BM403

INTRODUCTION

Wal-mart Vs Kmart

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The Wal-Mart example demonstrates how a company obtained a substantial competitive advantage by improving basic operational activities such as controlling its supply chain. The automobile industry “case of Toyota, GM etc” shows how losing an operation as focus can drive a firm into bankruptcy.

Today, in our international market place, consumers purchase their products and services from the provider that offers them the most “value” for their money. To illustrate this, you may be doing your assignments on a Japanese made laptop, driving a Germany or Japanese car, watching a TV programme on television set made in China etc. however, most of our services – banking, insurance, personal care - are domestic, although some of these are owned or outsourced to foreign firms.

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There is a reason why most services are produced by domestic firms while products may be produced in part, or wholly by foreign firms, and it concerns an area of business known as operations. A great many societal changes that are occurring today intimately involve activities associated with operations. For example, there is great pressure among competing nations to increase their exports. And business are intent on building efficient and effective supply chains, improving their processes through “six sigma” and successfully applying the precepts of “lean management” and other operations – based programmes.

Another characteristic of our modern society is the explosion of new technology, an important aspect of operations. Technologies such as cell phones, e-mail, notebook computers, personal digital assistants, and the Web etc are profoundly affecting business and are fundamentally changing the nature of work. For example, many banks around the world are shifting their focus from building new branch locations to using the web as a way to establish and develop new customer relationships. Banks rely on technology to carry out routine activities as well, such as Electronic Funds Transfer (EFT) instantly across cities, regions and oceans.

Operations

Why do we argue that operations be considered the heart of every organisation? Fundamentally, organisations exist to create value and operations involves tasks that create value. Hammer (2004) maintains that operational innovation can provide organisations with long-term strategic advantages over their competitors.

Consider Innscor (Chicken Inn) as an example. This firm uses a number of inputs, including ingredients, labour, equipment, and facilities; transforms them in a way that adds value to them (eg by frying); and obtains an output, such as a two piecer that can be sold at a profit. This conversion process is termed a production system is illustrated in figure 1.1. The elements of the figure represent what is known as a system: a purposeful collection of people, objects, and procedures for operating within an environment. Purposeful: systems are not merely arbitrary groupings but goal-directed or purposeful collections. Managing and running a production system efficiently and effectively is at the heart of the operations activities.

Operations is concerned with transforming inputs into useful outputs according to the agreed upon strategy and thereby adding value to some entity; this constitutes the primary activity of virtually every organisation.

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Systems Perspective

As figure 1.1 illustrates, a production system is defined in-terms of the environment, a strategy, a set of inputs, the transformation process, the outputs and some mechanism for controlling the overall system. The strategy includes such elements as, what customers value, the vision and mission of the organisation, an appropriate framework to execute the vision and the core capabilities of the organisation.

The environment includes those factors that are outside the actual production system but that influence it in some ways. Because of its influence we need to consider the environment, even though it is beyond the control of decision makers within the system. Think about how changes in customer needs, competitor’s new products, or a new advance in technology can influence the level of satisfaction with a production system’s current outputs.

Because the world around us is constantly changing, it is necessary to monitor the production system and take action when the system is not meeting its strategic goals. It may be found that the current strategy is no longer appropriate, indicating a need to revise the strategy. On the other hand, it may be

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found that the strategy is fine but that the inputs or transformation processes, or both, should be modified in some way.

In either case, it is important to continuously collect data from the environment, the transformation processes, and the outputs, compare that data to the strategic plan, and if substantial deviations exist, design and implement improvements to the system or perhaps the strategy, so that results agree with the strategic goals.

Thinking in terms of systems provides decision makers with numerous advantages. For instance the systems perspective provides decision makers with a broad and complete picture of an entire system. Furthermore, the systems perspective emphasizes the relationships between the various system components. Without considering these relationships, decision makers are prone to a problem called sub-optimisation. Sub-optimisation occurs when one part of the system is improved to be detriment of other parts of the system, and perhaps the organisation as a whole. For example, if a retailer decides to broaden its product line in an effort to increase sales, this could actually end up hurting the retailer as a whole if it does not have sufficient shelf space or service personnel available to accommodate the broader product line. Thus, decisions need to be evaluated in terms of their effect on the entire system, not simply in terms of how they will affect one component of the system.

Inputs

The set of inputs used in a production system is more complex than might be supposed and typically involves many other areas such as marketing, finance, human resource management and engineering. Obvious inputs include raw materials, capital, labour, equipment, facilities and supplies etc. supplies are distinguished from raw materials by the fact that they are not usually a part of the final output. Oil, paper clips, pens, tape and other such items are commonly classified as supplies because they only aid in producing the output.

Transformation processes

The transformation processes are the part of the system that adds value to the inputs. Value can be added to an entity in a number of ways. Four major ways are:

1. Alter: something can be changed structurally. That would be physical change and this approach is basic to our manufacturing industries where goods are cut, stamped, formed, assembled etc. Other more subtle, alterations may also have value, sensual alterations, such as heat when we are cold, or music or beauty may be highly valued on certain occasions. Beyond this even psychological alterations can have value such as the feeling of worth from obtaining a university degree.

2. Transport: An entity, again in including ourselves, may have more value if it is located somewhere other than where it currently is. We may appreciate having things brought to us, such as flowers or removed from us such as garbage.

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3. Store: the value of an entity may be enhanced for us if it is kept in a protected environment for some period of time. Some examples are title deeds or motor vehicle registration books kept in a safe deposit box or ourselves staying in a hotel.

4. Inspect: an entity may be more valued because we better understand its properties. This may apply to something we own, plan to use, or considering to purchase. Medical exams, elevator certifications and jewelry appraisals fall into this category.

Outputs

Two types of outputs commonly results from a production process: services and products. Generally, products are physical goods such as a personal computer, and services are abstract or nonphysical. More specifically we can consider the characteristics in Table 1.1 to help distinguish between the two

However, this classification may be confusing. For example, consider a pizza delivery chain. Does this organisation produce a product or provide a service? Suppose that instead of delivering the pizzas to the actual customer, it made the pizzas in a factory and sold them in the frozen foods section of grocery stores. Clearly the actual process of making pizzas for immediate consumption or to be frozen involves basically the same tasks, although one may be on a larger scale and use more automated equipment. The point is, however, that both organizations produce a pizza and defining one organization as a service and the other as a manufacturer seems arbitrary. In addition, both products and services can be produced as commodities or individually customized.

This ambiguity is avoided by adopting the point that any physical entity accompanying a transformation that adds value is a facilitating good (eg, the pizza). In many cases there may be no facilitating goods and these are referred to as pure services. A reason for not making a distinction between manufacturing and service is that when a company thinks of itself as a manufacturer it tends to focus itself on measures of internal performance such as efficiency and utilization. But when companies consider themselves as providing a service they tend to focus externally and ask questions such as “how can we serve our customers better”.

This is not to imply that improving internal performance measures is not desirable. Rather, it suggests that improved customer services should be the primary impetus for all improvement efforts. It is

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generally not advisable to seek internal improvements if these improvements do not ultimately lead to corresponding improvements in customer service and customer satisfaction.

Control

Suppose that in our production system we make a mistake, we must be able to observe this through for example, accounting records (measurement data), compare it to standard to see how serious the error is and then if needed, plan and implement some improvements. If the changes are not significantly affecting the outputs, then no control actions are needed.

Operations Activities

Operations include not only those activities associated specifically with the production system but also a variety of other activities. For example, purchasing or procurement activities are concerned with obtaining many of the inputs needed in the production system. Because of the important interdependencies of these activities, many organisations are attempting to manage these activities as one process commonly referred to as supply chain management. These processes are classified in Table 1.3 below.

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Customer Value

Customers support the provider of goods and services who offers them with the most “value”. The equation for value is conceptually clear: value = perceived benefits/cost.

The perceived benefits can take a wide variety of forms, but the costs are usually more straightforward:

The upfront monetary investment Other monetary life cycle costs of maintenance and such The hassles involved in obtaining the product or service such as travel distance, financing for

upfront investment, friendliness of service etc.

In contrast, the benefits can be multiple to include; innovativeness, functionality, quality, customization and responsiveness.

Innovativeness: many people (called “early adopters”) will buy products and services simply because

they are so innovative, or major improvements over what has been available formerly. It is the field of R&D that is primarily responsible for developing innovative new product and service ideas. R&D activities should focus at creating and developing (but not producing) the organisation’s outputs. On occasion, R&D also creates new production methods by which outputs, either new or old, may be produced.

Research itself is typically divided into two types: pure research and applied research. Pure research is simply working with basic technology to develop new knowledge. Applied research is attempting to develop new knowledge along particular lines. For example, pure research might focus on developing a material that conducts electricity with zero resistance, where as applied research could focus on further developing this material to be used in products for customers.

Development: is the attempt to utilize the findings of research and expand the possible applications, often consisting of modifications or extensions to existing outputs to meet customer’s interests. Figure 1.3 illustrates the range of applicability of development as the output becomes more clearly defined. In the early years of a new output, development is oriented toward removing “bugs” increasing performance, improving quality and so on. In the middle years, options and variants of the output are developed. In the later years, development is oriented toward extensions of the output that will prolong its life.

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Mortality Curve

Unfortunately, the returns from R&D are frequently meager, whereas the costs are great. Figure 1.4

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illustrates the mortality curve (fallout rate) associated with the concurrent design, evaluation and selection for a hypothetical group of 50 potential chemical products, assuming that the 50 candidate products are available for consideration in year 3. (The first three years, on the average are required for the necessary research preceding each candidate products). Initial evaluation and screening reduce the 50 to 22, and economic analysis further reduces the number to about 9. Development reduces this number even more, to about 5, and design and testing reduce it to perhaps 3. By the time construction (for production), market development and a year’s commercialization are completed, there is only one successful product left (sometimes there are none).

Two alternatives to research frequently used by organisations are imitation of proven new idea (ie employing a second to market strategy) or outright purchase of someone else’s invention. The outright purchase strategy is becoming increasingly popular in those industries where bringing a new product to the market can be costly, such as pharmaceuticals and high technology. It is also employed in those industries where technology advances so rapidly that there isn’t enough time to employ a second to market strategy.

Although imitation does not put the organisation first in the market with the new product or service it does give the organisation any opportunity to study any possible defects in the original product or service and rapidly develop a better design frequently at a better price.

The second approach-purchasing an invention or the inventing company itself – eliminates the risks inherent in research, but it still requires the company to develop and market the product or service before knowing whether it will be successful.

Either root spares the organisation the risk and tremendous cost of conducting the actual research leading up to a new invention or improvement.

In addition to product research (as it is generally known), there is also process research, which involves the generation of new knowledge concerning how to produce outputs. Motorola, to take an example extensively uses project teams that conduct process development at the same time as product development.

Functionality

Functionality is not quality. Functionality involves the activities that the product or service is intended to perform, thereby providing benefits to the consumer. A contemporary example is the ubiquitous “cell phone”. These days it is probably rare to find a cell phone which is only a phone; many phones include a flashlight, camera and a way to send its picture or files to another person (Bluetooth) or provide access to the internet, as well as a myriad of other functions.

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Quality

Quality is a relative term, meaning different things to different people at different times. Moreover, quality is not an absolute but rather, is based on customer’s perceptions. Customer’s impressions can be influenced by a number of factors, including brand loyalty and an organisation’s reputation.

Quality dimensions:

Richard J Schonberger has compiled a list of multiple quality dimensions that customers often associate with products and services:

1. Conformance to specifications: this is the extent to which the product matches the design specifications, such as a pizza delivery shop that consistently meet its advertised delivery time of 30 minutes.

2. Performance: customers frequently equate the quality of products or services wit7h their performance. Examples of performance include how quality a car accelerates or the battery life of a cell phone.

3. Features: features are the options that a product or service offers, such as side impact airbags in cars and leather seats.

4. Quick response: quick response is associated with the amount of time required to react to customer’s demands.

5. Reliability: this is the probability that a product or service will perform as intended on any given trial or for a period of time, such as the probability that a car will start on any given morning.

6. Durability: this refers to how tough the product is, such as a notebook computer that still functions after being dropped.

7. Serviceability: refers to the ease with which maintenance or a repair can be performed.8. Aesthetics: these are factors that appeal to human senses, such as the taste of a steak or the

sound of a car’s engine. “when I grow up I want to be a bus or truck (gonyeti) driver”9. Humanity: humanity has to do with how the customer is treated, such as a private university

that maintain small classes so students are not treated like numbers by its lecturer.

NB It is worth noting that all the dimensions of quality are relevant to all products and services. Thus organisations need to identify the dimensions of quality that are relevant to the products and services they offer.

Quality benefits and costs

Many benefits are associated with providing products and services that have high quality. Customers are more pleased with a high quality product or service. They are more apt to encourage their friends to patronize the firm, as well as giving the firm their own repeat business. Top quality also establishes a reputation for the firm that is very difficult to obtain in any other manner, it allows the firm to charge premium prices. And typically, high quality products and services are not only the most profitable but

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also garner the largest market share. High quality tends to protect the firm from competitors, who may have to offer competing outputs at an especially low price. It also enhances the attractiveness of follow up products or services so that their chances of success are much improved. High quality minimizes risks of safety and health and reduces liability for the firm.

Traditionally, it was thought that making products and services of excellent quality would translate into high costs. Of course this view neglects the negative consequences of gaining a reputation for producing shoddy outputs. The Japanese have demonstrated that it is often possible to improve quality and lower costs at the same time. One explanation for this phenomenon is that it is simply cheaper to do a job right the first time (TGR) than to try fix it or rework it later (TGW – Dzokororo Haina Simba). And if quality is built into the production system it improves worker’s morale, reduces scrap and waste, smoothes work flows, improves control and reduces a wide variety of other costs. Two primary sets of costs are involved in quality: Control Costs and Failure Costs. Traditionally, these costs are broken down into four categories: prevention costs (including planning, training, product design, maintenance); appraisal costs (measuring testing, test equipment, inspectors, reports); internal costs of defects (extra labour and materials to repair, scrap, rework interruptions, expediting) and external costs of defects (ill-will, complaints, quick response to correct, warranties, insurance, recalls, lawsuits). The first two costs are incurred in attempting to control quality and the last two are the costs of failing to control quality.

Customization

Customization refers to offering a product or service exactly suited to a customer’s desires or needs. However, there is a range of accommodation to the customer’s needs, as illustrated in Figure 1.5 below. At the left, there is the completely standard, world class (excellence suitable for all markets) producer or service. Moving to the right is the standard with options, continuing on to variants and alternative models and ending at the right with made-to-order customization. In general, the more customization the better, it can be provided quickly, with acceptable quality and cost.

Flexibility

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However, to offer customization demands flexibility on the part of the firm. Professor David Upton (1994) defines flexibility as “the ability to change or react with little penalty in time, effort, cost or performance”. There are more than a dozen types of flexibility that include, design, volume routing through the production system, product mix etc. but having the right type of flexibility can offer the following major competitive advantages:

1. Faster matches to customer’s needs because changeover time from one product or service to another is quicker.

2. Closer matches to customer’s needs.3. Ability to supply the needed items in the volumes required for the markets as they develop.4. Faster design-to-market time to meet new customer needs.5. Lower cost of changing production to meet customer needs.6. Ability to offer a full line of products or services without the attendant cost of stocking large

inventories.7. Ability to meet market demands even if delays develop in the production or distribution

process.

Mass Customisation

Until recently it was widely believed that producing low cost standard product or services (at the far left of figure 1.5) required one type of transformation process and producing high-cost customized products (far right) required another type of process. However, in addition to vast improvements in operating efficiency, an unexpected by-product of continuous improvement programmes of the 1980s was substantial improvement in flexibility.

Indeed prior to this increasing efficiency meant that flexibility had to be sacrificed and vice-versa. Thus, with the emphasis on continuous improvement came the realization that increasing operating efficiency could also enhance flexibility. For example, many manufacturers initiated efforts to reduce the amount of time required set-up (or change over) equipment from the production of one product or service to another. Obviously all time spent setting up equipment is wasteful, since the equipment is not being used during this time. Consequently improving the amount of time a resource is used productively directly translates into improved efficiency. Thus the same reductions in machine set-up times also results in improved flexibility.

Specifically, with shorter machine set-up times, manufacturers could produce economically in smaller size batches, making it easier to switch from the production of one product to another. In response to this discovery that efficiency and flexibility can be improved simultaneously and may not have to be traded off, the strategy of mass customization emerged (Pine 1993 and Gilmore and Pine 1997). Organisations pursuing mass customization seek to produce low-cost, high-quality outputs in high variety. Of course, not all products and services lend themselves to being customized. This is particularly true of commodities such as sugar, gas, electricity and flour. On the other hand, mass customization is often quite applicable to products characterised by short life cycles, rapidly advancing technology or changing customer requirements.

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However, recent research suggest that successfully employing mass customization requires an organisation to first develop a transformation process that can constantly deliver high-quality outputs at a low cost.

Gilmore and Pine (1997) identified four mass customization strategies:

1. Collaborative customizers: the organisations establish a dialogue to help customers articulate their needs and then develop customized outputs to meet these needs. For example a computerized system to help customers select eyewear. The system combines a digital image of the customer’s face and then various styles of eyewear are displayed on the digital image. Once the customer is satisfied, the customized glasses are produced at the retail store within an hour.

2. Adaptive customizers: these organisations offers a standard product that customers can modify themselves, such as fast food hamburgers (ketchup, mustard etc).

3. Cosmetic customizers: these organisations produce a standard product but present it differently to different customers. For example peanuts can be packaged and or mixed in a variety of containers on the basis of specific needs of retailing customers.

4. Transparent customizers: these organisations provide custom products without the customers knowing that a product has been customised for them. For example, Amazon.com provides boo recommendations based on information about past purchases.

Responsiveness

The competitive advantage of faster, dependable response to new markets or to the individual customer’s needs have occasionally been noted in business media (Vessey 1991, Eisenhardt and Brown 1998 and Stalk 1988). For example, in a study of the US and Japanese robotics industry, the National Science Foundation(USA) found that the Japanese tend to be about 25% faster than Americans and spend 10% more than the Americans on developing more efficient production methods. The major difference is that the Americans spend more time and money on marketing, whereas the Japanese spend 5 times more than the Americans on developing more efficient production methods.

Table 1.4 indentifies a number of prerequisites for and advantages of a fast, dependable response. These include; higher quality, faster revenue generation, lower cost through elimination of overhead, reduction of inventories, greater efficiency, and fewer errors and scrap. One of the most important but least recognised advantages for managers is that by responding faster, they can allow a customer to delay an order until the exact need is known. Thus the customer does not have to change the order – a perennial headache for most operations managers.

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Strategy and competitiveness

Competiveness can be thought of as the long-term viability of a firm or organisation, or in a short-term context, it is the current success of as firm in the market place as measured by its markets share or its profitability.

Strategy

The organisation’s business strategy is a set of objectives, plans and policies for the organisation to compete successfully in its market. In effect a business strategy specifies what an organisation’s competitive advantage will be and how this advantage will be achieved and sustained. The aspect of the business strategy is defining the organisation’s core competencies and focus.

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Strategic Frameworks

Products of strategic planning include, a marketing strategy, a financial strategy, an R&D strategy, an operations strategy etc. as it happens there are a number of fairly well-defined such strategies. One that is common to many of the functional areas is related to the life cycle of the organisation’s products or services.

The life cycle

A number of functional strategies are tied to the stages in the standard life cycle of products and services. on the life cycle as output increases, the design of the output stabilizes and more competitors enter the market, frequently with more capital-intensive equipment. In the mature phase, the now high-volume output is a virtual commodity and the firm that can produce an acceptable version at the lowest cost usually controls the market.

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Operations processes

The design of the most appropriate processes depend substantially of course on the operations strategy an organisation is trying to execute. And not, only must it design the operations processes but also design a procedure for monitoring and controlling them in case they become ineffective or need to be changed to reflect changes in the market, environment or the organisation’s strategy. Overtime, we know we will have to improve our processes, both in terms of their variability, to make the outputs more consistent or less costly, and in terms of reducing waste in all forms (including time and human effort).

The contemporary concepts of six sigma and lean are used to reducing variation and for reducing waste, respectively.

Operations Managers should select and design transformation processes that can deliver those factors such as low cost, high quality, enhanced functionality, speed and so on – in an efficient and effective manner. If an organisation is using the wrong transformation process, either because the organisation has changed or the market has changed, the organisation will not be competitive on these critical value factors.

As noted earlier, the Sand Cone Model of additive and contemporary competitive strengths emphasizes operations that can deliver quality, delivery dependability, speed and low cost. The most important ingredient in achieving these strengths is selecting the most appropriate transformation process design and layout for the organisation’s operations.

There are 5 basic forms of transformation systems which are:

1) Continuous process2) Flow shop3) Job shop4) Cellular 5) Project

The continuous process industries are in many ways the most advanced, moving fluid material continuously through vats and pipes until a final product is obtained.

Flow shop produce discrete, usually standardized outputs on a continuous basis by means of assembly lines or mass production, often using automated equipment.

Cellular shops produce “families” of outputs within a variety of flow cells, but numerous cells within the plant can offer a range of families of outputs.

Job shops offer a wide range of possible outputs usually in batches by individualised processing departments. These departments typically consist of a set of largely identical equipment, as well as highly skilled workers.

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Finally, projects are temporary endeavors to achieve a unique outcome. The most commonly known projects are those performed on a massive scale when the labour and equipment are brought to each site rather than to a fixed production facility, such as dams, buildings, roadways, etc.

The general procedure for selecting a transformation system is to consider all alternative forms and combinations to devise the best strategy for obtaining the desired outputs. The major considerations, to designing the transformation system – efficiency, volume, effectiveness, capacity, lead time, flexibility and so on – are so interdependent that changing the system to alter one will change the others as well. The layout of the operations is another aspect that must be considered in the selection of the transformation system. The main purpose of layout analysis is to maximize the efficiency (cost-orientation) or effectiveness (e.g. quality, lead time, flexibility) of operations.

Other purposes also exist, such as reducing safety or health hazards, minimizing interference or noise between different operational areas, (facilitating crucial staff interactions, or maximizing customer’s exposure to products or services). In laying out service operations, the emphasis may be on accommodating the customer rather than on operations per-se. Moreover, capacity and layout analyses are frequently conducted simultaneously by analyzing service operations and the wait that the customer must endure.

The layout of parking lots, entry zones, reception rooms, waiting areas, service facilities and other points of customer, contact are top priority in service –oriented organisations such as clinics, stores, nightclubs, restaurants and banks. In a frequently changing environment, the transformation system and its layout will have to be constantly monitored and occasionally redesigned to cope with new demands, new products and services, new government regulations and new technology.

Forms of transformation systems

The continuous transformation process is commonly used to produce highly standardized outputs, usually fluidic products, in extremely large quantities/volumes. In some cases these outputs have become so standardized that there are virtually no real differences between the outputs of different firms. Examples of such commodities include water, gas, chemicals and electricity as well as cement.

The name continuous process reflects the typical practice of running these operations 24 hours a day. One reason for running these systems continuously is to spread their enormous fixed cost over a large volume as possible, thereby reducing unit cost. This is particularly important in commodity markets, where price can be the single most important factor in competing successfully. Another reason is that stopping and starting them can be prohibitively expensive.

The operations of these commodity industries are highly automated, with very specialized equipment and controls, often electronic and computerized. Such automation and the expense it entails are necessary because of strict processing requirements. Because of the highly specialized and automated

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nature of the equipment, changing the rate of the output can be quite difficult. The layout follows the processing stages of the product, and the output rate is controlled through equipment capacity and flow and mixture rates. Labour requirements are low and are devoted primarily to monitoring and maintaining the equipment. However, there are other differentiable forms of continuous processes. Some run for a short time making one product and the switch over to make another product, largely on demand and by the specification of individual customers, which is almost the opposite of commodity productions.

Flow shop

The flow shop is a transformation system similar to continuous process, the major difference being that in the flow shop there is a discrete product or service, whereas in continuous processes the end product is not naturally divisible. Thus in continuous process an additional step, such as bottling or canning, might be needed to get the product into discrete units. Like the continuous process, the flow shop treats all the outputs as basically the same, and the flow of work is thus relatively continuous.

Organisations that use this form are heavy automated, with large special purpose equipment. The characteristics of flow shop are fixed set of inputs, constant throughput times and a fixed set of outputs. Examples of the flow form for discrete products are steelmaking, and automobile assembly where as for services, some examples include car wash, processing insurance claims and the perennial fast food restaurant.

An organisation that produces or plans to produce a high volume of small variety of outputs will thus probably organise its operations as a flow shop. In doing so, the organisation will take advantage of the simplicity and the savings on variable costs that such an approach offers. Because outputs and operations are standardized, specialised equipment can be used to perform the necessary operations at low per-unit costs, and the relatively large fixed costs of the equipment are distributed over a large volume of outputs.

Continuous types of materials-handling equipment, such as conveyors – again operating at low per-unit costs – can be used because can be used because the operations are standardized and typically follow the same path for one operation to the next. This standadisation of treatment provides for a known, fixed throughput time, giving managers easier control of the system and more reliable delivery dates.

The flow shop is easier to manage for other reasons as well: routing, scheduling and control are all facilitated because each output does not have to be individually monitored and controlled. Standardization of operations means that fewer skilled workers can be used and each manager’s span of control can increase.

Advantages of the flow shop

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The primary advantage of a flow shop is the low-per unit cost that is attainable owing to specialized high volume equipment, bulk purchasing, lower labour cost/rates, efficient utilization of the facility, low in process inventories and simplified managerial control.

In addition, with everyone working on all the required tasks simultaneously, referred to as overlapping, product or service outputs are produced very quickly and fast response to changing markets is possible.

Because of the high rate of output, materials can often be bought in large quantities at a significant saving.

Also because operations are standardized, processing times tend to remain constant so that in-process inventories are not required to queue up for processing. This minimizes investment in in-process inventory and queue (buffer) space.

Furthermore, because a standardized product is produced, inventory control and purchasing decisions are routine.

Because the machines are specialized, less skilled labour/operators are needed and therefore, lower wages can be paid. In addition, fewer supervisors are needed, further reducing costs.

Since the flow shop is generally continuous, with materials handling often built into the system itself, the operations can be designed to perform compactly, and efficiently with narrow aisles, thereby maximum use of space.

The simplification in managerial control of a well-designed flow shop is also an advantage in that constant operations problems requiring unending managerial attention penalize the organisation by distracting managers from the normal duties of planning and decision making.

Disadvantages

Not only is a variety of output difficult to obtain, even changes in the rate of output are hard to make. Changing the rate of output may require using overtime, laying off workers, adding additional shifts, or temporarily closing the plant.

Also, because the equipment is so specialised, minor changes in the design of the product often require substantial changes in the equipment. Thus, important changes in product-design are infrequent and this could weaken the organisation’s market position.

A well-known problem in flow shop is boredom and absenteeism among the labour force due to a lack of challenge since the equipment performs the skilled tasks. In addition the constant, unending, repetitive nature of the manufacturing line can dehumanize the workers.

Because the rate of work flow is generally set (paced) by the line speed, incentive pay and other output based incentives are not possible. In the flow production if the line should stop for any unplanned

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reason, a breakdown of a machine or conveyor, a shortage of inputs, and so forth – production may come to an immediate halt unless work in process is stored at key points in the line.

Other requirements of the flow shop also add to its cost and its problem, for example, parts must be standardized so that they full fit together easily and quickly on the assembly line. And since all machines and labour must work at the same repetitive pace in order to coordinate operations, the work loads along the entire line are generally balanced to the pace of the slowest element. To keep the line running smoothly, a large support team is required, as well as large stocks all of which add to the expense.

Last, in the flow shop, simplicity in ongoing operation is achieved at the cost of complexity in the initial setup. The planning, design and installation of the typically complicated, special purpose, high volume equipment are mammoth tasks. The equipment is costly not only to set up originally but also to maintain and service.

Furthermore, such special-purpose equipment is very susceptible to obsolescence and is difficult to dispose of or to modify for other purposes.

Layout of the flow shop

The work flow should be subdivided sufficiently so that labour and equipment are utilized smoothly throughout the processing operations. If for example, one operation takes longer than all the others, this single operation (perhaps a machine) will become a bottleneck, delaying all the operations following it and restricting the output rate of the entire process.

Obtaining smooth utilization of workers and equipment across all operations involves assigning to groups tasks that take about the same amount of time to complete. This balancing applies to production lines where parts or outputs are produced, as well as to assembly lines where parts are assembled into final products.

Final assembly operations usually have more labour input and fewer fixed-equipment cycles and can therefore be subdivided more easily for smooth flow. Either two types of lines can be used. A paced line buses some sort of conveyor and moves the output along at a continuous rate, and operators do their work as the output passes by them. For longer operations the worker may walk or ride alongside the conveyor and then have to walk back to the starting workstation.

Willowvale motor industry assembly line is a common example of a paced line. Workers install doors, engines, hoods and the like as the conveyor moves past them.

In unpaced lines, the workers build up queues between workstations and can then vary their pace to meet the needs of the job or their personal desires, however, average daily output must remain the same. The advantage of unpaced line is that a worker can spend longer on the more difficult tasks or output and balance this with the easier outputs. Similarly, workers can vary their pace to add variety to

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a boring task. For example, a worker may work fat to get ahead of the pace for a few seconds before returning to the tasks.

However, unpaced lines cannot be used with large, bulky products because too much work in process storage space is required. More important, minimum output rates are difficult to maintain because short durations in one operation normally does not dovetail with long durations in the next operation. When long durations coincide, operations downstream from these operations may run out of work in process to work on and may thus be forced to sit idle.

For operations that can be smoothed to obtain the benefits of a production line, there are two main elements in designing the most efficient line. The first is formulating the situation by determining the necessary output rate, the available work time per day, the times for operational tasks, and the order of precedence of the operations.

The second element is actually to solve the balancing problem by subdividing and grouping the operations into balanced jobs.

Balancing the production line

Long-form Credit receives 1200 credit applications a day, on the average. Long Form competes on the basis of its ability to process applications within hours. Daily application processing tasks (tasks that must be completed before the next task) are listed below.

Tasks in credit application processing

Task Average time (minutes)

Immediately preceding tasks

A Open and stack applications 0.20 None B Process enclosed letter; make note of and handle any special

requirements0.37 a

C Check off form 1 for page 1 of application 0.21 aD Check off form 2 for page 2 application; file original copy of

application0.18 a

E Calculate credit limit from standardized tables according to forms 1 and 2

0.19 c, d

F Supervisor checks quotation in light of special processing of letter, notes type of form letter, address, and credit limit to return to applicant

0.39 b, e

G Secretary types in details on form letter and mails 0.36 fTotal 1.90

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Precedence graph for credit application

In balancing the line, the intent is to find a cycle time in which workstation can complete its tasks. A work station is usually a single person, but it may include any number of people responsible for completing all the tasks associated with the job for that station. Conceptually, at the end of this time every workstation passes its part on to the next station and of course, one item comes off the end of the line fully complete.

(industry now refers to the cycle time (based on the demand required) as takt time, since so many firms have erroneously used the term cycle time to refer instead to the time it takes to complete all the work to produce a finished item (i.e. its total throughput time)). Task elements are thus grouped for each workstation so as to utilize as much of this cycle time as possible but not to exceed it.

Each workstation will have a slightly different idle time within the cycle time.

Cycle time = available work time/demand

=(8hr*60min/hr)/1200 applications = 0.4min/application

The cycle time is determined from the required output rate. In this case, the average daily output rate must equal daily input rate; 1200.

If it is less than this figure, a backlog of applications will accumulate. If it is more than this, unnecessary idle time will result. Assuming an eight-hour day, 1200 applications per eight hours means completing 150 every hour or one every 0.4 minutes – this, then, is the cycle time. Adding up the task times in the table above, we can see that the total is 1.9minutes. since every workstation will do no more than 0.4 minutes work during each cycle, it is clear that a minimum of 1.9/0.4 = 4.75 workstations are needed – or always round up, five work stations.

Number of theoretical workstations, NT = ∑task time/cycle time = 1.9/0.4 = 4.75 or 5

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It may be, however that the work cannot be divided and balanced in 5 stations – that six or even seven may be needed. For example, precedence relationships may interfere with assigning two tasks to the same workstation. This is why we referred to NT as the theoretical number of workstations needed. If more workstations are actually needed than the theoretical number, the production line will be less efficient. The efficiency of the line NA Actual stations may be computed as follows:

Efficiency = output/input = total task time

= 1.9/(5*0.4) = 95% (if the line can be balanced with 5 stations)

Or 1.9/(6*0.4) = 79% (if 6 stations are required)

In the formula for efficiency, input is represented by the amount of work required to produce on unit, and output is represented by the amount of work that actually goes into producing one unit.

Now that the problem has been formulated, we can attempt to balance the line by assigning tasks to stations. We begin by assuming that all workers can do any of the tasks. Using the LOT Rule, select the task with the longest operation time next. The general procedure for line balancing is:

1. Construct a list of the tasks where predecessor tasks have already been completed.2. Consider each of these tasks, one at a time, in LOT order and place them within the station.3. As a task is tentatively placed in a station, new follower tasks can now be added to the list.4. Consider adding to the station any tasks in this list whose time fits within the remaining time for

that station.5. Continue in this manner until as little idle time as possible remains for the station.

For Long-form, the first tasks to consider are those with no preceding tasks. Thus, tasks A, taking 0.2 minutes of the 0.4 minutes available, is assigned to station 1. This the, makes task B (0.37) minutes, C (0.21 minute) and D (0.18 minutes) eligible for assignment. Trying the longest first B then C and Last D, we find that only D can be assigned to station 1 without exceeding the 0.4 minutes cycle time. Thus station 1 will include tasks A and D. since 0.02 minutes remains unassigned in Station 1 and no task is that short, we then consider assignments to station 2. Only B and C are eligible for assignment (since E requires that C be completed first) and B (0.37 minutes) will clearly require a station by itself. B is therefore assigned to station 2. Only C is now eligible for assignment, since F requires that both E and B be completed and E is not yet completed. But when we assign C (0.21 minutes) to station 3, task E (0.19 minutes) becomes available and can also be just accommodated in station 3; Task F (0.39 minutes), the next eligible task, requires its own station, this leaves G (0.36 minutes) to station 5. These assignments are illustrated below:

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Station task assignments

Station assignments

Job Shop

It gets its name because unique jobs must be produced. In this form of transformation system each output, or each small batch of outputs is processed differently. Therefore the flow of work through the facility tends to be intermittent. The general characteristics of the job shop are groupings of staff and equipment according to function, a large variety of outputs; a considerable amount of transport of staff, materials or recipients and large variations in system flow times (the time it takes for a complete job).

In general, each output takes a different route through the organisation, requires different operations, uses different inputs and takes a different amount of time. The transformation system is common when the inputs differ significantly in form, structure, materials or processing required. Specific examples that produces product and service in jobs include, hospitals, automobile repair shops, criminal justice system etc. by and large the job shop is especially appropriate for service organisations because services are often customized and hence, each service requires different operations.

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Clearly, the efficient management of a job shop is a difficult task, since every output must be treated differently. Furthermore, managers must also be sure that the available resources are being effeicnetly utilized. Often there is a difficulty trade-off between efficiency and flexibility of operations. Job based processes tend to emphasize flexibility over efficiency.

The figure below represents the flow through a job shop. Each particular job travels from one area to another and so on, according to the unique routing until it is fully processed. Temporary in process storage may occur between various operations while jobs are waiting for subsequent processing (standing in line for the coffee machine, in shops, banks, MSU registration and fees payment queues etc).

A generalized job shop operation.

Advantages of the job shop

The job shop is usually selected by organisations to provide them with the flexibility needed to respond to individual, small-volume demands (or even custom demands).

The ability to produce a wide variety of outputs at reasonable cost is thus the primary advantage of this form.

General-purpose equipment is used, and this is in greater demand and is usually available from more suppliers at a lower price than special-purpose equipment.

There is a larger base of experience with general equipment, therefore problems with installation and maintenance are more predictable and replacement parts are more widely available.

Because of the functional arrangement of the equipment, there are also other advantages. Resources for a function requiring special staff, materials or facilities, maybe centralized at the

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location of that function and the organisation can thus save expense through high utilization rates. Distractions or dangerous equipment, supplies, or activities may also be segregated from other operations in facilities that are soundproof, airtight, explosion-proof and so forth.

One advantage to the staff is that with more highly skilled work involving constantly varying jobs, responsibility and pride in one’s work are increased and boredom is reduced.

Other advantages to the staff are that concentrations of experience and expertise are available and morale increase when people with similar skills work together in centeralised locations.

Because all workers who perform similar activities are grouped together, each worker has the opportunity to learn from others, and the workers can easily collaborate to solve difficult problems.

Disadvantages

The general-purpose equipment of the job shop is usually slower than special-purpose equipment, resulting in higher variable costs per unit.

In addition, the cost of direct labour for the experienced staff necessary to operate general-purpose equipment further increases unit costs of production above what semi-skilled or unskilled workers would require.

Layout of the job shop

Because of the relative permanence, the layout of operations is probably one of the most crucial elements affecting the efficiency of a job shop. In general the problem of laying out operations in a job shop is quite complex. The difficulty stems from the variety of outputs and the constant changes in outputs that are characteristic of organisations with an intermittent transformation system. The optimal layout of the existing set of outputs may be relatively inefficient for the outputs to be produced six months from now.

This is particularly true of job shops where there is no proprietary product and only for-contract work is performed. One week such a shop might produce 1000 wheels, and the next week it might produce an 8000 liter tank. Therefore, a job shop layout is based on the historically stable output pattern of the organisation and expected changes in that pattern rather than on current operations or outputs.

A variety of factors can be important in the interrelations among the operations of job shop. If all the qualitative and quantitative factors can be analyzed and combined, the relative importance of locating, each department close or far from each of the other departments may be used to determine layout.

This approach is particularly significant.

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Directly specified closeness preferences

Consider the table below where 6 departments have been analyzed for the desirability of closeness to each other. Assuming we are given the organisation’s closeness preferences indicated by letter A, E, I, O, U and X with the meaning given in the table.

Directly Specified Closeness preferences

Closeness preferences layout: a) Initial layout (b) Final layout

One way of starting the layout process is simply to draw boxes representing the departments in the order given in the table and show closeness preferences on the arcs joining them as shown by (a) initial layout. The next step is to shift the departments with A on their arcs nearer each other and those with X away from each other. When, these have been shifted as much as possible the E arcs, then the I arcs, and finally the O arcs will be considered for relocation resulting in an improved layout (b).

Cost Volume Distance Model

In the CVD approach, the desirability of closeness is based on the total cost of moving material or people between departments. Clearly, a layout can never be completely reduced to just one such objective, but where the cost of movement is significantly, this approach produces reasonable first approximations.

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The objective is to minimize the costs of interrelations among operations by locating those operations that interrelate extensively close to one another. If we label one of the departments I and another department J, then the cost of moving materials between departments I and J depends on the distance between I and J, Dij. In addition, the cost will usually depend on the amount or volume moving from I to J, such as trips, case, volume, weight, or some other such measure, which we will denote by Vij. Then, if the cost of the flow from I to J per-unit amount per-unit distance is Cij, the total cost of I relating with J is CijVijDij. Not that C, V and D may have different values for different types of flows and that they need not have the same values from J to I as from I to J, since the flow in opposite directions may be of an entirely different nature. For example, information may be flowing from I to J, following a certain paperwork path; but sheet steel may flow from J to I, following a lift truck or conveyor belt path.

Adding the flows from I to everyone of N possible departments, we find that the total cost of department I interrelated with all other departments is:

∑j=1

n

C ijV ij D ij .

It is normally assumed that CijVijDij. = 0, because the distance from I to itself is zero. Adding together the costs for all the departments results in the total cost.

TC = ∑J=1

N

∑j=1

n

C ijV ij D ij .

The overall goal is to find the layout that minimizes this total cost. This may be done by evaluating the cost of promising layouts or as in the following simplified example, by evaluating all possible layouts.

The section of Business school containing the administrative offices of the operations management department is illustrated in the figure below. Each office approximately 10m by 10m, so the walking distance D between adjacent offices (i.e., office 1 and 2 and offices 2 and 3) is 10m, whereas the distance between diagonal offices (office 1 and 3) is approximately 15m. The average number of interpersonal trips made each day is given in a travel or load matrix as shown below. According to the load matrix, each day the assistant makes 5 trips to the Chairperson’s office and 17 trips to the secretary’s office. Thus, the assistant would travel 305m (10m*5 trips*17 trips) each day.

Assuming that the Chairperson is paid approximately twice as much as the secretary and the junior administrative assistant, determine if the current arrangement is best (i.e., least costly) in terms of transit time and if not, what arrangement would be better.

Before calculating total costs of all possible arrangements, some preliminary analysis is worthwhile. First, because of special utility connections, restrooms are usually not considered relocatable. Second, many arrangements are mirror images of other arrangements and thus need not be evaluated, since

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their cost will be the same, for example, interchanging offices 1 and 3 will result in the same lost as the current layout.

The essence of the problem is, then to determine which office should be located diagonally across from the restrooms. There are 3 alternatives Chairperson, assistant or secretary.

Office layout

Load matrix, Vij (trips)

Now, let us evaluate each of the three possibilities as the “diagonal office” – first the chairperson, then the assistant, and last the secretary. The costs will simply be denoted as 1 for the assistant and the secretary or 2 for the chairperson (who earns twice as much as the others). As noted, the V ij “volumes” will be the number of trips from I to J taken from the load matrix, and the distances will depend on who has the diagonal office across the restrooms. The calculations for each arrangement are shown here:

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To better understand these calculations, consider the current arrangement in which the chair has the office diagonal to the restrooms. In this case, the assistant must travel 305m each day, as each day the chair would have to travel 150m (10m*10 trips to the assistant) + (10m*5 trips to the secretary). Finally, the secretary would have to travel 445m each day (15m*13 trips to the assistant) + (10m*25 trips to the chair). Because the chair is paid twice as much as the secretary and the assistant, we weight the chair’s travel distance as twice that of the other two workers. Using this weighting scheme provides a total cost of the current office arrangement of 1050: that is 305 + (2*150) + 445. The best arrangement is to put the secretary in the office diagonal to the restrooms for a relative cost of 1025.

Selection of transformation systems

This section addresses the issue of selecting the appropriate transformation system, or mix of systems, to produce an output. From the preceding discussion, it should be clear that the five transformation systems are somewhat simplified extremes of what is likely to be found in practice. Few organisations use one of the five forms in a pure sense; most combine two or more forms in what we call a hybrid shop. For example in manufacturing computer keyboards, some parts and subassemblies are produced in job shops or cells but then feed into a flow shop at the final assembly line, where a batch of one model is produced. Then the line is modified to produce a batch of another model . Even in “custom” work, jobs are often handled in groups of generally common items throughout most of their processing, leaving minor finishing details such as the fabric on a couch or the facade of a house to give the impression of customizing.

Although services typically take the form of a job shop, the emphasis has recently been on trying to mass-produce them (using cells of flow shops) so as to increase volume and reduce unit costs. The problem for the operations manager is to decide what processing form(s) is most appropriate for the organisation, considering long run efficiency, effectiveness, lead time, capacity, quality and flexibility . Selection may be even more difficult because, as mentioned previously, it is possible to combine processing forms to attain efficiency in some portions of the production process and flexibility in other portions. It is clear that the tradeoffs must be well understood by the manager, and the expected benefits and costs must be well known.

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Considerations of volume and variety

One of the most important factors in the design of a transformation system is establishing the volume and variety of outputs the organisation will produce. High volumes tend to indicate that highly automated mass production will be necessary. High variety, on the other hand, implies the use of skilled labour and general-purpose tools and facilities.

A related consideration here is whether the output will be make-to-stock or make-to-order. A make-to-stock item is produced in batches of some size that is economical (for the firm) and then stocked (in a warehouse, on shelves etc). As customers purchase them, the items are withdrawn from stock. A make-to-order item is usually produced in a batch of a size set by the customer (sometimes just one) and is delivered to the customer upon its completion. Generally, make to stock items are produced in large volumes with low variety, whereas make to order items are produced in low volumes with high variety. (quite often every item is different).

The figure below based on the product process matrix developed by Hayes and Wheelwright (1979), illustrates these points as they relate to various transformation systems. The horizontal axis shows volume, as measured by the batch size, and the left vertical axis shoes the variety of outputs. Organizations making a single unit of output that varies each time (such as dams and custom built machines) use the project form or sometimes the job shop. Some services also fall into this region, as indicated by the upper left tip of the oval. Job shop and cellular systems, however, are mainly used when a considerable variety of outputs are required in relatively small batches. This is particularly characteristic of services. When the size of the batch increases significantly, with a corresponding decrease in variety, then a flow shop is appropriate. Some services also fall into this category. Last, when all the output is the same and the batch is extremely large (or essentially infinite, as in the ore, petroleum and food and drink industries), the continuous process is appropriate. Very few services exist here.

Effects of output characteristics on transformation systems – the product-process matrix

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Note that the standard, viable transformation forms lie on the diagonal of the product-process matrix. Operating at some point off this diagonal can be dangerous for the organization unless done carefully as a well planned strategy. Essentially, no organisations operate in the upper right or lower left segments of the grid. The lower left does, however represent manufacturing 200 years ago. If you wanted four identical dresses, say for your four children, they were made one at a time by hand (whether identical or all different, for that matter). Today, however, it is simply too expensive to produce items this way.; if the items are all identical, they are made in a large batch and then sold separately. In some cases, it is almost impossible to buy a single unit of some items, such as common roofing nails, you have to buy a blister-pack in Kilogrammes or so. Similarly, the upper right may represent manufacturing in the future, when advanced technology can turn out great masses of completely customised products as cheaply as standard items. Currently however, there is trouble doing this (in spite of such popular concepts as mass customization).

Note the overlap in the different forms. This means for example, that on occasion some organisations will use a flow shop for outputs with smaller batches or larger variety or both, than the outputs of organisations using a job shop. There are many possible reasons for this including economic and historical factors. The point is that the categories are not rigid, and many variations do occur. Many organisations also use hybrids or combinations of systems, such as producing components to stock but assembling finished products to order, as in the auto industry.

Note that in the above diagram, the general breakdown of make-to-order and make to stock with output variety and size of batch. Project forms (high variety, unit batch size) are almost always make to order and continuous processing forms (no variety, infinite batch size) are almost make to stock, though exceptions occasionally occur.

Product and Process Life Cycle

The life cycle of an output is how long it takes to develop, bring to market, and catch on, how quickly it grows in popularity, how different versions are developed for different market segments; how the output reaches market saturation, how price competition emerges. A similar life cycle occurs in the production system for an output. As a result a project form of transformation may be used for the development of a new output, may evolve into a job shop or cellular layout as a market develop. In the R and D stage of the production life cycle, many variations are investigated during the development of a product. As the output is being developed, prototypes are made in small volumes in a relatively inefficient, uncoordinated manner typically in a job shop. As demand grows and competitors enter the market, price competition begins and a cellular or flow system, with its high volume and low variable costs, becomes preferred. At the peak of the cycle, demand may increase to the point where such a system is justified.

This progress is illustrated in the figure below, which presents a breakeven analysis for each of four transformation systems. The dark bold line illustrates the lowest-cost system for each stage of the life cycle. At the stage of project development and initiation (R and D and initial production), the cost of fixed equipment is nil, and labour is the predominant contributor to high variable costs. In the expansion

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stage, the job shop allows some tradeoff of equipment for labour with a corresponding reduction in variable unit costs, thus, leading, at these volumes, to a reduction in overall unit costs. Finally, at high volumes characterizing maturity, a nearly complete replacement of expensive labour with equipment is possible, using cellular form and the flow shop.

Selection of transformation systems by stage of life cycle.

Be advised, however, that not all outputs can or should follow this sequence. The point is that the transformation system should evolve as the market and output evolve. But many organisations see their strength in operating a particular transformation system, such as R and D or low-cost production of large volumes. If their outputs evolve into another stage of the life cycle in which a different transformation system form is preferable, they drop the output (or license it to someone else) and switch to another output more appropriate to their strengths.

Failing to maintain this focus in the organisation’s production system can quickly result in a white elephant – a facility built to be efficient at one task but being inefficiently used for something else. This can also happen if the organisation, in an attempt to please every customer, mixes the production of outputs that require different transformation systems. Japanese plants are very carefully planned to maintain one strong focus in each plant. If an output requiring a different process is to be produced, new plant is acquired or built.

From the previous discussion it is clear that there is a close relationship between the design of a product or service and the design of the production system. Actually, the link is even closer that it seems. The figure below illustrates the relationship between the innovations throughout the life cycle of a product or service and innovations throughout the life cycle of its production system. At the left, when

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the product or service is introduced, innovations and changes in its design are frequent. At this point, the production system is more of the project or modeling/job shop form since the design is still changing (the number of product innovations is high). Toward the middle, the product design has largely stabilized, and cost competition is forcing innovations in the production process, particularly the substitution of cellular or flow shop machinery for labour (the number of process innovations is high). At the right, this phenomenon has subsided and innovations in production methods are primarily the result of competitor’s actions, government regulations, and other external factors.

Product-process innovations over time

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Product lice cycle diagram

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Company strategy

Introduction

*Best period to increase market share

*R & D and Engineering very critical

Growth

* practical to change price or quality image

* strengthen niche

*firm position via fresh promotion and distribution approaches

*poor time to increase markets

Maturity

* competitive costs are critical

Decline

*Cost control critical

Production and Operations

Strategy

*Product design and development critical

* frequent product and process design changes

* over capacity due to short production runs.

* highly skilled labour

*high production costs.

* forecasting is critical.

* product and process reliability important

*competitive product improvements and features improved

*increase capacity

*shift towards product orientation

* emphasis is on cost containment

*standardisation

*optimum capacity

*increased stability of manufacturing processes

*low skilled labour

*little product differentiation

*cost minimisation

*over capacity in the industry

*prune lines to eliminate items not retaining good margins

*reduce capacity

Service processes

As with the design of transformation systems for products, the design of transformation systems for services depends heavily on knowing exactly what characteristics of the service need to be emphasized: its explicit and implicit benefits, its cost, its time duration, its location, and accessibility. Knowing the importance of each of these allows the designer to make the necessary tradeoffs in costs and benefits to offer an effective yet reasonably priced service.

Unfortunately, service transformation systems are frequently implemented with little development or pretesting, which is also a major reason why so many of them fail. Almost cases the various forms and layouts of manufacturing transformation processes apply equally well to services. Flow shops are seen in fast food restaurants, job shops are seen in banks and hospitals, and projects are seen in individual services such as saloons and house construction.

However, one important service element that is usually missing from manufacturing transformation design is the extensive customer contact during delivery of the service. This presents both problem and also opportunities. For one thing, the customer will often add new inputs to the delivery system or make new demands on it that were not anticipated when it was designed.

In addition, customers do not arrive at smooth, even increments of time but instead tend to bunch up, as during lunch periods, and then complain when they have to wait for service. Furthermore, the customer’s biased perception of the server, and the server’s skills, can often influence their satisfaction with the quality of the service. Obviously, this can either be beneficial or harmful depending on the circumstances. On the other hand, having the customer involved in the delivery of a service can also present opportunities to improve it. Since customers know their own needs best, it is wise to let them aid in the preparation or delivery of the service – as with automatic teller machines, etc. in addition to improving the quality of the service, this can save the firm money by making it unnecessary to hire additional severs.

However, the customer can also negligently and quickly ruin a machine or a tool, and may even sue if injured by it, so the service firm must carefully consider how much self-service it is willing to let the customer perform.

Chase and Tansik (1983) devised a helpful way to view this customer contact when designing service. Chase’s suggestion is to evaluate whether the service is, in general, high contact or low contact, and what portions of the service, in particular are each.

The value of this analysis is that the service can be made both more efficient and more effective by separating these two portions and designing them differently. For example, the high contact portions of the service should be handled by workers who are skilled at social interaction whereas the low contact portion should employ more technical workers and take advantage of labour saving equipment.

Whenever possible, the low contact portion of a service should be decoupled from the high contact portion so that it may be conducted with efficiency, whereas the high contact portion is conducted with

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*little product differentiation

*cost minimisation

*over capacity in the industry

*prune lines to eliminate items not retaining good margins

*reduce capacity

grace and friendliness. Close analysis of the service tasks may also reveal opportunities for decreasing contact with the customer – through, for example, automated teller machines, phone service, self-service or the web, if this is appropriate – with a concomitant opportunity for improving both the efficiency and level of service.

In particular, allowing customers to use the web to obtain service (eg obtain account information, place orders) offers them convenient access, 24 hours per day, 365 days per year and immediate attention). (i.e. no longer being placed on hold for the next available representative).

SERVICE PROCESS DESIGN

Like the product-process matrix for manufacturing, Schemenner (1986) has developed a similar matrix for services that not only classifies four major and quite different types of services but gives some insights on how to design the best service system.

The service matrix is shown below. Service systems are divided into those with high versus low contact intensity customization (similar to Chase) and whether they are capital intensive or labour intensive.

Schemenner names each of the quadrants with an easily understood identifier that captures the essence of that quadrant: service factory, service shop, mass service and professional service.

The service matrix

Each of the quadrants represents a unique service transformation process, with unique managerial challenges and having unique characteristics. Those services at the high contact side of the matrix have low volumes with high customization and must attain their profitability through high process. Those on the other side with low contact and customization attain profitability through high volumes. The

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investment axis identifies whether the service provider puts their resources into expensive equipment or into labour. Thus, one axis is a combination of customer variety and volume (like the product process matrix) and the other axis is based on the inputs to conduct the service.

The matrix is also useful in identifying the managerial challenges for each of the quadrants. In the low contact left end, the managerial challenge is making the service appear warm and friendly so as to attract high volumes. If the level of the contact is high, the managerial challenge is trying to be optimally efficient in using capital and labour resources, while keeping prices high.

If the service is equipment intensive, the challenge is to keep capital investment costs low. If instead the service is labour intensive the challenge is to minimize wages and time spent on each customer. The matrix is also useful in designing a service. For example, a firm may decide to move from one quadrant to another to better use their resources or environment. For example, a tax preparation service may start as a high-priced professional service but then move either toward a more automated service shop through computer preparation of the forms, or a less-personalised mass service using less skilled tax preparers.

Distinction between a High and Low Contact Business

Design Decision High contact system Low contact system

Facility location Operations must be near the customer (ie walk in and out by customer)

Operations may be placed near supply, transport and labour (e.g. high density and heavy industrial sites, CBD)

Facility layout – should accommodate the customer's physical and psychological needs and expectations

Should meet the physiological and physical needs and expectations

Should focus on production and efficiency (DStv)

Product design Environment as well as physical products define the nature of service (customer is involved in product design)

Customer is not in the service environment so that the product can be defined by fewer attributes.

Process design Stages of the production process have direct and immediate effect on the customer (we are all service experts)

Customers are not involved in the majority of the processing steps

Scheduling Customer is in the production schedule and must be accommodated (when and where)

Customer is concerned mainly with completion dates

Production planning Orders cannot be stored, so smoothing of production flow will result in loss of business

Both backlogging and production smoothing are possible

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Worker skills Direct workforce constitute a major part of the products so to interact with the public (technical skills, human skills)

Direct workforce need only have technical skills

Quality control Quality standards are often in the eye of the beholder and thus are variable

Quality standards are generally measurable and thus fixed

Time standards Service time depends on the customer's needs so time standards are inherently loose

Work is performed on the customer's surrogate so time standards can be tight

Wage payment Variable output requires time based wage system

Flexible output permits output based wage systems

Capacity planning To avoid lost sales, capacity must be set to match peak demand (unless you have a monopoly)

Storage output permits capacity at some average demand level ie you can hire casuals, additional shifts or overtime to make up

Service Guarantees and Fail safing

Service guarantees are increasingly common among service providers who have confidence they can meet them, and who desire a competitive advantage in their industry. Package transformation companies were among the first to use them, and since then have been adopted by hotels, restaurants and pothers in those service businesses that have extensive contact with the public but a reputation for poor service.

There are four major elements of a service guarantee:

1. It must be meaningful to the customer in the sense that it in fact repays the customer for the failure of the service to meet his or her expectations. A guarantee with a trivial payoff that does not satisfy the customer will just increase the customer’s dissatisfaction and negate the purpose of the guarantee program in the first place.

2. The guarantee must be unconditional. Again, if there are “exceptions” that exclude the common reasons why the service might fail, the customer will only be more dissatisfied.

3. The guarantee must be easy to communicate and for the customer (and employees) to understand. If the guarantee is complex or complicated to explain, it will not come serve to attract customers to the service provider. And need to fully understand it and be able to execute the guarantee provisions.

4. The guarantee must be easy to use in the sense of immediately invoking it when a service failure occurs. If the customer has to return home, mail in a coupon, and wait for satisfaction, the guarantee programme will not achieve its purpose.

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THROUGHPUT / OPERATION CYCLE

Time has become a key success measure in business. Oftentimes, it is more important than other performance measures.

For example, in marketing a product's success or failure often depends on "time-to-market," or how quickly a new product becomes available to the customer.

One of many cycle time measures used in management, cycle time is the measure of a business cycle from beginning to end. Production cycle time refers to production activities, such as the total time required to produce a product.

Order processing cycle time is used in the front office to determine the total time required to process an order.

From a financial perspective, terms like cash-to-cash cycle time describe the amount of time a company takes to recover its financial investment. From a management perspective, cycle time is used to evaluate performance in all aspects of a business.

Cycle time has become the key measurement tool for the performance of a number of leading edge management concepts, including supply chain management (SCM), just-in-time (JIT) management, enterprise resources planning (ERP), theory of constraints management, and lean management.

Cycle time improvements in any of these areas have been linked to reduced costs, reduced inventories, and increased capacity.

The resource areas that are measured by cycle time include the measurement of financial flow, materials flow, and information flow. In each case, a delay or failure of any of these measures would indicate a failure of the entire business process.

Cycle time is best illustrated by a few examples. In marketing, time-to-market cycle time is the critical measure of success in the fashion, apparel, and technology industries. Companies that cannot get products to market quickly may get completely washed out. Time-to-market is the measure of time from idea inception through idea development, design and engineering, pilot, and finally production and customer availability. For example, the United States led the world in the idea phase of automotive air bag development. However, a slow design and engineering process enabled the Japanese to generally offer airbags in their vehicles several years before the United States.

Another example of cycle time is the production cycle time. This is the time from when an order is released on the production floor until completion and shipment to the customer. For the American automobile manufacturer this time is measured in weeks and, in some cases, months. For Toyota this time is approximately four hours. The repercussions for this are found is the staging of enormous amounts of work-in-process inventories. The actual "hands-on" production time in both cases is about the same. However, since the United States produces in large batch quantities, it effectively produces hundreds and thousands of cars at the same time. As a result, there is a lot of inventory staging and related work space requirements. This example illustrates the direct relationship between cycle time and inventory.

Another example of cycle time is order-processing time. Unfortunately, in far too many factories the paperwork time to process an order is longer than product production time. Order processing time starts when a phone call or fax initiates the order, and ends when the order is sent to production

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scheduling. This cycle time includes all paperwork-related steps, such as credit verification and order form completion.

In finance, performance measures such as cash-to-cash cycle time reflect a company's cash performance. This is the amount of time it takes from the time money is spent on a customer's product for the purchase of components until the "cash" is recovered from the customer in the form of a payment. In the computer industry the industry average cash-to-cash cycle time is 106 working days. For "best-in-class" companies this cash-to-cash cycle time is 21 working days, and for Dell Inc. it is a negative seven days. This example illustrates that the average computer company needs to finance its inventory investment for 106 days, whereas Dell has the advantage of being able to utilize its customers' cash to earn interest. Dell can then use this advantage to offer price incentives that the other computer manufacturers cannot.

A variant use of the term "cycle time" is found in industrial engineering. In this specific example, cycle time has a number of meanings—depending on the situation in which the term is used and the industry to which it is applied. It generally is considered to be a manufacturing term applied to an environment where a series of activities or tasks (each with a predetermined completion time known as task time) are performed in a specified sequence known as a "precedence relationship." However, the term can be used in the service sector if the rendering of the service requires a sequential series of tasks. As these tasks are completed at each operation or workstation, the product is passed on to the next workstation in the sequence until the product is complete and can be defined as a finished good.

The predetermined task times govern the range of possible cycle times. The minimum cycle time is equal to the longest task time in the series of tasks required to produce the product, while the maximum cycle time is equal to the sum of all the task times required for a finished good. For example, consider a product that requires five sequential tasks to manufacture. Task one takes 10 minutes to complete; task two, 12 minutes; task three, 20 minutes; task four, 8 minutes; and task five, 10 minutes. The minimum cycle time for this product would be 20 minutes (the longest time). Any cycle time less than 20 minutes would not allow the product to be made, because task three could not be completed. The maximum cycle time would be 60 minutes, or the sum of all task times in the sequence. This implies a range of possible cycle times of 20 to 55 minutes. However, the maximum cycle time would really only be feasible if there was no waste or non-value-added time in the process, such as delays between tasks. Some people refer to the sum of the task times as throughput time or the time required to move a product completely through the system.

However, in its more general usage cycle time is how long it takes for material to enter and exit a production facility. Depending on the industry, this definition is appropriate with slight modifications. For example, in the automobile collision repair industry cycle time refers to the time a car enters the facility for repair until the repair is completed.

FURTHER READING:

Blackstone, John H. Capacity Management. Cincinnati, OH: South-Western Publishing Co., 1989.

Cox, James F., III, and John H. Blackstone, Jr., eds. APICS Dictionary, 9th ed. Falls Church, VA: American Production and Inventory Control Society Inc., 1998.

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Plenert, Gerhard J. International Operations Management, Copenhagen, Denmark: Copenhagen Business School Press, 2002.

Stevenson, William J. Production/Operations Management, 6th ed. Boston: Irwin/McGraw-Hill, 1999.

Throughput (Manufacturing Cycle) Time

A Measure of Internal Business Process Performance:

Throughput time or manufacturing cycle time is an important measure of internal business process performance. Performance measures are found on the balanced scorecards of the companies. Examples of the some performance measures can be found on characteristics of balanced scorecard page. Most of the performance measures are self explanatory. However, three are not - delivery cycle time, throughput time, and manufacturing cycle efficiency (MCE). On this page, Throughput time or manufacturing cycle time is defined, explained and calculated.

Definition and Explanation:

The amount of time required to turn raw materials into completed product is called throughput time, or Manufacturing cycle time. The relation between the delivery cycle time and the throughput time is illustrated below:

Exhibit 1-1: Delivery Cycle Time and Throughput (Manufacturing Cycle) Time

The throughput time or manufacturing cycle time is made up of process time, inspection time, move time, and queue time. Process time is the amount of time work is actually done on the product. Inspection time is the amount of time spent ensuring that the product is not defective. Move time is the time required to move materials or partially completed products from workstation to workstation.

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Queue time is the amount of time a product spends waiting to be worked on, to be moved, to be inspected or to be shipped.

As shown at the bottom of exhibit 1-1, only one of these four activities adds value to the product - process time. The other three activities - inspecting, moving, and queuing - add no value to the product and should be eliminated as much as possible.

Formula: Throughput time = Process time + Inspection time + move time + Queue time

ExampleCalculation of Throughput Time or Manufacturing Cycle Time:

Novex Company keeps careful track of the time relating to orders and their production. During the most recent quarter, the following average times were recorded for each unit or order:

Wait time 17.0Inspection time 0.4Process time 2.0Move time 0.6Queue time 5.0

Goods are shipped as soon as production is completed.

Required:

Calculate the throughput time or manufacturing cycle time.

Solution:

*Throughput time = Process time + Inspection time + move time + Queue time

2.0 days + 0.4 days + 0.6 days + 5.0 days

= 8.0 days

DELIVERY CYCLE TIME

A Measure of Internal Business Process Performance:

Delivery cycle time is an important measure of internal business process performance. Performance measures are found on the balanced scorecards of the companies. Examples of the some performance measures can be found on characteristics of balanced scorecard page. Most of the performance measures are self explanatory. However, three are not - delivery cycle time, throughput time, and

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manufacturing cycle efficiency (MCE). On this page, deliver cycle time is defined, explained and calculated.

Definition and Explanation:

The amount of time from when an order is received from a customer to when the completed order is shipped is called delivery cycle time. This time is clearly a key concern to many customers, who would like the delivery cycle time to be as short as possible. Cutting the delivery cycle time may give a company a key competitive advantage - and may be necessary for survival. Consequently, many companies would include this performance measure on their balanced scorecard.

Delivery Cycle Time and Throughput (Manufacturing Cycle) Time

Formula:

Delivery Cycle Time = Wait time + Throughput time

ExampleCalculation of Delivery Cycle Time:

Novex Company keeps careful track of the time relating to orders and their production. During the most recent quarter, the following average times were recorded for each unit or order:

Wait time 17.0Inspection time 0.4Process time 2.0Move time 0.6Queue time 5.0

Goods are shipped as soon as production is completed.

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Required:

Calculate the delivery cycle time.

Solution:

Delivery Cycle Efficiency = Wait time + Through

= 17.0 days + 8.0 days*

= 25.0 days

*Throughput time = Process time + Inspection time + move time + Queue time

2.0 days + 0.4 days + 0.6 days + 5.0 days

= 8.0 days

Manufacturing Cycle Efficiency (MCE)

A Measure of Internal Business Process Performance:

Manufacturing cycle efficiency (MCE) is an important measure of internal business process performance. Performance measures are found on the balanced scorecards of the companies. Examples of the some performance measures can be found on characteristics of balanced scorecard page. Most of the performance measures are self explanatory. However, three are not - delivery cycle time, throughput time, and manufacturing cycle efficiency (MCE). On this page, manufacturing cycle efficiency (MCE) is defined, explained and calculated.

Definition and Explanation:

value added time as a percentage of throughput time is called manufacturing cycle efficiency.

Through concerted efforts to eliminate the non-value added activities such as inspecting, moving, and queuing, some companies have reduced their throughput time to only a fraction of previous levels. In turn, this has helped to reduce the delivery cycle time from months to only weeks or hours. Throughput time, which is considered to be a key measure in delivery performance, can be put into better perspective by computing the manufacturing cycle efficiency (MCE).

Exhibit 1-1: Delivery Cycle Time and Throughput (Manufacturing Cycle) Time

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Formula:

MCE = Value-added time / Throughput time

If the MCE is less than 1, then non-value added time is present in the production process. An MCE of 0.5, for example, would mean that half of the total production time consisted of inspection, moving, and similar non-value-added activities. In many manufacturing companies, it is less than 0.1 (10%), which means that 90% of the time a unit is in process is spent on activities that do not add value to the product. By monitoring the MCE, companies are able to reduce non-value-added activities and thus get products into the hands of customers more quickly and at a lower cost.

ExampleCalculation of Manufacturing Cycle Efficiency:

Novex Company keeps careful track of the time relating to orders and their production. During the most recent quarter, the following average times were recorded for each unit or order:

Wait time 17.0Inspection time 0.4Process time 2.0Move time 0.6Queue time 5.0

Goods are shipped as soon as production is completed.

Required:

Calculate manufacturing cycle efficiency.

Solution:

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MCE = Value-added time / Throughput time

MCE = 2.0 days* / 8.0 days**

= 0.25

*Only process time (2.0 days) represents value-added time

**Throughput time = Process time + Inspection time + move time + Queue time

= 2.0 days + 0.4 days + 0.6 days + 5.0 days

= 8.0 days

Monitoring and Control

As noted in the introduction section to this module, the production system, we have to monitor not only our processes, but our output and even the environment to make sure that our strategy, inputs and transformation processes are appropriate to achieve our goals. To do this we need to identify the key factors to be controlled, which in turn depend upon our goals for the production system. The monitoring system is a direct, connection between planning and control. But if it does not collect and report iofnromation on some significant element of the production system, control can be faulty.

Unfortunately, it is common to focus monitoring activities on data that are easily gathered, - rather than important – or to concentrate on “objective” measures that are easily defended at the expense of softer, more subjective data that may be more valuable for control. When monitoring output performance, we should concentrate primarily on measuring various facets of output rather than intensity of activity. It is crucial to remember that effective managers are not primarily interested in how hard their employees work-they are interested in the results achieved.

Performance criteria, standards and data collection procedures must be established for each of the factors to be measured. However, more often than not, standards and criteria change because of factors that are not under the control of the management. Standards may also be changed by the community as a response to some shift in public policy.

The monitoring process is based on the criteria and standards because they dictate, or at least constrain, the set of relevant measures.

Next, the information to be collected must be identified. This may consist of accounting data, operating data, engineering test data, customer reactions, regulations, competitor’s process, specification changes etc.

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The fundamental problem is to determine precisely which of all the available data should be collected. Perhaps the most common error made when monitoring data is to gather information that is clearly related to performance but has little or no probability of changing significantly from one collection period to another. Therefore, the first task is to identify the objectives desired from the production system. Data must be identified that measures achievement against these goals, and mechanisms designed that will gather and store such data.

Next, we must examine the purpose of these various processes, and find measures that provide us with insight into how these processes are performing.

There is arrange of ways to determine what measures to monitor and then if necessary, take action to control and these include, Balanced scorecard, strategy maps, ISO9000 and 14000, Failure Mode and Effect Analysis (FMEA).

Process Monitoring

From the above discussion, it is clear that there is a wide array of elements of the production system and environment that we may wish to monitor, but too much data can be worse than too little data since it obscures the information that may be most important in the various measures we are watching.

Stages of operational effectiveness

Wheelwright and Hayes (1985) suggest that organisation can progress through four stages of effectiveness in terms of the role their operations play in supporting and achieving the overall strategic objectives of the business’s production system.

As a diagnostic tool this framework helps determine the extent to which an organisation is utilizing its operations to support and possibly attain a sustainable competitive advantage. As a prescriptive tool, the framework helps focus an organisation an appropriate future courses of action because it is argued that stages cannot be skipped. Important managerial challenges are also identified for each stage of effectiveness.

Organisations in Stage 1 of the model are labeled internally neutral. These organisations tend to view operations as having little impact on the organisation competitive success, in fact, these organisations often consider the operations area as primarily a source of problems (e.g., quality problems, late shipments, too much capital tied upon in inventory). Thus, believing that operations have little strategic importance, the emphasis in these organisations is on minimizing the negative impact of operations.

Stage 2 is labeled externally neutral. Organisations at this stage attempt to match the operational practices of the industry. Thus, organisations in this stage still tend to view operations as having little strategic importance, but they at least try to follow standard industry practices. Because these

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organisations follow industry practice, they tend to be more reactive that proactive in the operations area. Furthermore, operational investments and improvements tend to be tied to reducing costs.

Stage 3 is called internally Supportive. In this stage, of development the organisation expects its operations to support the overall business strategy and competitive position. In many cases this is stated as a formal operations strategy. Thus, operational decisions are evaluated based on their consistency with and the extent to which they support the organisation’s overall mission. Internally supportive organisations tend to be more proactive in terms of identifying opportunities to support the organisation’s overall competitiveness. It is important to point out, however, that while stage 3 organisations expect operations to support the overall business strategy, operations is typically not involved in actually formulating it.

Stage 4 organisations depend on their operations to achieve a competitive advantage and are referred to as externally supportive. In effect, these organisations use core capabilities residing in the operations area to obtain a sustainable competitive advantage.

Because different parts of an organisation may evolve at different rates, determining an organisation’s stage of effectiveness may require making a judgment about where the balance of the organisation is positioned. Thus, it is possible that some departments or areas o a stage 2 organisation exhibit characteristics of a stage 3 organisation. However, if the majority of the organisation is most appropriately characterised as being in stage 2, eth the organisation should be categorized as being in stage 2.

Thus, evaluating an organisation’s evolution is based not on its most evolved area, but rather on the balance of its organisational practices. Using the definitions of the four operational effectiveness stages above, Dangayach and Deshmukh (2006) developed perceptual measures for each of these stages, some of which are given in the table below. By asking managers how closely their company followed each of these policy measures (on a 1-5 scale) they were able to classify firms into one of the four stages and relate them to the firm’s overall performance. Thus, these measures would be excellent items for monitoring the evolving competitive strength of manufacturing firms and taking control actions when they showed competitive slippage.

Measures of operational effectiveness

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Process Performance Measures

Operations managers are interested in process aspects such as cost, quality, flexibility, and speed.

Some of the process performance measures that communicate these aspects include:

Process capacity - The capacity of the process is its maximum output rate, measured in units produced per unit of time. The capacity of a series of tasks is determined by the lowest capacity task in the string. The capacity of parallel strings of tasks is the sum of the capacities of the two strings, except for cases in which the two strings have different outputs that are combined. In such cases, the capacity of the two parallel strings of tasks is that of the lowest capacity parallel string.

Capacity utilization - the percentage of the process capacity that actually is being used. Throughput rate (also known as flow rate ) - the average rate at which units flow past a specific

point in the process. The maximum throughput rate is the process capacity. Flow time (also known as throughput time or lead time) - the average time that a unit requires

to flow through the process from the entry point to the exit point. The flow time is the length of the longest path through the process. Flow time includes both processing time and any time the unit spends between steps.

Cycle time - the time between successive units as they are output from the process. Cycle time for the process is equal to the inverse of the throughput rate. Cycle time can be thought of as the time required for a task to repeat itself. Each series task in a process must have a cycle time less than or equal to the cycle time for the process. Put another way, the cycle time of the process is equal to the longest task cycle time. The process is said to be in balance if the cycle times are equal for each activity in the process. Such balance rarely is achieved.

Process time - the average time that a unit is worked on. Process time is flow time less idle time. Idle time - time when no activity is being performed, for example, when an activity is waiting for

work to arrive from the previous activity. The term can be used to describe both machine idle time and worker idle time.

Work In process - the amount of inventory in the process.

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Set-up time - the time required to prepare the equipment to perform an activity on a batch of units. Set-up time usually does not depend strongly on the batch size and therefore can be reduced on a per unit basis by increasing the batch size.

Direct labor content - the amount of labor (in units of time) actually contained in the product. Excludes idle time when workers are not working directly on the product. Also excludes time spent maintaining machines, transporting materials, etc.

Direct labor utilization - the fraction of labor capacity that actually is utilized as direct labor.

The Process Bottleneck

The process capacity is determined by the slowest series task in the process; that is, having the slowest throughput rate or longest cycle time. This slowest task is known as the bottleneck. Identification of the bottleneck is a critical aspect of process analysis since it not only determines the process capacity, but also provides the opportunity to increase that capacity.

Saving time in the bottleneck activity saves time for the entire process. Saving time in a non-bottleneck activity does not help the process since the throughput rate is limited by the bottleneck. It is only when the bottleneck is eliminated that another activity will become the new bottleneck and present a new opportunity to improve the process.

If the next slowest task is much faster than the bottleneck, then the bottleneck is having a major impact on the process capacity. If the next slowest task is only slightly faster than the bottleneck, then increasing the throughput of the bottleneck will have a limited impact on the process capacity.

Starvation and Blocking

Starvation occurs when a downstream activity is idle with no inputs to process because of upstream delays. Blocking occurs when an activity becomes idle because the next downstream activity is not ready to take it. Both starvation and blocking can be reduced by adding buffers that hold inventory between activities.

Process Improvement

Improvements in cost, quality, flexibility, and speed are commonly sought. The following lists some of the ways that processes can be improved.

Reduce work-in-process inventory - reduces lead time. Add additional resources to increase capacity of the bottleneck. For example, an additional

machine can be added in parallel to increase the capacity. Improve the efficiency of the bottleneck activity - increases process capacity. Move work away from bottleneck resources where possible - increases process capacity. Increase availability of bottleneck resources, for example, by adding an additional shift -

increases process capacity. Minimize non-value adding activities - decreases cost, reduces lead time. Non-value adding

activities include transport, rework, waiting, testing and inspecting, and support activities. Redesign the product for better manufacturability - can improve several or all process

performance measures.

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Flexibility can be improved by outsourcing certain activities. Flexibility also can be enhanced by postponement, which shifts customizing activities to the end of the process.

In some cases, dramatic improvements can be made at minimal cost when the bottleneck activity is severely limiting the process capacity. On the other hand, in well-optimized processes, significant investment may be required to achieve a marginal operational improvement. Because of the large investment, the operational gain may not generate a sufficient rate of return. A cost-benefit analysis should be performed to determine if a process change is worth the investment. Ultimately, net present value will determine whether a process "improvement" really is an improvement.

Balanced Scorecard

The balanced scorecard approach (Kaplan and Norton 1996) is a recognised tool for helping organisations translate their strategy into appropriate performance measures to monitor their success. Many organizations now realize that no single type of measure can provide insight into all the critical areas of business. Thus, the purpose of the balanced scorecard is to develop a set of measures that provides a comprehensive view of the organisation. Organisations that have developed a balanced scorecard report numerous benefits, including:

An effective way to clarify and gain consensus of the strategy. A mechanism for communicating the strategy throughout the entire organisation. A mechanism for aligning departmental and personal goals to the strategy. A way to ensure that strategic objectives are linked to annual budgets. Timely feedback related to improving the strategy.

One problem with traditional performance measurement systems based primarily on financial measures is that they often encourage short-sighted decisions such as reducing investments in product development, employee training, and information technology. The balanced scorecard approach corrects this problem by measuring performance in four major areas:

1. Financial performance2. Customer performance 3. Internal business process performance4. Organisational learning and growth

The financial performance measures included in the balanced scorecard are typically related to profitability, such as Return on Equity, Return on Capital, and Economic Value Added.

Customer performance measures focus on customer satisfaction, customer retention, customer profitability, market share, and customer acquisition.

The internal business process dimension addresses the issue of what the organisation must excel at to achieve its financial and customer objectives.

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Examples of performance measures for internal business processes include; quality, response time, cost, new product launch time, and the ratio of processing time to total throughput time.

Finally, the learning and growth dimension focuses on the infrastructure the organisation must build to sustain its competitive advantage. Learning and growth performance measures include employee satisfaction, employee retention, worker productivity, and the availability of timely and accurate information.

The Strategy Map

In extending their earlier work on the balanced scorecard, Kaplan and Norton (2000) proposed the development of strategy maps (see also Scholey 2005) as a way to illustrate and monitor the cause and effect relationships identified through the development of a balanced scorecard. In particular, strategy maps provide organisations with a tool that helps them better monitor important details about their strategic business processes, thereby enhancing their employee’s understanding of the strategy interactions which in turn facilitates implementing the business strategy.

Like the balanced scorecard, strategy maps address four perspectives: the financial perspective, the customer perspective and the learning and growing perspective. An example strategy maps for a department store that desires to improve its performance is shown in the figure below.

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Example strategy map for a departmental store

At the top of the strategy map the goal is specified, which in this example is to improve the store’s Return on Investment. Management has determined that the goal of improving the store’s Return On Investment can be accomplished by increasing revenue and or improving the store’s productivity. The remainder of the strategy map explicitly shows the chain of cause and effect relationships management has hypothesized about how the store’s Return on Investment can be improved. For example, it is believed that providing the sales associates with additional training will lead to improved selling skills, which would the result in increased sales per square metre of retail space and happier associates. Happier associates in turn should result in both friendly and courteous sales associates and less turnover among the associates. Ultimately, the strategy map hypothesizes that increased sales per square metre will help the store increase its revenue and its inventory turns, which then leads to revenue growth and productivity improvements.

ISO 9000 and ISO 14000

ISO 9000 was developed as a guideline for designing, manufacturing, selling and servicing products, in a sense, it is a sort of “checklist” of good business practices. Thus the intent of the ISO 9000 standard is that, if an organisation selects a supplier that is ISO 9000 certified, it has some assurance that the supplier follows accepted business practices in the areas specified in the standard. However, one criticism of ISO 9000 is that it does not require any specific actions, and therefore each organisation determines how it can best meet the requirements of the standard.

ISO 9000 was developed by the International organisation for Standardisation and first issued in March 1987. A major revision to ISO 9000 was completed in December 2008, and the new standard is commonly referred to as ISO 9000:2008. In 1993 the European Community required that companies in several industries become certified as a condition of conducting business in Europe.

ISO 14000 is a series of standards covering environmental management systems, environmental auditing, evaluation of environmental performance, environmental labeling and life cycle assessment. The focus of ISO 14001 or 14000 is on an organisation’s environmental management system without prescribing any specific standards of performance or levels of improvement.

Failure Mode and Effect Analysis (FMEA)

FMEA was developed by the space programme in the 1960s (Stamatis 2003) as a structured approach to help identify and prioritise for close monitoring and control those elements of a system that might give rise to failure. It employs a scoring model approach set up in a series of six straightforward steps as follows:

1. List the possible ways a production system might fail.

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2. Evaluate the severity (S) of the consequences of each type of failure on a 10 point scale where “1” is “no effect” and “10” is “very severe”.

3. For each cause of failure, estimate the like hood (L) of its occurrence on a 10 point scale where “1” is “remote” and “10” is “almost certain”.

4. Estimate the ability to detect (D) a failure associated with each cause. Using a 10 point scale, “1” means detectability is almost certain using normal monitoring or control systems and “10” means it is practically certain that failure will not be detected in time to avoid or mitigate it.

5. Find the Risk priority Number (RPN) where RPN = S*L*D.6. Consider ways to reduce the S, L and D for each cause of failure with a significantly high RPN.

The table below illustrates the application of the FMEA to a new concept fast food restaurant. Please note that in real situations the items of failure would be much more specific and narrow”: a particular machine, a particularly difficult process, a unique government regulation, and ot7her such items that clearly could result in missing our goals for the new concept.

We might invest additional time and effort in training our employees to offset the first threat but since L is low already, it might be more productive to find ways to detect this inadequacy faster such as surveying our customers to monitor their perceptions of our service. This might then reduce D to 3 instead % and thereby mitigate the threat. As well, we could include a question on the survey to help determine what marketing approaches are having the greatest results, reducing D from 8 to perhaps 5.

Process Control

Process Control is the act of reducing differences between a plan and reality for each process. Monitoring and comparing activities with plan, and then reporting these findings is to no avail if actions are not taken when reality deviates significantly from what was planned. Control has the primary purpose of ensuring that the process is in compliance with its objectives. In large production systems particularly, early control is crucial since the longer we wait, the more difficult to correct the deviation it becomes.

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Control is one of the manager’s most difficult tasks invariably involving both mechanistic and human elements. As well, control can be difficult because problems are rarely clear cut and hence the need for change and redirection may be fuzzy. Determining what to control raises further difficulties, did someone take an incorrect action or is the system to blame, or perhaps simply Mother Nature”.

A good control system should also possess some specific characteristics:

The system should be flexible, where possible, it should be able to react to unforeseen changes in system performance.

The system should be cost effective and the cost of control should never exceed the value of control. For example, bear in mind that control is not always less expensive than scrap.

The system should be as simple as possible to operate. The system must operate in a timely manner. Problems must be reported while there is still

time to do something about them. Sensors and monitors should be sufficiently precise to control the project within limits that are

truly functional for the organisation. The control system should be easy to maintain. The system should signal the manager if it goes out of order. The system should be capable of being extended or otherwise altered. Control systems should be fully documented when installed, and the documentation should

include a complete training programme in system operation. The system must operate in an ethical manner.

Statistical Process Control

One of management’s most difficult decisions in quality Control centers on whether an activity needs adjustment. This requires some form of inspection, and in quality Control there are two major types of inspection either:

1. Measuring something or 2. Simply determining the existence of a characteristic.

Type 1, inspection by measuring, is called inspection for variables, and usually relates to weight, length, temperature, diameter or some other variable that can be used.

Quality control is concerned with the quality conformance of process: Does the output of a process conform to the intent of design? Toward that end managers use statistical process control to evaluate the output of a process to determine its acceptability. To do this they take periodic samples from the process and compare them with a predetermined standard. If the sample results are not acceptable, they stop the process and take corrective action. If the sample results are acceptable, they allow the process to continue.

Managers make use of control charts to test for non randomness in data, which implies a process is out of control. Managers must establish a definition of out of control even a process that is functioning as it

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should will not yield output that conforms exactly to the standard, simply because of the natural (i.e., random) variations inherent in all processes, a manual or mechanical – a certain amount of variation is inevitable.

The main task of Quality Control is to distinguish random from non-random variability, because non-random variability means that a process is out of control. When a process is judged out of control, corrective action must be taken. This involves uncovering the cause of non-random variability (eg worn equipment, incorrect methods, failure to follow specified procedures) and correct it.

To ensure that corrective action is effective, the output of a process must be monitored for a sufficient period of time to verify that the problem has been eliminated.

Variations and Control

All processes that provide a good or a service exhibit a certain amount of “natural” variation in their output the variations are created by the combined influences of countless minor factors, each one so unimportant that even if it could be identified and eliminated, the decrease in process variability would be negligible. In effect, this variability is inherent in the process. It is often referred to as chance or random variation. Deming referred to this as common variability. The amount of inherent variability differs from process to process. For instance, older machines generally exhibit a higher degree of natural variability than newer machines, partly because of worn parts and partly because new machines may incorporate design improvements that lessen the variability in their output.

The other type of variability in process output is called assignable variation, the main source of assignable variation can usually be identified (assigned to a specific cause and eliminated). Deming called this special variation. Tool wear and equipment that needs adjustment, defective materials, human factors (carelessness, fatigue, noise and other distractions, failure to follow correct procedures etc).

When samples are taken form process output and sample statistics such as the sample mean or range are calculated, they exhibit the same variability; i.e., there is a variation in the values of sample means and variation in the values of sample ranges. The variability of a sample statistic can be described by its sampling distribution the random variability of sample statistics.

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NB: the sampling distribution of means is normal and it has less variability than the process

The goal of sampling is to determine whether non-random and thus correctible sources of variation are present in the output of a process.

Example:

Suppose there is a process for filling bottles with soft drinks. If the amount of soft drink in large number of bottles is measured accurately, we should discover slight differences among bottles. If these differences were arranged on a graph, the frequency distribution would reflect the process variability. The values would be clustered close to the process average (e.g., 16ml), but some of the values would vary somewhat from the mean. If we return to the process and take samples of 10 bottles each and compute the mean amount of soft drink in each vary, just as the individual values varied. They, too would have a distribution of values. If the process contained only random variability, the distribution of process values would represent the inherent process variability, and the distribution of sample means would represent the random variability of all possible sample means.

These two distributions are shown on the figure above. The sampling distribution exhibit mush less variability (i.e., it is less spread out) than the process distribution. This reflects the averaging that occurs in computing the sample means: high and low values in samples tend to offset each other, resulting in les variability among sample means than among individual values.

Note that the mean of the sampling distribution is exactly equal to the mean of the process.

Finally, note that the sampling distribution is a normal distribution, even if the process distribution is not normal. The central limit theorem provides the basis for the assumption that the sampling distribution will be normal or at least approximately normal, even, if the population (i.e., the process) is not.

The normal distribution can be used to judge whether a process is performing adequately. If the output reflects only random variability, one would conclude that the process is stable (i.e., in control). But if there is evidence of no-random variability, one would conclude that the process is unstable (i.e., out of control).

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To understand how the normal distribution is used, consider the following: approximately 95.5% of the area under a normal curve (and hence 95.5% of the sample means) will have values that are within ±2 standard deviations of the distribution mean, and approximately 99.7% of the sample means will have values that are within ±3 standard deviations of the distribution mean. These values are typically used for control limits and these are shown in the figure below.

Control charts

A control chart is a time-ordered plot of sample statistics, used to distinguish between random and non-random variability. The basis for the control chart is the sampling distribution, which essentially describes random variability. Control charts have two limits that separate random variation and non-random variation. The larger value is the upper control limit (UCL) and the smaller value is the lower control limit (LCL).

A sample statistic that falls between these two limits means (averages) suggest (but does not prove) randomness, while a value outside or on either limit suggest (but does not prove) non-randomness.

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There are four commonly used control charts. Two are used for variables and two are used for attributes. Attributes data are counted (e.g., the number of defective parts in a sample, the number of calls per day). Variables data are measured usually on a continuous scale (e.g., amount of time needed to complete a task, length or width of a part).

Control charts for variables

Mean and range charts are used to monitor variables. Control charts for means monitor the central tendency of a process and range charts monitor the dispersion of a process.

Mean charts. A mean control chart sometimes referred to as an ẋ (x-bar) chart, can be constructed ion one of two ways. The choice depends on what information is available. Although the value of the standard deviation of process, σ, is often known, if a reasonable estimate is available, one can compute control limits using these formulas:

Upper control limit (UCL): = x́ + zσ x́

Lower control limit (LCL): = x́ - zσx́

Where

σx́ = σ√n

σx́ = Standard deviation of distribution of sample means

σ = Process standard deviation

n = Sample size

z = Standard normal deviate

x́ = Average of sample means

The following example illustrates the use of these formulas.

A quality inspector took five samples, each with four observations, of the length of time to process loan application at a bank. The analyst computed the mean of each sample and then computed the grand mean. All values are in minutes. Use this information to obtain three-sigma (ie., z = 3) control limits for means of future times. It is known form previous experience that the standard deviation of the process is 0.02 minute.

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x́= 12.10+12.12+12.11+12.10+12.12

5 = 12.11

Using the formulas for Upper Control Limits (UCL) and Lower Control Limits (LCL), with z = 3, n = 4 observations per sample, and σ = 0.02, we find:

UCL: = 12.11 + 3( 0.02√ 4 ) = 12.14

LCL = 12.11 - 3( 0.02√ 4 ) = 12.08

NB: if one applied these control limits to these data, one would judge the process to be in control because all of the sample means have values that fall within the control limits. The fact that some of the individual measurements fall outside of the control limits (eg the first observation in sample 2 and the last observation in sample 3) is irrelevant. You can see why by referring to figures above; individual values are represented by the process distribution, a large portion of which lies outside the control limits for means.

A second approach is to us the sample range as a measure of process variability. The appropriate formulas for control limits are:

UCL = x́ + A2R

LCL = x́ - A2R

Where

A2 = A factor from table for three-sigma control limits for x́ and R charts.

R = Average of sample ranges

Example

Twenty samples of n = 8 have been taken from a cleaning operation. The average sample range for the 20 samples was 0.06 minute, and the average mean was 3 minutes. Determine three-sigma control limits for this process.

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Solution

x́ = 3 min R = 0.16, A2 = .37 for n = 8 (from table below)

UCL = x́ + A2R = 3 + 0.37(0.016) = 3.006 minutes

LCL = x́ - A2R = 3 – 0.37(0.016) = 2.994 minutes

Range charts

Range control charts (R –charts) are used to monitor process dispersion; they are sensitive to changes in process dispersion. Although the underlying sampling distribution is not normal, the concepts for use of range charts are much the same as those for use of mean charts. Control limits for range are found using the average sample range in conjunction with these formulas:

UCLR = D4R

LCLR = D3R

Where values of D3 and D4 are obtained from the table above pf factors for three-sigma control limits for x́ and R charts.

Example

Twenty –five samples of n = 10 observations have been taken from a milling process. The average sample range was 0.01 centimeter. Determine upper and lower control limits for sample ranges.

Solution

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R = 0.01 cm, n = 10

From the above table, for n = 10, D4 = 1.78 and D3 = 0.22

UCLR = 1.78(0.01) = 0.0178 or 0.018

LCLR = 0.22(0.01) = 0.0022 or 0.002

In the above example, a sample range of 0.018 centimeter or more would suggest that the process variability has increased. A sample range of 0.002 or less would imply that the process variability has decreased. In the former case, the means that the process was producing too much variation: we would want to investigate this in order to remove the cause of variation. In the latter case, even though decreased variability is desirable, we should want to determine what was causing it. Hence it can be beneficial to investigate both points beyond and lower than both control limits.

Using Mean and Range Charts

Control charts for attributes

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Control charts for attributes are used when the process characteristics is counted rather than measured. For example, the number of defective items in a sample is counted, whereas the length of each item is measured. There are two types of attribute control charts, one for the fraction of defective items in a sample (a p-chart) and one for the number of defects per unit (a c-chart). A p-chart is appropriate when the data consist of two categories of items. For instance, if glass bottles are inspected for chipping and cracking, both the good bottles and the defective ones can be counted. However, one can count the number of accidents that occur during a given period of time but not the number of accidents that did not occur. Similarly, one can count the number of scratches on a polished surface, the number of bacteria present in a water sample, and the number of crimes committed during the month of June but one cannot count the number of non-occurrences. In such cases, a c-chart is appropriate. See table 3 below representing p-chart and c-chart variables.

p-Chart

A p-chart is used to monitor the proportion of defective items generated by a process. The theoretical basis for a p-chart is the binomial distribution, although for large sample sizes, the normal distribution provides a good approximation to it. Conceptually, a p-chart is constructed and used in much the same way as a mean chart. The center line on a p-chart is the average fraction defective in the population, p. the standard deviation of the sampling distribution when p is known is:

σ p = √ p (1−p )n

Control limits are computed using the formulas:

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UCLp = p + zσ p

LCLp = p - zσ p

If p is know, it can be estimated from samples. That estimate, p, replaces p in the preceding formulas, as illustrated the next example.

Note: because the formula is an approximation, it sometimes happens that the computed LCL is negative. In those instances, zero is used as the lower limit.

Example

An inspector counted the number of defective monthly billing statements of a company telephone in each of 20 samples. Using the following information, construct a control chart that will describe 99.74% of the chance variation in the process when the process is in control. Each sample contained 100 statements.

Sample Number of Defectives

Sample Number of Defectives

1 4 11 82 10 12 123 12 13 94 3 14 105 9 15 216 11 16 107 10 17 88 22 18 129 13 19 1010 10 20 16Total 220

Solution

z for 99.74 is 3.00 (from appendix Table A).

p = Totalnumber of defectivesTotalnumber of observations =

22020 (100 )

=0.11

σ̂ = √ p (1−p )n

= √ 0.11 (1−0.11)100

= 0.03

Control limits are:

UCLP = p + z(σ̂ p) = 0.11 + 3.00(0.03) = 0.20

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LCLP = p – z(σ̂ p) = 0.11 – 3.00(0.03) = 0.02

Plotting the control limits and the sample fraction defective, you can see that the process in not in

control: sample 8 ( 22100 )=0.22 and sample 15 ( 21100 )=0.21 are above the upper control limit.

C-chart

When the goal is to control the number of occurrences (e.g. defects) per unit, a c-chart is used. Units

might be automobiles, hotels, typed pages, or rolls of carpet. The underlying sampling distribution is the

Poisson distribution. Use of the Poisson distribution assumes that defects occur over some continuous

region and that the probability of more than one defect at any particular spot is negligible. The mean

number of defects per unit is c and the standard deviation is √c. For practical reasons, the normal

approximation to the Poisson is used. The control limits are:

UCLc = c + z√c

LCLc = c - z√c

If the value of c is unknown, as is generally the case, the sample estimate, c, is used in place of c.

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Rolls of coiled wire are monitored using a c-chart. Eighteen rolls have been examined and the number of

defects per roll has been recorded in the following table. Is the process in control? Plot the values on a

control chart using three standard deviation control limits.

Sample Number of Defectives Sample Number of Defectives

1 3 10 1

2 2 11 3

3 4 12 4

4 5 13 2

5 1 14 4

6 2 15 2

7 4 16 1

8 1 17 3

9 2 18 1

Total 45

Solution

c = 4518

=2.5

UCLc = c + 3√c = 2.5 + 3√2.5 = 7.24

LCLc = c - 3√c = 2.5 - 3√2 .5 = -0.66 →0

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If the process average is unknown, it can be estimated from sample data, using c = Number of defects

÷ Number of samples

When the computed lower control limit is negative, the effective lower limit is zero. The calculation

sometimes produces a negative lower limit due to the use of the normal distribution to approximate the

Poisson distribution. The normal is symmetrical, whereas the Poisson is not symmetrical when c is close

to zero.

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