Operations Management

1. Lesson 4 Capacity Planning and Forecasting Operations Management

2. What you will learn in this unit: • Capacity Planning • Making Capacity Planning Decisions • Forecasting Process • Types of Forecasting Methods • Qualitative Methods • Quantitative Methods

3. Capacity planning • Capacityis the maximum output rate of a production or service facility • Capacity planningis the process of establishing the output rate that may be needed at a facility. Setting the effective capacity of the operation to meet the required demands

4. Measuring Capacity Examples • There is no one best way to measure capacity • Output measures like kegs per day are easier to understand • With multiple products, inputs measures work better

5. Capacity Information Needed • Design capacity: • Maximum output rate under ideal conditions • A bakery can make 30 custom cakes per day when pushed at holiday time • Effective capacity: • Maximum output rate under normal (realistic) conditions • On the average this bakery can make 20 custom cakes per day

6. Calculating Capacity Utilization • Measures how much of the available capacity is actually being used: • Measures effectiveness • Use either effective or design capacity in denominator

7. Example of Computing Capacity Utilization: In the bakery example the design capacity is 30 custom cakes per day. Currently the bakery is producing 28 cakes per day. What is the bakery’s capacity utilization relative to both design and effective capacity? • The current utilization is only slightly below its design capacity and considerably above its effective capacity • The bakery can only operate at this level for a short period of time

8. How Much Capacity Is Best? • The Best Operating Level is the output that results in the lowest average unit cost • Economies of Scale: • Where the cost per unit of output drops as volume of output increases • Spread the fixed costs of buildings & equipment over multiple units, allow bulk purchasing & handling of material • Diseconomies of Scale: • Where the cost per unit rises as volume increases • Often caused by congestion (overwhelming the process with too much work-in-process) and scheduling complexity

9. Best Operating Level and Size • Alternative 1: Purchase one large facility, requiring one large initial investment • Alternative 2: Add capacity incrementally in smaller chunks as needed

10. Other Capacity Considerations • Focused factories: • Small, specialized facilities with limited objectives • Plant within a plant (PWP): • Segmenting larger operations into smaller operating units with focused objectives • Subcontractor networks: • Outsource non-core items to free up capacity for what you do well • Capacity cushions: • Plan to underutilize capacity to provide flexibility

11. Making Capacity Planning Decisions • The three-step procedure for making capacity planning decisions is as follows: • Step 1: Identify Capacity Requirements • Step 2: Develop Capacity Alternatives • Step 3: Evaluate Capacity Alternatives

12. Good forecasts are essential for effective capacity planning. But so is an understanding of demand uncertainty because it allows you to judge the risks to service level. Only 5% chance of demand being higher than this Distribution of demand DEMAND DEMAND Only 5% chance of demand being lower than this TIME TIME When demand uncertainty is high the risks to service level of under provision of capacity are high.

13. Forecasting Steps • What needs to be forecast? • Level of detail, units of analysis & time horizon required • What data is available to evaluate? • Identify needed data & whether it’s available • Select and test the forecasting model • Cost, ease of use & accuracy • Generate the forecast • Monitor forecast accuracy over time

14. Types of Forecasting Models • Qualitative methods: • Forecasts generated subjectively by the forecaster • Quantitative methods: • Forecasts generated through mathematical modeling

15. Quantitative Methods • Time Series Models: • Assumes the future will follow same patterns as the past • Causal Models: • Explores cause-and-effect relationships • Uses leading indicators to predict the future • E.g. housing starts and appliance sales

16. Time Series Data Composition • Data = historic pattern + random variation • Historic pattern to be forecasted: • Level (long-term average) • Trend • Seasonality • Cycle • Random Variation cannot be predicted

17. Time Series Patterns

18. Causal Models • Often, leading indicators can help to predict changes in future demand e.g. housing starts • Causal models establish a cause-and-effect relationship between independent and dependent variables • A common tool of causal modeling is linear regression: • Additional related variables may require multiple regression modeling

19. Linear Regression • Identifydependent (y) and independent (x) variables • Solve for the slope of the line • Solve for the y intercept • Develop your equation for the trend line Y=a + bX

20. Linear Regression Problem: A maker of golf shirts has been tracking the relationship between sales and advertising dollars. Use linear regression to find out what sales might be if the company invested $53,000 in advertising next year.

21. How Good is the Fit? • Correlation coefficient (r) measures the direction and strength of the linear relationship between two variables. The closer the r value is to 1.0 the better the regression line fits the data points. • Coefficient of determination ( ) measures the amount of variation in the dependent variable about its mean that is explained by the regression line. Values of ( ) close to 1.0 are desirable.

22. How do you cope with fluctuations in demand? Adjust output to match demand Change demand Absorb Demand

23. Part finished, Queues Backlogs Absorb demand Have excess capacity Keep output level Make customer wait Make to stock Finished Goods, or Customer Inventory

24. Types of Aggregate Plans • Level Aggregate Plans • Maintains a constant workforce • Sets capacity to accommodate average demand • Often used for make-to-stock products like appliances • Disadvantage- builds inventory and/or uses back orders • Chase Aggregate Plans • Produces exactly what is needed each period • Sets labor/equipment capacity to satisfy period demands • Disadvantage- constantly changing short term capacity

25. Absorb Demand • Level capacity plan • Anticipation inventory

26. Principles of the Chase Method • The chase method helps firms match production and demand by hiring and firing workers as necessary to control output

27. Adjust output to match demand • Chase capacity plan • Adjustment methods • Overtime & idle time • Workforce size • Part-time staff • Sub-contracting

28. The tasks of capacity planning Some key questions Forecast Demand or Revenue Potential Can you predict the most likely demand at any point in time? Can you predict the uncertainty in demand at any point in time? Do you have realistic work standards?? Do you understand the capacity constraints of all the necessary resources? Calculate Capability of Operations Resources Allocate Resources Over Time What are the options for capacity allocation? What are their cost, revenue, work capital and service level implications? What are their flexibility implications? Do you monitor actual demand against forecast? Do you adapt forecasts accordingly?Do you replan capacity accordingly? Design “Capacity Control” Mechanisms