When production stages have different capacities What are the options for dealing with the imbalance?

• What is capacity management and why is it an important focus for firms?
• How does one execute capacity planning?
• Compare and contrast capacity planning for service and manufacturing businesses.

500-600 word response

LO5–1

Explain what capacity management is and why it is strategically important.

A dictionary definition of capacity is “the ability to hold, receive, store, or accommodate.” In a general business sense, it is most frequently viewed as the amount of output that a system is capable of achieving over a specific period of time. In a service setting, this might be the number of customers that can be handled between noon and 1:00 P.M. In manufacturing, this might be the number of automobiles that can be produced in a single shift.

When looking at capacity, operations managers need to look at both resource inputs and product outputs. For planning purposes, real (or effective) capacity depends on what is to be produced. For example, a firm that makes multiple products inevitably can produce more of one kind than of another with a given level of resource inputs. Thus, while the managers of an automobile factory may state that their facility has 6,000 production hours available per year, they are also thinking that these hours can be used to make either 150,000 two-door models or 120,000 four-door models (or some mix of the two- and four-door models). This reflects their knowledge of what their current technology and labor force inputs can produce and the product mix that is to be demanded from these resources.

While many industries measure and report their capacity in terms of outputs, those whose product mix is very uncertain often express capacity in terms of inputs. For example, hospital capacity is expressed as the number of beds because the number of patients served and the types of services provided will depend on patient needs.

An operations and supply chain management view also emphasizes the time dimension of capacity. That is, capacity must also be stated relative to some period of time. This is evidenced in the common distinction drawn between long-range, intermediate-range, and short-range capacity planning.

Capacity planning is generally viewed in three time durations:

Strategy

Long range—greater than one year. Where productive resources (such as buildings, equipment, or facilities) take a long time to acquire or dispose of, long-range capacity planning requires top management participation and approval.

Intermediate range—monthly or quarterly plans for the next 6 to 18 months. Here, capacity may be varied by such alternatives as hiring, layoffs, new tools, minor equipment purchases, and subcontracting.

Short range—less than one month. This is tied into the daily or weekly scheduling process and involves making adjustments to eliminate the variance between planned and actual output. This includes alternatives such as overtime, personnel transfers, and alternative production routings.

In this chapter, our focus is on capacity planning related to the long-term decisions. These involve the purchase of highly capital-intensive items, such as buildings, equipment, and other assets. The medium-term capacity-related decisions are considered as part of the aggregate operations planning decisions, which are the topic of Chapter 19. Short-term capacity planning is discussed in the context of the different types of processes discussed in the book: manufacturing in Chapter 7, service in Chapter 9, and material requirements planning in Chapter 21.

Although there is no one person with the job title “capacity manager,” there are several managerial positions charged with the effective use of capacity. Capacity is a relative term; in an operations management context, it may be defined as the amount of resource inputs available relative to output requirements over a particular period of time.

The objective of strategic capacity planning is to provide an approach for determining the overall capacity level of capital-intensive resources—facilities, equipment, and overall labor force size—that best supports the company’s long-term competitive strategy. The capacity level selected has a critical impact on the firm’s response rate, its cost structure, its inventory policies, and its management and staff support requirements. If capacity is inadequate, a company may lose customers through slow service or by allowing competitors to enter the market. If capacity is excessive, a company may have to reduce prices to stimulate demand; underutilize its workforce; carry excess inventory; or seek additional, less profitable products to stay in business.

Strategic capacity planning

Finding the overall capacity level of capital-intensive resources to best support the firm’s long-term strategy.

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Capacity Utilization

The term capacity implies an attainable rate of output, for example, 480 cars per day, but says nothing about how long that rate can be sustained. Thus, we do not know if this 480 cars per day is a one-day peak or a six-month average. To avoid this problem, the concept of best operating level is used. This is the level of capacity for which process was designed and thus is the volume of output at which average unit cost is minimized. Determining this minimum is difficult because it involves a complex trade-off between the allocation of fixed overhead costs and the cost of overtime, equipment wear, defect rates, and other costs.

Capacity

The output that a system is capable of achieving over a period of time.

Best operating level

Output level where average unit cost is minimized.

An important measure is the capacity utilization rate, which reveals how close a firm is to its best operating level:

Capacity utilization rate

Measure of how close the firm’s current output rate is to its best operating level (percent).

$\begin{array}{cc}Capacityutilizationrate=\frac{Capacityused}{Bestoperatinglevel}& \left[5.1\right]\end{array}$

So, for example, if our plant’s best operating level was 500 cars per day and the plant was currently operating at 480 cars per day, the capacity utilization rate would be 96 percent.

$Capacityutilizationrate=\frac{480}{500}=.96\text{or 96\%}$

The capacity utilization rate is expressed as a percentage and requires that the numerator and denominator be measured in the same units and time periods (such as machine hours/day, barrels of oil /day, or dollars of output /day).

Economies and Diseconomies of Scale

The basic notion of economies of scale is that as a plant gets larger and volume increases, the average cost per unit of output drops. This is partially due to lower operating and capital cost, because a piece of equipment with twice the capacity of another piece typically does not cost twice as much to purchase or operate. Plants also gain efficiencies when they become large enough to fully utilize dedicated resources (people and equipment) for information technology, material handling, and administrative support.

Economies of scale

Idea that as the plant gets larger and volume increases, the average cost per unit drops. At some point, the plant gets too large and cost per unit increases.

At some point, the size of a plant becomes too large and diseconomies of scale become a problem. These diseconomies may surface in many different ways. For example, maintaining the demand required to keep the large facility busy may require significant discounting of the product. The U.S. automobile manufacturers continually face this problem. Another typical example involves using a few large-capacity pieces of equipment. Minimizing equipment downtime is essential in this type of operation. M&M Mars, for example, has highly automated, high-volume equipment to make M&Ms. A single packaging line moves 2.6 million M&Ms each hour. Even though direct labor to operate the equipment is very low, the labor required to maintain the equipment is high.

In many cases, the size of a plant may be influenced by factors other than the internal equipment, labor, and other capital expenditures. A major factor may be the cost to transport raw materials and finished product to and from the plant. A cement factory, for example, would have a difficult time serving customers more than a few hours from its plant. Similarly, automobile companies such as Ford, Honda, Nissan, and Toyota have found it advantageous to locate plants within specific international markets. The anticipated size of these intended markets will largely dictate the size and capacity of the plants.

Jaguar, the luxury automobile producer, recently found it had too many plants. Jaguar was employing 8,560 workers in three plants that produced 126,122 cars, about 15 cars per employee. In comparison, Volvo’s plant in Torslanda, Sweden, was nearly twice as productive, building 158,466 cars with 5,472 workers, or 29 cars per employee. By contrast, BMW AG’s Mini unit made 174,000 vehicles at a single British plant with just 4,500 workers, or 39 cars per employee.

M&M’S PRODUCTION.

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Capacity Focus

The concept of a focused factory holds that a production facility works best when it focuses on a fairly limited set of production objectives. This means, for example, that a firm should not expect to excel in every aspect of manufacturing performance: cost, quality, delivery speed and reliability, changes in demand, and flexibility to adapt to new products. Rather, it should select a limited set of tasks that contribute the most to corporate objectives. Typically the focused factory would produce a specific product or related group of products. A focused factory allows capacity to be focused on producing those specific items.

Focused factory

A facility designed around a limited set of production objectives. Typically the focus would relate to a specific product or product group.

The capacity focus concept can be operationalized through the mechanism of plant within a plant—or PWP. A focused factory (Exhibit 5.1) may have several PWPs, each of which may have separate suborganizations, equipment and process policies, workforce management policies, production control methods, and so forth, for different products—even if they are made under the same roof. This, in effect, permits finding the best operating level for each department of the organization and thereby carries the focus concept down to the operating level.

Plant within a plant (PWP)

An area in a larger facility that is dedicated to a specific production objective (for example, product group). This can be used to operationalize the focused factory concept.

Capacity Flexibility

Capacity flexibility means having the ability to rapidly increase or decrease production levels, or to shift production capacity quickly from one product or service to another. Such flexibility is achieved through flexible plants, processes, and workers, as well as through strategies that use the capacity of other organizations. Increasingly, companies are taking the idea of flexibility into account as they design their supply chains. Working with suppliers, they can build capacity into their whole systems.

Flexible Plants Perhaps the ultimate in plant flexibility is the zero-changeover-time plant. Using movable equipment, knockdown walls, and easily accessible and reroutable utilities, such a plant can quickly adapt to change. An analogy to a familiar service business captures the flavor well: a plant with equipment that is easy to install and easy to tear down and move—like the Ringling Bros.–Barnum and Bailey Circus in the old tent-circus days.

Flexible Processes Flexible processes are epitomized by flexible manufacturing systems on the one hand and simple, easily set up equipment on the other. Both of these technological approaches permit rapid low-cost switching from one product to another, enabling what are sometimes referred to as economies of scope. (By definition, economies of scope exist when multiple products can be combined and produced at one facility at a lower cost than they can be produced separately.)

Economies of scope

When multiple products can be produced at lower cost in combination than they can be separately.

Flexible Workers Flexible workers have multiple skills and the ability to switch easily from one kind of task to another. They require broader training than specialized workers and need managers and staff support to facilitate quick changes in their work assignments.

exhibit 5.1 Focused Factories—Plant within a Plant

THIS COMPANY NEEDS TO PRODUCE TWO DIFFERENT PRODUCTS. PRODUCT A IS HIGH VOLUME AND STANDARD (THERE IS NO VARIATION IN HOW IT IS MADE). PRODUCT B IS LOW VOLUME AND NEEDS TO BE CUSTOMIZED TO EACH ORDER. THIS PLANT IS DIVIDED INTO THREE DISTINCT AREAS THAT OPERATE INDEPENDENTLY. THE PRODUCT LINE A AREA IS A HIGH-VOLUME ASSEMBLY LINE DESIGNED TO PRODUCE A. B MACHINE SHOP IS AN AREA WHERE CUSTOM PARTS ARE MADE FOR PRODUCT B. ASSEMBLY B IS WHERE PRODUCT B IS ASSEMBLED BASED ON EACH CUSTOMER ORDER. THIS FACTORY, WITH ITS PLANTS WITHIN A PLANT, CAN OPERATE MORE EFFICIENTLY THAN IF BOTH PRODUCTS WERE MADE WITH A SINGLE COMMON PRODUCTION PROCESS.

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CAPACITY ANALYSIS

Considerations in Changing Capacity

Many issues must be considered when adding or decreasing capacity. Three important ones are maintaining system balance, frequency of capacity additions or reductions, and use of external capacity.

LO5–2

Exemplify how to plan capacity.

Strategy

Maintaining System Balance In a perfectly balanced plant with three production stages, the output of stage 1 provides the exact input requirement for stage 2. Stage 2’s output provides the exact input requirement for stage 3, and so on. In practice, however, achieving such a “perfect” design is usually both impossible and undesirable. One reason is that the best operating levels for each stage generally differ. For instance, department 1 may operate most efficiently over a range of 90 to 110 units per month, whereas department 2, the next stage in the process, is most efficient at 75 to 85 units per month, and department 3 works best over a range of 150 to 200 units per month. Another reason is that variability in product demand and the processes themselves may lead to imbalance.

There are various ways of dealing with imbalance. One is to add capacity to stages that are bottlenecks. This can be done by temporary measures, such as scheduling overtime, leasing equipment, or purchasing additional capacity through subcontracting. A second way is through the use of buffer inventories in front of the bottleneck stage to ensure that it always has something to work on. A third approach involves duplicating or increasing the facilities of one department on which another is dependent. All these approaches are increasingly being applied to supply chain design. This supply planning also helps reduce imbalances for supplier partners and customers.

Frequency of Capacity Additions There are two types of costs to consider when adding capacity: the cost of upgrading too frequently and that of upgrading too infrequently. Upgrading capacity too frequently is expensive. Direct costs include removing and replacing old equipment and training employees on the new equipment. In addition, the new equipment must be purchased, often for considerably more than the selling price of the old. Finally, there is the opportunity cost of idling the plant or service site during the changeover period.

Conversely, upgrading capacity too infrequently is also expensive. Infrequent expansion means that capacity is purchased in larger chunks. Any excess capacity that is purchased must be carried as overhead until it is utilized. (Exhibit 5.2 illustrates frequent versus infrequent capacity expansion.)

External Sources of Operations and Supply Capacity In some cases, it may be cheaper not to add capacity at all, but rather to use some existing external source of capacity. Two common strategies used by organizations are outsourcing and sharing capacity. An example of outsourcing is Dell Computer using a Chinese company to assemble its notebook computers. An example of sharing capacity is two domestic airlines flying different routes with different seasonal demands exchanging aircraft (suitably repainted) when one’s routes are heavily used and the other’s are not. A new twist is airlines sharing routes—using the same flight number even though the airline company may change through the route. Outsourcing is covered in more depth in Chapter 16.

EMPLOYEES WORK ON A PRODUCTION LINE OF DELL NOTEBOOK COMPUTERS AT A NEW PLANT OF WISTRON GROUP, WHICH IS THE MAIN PARTNER OF DELL AND LENOVO, IN CHENGDU, SICHUAN PROVINCE OF CHINA.

Decreasing Capacity Although we normally think in terms of expansions, shedding capacity in response to decreased demand can create significant problems for a firm. Temporary strategies such as scheduling fewer hours or scheduling an extended shutdown period are often used. More permanent reductions in capacity would typically require the sale of equipment or possibly even the liquidation of entire facilities.

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exhibit 5.2 Frequent versus Infrequent Capacity Expansion

Determining Capacity Requirements

In determining capacity requirements, we must address the demands for individual product lines, individual plant capabilities, and allocation of production throughout the plant network. Typically this is done according to the following steps:

Analytics

1. Use forecasting techniques (see Chapter 18) to predict sales for individual products within each product line.

2. Calculate equipment and labor requirements to meet product line forecasts.

3. Project labor and equipment availabilities over the planning horizon.

Often the firm then decides on some capacity cushion that will be maintained between the projected requirements and the actual capacity measured as a percentage in excess of the expected demand. A capacity cushion is an amount of capacity in excess of expected demand. For example, if the expected annual demand on a facility is $10 million in products per year and the design capacity is $12 million per year, it has a 20 percent capacity cushion. A 20 percent capacity cushion equates to an 83 percent utilization rate (100%/120%).

Capacity cushion

Capacity in excess of expected demand.

When a firm’s design capacity is less than the capacity required to meet its demand, it is said to have a negative capacity cushion. If, for example, a firm has a demand of $12 million in products per year but can produce only $10 million per year, it has a negative capacity cushion of 16.7 percent.

We now apply these three steps to an example.

EXAMPLE 5.1: Determining Capacity Requirements

For a step-by-step walkthrough of this example, visit www.mhhe.com/jacobs14e_sbs_ch05.

Stewart Company produces two brands of salad dressings: Paul’s and Newman’s. Each is available in bottles and single-serving plastic bags. Management would like to determine equipment and labor requirements for its packing operation for the next five years. The demand for the two flavors and for each packaging option is given in this table. The company has three machines that can package 150,000 bottles each year (each machine has two operators). It also has five machines that can package 250,000 plastic bags per year (each

of these machines has three operators). Will the company have enough packaging capacity to meet future demand?

For the Excel template, visit www.mhhe.com/jacobs14e.

SOLUTION

Step 1. Use forecasting techniques to predict sales for individual products within each product line. The marketing department, which is now running a promotional campaign for Newman’s dressing, provided the forecast demand values given in the above table (in thousands) for the next five years. The campaign is expected to continue for the next two years. The table of expected future demand is presented above.

Step 2. Calculate equipment and labor requirements to meet product line forecasts. Currently, three machines that can package up to 150,000 bottles each per year are available. Each machine requires two operators and can produce bottles of both Newman’s and Paul’s dressings. Six bottle machine operators are available. Also, five machines that can package up to 250,000 plastic bags each per year are available. Three operators are required for each machine, which can produce plastic bags of both Newman’s and Paul’s dressings. Currently, 15 plastic bag machine operators are available.

Total product line forecasts can be calculated from the preceding table by adding the yearly demand for bottles and plastic bags as follows:

We can now calculate equipment and labor requirements for the current year (year 1). Because the total available capacity for packaging bottles is 450,000/year (3 machines × 150,000 each), we will be using 135/450 = 0.3 of the available capacity for the current year, or 0.3 × 3 = 0.9 machine. Similarly, we will need 300/1,250 = 0.24 of the available capacity for plastic bags for the current year, or 0.24 × 5 = 1.2 machines. The total number of crew required to support our forecast demand for the first year will equal the crew required for the bottle machine plus the crew required for the plastic bag machine.

The labor requirement for year 1’s bottle operation is

$\begin{array}{ccc}0.9\text{bottle machine}\times 2\text{operators}& =& 1.8\text{operators}\\ 1.2\text{bag machines}\times 3\text{operators}& =& 3.6\text{operators}\end{array}$

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Step 3. Project labor and equipment availabilities over the planning horizon. We repeat the preceding calculations for the remaining years:

A positive capacity cushion exists for all five years because the available capacity for both operations always exceeds the expected demand. Stewart Company can now begin to develop the intermediate-range sales and operations plan for the two production lines.

USING DECISION TREES TO EVALUATE CAPACITY ALTERNATIVES

LO5–3

Evaluate capacity alternatives using decision trees.

A convenient way to lay out the steps of a capacity problem is through the use of decision trees. The tree format helps not only in understanding the problem but also in finding a solution. A decision tree is a schematic model of the sequence of steps in a problem and the conditions and consequences of each step. In recent years, a few commercial software packages have been developed to assist in the construction and analysis of decision trees. These packages make the process quick and easy.

Decision trees are composed of decision nodes with branches extending to and from them. Usually squares represent decision points and circles represent chance events. Branches from decision points show the choices available to the decision maker; branches from chance events show the probabilities for their occurrence.

In solving decision tree problems, we work from the end of the tree backward to the start of the tree. As we work back, we calculate the expected values at each step. In calculating the expected value, the time value of money is important if the planning horizon is long.

Once the calculations are made, we prune the tree by eliminating from each decision point all branches except the one with the highest payoff. This process continues to the first decision point, and the decision problem is thereby solved.

We now demonstrate an application of capacity planning for Hackers Computer Store.

Analytics

EXAMPLE 5.2: Decision Trees

For a step-by-step walkthrough of this example, visit www.mhhe.com/jacobs14e_sbs_ch05.

The owner of Hackers Computer Store is considering what to do with his business over the next five years. Sales growth over the past couple of years has been good, but sales could grow substantially if a major proposed electronics firm is built in his area. Hackers’ owner sees three options. The first is to enlarge his current store, the second is to locate at a new site, and the third is to simply wait and do nothing. The process of expanding or moving would take little time, and, therefore, the store would not lose revenue. If nothing were done the first year and strong growth occurred, then the decision to expand could be reconsidered. Waiting longer than one year would allow competition to move in and would make expansion no longer feasible.

The assumptions and conditions are as follows:

1. Strong growth as a result of the increased population of computer fanatics from the new electronics firm has a 55 percent probability.

2. Strong growth with a new site would give annual returns of $195,000 per year. Weak growth with a new site would mean annual returns of $115,000.

3. Strong growth with an expansion would give annual returns of $190,000 per year. Weak growth with an expansion would mean annual returns of $100,000.

4. At the existing store with no changes, there would be returns of $170,000 per year if there is strong growth and $105,000 per year if growth is weak.

5. Expansion at the current site would cost $87,000.

6. The move to the new site would cost $210,000.

7. If growth is strong and the existing site is enlarged during the second year, the cost would still be $87,000.

8. Operating costs for all options are equal.

SOLUTION

We construct a decision tree to advise Hackers’ owner on the best action. Exhibit 5.3 shows the decision tree for this problem. There are two decision points (shown with the square nodes) and three chance occurrences (round nodes).

The values of each alternative outcome shown on the right of the diagram in Exhibit 5.4 are calculated as follows:

For the Excel template, visit www.mhhe.com/jacobs14e.

exhibit 5.3 Decision Tree for Hackers Computer Store Problem

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exhibit 5.4 Decision Tree Analysis

Working from the rightmost alternatives, which are associated with the decision of whether to expand, we see that the alternative of doing nothing has a higher value than the expansion alternative. We therefore eliminate the expansion in the second year alternatives. What this means is that if we do nothing in the first year and we experience strong growth, then in the second year it makes no sense to expand.

Now we can calculate the expected values associated with our current decision alternatives. We simply multiply the value of the alternative by its probability and sum the values. The expected value for the alternative of moving now is $585,000. The expansion alternative has an expected value of $660,500, and doing nothing now has an expected value of $703,750. Our analysis indicates that our best decision is to do nothing (both now and next year)!

Due to the five-year time horizon, it may be useful to consider the time value of the revenue and cost streams when solving this problem. If we assume a 16 percent interest rate, the first alternative outcome (move now, strong growth) has a discounted revenue valued at $428,487 (195,000 × 3.274293654) minus the $210,000 cost to move immediately. Exhibit 5.5 shows

exhibit 5.5 Decision Tree Analysis Using Net Present Value Calculations

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the analysis considering the discounted flows. Details of the calculations are given below. The present value table in Appendix E can be used to look up the discount factors. In order to make our calculations agree with those completed by Excel (in Excel, calculate the discount factor = (1 + interest rate) ^ (−years), we have used discount factors that are calculated to 10 digits of precision. The only calculation that is a little tricky is the one for revenue when we do nothing now and expand at the beginning of next year. In this case, we have a revenue stream of $170,000 the first year, followed by four years at $190,000. The first part of the calculation (170,000 × .862068966) discounts the first-year revenue to the present. The next part (190,000 × 2.798180638) discounts the next four years to the start of year 2. We then discount this four-year stream to present value.

For the Excel template, visit www.mhhe.com/jacobs14e.

PLANNING SERVICE CAPACITY

Capacity Planning in Services versus Manufacturing

Although capacity planning in services is subject to many of the same issues as manufacturing capacity planning, and facility sizing can be done in much the same way, there are several important differences. Service capacity is more time- and location-dependent, it is subject to more volatile demand fluctuations, and utilization directly impacts service quality.

LO5–4

Compare capacity planning in services to capacity planning in manufacturing.

Time Unlike goods, services cannot be stored for later use. As such, in services, managers must consider time as one of their supplies. The capacity must be available to produce a service when it is needed. For example, a customer cannot be given a seat that went unoccupied on a previous airline flight if the current flight is full. Nor could the customer purchase a seat on a particular day’s flight and take it home to be used at some later date.

Location In face-to-face settings, the service capacity must be located near the customer. In manufacturing, production takes place, and then the goods are distributed to the customer. With services, however, the opposite is true. The capacity to deliver the service must first be distributed to the customer (either physically or through some communications medium, such as the telephone), then the service can be produced. A hotel room or rental car that is available in another city is not much use to the customer—it must be where the customer is when that customer needs it.

Strategy

Volatility of Demand The volatility of demand on a service delivery system is much higher than that on a manufacturing production system for three reasons. First, as just mentioned, services cannot be stored. This means that inventory cannot smooth the demand as in manufacturing. The second reason is that the customers interact directly with

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the production system—and these customers often have different needs, will have different levels of experience with the process, and may require a different number of transactions. This contributes to greater variability in the processing time required for each customer and hence greater variability in the minimum capacity needed. The third reason for the greater volatility in service demand is that it is directly affected by consumer behavior. Influences on customer behavior ranging from the weather to a major event can directly affect demand for different services. Go to any restaurant near your campus during spring break and it will probably be almost empty. This behavioral effect can be seen over even shorter time frames, such as the lunch-hour rush at a bank’s drive-through window. Because of this volatility, service capacity is often planned in increments as small as 10 to 30 minutes, as opposed to the one-week increments more common in manufacturing.

Capacity Utilization and Service Quality

Planning capacity levels for services must consider the day-to-day relationship between service utilization and service quality. Exhibit 5.6shows a service situation using waiting line terms (arrival rates and service rates). The term arrival rate refers to the average number of customers that come to a facility during a specific period of time. The service rate is the average number of customers that can be processed over the same period of time when the facility is operating at maximum capacity. The best operating point is near 70 percent of the maximum capacity. This is enough to keep servers busy but allows enough time to serve customers individually and keep enough capacity in reserve so as not to create too many managerial headaches. In the critical zone, customers are processed through the system, but service quality declines. Above the critical zone, where customers arrive at a rate faster than they can be served, the line builds up and it is likely that many customers may never be served. (Details related to how waiting lines operate relative to capacity are presented in Chapter 10, “Waiting Line Analysis and Simulation.”)

The optimal utilization rate is very context specific. Low rates are appropriate when both the degree of uncertainty and the stakes are high. For example, hospital emergency rooms and fire departments should aim for low utilization because of the high level of uncertainty and the life-or-death nature of their activities. Relatively predictable services such as commuter trains or service facilities without customer contact (for example, postal sorting operations) can plan to operate much nearer to 100 percent utilization. Interestingly, there is a third group for which high utilization is desirable. All sports teams like sellouts, not only because of the virtually 100 percent contribution margin of each customer, but because a full house creates

Typically a firm can run a factory at a much higher capacity utilization rate than a service facility such as a call center. Less predictable demand requires a lower capacity operating point for good service.

exhibit 5.6 Relationship between the Rate of Service Utilization (ρ) and Service Quality

Source: J. Haywood–Farmer and J. Nollet, Services Plus: Effective Service Management (Boucherville, Quebec, Canada: G. Morin Publisher Ltd., 1991), p. 59.

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OHIO STATE FANS CHEER THE BUCKEYES ON DURING THE FIRST HALF OF THEIR GAME AGAINST YOUNGSTOWN STATE.

an atmosphere that pleases customers, motivates the home team to perform better, and boosts future ticket sales. Stage performances and bars share this phenomenon.