Operations Management Lesson 4 Capacity Planning and Forecasting

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

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

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

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

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

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

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

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

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

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

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.

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

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

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

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

Time Series Patterns

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

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

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

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.

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

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

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

Absorb Demand Level capacity plan Anticipation inventory

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

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

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?