategies can be part of another strategy such as cost reduction, increased productivity, or time, all of which benefit from higher quality.

Time-based strategies
focus on reducing the time required to accomplish various activities (e.g., develop new products or services and market them, respond to a change in customer demand, or deliver a product or perform a service). By doing so, organizations seek to improve service to the customer and to gain a competitive advantage over rivals who take more time to accomplish the same tasks.

Time-based strategies focus on reducing the time needed to conduct the various activities in a process. The rationale is that by reducing time, costs are generally less, productivity is higher, quality tends to be higher, product innovations appear on the market sooner, and customer service is improved.

Organizations have achieved time reduction in some of the following:

Planning time: The time needed to react to a competitive threat, to develop strategies and select tactics, to approve proposed changes to facilities, to adopt new technologies, and so on.

Product/service design time: The time needed to develop and market new or redesigned products or services.

Processing time: The time needed to produce goods or provide services. This can involve scheduling, repairing equipment, methods used, inventories, quality, training, and the like.

Changeover time: The time needed to change from producing one type of product or service to another. This may involve new equipment settings and attachments, different methods, equipment, schedules, or materials.

Delivery time: The time needed to fill orders.

Response time for complaints: These might be customer complaints about quality, timing of deliveries, and incorrect shipments. These might also be complaints from employees about working conditions (e.g., safety, lighting, heat or cold), equipment problems, or quality problems.

It is essential for marketing and operations personnel to collaborate on strategy formulation in order to ensure that the buying criteria of the most important customers in each market segment are addressed.

Agile operations is a strategic approach for competitive advantage that emphasizes the use of flexibility to adapt and prosper in an environment of change. Agility involves a blending of several distinct competencies such as cost, quality, and reliability along with flexibility. Processing aspects of flexibility include quick equipment changeovers, scheduling, and innovation. Product or service aspects include varying output volumes and product mix.

Successful agile operations requires careful planning to achieve a system that includes people, flexible equipment, and information technology. Reducing the time needed to perform work is one of the ways an organization can improve a key metric:
productivity.

Computing Productivity

Productivity measures can be based on a single input (partial productivity), on more than one input (multifactor productivity), or on all inputs (total productivity).
Table 2.7 lists some examples of productivity measures. The choice of productivity measure depends primarily on the purpose of the measurement. If the purpose is to track improvements in labor productivity, then labor becomes the obvious input measure.

TABLE 2.7

Some examples of different types of productivity measures

Partial measures are often of greatest use in operations management.
Table 2.8 provides some examples of partial productivity measures.

TABLE 2.8

Some examples of partial productivity measures

The units of output used in productivity measures depend on the type of job performed. The following are examples of labor productivity:

Similar examples can be listed for
machine productivity (e.g., the number of pieces per hour turned out by a machine).

page 58 

Calculations of multifactor productivity measure inputs and outputs using a common unit of measurement, such as cost. For instance, the measure might use cost of inputs and units of the output:

Note: The unit of measure must be the same for all factors in the denominator

page 59 

Productivity measures are useful on a number of levels. For an individual department or organization, productivity measures can be used to track performance
over time. This allows managers to judge performance and to decide where improvements are needed. For example, if productivity has slipped in a certain area, operations staff can examine the factors used to compute productivity to determine what has changed and then devise a means of improving productivity in subsequent periods.

Productivity measures also can be used to judge the performance of an entire industry or the productivity of a country as a whole. These productivity measures are
aggregate measures.

In essence, productivity measurements serve as scorecards of the effective use of resources. Business leaders are concerned with productivity as it relates to
competitiveness: If two firms both have the same level of output but one requires less input because of higher productivity, that one will be able to charge a lower price and consequently increase its share of the market. Or that firm might elect to charge the same price, thereby reaping a greater profit. Government leaders are concerned with national productivity because of the close relationship between productivity and a nation’s standard of living. High levels of productivity are largely responsible for the relatively high standards of living enjoyed by people in industrial nations. Furthermore, wage and price increases not accompanied by productivity increases tend to create inflationary pressures on a nation’s economy.

Advantages of domestic-based operations for domestic markets often include higher worker productivity, better control of quality, avoidance of intellectual property losses, lower shipping costs, political stability, low inflation, and faster delivery.

Productivity in the Service Sector

Service productivity is more problematic than manufacturing productivity. In many situations, it is more difficult to measure, and thus to manage, because it involves intellectual activities and a high degree of variability. Think about medical diagnoses, surgery, consulting, legal services, customer service, and computer repair work. This makes productivity improvements more difficult to achieve. Nonetheless, because service is becoming an increasingly large portion of our economy, the issues related to service productivity will have to be dealt with. It is interesting to note that government statistics normally do not include service firms.

page 60 

A useful measure closely related to productivity is
process yield. Where products are involved, process yield is defined as the ratio of output of good product (i.e., defective product is not included) to the quantity of raw material input. Where services are involved, process yield measurement is often dependent on the particular process. For example, in a car rental agency, a measure of yield is the ratio of cars rented to cars available for a given day. In education, a measure for college and university admission yield is the ratio of student acceptances to the total number of students approved for admission. For subscription services, yield is the ratio of new subscriptions to the number of calls made or the number of letters mailed. However, not all services lend themselves to a simple yield measurement. For example, services such as automotive, appliance, and computer repair don’t readily lend themselves to such measures.

Factors that Affect Productivity

Numerous factors affect productivity. Generally, they are methods, capital, quality, technology, and management.

A commonly held misconception is that workers are the main determinant of productivity. According to that theory, the route to productivity gains involves getting employees to work harder. However, the fact is that many productivity gains in the past have come from
technological improvements. Familiar examples include:

However, technology alone won’t guarantee productivity gains; it must be used wisely and thoughtfully. Without careful planning, technology can actually
reduce productivity, especially if it leads to inflexibility, high costs, or mismatched operations. Another current productivity pitfall results from employees’ use of computers or smartphones for nonwork-related activities (playing games or checking stock prices or sports scores on the internet or smartphones, and texting friends and relatives). Beyond all of these is the dip in productivity that results while employees learn to use new equipment or procedures that will eventually lead to productivity gains after the learning phase ends.

page 61 

Other factors that affect productivity include the following:

Standardizing processes and procedures wherever possible to reduce variability can have a significant benefit for both productivity and quality.

Quality differences may distort productivity measurements. One way this can happen is when comparisons are made over time, such as comparing the productivity of a factory now with one 30 years ago. Quality is now much higher than it was then, but there is no simple way to incorporate quality improvements into productivity measurements.

Use of the internet can lower costs of a wide range of transactions, thereby increasing productivity. It is likely that this effect will continue to increase productivity in the foreseeable future.

Computer viruses can have an immense negative impact on productivity.

Searching for lost or misplaced items wastes time, hence negatively affecting productivity.

Scrap rates have an adverse effect on productivity, signaling inefficient use of resources.

New workers tend to have lower productivity than seasoned workers. Thus, growing companies may experience a productivity lag.

Safety should be addressed. Accidents can take a toll on productivity.

A shortage of technology-savvy workers hampers the ability of companies to update computing resources, generate and sustain growth, and take advantage of new opportunities.

Layoffs often affect productivity. The effect can be positive and negative. Initially, productivity may increase after a layoff, because the workload remains the same but fewer workers do the work—although they have to work harder and longer to do it. However, as time goes by, the remaining workers may experience an increased risk of burnout, and they may fear additional job cuts. The most capable workers may decide to leave.

Labor turnover has a negative effect on productivity; replacements need time to get up to speed.

Design of the workspace can impact productivity. For example, having tools and other work items within easy reach can positively impact productivity.

Incentive plans that reward productivity increases can boost productivity.

And there are still other factors that affect productivity, such as
equipment breakdowns and
shortages of parts or materials. The education level and training of workers and their health can greatly affect productivity. The opportunity to obtain lower costs due to higher productivity elsewhere is a key reason many organizations turn to
outsourcing. Hence, an alternative to outsourcing can be improved productivity. Moreover, as a part of their strategy for quality, the best organizations strive for
continuous improvement. Productivity improvements can be an important aspect of that approach.

Improving Productivity

A company or a department can take a number of key steps toward improving productivity:

1. Develop productivity measures for all operations. Measurement is the first step in managing and controlling an operation.

2. Look at the system as a whole in deciding which operations are most critical. It is overall productivity that is important. Managers need to reflect on the value of potential productivity improvements
   before okaying improvement efforts. The issue is
   effectiveness. There are several aspects of this. One is to make sure the result will be something customers want. For example, if a company is able to increase its output through productivity improvements, but then is unable to sell the increased output, the increase in productivity isn’t effective. Second, it is important to adopt a systems viewpoint: A productivity increase in one part of an operation that doesn’t increase the productivity of the system would not be effective. For example, suppose a system consists of a sequence of two operations, where the output of the first operation is the input to the second operation, and each operation can complete its part of the process at a rate of 20 units per hour. If the productivity of the first operation is increased, but the productivity of the second operation is not, the output of the system will still be 20 units per hour.

3. Develop methods for achieving productivity improvements, such as soliciting ideas from workers (perhaps organizing teams of workers, engineers, and managers), studying how other firms have increased productivity, and reexamining the way work is done.

4. Establish reasonable goals for improvement.

5. Make it clear that management supports and encourages productivity improvement. Consider incentives to reward workers for contributions.

6. Measure improvements and publicize them.

page 62 

Don’t confuse productivity with
efficiency. Efficiency is a narrower concept that pertains to getting the most out of a
fixed set of resources; productivity is a broader concept that pertains to effective use of overall resources. For example, an efficiency perspective on mowing a lawn with a hand mower would focus on the best way to use the hand mower; a productivity perspective would include the possibility of using a power mower.

Fracking productivity improvement is another example. Drilling methods have become more effective. Drillers are now adopting a hydraulic fracturing method pioneered by companies such as Liberty Resources and EOG Resources that uses larger amounts of water and minerals. Although it is a more costly process, it has increased production rates in the first year of a well’s life, after which output tends to drop off dramatically. Processes such as these have reduced the break-even cost of producing a barrel of oil and kept profitable some acreage that drillers might otherwise have left idle.

Executive Opinions

A small group of upper-level managers (e.g., in marketing, operations, and finance) may meet and collectively develop a forecast. This approach is often used as a part of long-range planning and new product development. It has the advantage of bringing together the considerable knowledge and talents of various managers. However, there is the risk that the view of one person will prevail, and the possibility that diffusing responsibility for the forecast over the entire group may result in less pressure to produce a good forecast.

page 81 

Salesforce Opinions

Members of the sales staff or the customer service staff are often good sources of information because of their direct contact with consumers. They are often aware of any plans the customers may be considering for the future. There are, however, several drawbacks to using salesforce opinions. One is that staff members may be unable to distinguish between what customers would
like to do and what they actually
will do. Another is that these people are sometimes overly influenced by recent experiences. Thus, after several periods of low sales, their estimates may tend to become pessimistic. After several periods of good sales, they may tend to be too optimistic. In addition, if forecasts are used to establish sales quotas, there will be a conflict of interest because it is to the salesperson’s advantage to provide low sales estimates.

Consumer Surveys

Because it is the consumers who ultimately determine demand, it seems natural to solicit input from them. In some instances, every customer or potential customer can be contacted. However, usually there are too many customers or there is no way to identify all potential customers. Therefore, organizations seeking consumer input usually resort to consumer surveys, which enable them to
sample consumer opinions. The obvious advantage of consumer surveys is that they can tap information that might not be available elsewhere. On the other hand, a considerable amount of knowledge and skill is required to construct a survey, administer it, and correctly interpret the results for valid information. Surveys can be expensive and time-consuming. In addition, even under the best conditions, surveys of the general public must contend with the possibility of irrational behavior patterns. For example, much of the consumer’s thoughtful information gathering before purchasing a new car is often undermined by the glitter of a new car showroom or a high-pressure sales pitch. Along the same lines, low response rates to a mail survey should—but often don’t—make the results suspect.

If these and similar pitfalls can be avoided, surveys can produce useful information.

Other Approaches

A manager may solicit opinions from a number of other managers and staff people. Occasionally, outside experts are needed to help with a forecast. Advice may be needed on political or economic conditions in the United States or a foreign country, or some other aspect of importance with which an organization lacks familiarity.

Another approach is the

Delphi method
, an iterative process intended to achieve a consensus forecast. This method involves circulating a series of questionnaires among individuals who possess the knowledge and ability to contribute meaningfully. Responses are kept anonymous, which tends to encourage honest responses and reduces the risk that one person’s opinion will prevail. Each new questionnaire is developed using the information extracted from the previous one, thus enlarging the scope of information on which participants can base their judgments.

The Delphi method has been applied to a variety of situations, not all of which involve forecasting. The discussion here is limited to its use as a forecasting tool.

As a forecasting tool, the Delphi method is useful for
technological forecasting; that is, for assessing changes in technology and their impact on an organization. Often, the goal is to predict
when a certain event will occur. For instance, the goal of a Delphi forecast might be to predict when video telephones might be installed in at least 50 percent of residential homes or when a vaccine for a disease might be developed and ready for mass distribution. For the most part, these are long-term, single-time forecasts, which usually have very little hard information to go by or data that are costly to obtain, so the problem does not lend itself to analytical techniques. Rather, judgments of experts or others who possess sufficient knowledge to make predictions are used.

Naive Methods

A simple but widely used approach to forecasting is the naive approach. A

naive forecast
uses a single previous value of a time series as the basis of a forecast. The naive approach can be used with a stable series (variations around an average), with seasonal variations, or with trend. With a stable series, the last data point becomes the forecast for the next period. Thus, if demand for a product last week was 20 cases, the forecast for this week is 20 cases. With seasonal variations, the forecast for this “season” is equal to the value of the series last “season.” For example, the forecast for demand for turkeys this Thanksgiving season is equal to demand for turkeys last Thanksgiving; the forecast of the number of checks cashed at a bank on the first day of the month next month is equal to the number of checks cashed on the first day of this month; and the forecast for highway traffic volume this Friday is equal to the highway traffic volume last Friday. For data with trend, the forecast is equal to the last value of the series plus or minus the difference between the last two
page 83values of the series. For example, suppose the last two values were 50 and 53. The next forecast would be 56:

Although at first glance the naive approach may appear
too simplistic, it is nonetheless a legitimate forecasting tool. Consider the advantages: It has virtually no cost, it is quick and easy to prepare because data analysis is nonexistent, and it is easily understandable. The main objection to this method is its inability to provide highly accurate forecasts. However, if resulting accuracy is acceptable, this approach deserves serious consideration. Moreover, even if other forecasting techniques offer better accuracy, they will almost always involve a greater cost. The accuracy of a naive forecast can serve as a standard of comparison against which to judge the cost and accuracy of other techniques. Thus, managers must answer the question: Is the increased accuracy of another method worth the additional resources required to achieve that accuracy?

page 84 

Techniques for Averaging

Historical data typically contain a certain amount of random variation, or
white noise, that tends to obscure systematic movements in the data. This randomness arises from the combined influence of many—perhaps a great many—relatively unimportant factors, and it cannot be reliably predicted. Averaging techniques smooth variations in the data. Ideally, it would be desirable to completely remove any randomness from the data and leave only “real” variations, such as changes in the demand. As a practical matter, however, it is usually impossible to distinguish between these two kinds of variations, so the best one can hope for is that the small variations are random and the large variations are “real.”

Averaging techniques smooth fluctuations in a time series because the individual highs and lows in the data offset each other when they are combined into an average. A forecast based on an average thus tends to exhibit less variability than the original data (see
Figure 3.2). This can be advantageous because many of these movements merely reflect random variability rather than a true change in the series. Moreover, because responding to changes in expected demand often entails considerable cost (e.g., changes in production rate, changes in the size of a workforce, inventory changes), it is desirable to avoid reacting to minor variations. Thus, minor variations are treated as random variations, whereas larger variations are viewed as more likely to reflect “real” changes, although these, too, are smoothed to a certain degree.

Averaging techniques generate forecasts that reflect recent values of a time series (e.g., the average value over the last several periods). These techniques work best when a series tends to vary around an average, although they also can handle step changes or gradual changes in the level of the series. Three techniques for averaging are described in this section:

1. Moving average

2. Weighted moving average

3. Exponential smoothing

Moving Average One weakness of the naive method is that the forecast just
traces the actual data, with a lag of one period; it does not smooth at all. But by expanding the amount of historical data a forecast is based on, this difficulty can be overcome. A

moving average
forecast uses a
number of the most recent actual data values in generating a forecast. The moving average forecast can be computed using the following equation:

where

For example, MA
₃ would refer to a three-period moving average forecast, and MA
₅ would refer to a five-period moving average forecast.

page 85 

Note that in a moving average, as each new actual value becomes available, the forecast is updated by adding the newest value and dropping the oldest and then recomputing the average. Consequently, the forecast “moves” by reflecting only the most recent values.

In computing a moving average, including a
moving total column—which gives the sum of the
n most current values from which the average will be computed—aids computations. To update the moving total: Subtract the oldest value from the newest value and add that amount to the moving total for each update.

Figure 3.3 illustrates a three-period moving average forecast plotted against actual demand over 31 periods. Note how the moving average forecast
lags the actual values and how
smooth the forecasted values are compared with the actual values.

The moving average can incorporate as many data points as desired. In selecting the number of periods to include, the decision maker must take into account that the number of data points in the average determines its sensitivity to each new data point: The fewer the data points in an average, the more sensitive (responsive) the average tends to be. (See
Figure 3.4A.)

If responsiveness is important, a moving average with relatively few data points should be used. This will permit quick adjustment to, say, a step change in the data, but it also will
page 86cause the forecast to be somewhat responsive even to random variations. Conversely, moving averages based on more data points will smooth more but be less responsive to “real” changes. Hence, the decision maker must weigh the cost of responding more slowly to changes in the data against the cost of responding to what might simply be random variations. A review of forecast errors can help in this decision.

The advantages of a moving average forecast are that it is easy to compute and easy to understand. A possible disadvantage is that all values in the average are weighted equally. For instance, in a 10-period moving average, each value has a weight of 1/10. Hence, the oldest value has the
same weight as the most recent value. If a change occurs in the series, a moving average forecast can be slow to react, especially if there are a large number of values in the average. Decreasing the number of values in the average increases the weight of more recent values, but it does so at the expense of losing potential information from less recent values.

Weighted Moving Average A

weighted average
is similar to a moving average, except that it typically assigns more weight to the most recent values in a time series. For instance, the most recent value might be assigned a weight of .40, the next most recent value a weight of .30, the next after that a weight of .20, and the next after that a weight of .10. Note that the weights must sum to 1.00, and that the heaviest weights are assigned to the most recent values.

where

Note that if four weights are used, only the
four most recent demands are used to prepare the forecast.

The advantage of a weighted average over a simple moving average is that the weighted average is more reflective of the most recent occurrences. However, the choice of weights is somewhat arbitrary and generally involves the use of trial and error to find a suitable weighting scheme.

Exponential Smoothing

Exponential smoothing
is a sophisticated weighted averaging method that is still relatively easy to use and understand. Each new forecast is based on the previous forecast plus a percentage of the difference between that forecast and the actual value of the series at that point. That is:

where (Actual − Previous forecast) represents the forecast error and
α is a percentage of the error. More concisely,

where

The smoothing constant
α represents a percentage of the forecast error. Each new forecast is equal to the previous forecast plus a percentage of the previous error. For example, suppose the previous forecast was 42 units, actual demand was 40 units, and
α = .10. The new forecast would be computed as follows:

Then, if the actual demand turns out to be 43, the next forecast would be

An alternate form of Formula 3–3a reveals the weighting of the previous forecast and the latest actual demand:

For example, if
α = .10, this would be

The quickness of forecast adjustment to error is determined by the smoothing constant,
α. The closer its value is to zero, the slower the forecast will be to adjust to forecast errors (i.e., the greater the smoothing). Conversely, the closer the value of
α is to 1.00, the greater the responsiveness and the less the smoothing. This is illustrated in
Figure 3.4B.

page 88 

Selecting a smoothing constant is basically a matter of judgment or trial and error, using forecast errors to guide the decision. The goal is to select a smoothing constant that balances the benefits of smoothing random variations with the benefits of responding to real changes if and when they occur. Commonly used values of
α range from .05 to .50. Low values of
α are used when the underlying average tends to be stable; higher values are used when the underlying average is susceptible to change.

Some computer packages include a feature that permits automatic modification of the smoothing constant if the forecast errors become unacceptably large.

Exponential smoothing is one of the most widely used techniques in forecasting, partly because of its ease of calculation and partly because of the ease with which the weighting scheme can be altered—simply by changing the value of
α.

Note:
Exponential smoothing should begin several periods back to enable forecasts to adjust to the data, instead of starting one period back. A number of different approaches can be used to obtain a
starting forecast, such as the average of the first several periods, a subjective estimate, or the first actual value as the forecast for period 2 (i.e., the naive approach). For simplicity, the naive approach is used in this book. In practice, using an average of, say, the first three values as a forecast for period 4 would provide a better starting forecast because that would tend to be more representative.

Other Forecasting Methods

You may find two other approaches to forecasting interesting. They are briefly described in this section.

Focus Forecasting Some companies use forecasts based on a “best recent performance” basis. This approach, called

focus forecasting
, was developed by Bernard T. Smith,
page 89and is described in several of his books.

¹
It involves the use of several forecasting methods (e.g., moving average, weighted average, and exponential smoothing) all being applied to the last few months of historical data after any irregular variations have been removed. The method that has the highest accuracy is then used to make the forecast for the next month. This process is used for each product or service, and is repeated monthly.

Diffusion Models When new products or services are introduced, historical data are not generally available on which to base forecasts. Instead, predictions are based on rates of product adoption and usage spread from other established products, using mathematical diffusion models. These models take into account such factors as market potential, attention from mass media, and word of mouth. Although the details are beyond the scope of this text, it is important to point out that diffusion models are widely used in marketing and to assess the merits of investing in new technologies.

Techniques for Trend

Analysis of trend involves developing an equation that will suitably describe trend (assuming that trend is present in the data). The trend component may be linear, or it may not. Some commonly encountered nonlinear trend types are illustrated in
Figure 3.5. A simple plot of the data often can reveal the existence and nature of a trend. The discussion here focuses exclusively on
linear trends because these are fairly common.

There are two important techniques that can be used to develop forecasts when trend is present. One involves use of a trend equation; the other is an extension of exponential smoothing.

Trend Equation A

linear trend equation
has the form

where

page 90 

For example, consider the trend equation
F

_t
= 45 + 5
t. The value of
F

_t
when
t = 0 is 45, and the slope of the line is 5, which means that, on average, the value of
F

_t
will increase by five units for each time period. If
t = 10, the forecast,
F

_t
, is 45 + 5(10) = 95 units. The equation can be plotted by finding two points on the line. One can be found by substituting some value of
t into the equation (e.g.,
t = 10) and then solving for
F

_t
. The other point is
a (i.e.,
F

_t
at
t = 0). Plotting those two points and drawing a line through them yields a graph of the linear trend line.

The coefficients of the line,
a and
b, are based on the following two equations:

where

Note that these two equations are identical to those used for computing a linear regression line, except that
t replaces
x in the equations. Values for the trend equation can be obtained easily by using the Excel template.

page 92 

Trend-Adjusted Exponential Smoothing

A variation of simple exponential smoothing can be used when a time series exhibits a
linear trend. It is called

trend-adjusted exponential smoothing
, or sometimes
double smoothing, to differentiate it from simple exponential smoothing, which is appropriate only when data vary around an average or have step or gradual changes. If a series exhibits a trend, and simple smoothing is used on it, the forecasts will all lag the trend: If the data are increasing, each forecast will be too low; if decreasing, each forecast will be too high.

The trend-adjusted forecast (TAF) is composed of two elements—a smoothed error and a trend factor.

where

and

where

In order to use this method, one must select values of
α and
β (usually through trial and error) and make a starting forecast and an estimate of trend.

Using the cell phone data from the previous example (where it was concluded that the data exhibited a linear trend), use trend-adjusted exponential smoothing to obtain forecasts for periods 6 through 11, with
α = .40 and
β = .30.

page 93 

The initial estimate of trend is based on the net change of 28 for the
three changes from period 1 to period 4, for an average of 9.33. The Excel spreadsheet is shown in
Table 3.2. Notice that an initial estimate of trend is estimated from the first four values and that the starting forecast (period 5) is developed using the previous (period 4) value of 728 plus the initial trend estimate:

TABLE 3.2

Using the Excel template for trend-adjusted smoothing

Source: Microsoft

Unlike a linear trend line, trend-adjusted smoothing has the ability to adjust to
changes in trend. Of course, trend projections are much simpler with a trend line than with trend-adjusted forecasts, so a manager must decide which benefits are most important when choosing between these two techniques for trend.

Techniques for Seasonality

Seasonal variations
in time-series data are regularly repeating upward or downward movements in series values that can be tied to recurring events.
Seasonality may refer to regular annual variations. Familiar examples of seasonality are weather variations (e.g., sales of winter and summer sports equipment) and vacations or holidays (e.g., airline travel, greeting card sales, visitors at tourist and resort centers). The term
seasonal variation is also applied to daily, weekly, monthly, and other regularly recurring patterns in data. For example, rush hour traffic occurs twice a day—incoming in the morning and outgoing in the late afternoon. Theaters and restaurants often experience weekly demand patterns, with demand higher later in the week. Banks may experience daily seasonal variations (heavier traffic during the noon hour and just before closing), weekly variations (heavier toward the end of the week), and monthly variations (heaviest around the beginning of the month because of Social Security, payroll, and welfare checks being cashed or deposited). Mail volume; sales of toys, beer, automobiles, and turkeys; highway usage; hotel registrations; and gardening also exhibit seasonal variations.

page 94 

Seasonality in a time series is expressed in terms of the amount that actual values deviate from the
average value of a series. If the series tends to vary around an average value, then seasonality is expressed in terms of that average (or a moving average); if trend is present, seasonality is expressed in terms of the trend value.

There are two different models of seasonality: additive and multiplicative. In the
additive model, seasonality is expressed as a
quantity (e.g., 20 units), which is added to or subtracted from the series average in order to incorporate seasonality. In the
multiplicative model, seasonality is expressed as a
percentage of the average (or trend) amount (e.g., 1.10), which is then used to multiply the value of a series to incorporate seasonality.
Figure 3.6 illustrates the two models for a linear trend line. In practice, businesses use the multiplicative model much more widely than the additive model, because it tends to be more representative of actual experience, so we will focus exclusively on the multiplicative model.

The seasonal percentages in the multiplicative model are referred to as

seasonal relatives
or
seasonal indexes. Suppose that the seasonal relative for the quantity of toys sold in May at a store is 1.20. This indicates that toy sales for that month are 20 percent above the monthly average. A seasonal relative of .90 for July indicates that July sales are 90 percent of the monthly average.

Knowledge of seasonal variations is an important factor in retail planning and scheduling. Moreover, seasonality can be an important factor in capacity planning for systems that must be designed to handle peak loads (e.g., public transportation, electric power plants, highways, and bridges). Knowledge of the extent of seasonality in a time series can enable one to
remove seasonality from the data (i.e., to seasonally adjust data) in order to discern other patterns
page 95or the lack of patterns in the series. Thus, one frequently reads or hears about “seasonally adjusted unemployment” and “seasonally adjusted personal income.”

The next section briefly describes how seasonal relatives are used.

Using Seasonal Relatives Seasonal relatives are used in two different ways in forecasting. One way is to
deseasonalize
data; the other way is to
incorporate seasonality in a forecast.

To deseasonalize data is to remove the seasonal component from the data in order to get a clearer picture of the nonseasonal (e.g., trend) components. Deseasonalizing data is accomplished by
dividing each data point by its corresponding seasonal relative (e.g., divide November demand by the November relative, divide December demand by the December relative, and so on).

Incorporating seasonality in a forecast is useful when demand has both trend (or average) and seasonal components. Incorporating seasonality can be accomplished in this way:

1. Obtain trend estimates for desired periods using a trend equation.

2. Add seasonality to the trend estimates by
   multiplying (assuming a multiplicative model is appropriate) these trend estimates by the corresponding seasonal relative (e.g., multiply the November trend estimate by the November seasonal relative, multiply the December trend estimate by the December seasonal relative, and so on).

Example 4 illustrates these two techniques.

page 96 

Computing Seasonal Relatives A widely used method for computing seasonal relatives involves the use of a

centered moving average
. This approach effectively accounts for any trend (linear or curvilinear) that might be present in the data. For example,
Figure 3.7 illustrates how a three-period centered moving average closely tracks the data originally shown in
Figure 3.3.

Manual computation of seasonal relatives using the centered moving average method is a bit cumbersome, so the use of software is recommended. Manual computation is illustrated in Solved Problem 4 at the end of the chapter. The Excel template (on the website) is a simple and convenient way to obtain values of seasonal relatives (indexes). Example 5 illustrates this approach.

For practical purposes, you can round the relatives to two decimal places. Thus, the seasonal (standard) index values are:

Computing Seasonal Relatives Using the Simple Average Method The simple average (SA) method is an alternative way to compute seasonal relatives. Each seasonal relative is the average for that season divided by the average of all seasons. This method is illustrated in Example 5, where the seasons are days. Note that there is no need to standardize the relatives when using the SA method.

page 97 

page 98 

The obvious advantage of the SA method compared to the centered MA method is the simplicity of computations. When the data have a stationary mean (i.e., variation around an average), the SA method works quite well, providing values of relatives that are quite close to those obtained using the centered MA method, which is generally accepted as accurate. Conventional wisdom is that the SA method should not be used when linear trend is present in the data. However, it can be used to obtain fairly good values of seasonal relatives as long as the ratio of the intercept to the slope is large, or when variations are large relative to the slope, shown as follows. Also, the larger the ratio, the smaller the error. The general relationship is illustrated in the following figure.

Techniques for Cycles

Cycles are up-and-down movements similar to seasonal variations but of longer duration—say, two to six years between peaks. When cycles occur in time-series data, their frequent irregularity makes it difficult or impossible to project them from past data because turning points are difficult to identify. A short moving average or a naive approach may be of some value, although both will produce forecasts that lag cyclical movements by one or several periods.

The most commonly used approach is explanatory: Search for another variable that relates to, and
leads, the variable of interest. For example, the number of housing starts (i.e., permits to build houses) in a given month often is an indicator of demand a few months later for products and services directly tied to construction of new homes (landscaping; sales of washers and dryers, carpeting, and furniture; new demands for shopping, transportation, schools). Thus, if an organization is able to establish a high correlation with such a
leading variable (i.e., changes in the variable precede changes in the variable of interest), it can develop an equation that describes the relationship, enabling forecasts to be made. It is important that a persistent relationship exists between the two variables. Moreover, the higher the correlation, the better the chances that the forecast will be on target.

Simple Linear Regression

The simplest and most widely used form of regression involves a linear relationship between two variables. A plot of the values might appear like that in
Figure 3.8. The object in linear regression is to obtain an equation of a straight line that minimizes the sum of squared vertical
page 99deviations of data points from the line (i.e., the
least squares criterion). This

least squares line
has the equation

where

(
Note: It is conventional to represent values of the predicted variable on the
y axis and values of the predictor variable on the
x axis.)
Figure 3.9 is a general graph of a linear regression line.

The coefficients
a and
b of the line are based on the following two equations:

where

page 100 

TABLE 3.3

Using the Excel template for linear regression

Source: Microsoft

One indication of how accurate a prediction might be for a linear regression line is the amount of scatter of the data points around the line. If the data points tend to be relatively close to the line, predictions using the linear equation will tend to be more accurate than if the data points are widely scattered. The scatter can be summarized using the

standard error of estimate
. It can be computed by finding the vertical difference between each data point and the
page 101computed value of the regression equation for that value of
x, squaring each difference, adding the squared differences, dividing by
n − 2, and then finding the square root of that value.

where

For the data given in
Table 3.3, the error column shows the
y −
y
_c differences. Squaring each error and summing the squares yields .01659. Hence, the standard error of estimate is

One application of regression in forecasting relates to the use of indicators. These are uncontrollable variables that tend to lead or precede changes in a variable of interest. For example, changes in the Federal Reserve Board’s discount rate may influence certain business activities. Similarly, an increase in energy costs can lead to price increases for a wide range of products and services. Careful identification and analysis of indicators may yield insight into possible future demand in some situations. There are numerous published indexes and websites from which to choose.

²
These include:

Net change in inventories on hand and on order

Interest rates for commercial loans

Industrial output

Consumer price index (CPI)

The wholesale price index

Stock market prices

page 102 

Other potential indicators are population shifts, local political climates, and activities of other firms (e.g., the opening of a shopping center may result in increased sales for nearby businesses). Three conditions are required for an indicator to be valid:

1. The relationship between movements of an indicator and movements of the variable should have a logical explanation.

2. Movements of the indicator must precede movements of the dependent variable by enough time so that the forecast isn’t outdated before it can be acted upon.

3. A fairly high correlation should exist between the two variables.

Correlation
measures the strength and direction of relationship between two variables. Correlation can range from −1.00 to +1.00. A correlation of +1.00 indicates that changes in one variable are always matched by changes in the other; a correlation of −1.00 indicates that increases in one variable are matched by decreases in the other; and a correlation close to zero indicates little
linear relationship between two variables. The correlation between two variables can be computed using the equation

The square of the correlation coefficient,
r
², provides a measure of the percentage of variability in the values of
y that is “explained” by the independent variable. The possible values of
r
² range from 0 to 1.00. The closer
r
² is to 1.00, the greater the percentage of explained variation. A high value of
r
², say .80 or more, would indicate that the independent variable is a good predictor of values of the dependent variable. A low value, say .25 or less, would indicate a poor predictor, and a value between .25 and .80 would indicate a moderate predictor.

Comments on the Use of Linear Regression Analysis

Use of simple regression analysis implies that certain assumptions have been satisfied. Basically, these are as follows:

• Variations around the line are random. If they are random, no patterns such as cycles or trends should be apparent when the line and data are plotted.

• Deviations around the average value (i.e., the line) should be normally distributed. A concentration of values close to the line with a small proportion of larger deviations supports the assumption of normality.

• Predictions are being made only within the range of observed values.

If the assumptions are satisfied, regression analysis can be a powerful tool. To obtain the best results, observe the following:

• Always plot the data to verify that a linear relationship is appropriate.

• The data may be time-dependent. Check this by plotting the dependent variable versus time; if patterns appear, use analysis of time series instead of regression, or use time as an independent variable as part of a
  multiple regression analysis.

• A small correlation may imply that other variables are important.

In addition, note these weaknesses of regression:

• Simple linear regression applies only to linear relationships with
  one independent variable.

• One needs a considerable amount of data to establish the relationship—in practice, 20 or more observations.

• All observations are weighted equally.

page 103 

page 104 

Nonlinear and Multiple Regression Analysis

Simple linear regression may prove inadequate to handle certain problems because a linear model is inappropriate or because more than one predictor variable is involved. When nonlinear relationships are present, you should employ nonlinear regression; models that involve more than one predictor require the use of multiple regression analysis. While these analyses are beyond the scope of this text, you should be aware that they are often used. Multiple regression forecasting substantially increases data requirements.

Summarizing Forecast Accuracy

Forecast accuracy is a significant factor when deciding among forecasting alternatives. Accuracy is based on the historical error performance of a forecast.

Three commonly used measures for summarizing historical errors are the

mean absolute deviation (MAD)
, the

mean squared error (MSE)
, and the

mean absolute percent error (MAPE)
. MAD is the average absolute error, MSE is the average of squared errors, and MAPE is the average absolute percent error. The formulas used to compute MAD,

³
MSE, and MAPE are as follows:

Example 9 illustrates the computation of MAD, MSE, and MAPE.

page 107 

From a computational standpoint, the difference between these measures is that MAD weights all errors evenly, MSE weights errors according to their
squared values, and MAPE weights according to
relative error.

One use for these measures is to compare the accuracy of alternative forecasting methods. For instance, a manager could compare the results to determine which one yields the
lowest MAD, MSE, or MAPE for a given set of data. Another use is to track error performance over time to decide if attention is needed. Is error performance getting better or worse, or is it staying about the same?

In some instances, historical error performance is secondary to the ability of a forecast to respond to changes in data patterns. Choice among alternative methods would then focus on the cost of not responding quickly to a change relative to the cost of responding to changes that are not really there (i.e., random fluctuations).

Overall, the operations manager must settle on the relative importance of historical performance versus responsiveness and whether to use MAD, MSE, or MAPE to measure historical performance. MAD is the easiest to compute, but weights errors linearly. MSE squares errors, thereby giving more weight to larger errors, which typically cause more problems. MAPE should be used when there is a need to put errors in perspective. For example, an error of 10 in a forecast of 15 is huge. Conversely, an error of 10 in a forecast of 10,000 is insignificant. Hence, to put large errors in perspective, MAPE would be used. Another use of MAPE is when there is a need to compare forecast errors for
different products or services. One example would be forecasts for store brands versus national brands.

What Does Product and Service Design Do?

The primary focus of product or service design should be on customer satisfaction. The various activities and responsibilities of product and service design include the following (functional interactions are shown in parentheses):

1. Translate customer wants and needs into product and service requirements (marketing, operations)

2. Refine existing products and services (marketing)

3. Develop new products and/or services (marketing, operations)

4. Formulate quality goals (marketing, operations)

5. Formulate cost targets (accounting, finance, operations)

6. Construct and test prototypes (operations, marketing, engineering)

7. Document specifications

8. Translate product and service specifications into
   process specifications (engineering, operations)

Product and service design involves or affects nearly every functional area of an organization. However, marketing and operations have major involvement.

page 141 

Objectives of Product and Service Design

Primary consideration: Customer satisfaction.

Secondary considerations: Cost or profit, quality, ability to produce a product or provide a service, ethics/safety, and sustainability.

Key Questions

From a buyer’s standpoint, most purchasing decisions entail two fundamental considerations; one is cost and the other is quality or performance. From the organization’s standpoint, the key questions are:

1. Is there demand for it? What is the potential size of the market, and what is the expected demand profile (will demand be long term or short term, will it grow slowly or quickly)?

2. Can we do it? Do we have the necessary knowledge, skills, equipment, capacity, and supply chain capability? For products, this is known as
   
   manufacturability
   ; for services, this is known as
   
   serviceability
   . Also, is outsourcing some or all of the work an option?

1. What level of quality is appropriate? What do customers expect? What level of quality do competitors provide for similar items? How would it fit with our current offerings?

2. Does it make sense from an economic standpoint? What are the potential liability issues, ethical considerations, sustainability issues, costs, and profits? For nonprofits, is the cost within budget?

Reasons for Product and Service Design or Redesign

Product and service design typically has
strategic implications for the success and prosperity of an organization. Consequently, decisions in this area are some of the most fundamental that managers must make. Product and service design or redesign should be closely tied to an organization’s strategy.

Organizations become involved in product and service design or redesign for a variety of reasons. The main forces that initiate design or redesign are market opportunities and threats. The factors that give rise to market opportunities and threats can be one or more
changes:

• Economic (e.g., low demand, excessive warranty claims, the need to reduce costs)

• Social and demographic (e.g., aging baby boomers, population shifts)

• Political, liability, or legal (e.g., government changes, safety issues, new regulations)

• Competitive (e.g., new or changed products or services, new advertising/promotions)

• Cost or availability (e.g., of raw materials, components, labor, water, energy)

• Technological (e.g., in product components, processes)

While each of these factors may seem obvious, let’s reflect a bit on technological changes, which can create a need for product or service design changes in several different ways. An obvious way is new technology that can be used directly in a product or service (e.g., a faster, smaller microprocessor that spawns a new generation of smartphones). Technology also can indirectly affect product and service design: Advances in processing technology may require altering an existing design to make it compatible with the new processing technology. Still another way that technology can impact product design is illustrated by digital recording technology that allows television viewers to skip commercials when they view a recorded program. This means that advertisers (who support a television program) can’t get their message to viewers. To overcome this, some advertisers have adopted a strategy of making their products an integral part of a television program, say by having their products prominently displayed and/or mentioned by the actors as a way to call viewers’ attention to their products without the need for commercials.

The following reading suggests another potential benefit of product redesign.

page 142 

Cradle-to-Grave Assessment

Cradle-to-grave assessment
, also known as life cycle analysis, is the assessment of the environmental impact of a product or service throughout its useful life, focusing on such factors as global warming (the amount of carbon dioxide released into the atmosphere), smog formation, oxygen depletion, and solid waste generation. For products, cradle-to-grave analysis takes into account impacts in every phase of a product’s life cycle, from raw material extraction from the earth, or the growing and harvesting of plant materials, through fabrication of parts and assembly operations,
page 147or other processes used to create products, as well as the use or consumption of the product, and final disposal at the end of a product’s useful life. It also considers energy consumption, pollution and waste, and transportation in all phases. Although services generally involve less use of materials, cradle-to-grave assessment of services is nonetheless important, because services consume energy and involve many of the same or similar processes that products involve.

The goal of cradle-to-grave assessment is to choose products and services that have the least environmental impact, while still taking into account economic considerations. The procedures of cradle-to-grave assessment are part of the ISO 14000 environmental management standards, which are discussed in
Chapter 9.

End-of-Life Programs

End-of-life (EOL) programs deal with products that have reached the end of their useful lives. The products include both consumer products and business equipment. The purpose of these programs is to reduce the dumping of products, particularly electronic equipment, in landfills or third-world countries, as has been the common practice, or incineration, which converts materials into hazardous air and water emissions and generates toxic ash. Although the programs are not limited to electronic equipment, that equipment poses problems because it typically contains toxic materials such as lead, cadmium, chromium, and other heavy metals. IBM provides a good example of the potential of EOL programs. Over the last 15 years, it has collected about 2 billion pounds of product and product waste.

The Three Rs: Reduce, Reuse, and Recycle

Designers often reflect on three particular aspects of potential cost savings and reducing environmental impact: reducing the use of materials through value analysis; refurbishing and then reselling returned goods that are deemed to have additional useful life, which is referred to as remanufacturing; and reclaiming parts of unusable products for recycling.

Reduce: Value Analysis

Value analysis
refers to an examination of the
function of parts and materials in an effort to reduce the cost and/or improve the performance of a product. Typical questions that would be asked as part of the analysis include: Could a cheaper part or material be used? Is the function necessary? Can the function of two or more parts or components be performed by a single part for a lower cost? Can a part be simplified? Could product specifications be relaxed, and would this result in a lower price? Could standard parts be substituted for nonstandard parts?
Table 4.1 provides a checklist of questions that can guide a value analysis.

TABLE 4.1

Overview of value analysis

The following reading describes how Kraft Foods is working to reduce water and energy use, CO
₂ and plant waste, and packaging.

page 148 

Reuse: Remanufacturing

An emerging concept in manufacturing is the remanufacturing of products.

Remanufacturing
refers to refurbishing used products by replacing worn-out or defective components, and reselling the products. This can be done by the original manufacturer, or another company. Among the products that have remanufactured components are automobiles, printers, copiers, cameras, computers, and telephones.

There are a number of important reasons for doing this. One is that a remanufactured product can be sold for about 50 percent of the cost of a new product. Another is that the process requires mostly unskilled and semiskilled workers. Also, in the global market, European lawmakers are increasingly requiring manufacturers to take back used products, because this means fewer products end up in landfills and there is less depletion of natural resources, such as raw materials and fuel.

page 149 

Designing products so they can be more easily taken apart has given rise to yet another design consideration:

Design for disassembly (DFD)
.

Recycle

Recycling is sometimes an important consideration for designers.

Recycling
means recovering materials for future use. This applies not only to manufactured parts but also to materials used during production, such as lubricants and solvents. Reclaimed metal or plastic parts may be melted down and used to make different products. (See readings above and on next page.)

Companies recycle for a variety of reasons, including

1. Cost savings

2. Environment concerns

3. Environmental regulations

An interesting note: Companies that want to do business in the European Union must show that a specified proportion of their products are recyclable.

The pressure to recycle has given rise to the term

design for recycling (DFR)
, referring to product design that takes into account the ability to disassemble a used product to recover the recyclable parts.

page 150 

Strategies for Product or Service Life Stages

Most, but not all, products and services go through a series of stages over their useful life, sometimes referred to as their life cycle, as shown in
Figure 4.1. Demand typically varies by phase. Different phases call for different strategies. In every phase, forecasts of demand and cash flow are key inputs for strategy.

When a product or service is introduced, it may be treated as a curiosity item. Many potential buyers may suspect that all the bugs haven’t been worked out and that the price may drop after the introductory period. Strategically, companies must carefully weigh the trade-offs in getting all the bugs out versus getting a leap on the competition, as well as getting to market at an advantageous time. For example, introducing new high-tech products or features during peak back-to-school buying periods or holiday buying periods can be highly desirable.

It is important to have a reasonable forecast of initial demand so an adequate supply of product or an adequate service capacity is in place.

Over time, design improvements and increasing demand yield higher reliability and lower costs, leading the growth in demand. In the growth phase, it is important to obtain accurate projections of the demand growth rate and how long that will persist, and then to ensure that capacity increases coincide with increasing demand.

In the next phase, the product or service reaches maturity, and demand levels off. Few, if any, design changes are needed. Generally, costs are low and productivity is high. New uses for products or services can extend their life and increase the market size. Examples include baking soda, duct tape, and vinegar. The maker of LEGOs has found a way to grow its market, as described in the following reading.

In the decline phase, decisions must be made about whether to discontinue a product or service and replace it with new ones or abandon the market, or to attempt to find new uses or new users for the existing product or service. For example, duct tape and baking
page 152soda are two products that have been employed well beyond their original uses of taping heating and cooling ducts and cooking. The advantages of keeping existing products or services can be tremendous. The same workers can produce the product or provide the service using much of the same equipment, the same supply chain, and perhaps the same distribution channels. Consequently, costs tend to be very low, and additional resource needs and training needs are low.

page 153 

Some products do not exhibit life cycles: wooden pencils; paper clips; nails; knives, forks, and spoons; drinking glasses; and similar items. However, most new products do.

Some service life cycles are related to the life cycles of products. For example, as older products are phased out, services such as installation and repair of the older products also phase out.

Wide variations exist in the amount of time a particular product or service takes to pass through a given phase of its life cycle: Some pass through various stages in a relatively short period; others take considerably longer. Often, it is a matter of the basic
need for the item and the
rate of technological change. Some toys, novelty items, and style items have a life cycle of less than one year, whereas other, more useful items, such as clothes washers and dryers, may last for many years before yielding to technological change.

Product Life Cycle Management

Product life cycle management (PLM)
is a systematic approach to managing the series of changes a product goes through, from its conception, design, and development, through production and any redesign, to its end of life. PLM incorporates everything related to a particular product. That includes data pertaining to production processes, business processes, people, and anything else related to the product.

PLM software can be used to automate the management of product-related data and integrate the data with other business processes, such as enterprise resource planning (discussed in
Chapter 12). A goal of PLM is to eliminate waste and improve efficiency. For example, PLM is considered to be an integral part of lean production (discussed in
Chapter 14).

There are three phases of PLM application:

• Beginning of life, which involves design and development;

• Middle of life, which involves working with suppliers, managing product information and warranties; and

• End of life, which involves strategies for product discontinuance, disposal, or recycling.

Although PLM is generally associated with manufacturing, the same management structure can be applied to software development and services.

Degree of Standardization

An important issue that often arises in both product/service design and process design is the degree of standardization.

Standardization
refers to the extent to which there is absence of variety in a product, service, or process. Standardized products are made in large quantities of identical items; calculators, computers, and 2 percent milk are examples. Standardized service implies that every customer or item processed receives essentially the same service. An automatic car wash is a good example: Each car, regardless of how clean or dirty it is, receives the same service. Standardized processes deliver standardized service or produce standardized goods.

Standardization carries a number of important benefits, as well as certain disadvantages. Standardized products are immediately available to customers. Standardized products mean
interchangeable parts, which greatly lower the cost of production while increasing productivity and making replacement or repair relatively easy compared with that of customized parts. Design costs are generally lower. For example, automobile producers standardize key components of automobiles across product lines; components such as brakes, electrical systems, and other “under-the-skin” parts would be the same for all car models. By reducing variety, companies save time and money while increasing the quality and reliability of their products.

Another benefit of standardization is reduced time and cost to train employees and reduced time to design jobs. Similarly, the scheduling of work, inventory handling, and purchasing and accounting activities become much more routine, and quality is more consistent.

Lack of standardization can at times lead to serious difficulties and competitive struggles. For example, the use of the English system of measurement by U.S. manufacturers, while most of the rest of the world’s manufacturers use the metric system, has led to problems
page 154in selling U.S. goods in foreign countries and in buying foreign machines for use in the United States. This may make it more difficult for U.S. firms to compete in the European Union.

Standardization also has disadvantages. A major one relates to the reduction in variety. This can limit the range of customers to whom a product or service appeals. And that creates a risk that a competitor will introduce a better product or greater variety and realize a competitive advantage. Another disadvantage is that a manufacturer may freeze (standardize) a design prematurely and, once the design is frozen, find compelling reasons to resist modification.

Obviously, designers must consider important issues related to standardization when making choices. The major advantages and disadvantages of standardization are summarized in
Table 4.2.

TABLE 4.2

Advantages and disadvantages of standardization

Designing for Mass Customization

Companies like standardization because it enables them to produce high volumes of relatively low-cost products, albeit products with little variety. Customers, on the other hand, typically prefer more variety, although they like the low cost. The question for producers is how to resolve these issues without (1) losing the benefits of standardization, and (2) incurring a host of problems that are often linked to variety. These include increasing the resources needed to achieve design variety; increasing variety in the production process, which would add to the skills necessary to produce products, causing a decrease in productivity; creating an additional inventory burden during and after production, by having to carry replacement parts for the increased variety of parts; and adding to the difficulty of diagnosing and repairing product failures. The answer, at least for some companies, is

mass customization
, a strategy of producing standardized goods or services, but incorporating some degree of customization in the final product or service. Several tactics make this possible. One is
delayed differentiation, and another is
modular design. (See reading on following page.)

Delayed differentiation
is a
postponement tactic: the process of producing, but not quite completing, a product or service, postponing completion until customer preferences or specifications are known. There are a number of variations of this. In the case of goods, almost-finished units might be held in inventory until customer orders are received, at which time customized features are incorporated, according to customer requests. For example, furniture makers can produce dining room sets, but not apply stain, allowing customers a choice of stains. Once the choice is made, the stain can be applied in a relatively short time, thus eliminating a long wait for customers, giving the seller a competitive advantage. Similarly, various e-mail or internet services can be delivered to customers as standardized packages, which can then be modified according to the customer’s preferences. HP printers that are made in the United States but intended for foreign markets are mostly completed in domestic assembly plants and then finalized closer to the country of use. The result of delayed differentiation is a product or service with customized features that can be quickly produced, appealing to the customers’ desire for variety and speed of delivery, and yet one that for the most part is standardized, enabling the producer to realize the benefits of standardized production. This technique is not new. Manufacturers of men’s clothing, for example, produce suits with pants that have legs that are unfinished, allowing customers to tailor choices as to the exact length and whether to have cuffs or no cuffs. What is new is the extent to which business organizations are finding ways to incorporate this concept into a broad range of products and services.

page 155 

Modular design
is a form of standardization. Modules represent groupings of component parts into subassemblies, usually to the point where the individual parts lose their separate identity. One familiar example of modular design is computers, which have modular parts that can be replaced if they become defective. By arranging modules in different configurations, different computer capabilities can be obtained. For mass customization, modular design enables producers to quickly assemble products with modules to achieve a customized configuration for an individual customer, avoiding the long customer wait that would occur if individual parts had to be assembled. Dell Computers has successfully used this concept to become a dominant force in the PC industry by offering consumers the opportunity to configure modules according to their own specifications. Many other computer manufacturers now use a similar approach. Modular design also is found in the construction industry. One firm in Rochester, New York, makes prefabricated motel rooms complete with wiring, plumbing, and even room decorations in its factory and then moves the complete rooms by rail to the construction site, where they are integrated into the structure.

One advantage of modular design of equipment compared with nonmodular design is that failures are often easier to diagnose and remedy because there are fewer pieces to investigate. Similar advantages are found in the ease of repair and replacement; the faulty module is
page 156conveniently removed and replaced with a good one. The manufacture and assembly of modules generally involve simplifications: Fewer parts are involved, so purchasing and inventory control become more routine, fabrication and assembly operations become more standardized, and training costs often are relatively low.

The main disadvantages of modular design stem from the decrease in variety: The number of possible configurations of modules is much less than the number of possible configurations based on individual components. Another disadvantage that is sometimes encountered is the inability to disassemble a module in order to replace a faulty part; the entire module must be scrapped—usually at a higher cost.

Reliability

Reliability
is a measure of the ability of a product, a part, a service, or an entire system to perform its intended function under a prescribed set of conditions. The importance of reliability is underscored by its use by prospective buyers in comparing alternatives, and by sellers as one determinant of price. Reliability also can have an impact on repeat sales, reflect on the product’s image, and, if it is too low, create legal implications. Reliability is also a consideration for sustainability: The higher the reliability of a product, the fewer the resources that will be needed to maintain it, and the less frequently it will involve the three Rs.

The term

failure
is used to describe a situation in which an item does not perform as intended. This includes not only instances in which the item does not function at all, but also instances in which the item’s performance is substandard or it functions in a way not intended. For example, a smoke alarm might fail to respond to the presence of smoke (not operate at all), it might sound an alarm that is too faint to provide an adequate warning (substandard performance), or it might sound an alarm even though no smoke is present (unintended response).

Reliabilities are always specified with respect to certain conditions, called

normal operating conditions
. These can include load, temperature, and humidity ranges, as well as operating procedures and maintenance schedules. Failure of users to heed these conditions often results in premature failure of parts or complete systems. For example, using a passenger car to tow heavy loads will cause excess wear and tear on the drive train; driving over potholes or curbs often results in untimely tire failure; and using a calculator to drive nails might have a marked impact on its usefulness for performing mathematical operations.

Improving Reliability Reliability can be improved in a number of ways, some of which are listed in
Table 4.3.

TABLE 4.3

Potential ways to improve reliability

Because overall system reliability is a function of the reliability of individual components, improvements in their reliability can increase system reliability. Unfortunately, inadequate production or assembly procedures can negate even the best of designs, and this is often a source of failures. System reliability can be increased by the use of backup components. Failures in actual use often can be reduced by upgrading user education and refining maintenance recommendations or procedures. Finally, it may be possible to increase the overall reliability of the system by simplifying the system (thereby reducing the number of components that could cause the system to fail) or altering component relationships (e.g., increasing the reliability of interfaces).

A fundamental question concerning improving reliability is: How much reliability is needed? Obviously, the reliability needed for a household light bulb isn’t in the same category
page 157as the reliability needed for an airplane. So the answer to the question depends on the potential benefits of improvements and on the cost of those improvements. Generally speaking, reliability improvements become increasingly costly. Thus, although benefits initially may increase at a much faster rate than costs, the opposite eventually becomes true. The optimal level of reliability is the point where the incremental benefit received equals the incremental cost of obtaining it. In the short term, this trade-off is made in the context of relatively fixed parameters (e.g., costs). However, in the longer term, efforts to improve reliability and reduce costs can lead to higher optimal levels of reliability.

Robust Design

Some products or services will function as designed only within a narrow range of conditions, while others will perform as designed over a much broader range of conditions. The latter have

robust design
. Consider a pair of fine leather boots—obviously not made for trekking through mud or snow. Now consider a pair of heavy rubber boots—just the thing for mud or snow. The rubber boots have a design that is more
robust than that of the fine leather boots.

The more robust a product or service, the less likely it will fail due to a change in the environment in which it is used or in which it is performed. Hence, the more designers can build robustness into the product or service, the better it should hold up, resulting in a higher level of customer satisfaction.

A similar argument can be made for robust design as it pertains to the production process. Environmental factors can have a negative effect on the quality of a product or service. The more resistant a design is to those influences, the less likely is a negative effect. For example, many products go through a heating process: food products, ceramics, steel, petroleum products, and pharmaceutical products. Furnaces often do not heat uniformly; heat may vary either by position in an oven or over an extended period of production. One approach to this problem might be to develop a superior oven; another might be to design a system that moves the product during heating to achieve uniformity. A robust-design approach would develop a product that is unaffected by minor variations in temperature during processing.

Taguchi’s Approach Japanese engineer Genichi Taguchi’s approach is based on the concept of robust design. His premise is that it is often easier to design a product that is insensitive to environmental factors, either in manufacturing or in use, than to control the environmental factors.

The central feature of Taguchi’s approach—and the feature used most often by U.S. companies—is
parameter design. This involves determining the specification settings for both the product and the process that will result in robust design in terms of manufacturing variations, product deterioration, and conditions during use.

The Taguchi approach modifies the conventional statistical methods of experimental design. Consider this example. Suppose a company will use 12 chemicals in a new product it intends to produce. There are two suppliers for these chemicals, but the chemical concentrations vary slightly between the two suppliers. Classical design of experiments would require 2
¹² = 4,096 test runs to determine which combination of chemicals would be optimum. Taguchi’s approach would involve only testing a portion of the possible combinations. Relying on experts to identify the variables that would be most likely to affect important performance, the number of combinations would be dramatically reduced, perhaps to, say, 32. Identifying the best combination in the smaller sample might be a near-optimal combination instead of the optimal combination. The value of this approach is its ability to achieve major advances in product or process design fairly quickly, using a relatively small number of experiments.

Critics charge that Taguchi’s methods are inefficient and incorrect, and often lead to non-optimal solutions. Nonetheless, his methods are widely used and have been credited with helping to achieve major improvements in U.S. products and manufacturing processes.

page 158 

Degree of Newness

Product or service design change can range from the modification of an existing product or service to an entirely new product or service:

1. Modification of an existing product or service

2. Expansion of an existing product line or service offering

3. Clone of a competitor’s product or service

4. New product or service

The degree of change affects the newness to the organization and the newness to the market. For the organization, a low level of newness can mean a fairly quick and easy transition to producing the new product, while a high level of newness would likely mean a slower and more difficult, and therefore more costly, transition. For the market, a low level of newness would mean little difficulty with market acceptance, but possibly low profit potential. Even in instances of low profit potential, organizations might use this strategy to maintain market share. A high level of newness, on the other hand, might mean more difficulty with acceptance, or it might mean a rapid gain in market share with a high potential for profits. Unfortunately, there is no way around these issues. It is important to carefully assess the risks and potential benefits of any design change, taking into account clearly identified customer wants.

Quality Function Deployment

Obtaining input from customers is essential to assure that they will want what is offered for sale. Although obtaining input can be informal through discussions with customers, there is a formal way to document customer wants.

Quality function deployment (QFD)
is a structured approach for integrating the “voice of the customer” into both the product and service development process. The purpose is to ensure that customer requirements are factored into every aspect of the process. Listening to and understanding the customer is the central feature of QFD. Requirements often take the form of a general statement such as, “It should be easy to adjust the cutting height of the lawn mower.” Once the requirements are known, they must be translated into technical terms related to the product or service. For example, a statement about changing the height of the lawn mower may relate to the mechanism used to accomplish that, its position, instructions for use, tightness of the spring that controls the mechanism, or materials needed. For manufacturing purposes, these must be related to the materials, dimensions, and equipment used for processing.

The structure of QFD is based on a set of matrices. The main matrix relates customer requirements (what) and their corresponding technical requirements (how). This matrix is illustrated in
Figure 4.2. The matrix provides a structure for data collection.

Source: Ernst and Young Consulting Group,
Total Quality (Homewood, IL: Dow-Jones Irwin, 1991), p. 121.

page 159 

Additional features are usually added to the basic matrix to broaden the scope of analysis. Typical additional features include importance weightings and competitive evaluations. A correlational matrix is usually constructed for technical requirements; this can reveal conflicting technical requirements. With these additional features, the set of matrices has the form illustrated in
Figure 4.3. It is often referred to as the
house of quality because of its house-like appearance.

An analysis using this format is shown in
Figure 4.4. The data relate to a commercial printer (customer) and the company that supplies the paper. At first glance, the display appears complex. It contains a considerable amount of information for product and process planning. Therefore, let’s break it up into separate parts and consider them one at a time. To start, a key part is the list of customer requirements on the left side of the figure. Next, note the technical requirements, listed vertically near the top. The key relationships and their degree of importance are shown in the center of the figure. The circle with a dot inside indicates the strongest positive relationship; that is, it denotes the most important technical requirements for satisfying customer requirements. Now look at the “importance to customer” numbers that are shown next to each customer requirement (3 is the most important). Designers will take into account the importance values and the strength of correlation in determining where to focus the greatest effort.

Next, consider the correlation matrix at the top of the “house.” Of special interest is the strong negative correlation between “paper thickness” and “roll roundness.” Designers will have to find some way to overcome that or make a trade-off decision.

On the right side of the figure is a competitive evaluation comparing the supplier’s performance on the customer requirements with each of the two key competitors (A and B). For example, the supplier (X) is worst on the first customer requirement and best on the third customer requirement. The line connects the X performances. Ideally, design will cause all of the Xs to be in the highest positions.

Across the bottom of
Figure 4.4 are importance weightings, target values, and technical evaluations. The technical evaluations can be interpreted in a manner similar to that of the competitive evaluations (note the line connecting the Xs). The target values typically contain technical specifications, which we will not discuss. The importance weightings are the sums of values assigned to the relationships (see the lower right-hand key for relationship weights). The 3 in the first column is the product of the importance to the customer, 3, and the small (Δ) weight, 1. The importance weightings and target evaluations help designers focus on desired results. In this example, the first technical requirement has the lowest importance weighting, while the next four technical requirements all have relatively high importance weightings.

page 160 

The house of quality approach involves a sequence of “houses,” beginning with design characteristics, which leads to specific components, then production processes, and finally, a quality plan. The sequence is illustrated in
Figure 4.5. Although the details of each house are beyond the scope of this text,
Figure 4.5 provides a conceptual understanding of the progression involved.

The Kano Model

The
Kano model is a theory of product and service design developed by Dr. Noriaki Kano, a Japanese professor, who offered a perspective on customer perceptions of quality different from the traditional view that “more is better.” Instead, he proposed different categories of quality and posited that understanding them would better position designers to assess and address quality needs. His model provides insights into the attributes that are perceived to be
page 161important to customers. The model employs three definitions of quality: basic, performance, and excitement.

Basic quality refers to customer requirements that have only a limited effect on customer satisfaction if present, but lead to dissatisfaction if not present. For example, putting a very short cord on an electrical appliance will likely result in customer dissatisfaction, but beyond a certain length (e.g., 4 feet), adding more cord will not lead to increased levels of customer satisfaction. Performance quality refers to customer requirements that generate satisfaction or dissatisfaction in proportion to their level of functionality and appeal. For example, increasing the tread life of a tire or the amount of time house paint will last will add to customer satisfaction. Excitement quality refers to a feature or attribute that was unexpected by the customer and causes excitement (the “wow” factor), such as a voucher for dinner for two at the hotel restaurant when checking in.
Figure 4.6A portrays how the three definitions of quality influence customer satisfaction or dissatisfaction relative to the degree of implementation. Note that features that are perceived by customers as basic quality result in dissatisfaction if they are missing or at low levels, but do not result in customer satisfaction if they are present, even at high levels. Performance factors can result in satisfaction or dissatisfaction, depending on the degree to which they are present. Excitement factors, because they are unexpected, do not result in dissatisfaction when they are absent or at low levels, but have the potential for disproportionate levels of satisfaction if they are present.

Over time, features that excited become performance features, and performance features soon become basic quality features, as illustrated in
Figure 4.6B. The rates at which various design elements are migrating is an important input from marketing that will enable designers to continue to satisfy and delight customers and not waste efforts on improving what have become basic quality features.

The lesson of the Kano model is that design elements that fall into each aspect of quality must first be determined. Once basic needs have been met, additional efforts in those areas should not be pursued. For performance features, cost–benefit analysis comes into play, and these features should be included as long as the benefit exceeds the cost. Excitement features pose somewhat of a challenge. Customers are not likely to indicate excitement factors in surveys because they don’t know that they want them. However, small increases in such factors produce disproportional increases in customer satisfaction and generally increase brand loyalty, so it is important for companies to strive to identify and include these features when economically feasible.

The Kano model can be used in conjunction with QFD, as well as in Six Sigma projects (see
Chapter 9 for a discussion of Six Sigma).

Concurrent Engineering

To achieve a smoother transition from product design to production, and to decrease product development time, many companies are using
simultaneous development, or concurrent engineering. In its narrowest sense,

concurrent engineering
means bringing design and manufacturing engineering people together early in the design phase to simultaneously develop the product and the processes for creating the product. More recently, this concept has been enlarged to include manufacturing personnel (e.g., materials specialists) and marketing and purchasing personnel in loosely integrated, cross-functional teams. In addition, the views of suppliers and customers are frequently sought. The purpose, of course, is to achieve product designs that reflect customer wants, as well as manufacturing capabilities.

Traditionally, designers developed a new product without any input from manufacturing, and then turned over the design to manufacturing, which would then have to develop a process for making the new product. This “over-the-wall” approach created tremendous challenges for manufacturing, generating numerous conflicts and greatly increasing the time needed to successfully produce a new product. It also contributed to an “us versus them” mentality.

For these and similar reasons, the simultaneous development approach has great appeal. Among the key advantages of this approach are the following:

1. Manufacturing personnel are able to identify production capabilities and capacities. Very often, they have some latitude in design in terms of selecting suitable materials and processes. Knowledge of production capabilities can help in the selection process. In addition, cost and quality considerations can be greatly influenced by design, and conflicts during production can be greatly reduced.

2. Design or procurement of critical tooling, some of which might have long lead times, can occur early in the process. This can result in a major shortening of the product development process, which could be a key competitive advantage.

3. The technical feasibility of a particular design or a portion of a design can be assessed early on. Again, this can avoid serious problems during production.

4. The emphasis can be on
   problem resolution instead of
   conflict resolution.

page 164 

However, despite the advantages of concurrent engineering, a number of potential difficulties exist in this co-development approach. Two key ones are the following:

• Long-standing boundaries between design and manufacturing can be difficult to overcome. Simply bringing a group of people together and thinking they will be able to work together effectively is probably naive.

• There must be extra communication and flexibility if the process is to work, and these can be difficult to achieve.

Hence, managers should plan to devote special attention if this approach is to work.

Computer-Aided Design (CAD)

Computers are increasingly used for product design.

Computer-aided design (CAD)
uses computer graphics for product design. The designer can modify an existing design or create a new one on a monitor by means of a light pen, a keyboard, a joystick, or a similar device. Once the design is entered into the computer, the designer can maneuver it on the screen: It can be rotated to provide the designer with different perspectives, it can be split apart to give the designer a view of the inside, and a portion of it can be enlarged for closer examination. The designer can obtain a printed version of the completed design and file it electronically, making it accessible to people in the firm who need this information (e.g., marketing, operations).

A growing number of products are being designed in this way, including transformers, automobile parts, aircraft parts, integrated circuits, and electric motors.

A major benefit of CAD is the increased productivity of designers. No longer is it necessary to laboriously prepare mechanical drawings of products or parts and revise them repeatedly to correct errors or incorporate revisions. A rough estimate is that CAD increases the productivity of designers from 3 to 10 times. A second major benefit of CAD is the creation of a database for manufacturing that can supply needed information on product geometry and dimensions, tolerances, material specifications, and so on. It should be noted, however, that CAD needs this database to function and that this entails a considerable amount of effort.

page 165 

Some CAD systems allow the designer to perform engineering and cost analyses on proposed designs. For instance, the computer can determine the weight and volume of a part and do stress analysis as well. When there are a number of alternative designs, the computer can quickly go through the possibilities and identify the best one, given the designer’s criteria. CAD that includes finite element analysis (FEA) capability can greatly shorten the time to market of new products. It enables developers to perform simulations that aid in the design, analysis, and commercialization of new products. Designers in industries such as aeronautics, biomechanics, and automotives use FEA.

Production Requirements

As noted earlier in the chapter, designers must take into account
production capabilities. Design needs to clearly understand the capabilities of production (e.g., equipment, skills, types of materials, schedules, technologies, special abilities). This helps in choosing designs that match capabilities. When opportunities and capabilities do not match, management must consider the potential for expanding or changing capabilities to take advantage of those opportunities.

Forecasts of future demand can be very useful, supplying information on the timing and volume of demand, and information on demands for new products and services.

Manufacturability is a key concern for manufactured goods: Ease of fabrication and/or assembly is important for cost, productivity, and quality. With services, ease of providing the service, cost, productivity, and quality are of great concern.

The term

design for manufacturing (DFM)
is used to indicate the designing of products that are compatible with an organization’s capabilities. A related concept in manufacturing is

design for assembly (DFA)
. A good design must take into account not only how a product will be fabricated, but also how it will be assembled. Design for assembly focuses on reducing the number of parts in an assembly, as well as on the assembly methods and sequence that will be employed. Another, more general term,

manufacturability
, is sometimes used when referring to the ease with which products can be fabricated and/or assembled.

Component Commonality

Companies often have multiple products or services to offer customers. Often, these products or services have a high degree of similarity of features and components. This is particularly true of
product families, but it is also true of many services. Companies can realize significant benefits when a part can be used in multiple products. For example, car manufacturers employ this tactic by using internal components such as water pumps, engines, and transmissions on several automobile nameplates. In addition to the savings in design time, companies reap benefits through standard training for assembly and installation, increased opportunities for savings by buying in bulk from suppliers, and commonality of parts for repair, which reduces the inventory that dealers and auto parts stores must carry. Similar benefits accrue in services. For example, in automobile repair, component commonality means less training is needed because the variety of jobs is reduced. The same applies to appliance repair, where commonality and
substitutability of parts are typical. Multiple-use forms in financial and medical services are other examples. Computer software often comprises a number of modules that are commonly used for similar applications, thereby saving the time and cost to write the code for major portions of the software. Tool manufacturers use a design that allows tool users to attach different power tools to a common power source. Similarly, HP has a universal power source that can be used with a variety of computer hardware.

Overview of Service Design

Service design begins with the choice of a service strategy, which determines the nature and focus of the service, and the target market. This requires an assessment by top management of the potential market and profitability (or need, in the case of a nonprofit organization) of a particular service, and an assessment of the organization’s ability to provide the service. Once decisions on the focus of the service and the target market have been made, the customer requirements and expectations of the target market must be determined.

Two key issues in service design are the degree of variation in service requirements and the degree of customer contact and customer involvement in the delivery system. These have an impact on the degree to which service can be standardized or must be customized. The lower the degree of customer contact and service requirement variability, the more standardized the service can be. Service design with no contact and little or no processing variability is very much like product design. Conversely, high variability and high customer contact generally mean the service must be highly customized. A related consideration in service design is the opportunity for selling: The greater the degree of customer contact, the greater the opportunities for selling.

Differences between Service Design and Product Design

Service operations managers must contend with issues that may be insignificant or nonexistent for managers in a production setting. These include the following:

1. Products are generally tangible; services are generally intangible. Consequently, service design often focuses more on intangible factors (e.g., peace of mind, ambiance) than does product design.

2. In many instances, services are created and delivered at the same time (e.g., a haircut, a car wash). In such instances, there is less latitude in finding and correcting errors
   before the customer has a chance to discover them. Consequently, training, process design, and customer relations are particularly important.

3. Services cannot be inventoried. This poses restrictions on flexibility and makes capacity issues very important.

4. Services that are highly visible to consumers and must be designed with that in mind; this adds an extra dimension to process design, one that usually is not present in product design.

5. Some services have low barriers to entry and exit. This places additional pressures on service design to be innovative and cost-effective.

6. Location is often important to service design, with convenience as a major factor. Hence, design of services and choice of location are often closely linked.

   page 167 

7. Service systems range from those with little or no customer contact to those that have a very high degree of customer contact. Here are some examples of those different types:

   Insulated technical core; little or no customer contact (e.g., software development)

   Production line; little or no customer contact (e.g., automatic car wash)

   Personalized service (e.g., haircut, medical service)

   Consumer participation (e.g., diet program, dance lessons)

   Self-service (e.g., supermarket)

   If there is little or no customer contact, service system design is like product system design.

8. Demand variability alternately creates customer waiting times, which sometimes leads to lost sales, or idle service resources.

When demand variability is a factor, designers may approach service design from one of two perspectives. One is a cost and efficiency perspective, and the other is a customer perspective. Waiting line analysis (see
Chapter 18) can be especially useful in this regard.

Basing design objectives on cost and efficiency is essentially a “product design approach” to service design. Because customer participation makes both quality and demand variability more difficult to manage, designers may opt to limit customer participation in the process where possible. Alternatively, designers may use staff flexibility as a means of dealing with demand variability.

In services, a significant aspect of perceived quality relates to the intangibles that are part of the service package. Designers must proceed with caution because attempts to achieve a high level of efficiency tend to depersonalize service and to create the risk of negatively altering the customer’s perception of quality. Such attempts may involve the following:

• Reducing consumer choices makes service more efficient, but it can be both frustrating and irritating for the customer. An example would be a cable company that bundles channels, rather than allowing customers to pick only the channels they want.

• Standardizing or simplifying certain elements of service can reduce the cost of providing a service, but it risks eliminating features that some customers value, such as personal attention.

• Incorporating flexibility in capacity management by employing part-time or temporary staff may involve the use of less-skilled or less-interested people, and service quality may suffer.

Design objectives based on customer perspective require understanding the customer experience, and focusing on how to maintain control over service delivery to achieve customer satisfaction. The customer-oriented approach involves determining consumer wants and needs in order to understand relationships between service delivery and perceived quality. This enables designers to make enlightened choices in designing the delivery system.

Of course, designers must keep in mind that while depersonalizing service delivery for the sake of efficiency can negatively impact perceived quality, customers may not want or be willing to pay for highly personalized service either, so trade-offs may have to be made.

Phases in the Service Design Process

Table 4.5 lists the phases in the service design process. As you can see, they are quite similar to the phases of product design, except that the delivery system also must be designed.

TABLE 4.5

Phases in service design process

page 168 

Service Blueprinting

A useful tool for conceptualizing a service delivery system is the

service blueprint
, which is a method for describing and analyzing a service process. A service blueprint is much like an architectural drawing, but instead of showing building dimensions and other construction features, a service blueprint shows the basic customer and service actions involved in a service operation.
Figure 4.7 illustrates a simple service blueprint for a restaurant. At the top of the figure are the customer actions, and just below are the related actions of the direct contact service people. Next are what are sometimes referred to as “backstage contacts”—in this example, the kitchen staff—and below those are the support, or “backroom,” operations. In this example, support operations include the reservation system, ordering of food and supplies, cashier, and the outsourcing of laundry service.
Figure 4.7 is a simplified illustration—typically, time estimates for actions and operations would be included.

The major steps in service blueprinting are as follows:

1. Establish boundaries for the service and decide on the level of detail needed.

2. Identify and determine the sequence of customer and service actions and interactions. A flowchart can be a useful tool for this.

3. Develop time estimates for each phase of the process, as well as time variability.

4. Identify potential failure points and develop a plan to prevent or minimize them, as well as a plan to respond to service errors.

Characteristics of Well-Designed Service Systems

There are a number of characteristics of well-designed service systems. They can serve as guidelines in developing a service system. They include the following:

• Being consistent with the organization’s mission.

• Being user-friendly.

• Being robust if variability is a factor.

  page 169 

• Being easy to sustain.

• Being cost-effective.

• Having value that is obvious to customers.

• Having effective linkages between back-of-the-house operations (i.e., no contact with the customer) and front-of-the-house operations (i.e., direct contact with customers). Front operations should focus on customer service, while back operations should focus on speed and efficiency.

• Having a single, unifying theme, such as convenience or speed.

• Having design features and checks that will ensure service that is reliable and of high quality.

Challenges of Service Design

Variability is a major concern in most aspects of business operations, and it is particularly so in the design of service systems. Requirements tend to be variable, both in terms of differences in what customers want or need, and in terms of the timing of customer requests. Because services generally cannot be stored, there is the additional challenge of balancing supply and demand. This is less of a problem for systems in which the timing of services can be scheduled (e.g., doctor’s appointment), but not so in others (e.g., emergency room visit).

Another challenge is that services can be difficult to describe precisely and are dynamic in nature, especially when there is a direct encounter with the customer (e.g., personal services), due to the large number of variables.

Guidelines for Successful Service Design

1. Define the service package in detail. A service blueprint may be helpful for this.

2. Focus on the operation from the customer’s perspective. Consider how customer expectations and perceptions are managed during and after the service.

3. Consider the image that the service package will present both to customers and to prospective customers.

4. Recognize that designers’ familiarity with the system may give them quite a different perspective than that of the customer, and take steps to overcome this.

5. Make sure that managers are involved and will support the design once it is implemented.

6. Define quality for both tangibles and intangibles. Intangible standards are more difficult to define, but they must be addressed.

7. Make sure that recruitment, training, and reward policies are consistent with service expectations.

   page 170 

8. Establish procedures to handle both predictable and unpredictable events.

9. Establish systems to monitor, maintain, and improve service.

Finding the Probability of Functioning When Activated

The probability that a system or a product will operate as planned is an important concept in system and product design. Determining that probability when the product or system consists of a number of
independent components requires the use of the rules of probability for independent events.

Independent events
have no relation to the occurrence or nonoccurrence of each other. What follows are three examples illustrating the use of probability rules to determine whether a given system will operate successfully.

Rule 1. If two or more events are independent and
success is defined as the probability that all of the events occur, then the probability of success is equal to the product of the probabilities of the events.

Example
Suppose a room has two lamps, but to have adequate light both lamps must work (success) when turned on. One lamp has a probability of working of .90, and the other has a probability of working of .80. The probability that both will work is .90 × .80 = .72. Note that the order of multiplication is unimportant: .80 × .90 = .72. Also note that if the room had three lamps, three probabilities would have been multiplied.

This system can be represented by the following diagram:

Even though the individual components of a system might have high reliabilities, the system as a whole can have considerably less reliability because all components that are in series (as are the ones in the preceding example) must function. As the number of components in a series increases, the system reliability decreases. For example, a system that has eight components in a series, each with a reliability of .99, has a reliability of only .99
⁸ = .923.

Obviously, many products and systems have a large number of component parts that must all operate, and some way to increase overall reliability is needed. One approach is to use

redundancy
in the design. This involves providing backup parts for some items.

Rule 2. If two events are independent and
success is defined as the probability that
at least one of the events will occur, the probability of success is equal to the probability of either one plus 1.00 minus that probability multiplied by the other probability.

Example
There are two lamps in a room. When turned on, one has a probability of working of .90 and the other has a probability of working of .80. Only a single lamp is needed to light for success. If one fails to light when turned on, the other lamp is turned on. Hence, one of the lamps is a backup in case the other one fails. Either lamp can be treated as the backup; the probability of success will be the same. The probability of success is .90 + (1 − .90) × .80 = .98. If the .80 light is first, the computation would be .80 + (1 − .80) × .90 = .98.

This system can be represented by the following diagram:

Rule 3. If two or more events are involved and success is defined as the probability that at least one of them occurs, the probability of success is 1 −
p (all fail).