Copyright 2013 John Wiley & Sons, Inc.

Chapter 8

Capacity, Scheduling, and Location Planning

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Overview

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Introduction

• Capacity represents the rate at which a transformation system can create outputs

• Capacity planning applies to both manufacturing and service organizations

• Capacity options can be categorized as short-term or long-term– Changing staffing level is short-term– Building new building is long-term

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Introduction (Continued)

• Shorter product life cycles add further complications

• Volatile demand can further complicate capacity planning

• Capacity and location are important elements of a competitive strategy

• Capacity planning decisions are driven by projected demand estimates

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Long-Term Capacity Planning

• Capacity and location decisions are highly strategic because they are very expensive investments

• Once made, capacity and location decisions are not easily changed or reversed

• These decisions must be carefully and thoroughly analyzed beforehand

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Capacity Considerations

• Capacity measures must include a time dimension

• Capacity planning must consider the capacity to produce multiple outputs

• Having adequate capacity is clearly a generic problem– It is common to all types of organizations

• In pure service organizations capacity is a special problem because the output cannot normally be stored for later use

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Capacity Considerations (Continued)

• A variety of restrictions can limit capacity– Fast-food restaurant may be limited by order-

takers, number of cooks, machinery, space in restaurant, and so on

– Limiting factors are bottlenecks

• Often there are natural loses (waste, scrape, defects) that limit capacity

• Demand (and therefore capacity needs) may be a function of where the facility is located

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Capacity Planning Strategies

• Facility size planning

• Economies of scale and scope

• Capacity planning for multiple outputs

• Timing of capacity increments

Slides on each of these

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Facility Size Planning

• When plants are operated at their lowest-cost production level, larger facilities will have lower costs

• Known as economies of scale

• If production is at lower level, the advantage of a larger facility may be lost

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Economies of Scale and Scope

• Obtaining lower costs through larger facilities is known as economies of scale– Spreads fixed costs over larger volumes

• There are limits to this benefit

• The use of advanced, flexible technologies is economies of scope– Spreads fixed costs over a wide variety of

outputs

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Short Term Capacity Planning

• Primarily related to issues of…– Scheduling– Labor shifts– Balancing of resource capacities– Other such issues

• Not usually related to location decisions

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Short-Term Capacity Alternatives

• Increase Resources

• Improve Resource Use

• Modify the Output

• Modify the Demand

• Do Not Meet Demand

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Capacity Planning for Services

• Large fluctuations in demand

• Inventory often not an option

• Problem often is to match staff availability with customer demand

• May attempt to shift demand to off-peak periods

• Can measure capacity in terms of inputs

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The Learning Curve

• Learning curve is an extremely important aspect of capacity planning

• Learning curve effect is the ability of humans to increase their productivity through learning

• Particularly important in new and unfamiliar processes

• Each time the output doubles, the labor hours decrease to a fixed percentage of their previous value

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Queuing and the Psychology of Waiting

• An important element of capacity concerns waiting lines that build up in front of operations– Also known as queues

• Queuing theory provides a mechanism to determine several key performance measures of an operating system

• Wiley Web site has a discussion of the theory, equations, and some example calculations

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Principles of Waiting

1. Unoccupied time feels longer than occupied time

2. Pre-service waiting feels longer than in-service waiting

3. Anxiety makes waiting seem longer

4. Uncertain waiting is longer than known, finite waiting

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Principles of Waiting (Continued)

5. Unexplained waiting is longer than explained waiting

6. Unfair waiting is longer than fair waiting

7. Solo waiting is longer than group waiting

8. The more valuable the service, the longer it is worth waiting for

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Simple Moving Average Formula

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Simple Moving Average – Example

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Weighted Moving Average

• The simple moving average formula implies equal weighting for all periods.

• A weighted moving average allows unequal weighting of prior time periods.– The sum of the weights must be equal to one.– Often, more recent periods are given higher

weights than periods farther in the past.

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Selecting Weights

• Experience and/or trial-and-error are the simplest approaches.

• The recent past is often the best indicator of the future, so weights are generally higher for more recent data.

• If the data are seasonal, weights should reflect this appropriately.

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Exponential SmoothingA weighted average method that includes all past data in the

forecasting calculationMore recent results weighted more heavilyThe most used of all forecasting techniquesAn integral part of computerized forecastingWell accepted for six reasons

1. Exponential models are surprisingly accurate.2. Formulating an exponential model is relatively easy.3. The user can understand how the model works.4. Little computation is required to use the model.5. Computer storage requirements are small.6. Tests for accuracy are easy to compute.

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Exponential Smoothing ModelExponential Smoothing Model

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Exponential Smoothing Example

Week Demand Forecast

1 820 820

2 775 820

3 680 811

4 655 785

5 750 759

6 802 757

7 798 766

8 689 772

9 775 756

10 760

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Forecast Errors

Forecast error is the difference between the forecast value and what actually occurred.

All forecasts contain some level of error.Sources of error

Bias – when a consistent mistake is made Random – errors that are not explained by the model

being usedMeasures of error

Mean absolute deviation (MAD) Mean absolute percent error (MAPE) Tracking signal

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Forecast Error Measurements

• Ideally, MAD will be zero (no forecasting error).

• Larger values of MAD indicate a less accurate model.

MAPE scales the forecast error to the magnitude of demand.

Tracking signal indicates whether forecast errors are accumulating over time (either positive or negative errors).

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Computing Forecast Error

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