1. Operations Management
Lesson 4
Capacity Planning and Forecasting

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
Capacity is the maximum output rate of a production or service facility
Capacity planning is 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. Distribution of demand
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: Causal 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 Identify dependent (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.
Sales $ (Y)
Adv.$ (X) XY
X^2
Y^2 1
130 32
4160
2304
16,900 2
151 52
7852
2704
22,801 3
150 50
7500
2500
22,500 4
158 55
8690
3025
24964 5
153.85 53
Tot
589
189
28202
9253
87165
Avg
147.25
47.25

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
Absorb Demand
Change demand

23. Absorb demand
Have excess capacity
Keep output level
Make to stock
Make customer wait
Part finished,
Queues
Finished Goods, or
Backlogs
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
When demand increases, production must be increased, therefore additional workers are hired in order to meet this new demand. When demand decreases production will be reduced, therefore, workers are laid off or fired.
Reid & Sanders, Operations management, c Wiley 2003

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?
Calculate Capability of Operations Resources
Do you have realistic work standards??
Do you understand the capacity constraints of all the necessary 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?
Design “Capacity Control” Mechanisms
Do you monitor actual demand against forecast?
Do you adapt forecasts accordingly? Do you replan capacity accordingly?