OPIM 204 – Aggregate Planning 1 Aggregate Planning OPIM 3104 Instructor: Jose Cruz

OPIM 204 – Aggregate Planning 2 Lecture Outline Aggregate Planning Process Strategies for Adjusting Capacity Strategies for Managing Demand Quantitative Techniques for Aggregate Production Planning Aggregate Planning for Services

OPIM 204 – Aggregate Planning 3 Aggregate Planning Determine the resource capacity needed to meet demand over an intermediate time horizon –Aggregate refers to product lines or families –Aggregate planning matches supply and demand Objectives –Establish a company wide game plan for allocating resources –Develop an economic strategy for meeting demand

OPIM 204 – Aggregate Planning 4 Aggregate Planning Process

OPIM 204 – Aggregate Planning 5 Meeting Demand Strategies Adjusting capacity –Resources necessary to meet demand are acquired and maintained over the time horizon of the plan –Minor variations in demand are handled with overtime or under-time Managing demand –Proactive demand management

OPIM 204 – Aggregate Planning 6 Strategies for Adjusting Capacity Level production –Producing at a constant rate and using inventory to absorb fluctuations in demand Chase demand –Hiring and firing workers to match demand Peak demand –Maintaining resources for high-demand levels Overtime and under-time –Increasing or decreasing working hours Subcontracting –Let outside companies complete the work Part-time workers –Hiring part time workers to complete the work Backordering –Providing the service or product at a later time period

OPIM 204 – Aggregate Planning 7 Level Production Demand Units Time Production

OPIM 204 – Aggregate Planning 8 Chase Demand Demand Units Time Production

OPIM 204 – Aggregate Planning 9 Strategies for Managing Demand Shifting demand into other time periods –Incentives –Sales promotions –Advertising campaigns Offering products or services with counter- cyclical demand patterns Partnering with suppliers to reduce information distortion along the supply chain

OPIM 204 – Aggregate Planning 10 Quantitative Techniques For APP Pure Strategies Mixed Strategies Linear Programming Transportation Method Other Quantitative Techniques

OPIM 204 – Aggregate Planning 11 Pure Strategies Hiring cost= $100 per worker Firing cost= $500 per worker Regular production cost per pound = $2.00 Regular production cost per pound = $2.00 Inventory carrying cost= $0.50 pound per quarter Inventory carrying cost= $0.50 pound per quarter Production per employee= 1,000 pounds per quarter Production per employee= 1,000 pounds per quarter Beginning work force= 100 workers Beginning work force= 100 workers QUARTERSALES FORECAST (LB) Spring80,000 Summer50,000 Fall120,000 Winter150,000 Example:

OPIM 204 – Aggregate Planning 12 Level Production Strategy Level production = 100,000 pounds (50, , , ,000) 4 Spring80,000100,00020,000 Summer50,000100,00070,000 Fall120,000100,00050,000 Winter150,000100, ,000140,000 Cost of Level Production Strategy (400,000 X $2.00) + (140,00 X $.50) = $870,000 SALESPRODUCTION QUARTERFORECASTPLANINVENTORY

OPIM 204 – Aggregate Planning 13 Chase Demand Strategy Spring80,00080, Summer50,00050, Fall120,000120, Winter150,000150, SALESPRODUCTIONWORKERSWORKERSWORKERS SALESPRODUCTIONWORKERSWORKERSWORKERS QUARTERFORECASTPLANNEEDEDHIREDFIRED Cost of Chase Demand Strategy (400,000 X $2.00) + (100 x $100) + (50 x $500) = $835,000

OPIM 204 – Aggregate Planning 14 Mixed Strategy Combination of Level Production and Chase Demand strategies Examples of management policies –no more than x% of the workforce can be laid off in one quarter –inventory levels cannot exceed x dollars Many industries may simply shut down manufacturing during the low demand season and schedule employee vacations during that time

OPIM 204 – Aggregate Planning 15 Aggregate Planning for Services 1.Most services can’t be inventoried 2.Demand for services is difficult to predict 3.Capacity is also difficult to predict 4.Service capacity must be provided at the appropriate place and time 5.Labor is usually the most constraining resource for services

OPIM 204 – Aggregate Planning 16 Yield Management

OPIM 204 – Aggregate Planning 17 Yield Management P( n < x )  C u C u + C o where n = number of no-shows x = number of rooms or seats overbooked C u = cost of underbooking; i.e., lost sale C o = cost of overbooking; i.e., replacement cost P= probability

OPIM 204 – Aggregate Planning 18 Yield Management (cont.)

OPIM 204 – Aggregate Planning 19 Yield Management: Example NO-SHOWSPROBABILITYP(N < X) Optimal probability of no-shows P(n < x)  = =.517 C u C u + C o Hotel should be overbooked by two rooms