1. Copyright 2013 John Wiley & Sons, Inc. Chapter 8 Capacity, Scheduling, and Location Planning

2. 8-2 Overview

3. 8-3 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

4. 8-4 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

5. 8-5 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

6. 8-6 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

7. 8-7 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

8. 8-8 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

9. 8-9 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

10. 8-10 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

11. 8-11 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

12. 8-12 Short-Term Capacity Alternatives Increase Resources Improve Resource Use Modify the Output Modify the Demand Do Not Meet Demand

13. 8-13 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

14. 8-14 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

15. 8-15 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

16. 8-16 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

17. 8-17 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

18. 8-18 Simple Moving Average Formula  18-18

19. 8-19 Simple Moving Average – Example 18-19

20. 8-20 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. 18-20

21. 8-21 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. 18-21

22. 8-22 Exponential Smoothing  A weighted average method that includes all past data in the forecasting calculation  More recent results weighted more heavily  The most used of all forecasting techniques  An integral part of computerized forecasting  Well 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. 18-22

23. 8-23 18-23 Exponential Smoothing Model

24. 8-24 Exponential Smoothing Example WeekDemandForecast 1820 2775 820 3680 811 4655 785 5750 759 6802 757 7798 766 8689 772 9775 756 10 760 18-24

25. 8-25 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 used  Measures of error  Mean absolute deviation (MAD)  Mean absolute percent error (MAPE)  Tracking signal 18-25

26. 8-26 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). 18-26

27. 8-27 Computing Forecast Error 18-27