1 / 38

Chapter 9

Chapter 9. Capacity and Aggregate Planning. Capacity Planning. Establishes overall level of productive resources Affects lead time responsiveness, cost & competitiveness Determines when and how much to increase capacity. Capacity Expansion. Volume & certainty of anticipated demand

gratia
Download Presentation

Chapter 9

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Chapter 9 Capacity and Aggregate Planning

  2. Capacity Planning • Establishes overall level of productive resources • Affects lead time responsiveness, cost & competitiveness • Determines when and how much to increase capacity

  3. Capacity Expansion • Volume & certainty of anticipated demand • Strategic objectives for growth • Costs of expansion & operation • Incremental or one-step expansion

  4. Capacity Expansion Strategies

  5. (a) Capacity lead strategy (b) Capacity lag strategy Capacity Demand Units Units Demand Capacity Time Time (c) Average capacity strategy (d) Incremental vs. one-step expansion One-step expansion Capacity Units Units Incremental expansion Demand Demand Time Time Capacity Expansion Strategies Figure 9.1

  6. Average cost per room # Rooms Best Operating Levels Figure 9.2

  7. Average cost per room Best operating level Economies of scale Diseconomies of scale 250 500 1000 # Rooms Best Operating Levels Figure 9.2

  8. Aggregate Production Planning (APP) • Matches market demand to company resources • Plans production 6 months to 12 months in advance • Expresses demand, resources, and capacity in general terms • Develops a strategy for economically meeting demand • Establishes a company-wide game plan for allocating resources

  9. Inputs and Outputs to APP

  10. Capacity Constraints Strategic Objectives Company Policies Aggregate Production Planning Demand Forecasts Financial Constraints Size of Workforce Production per month (in units or $) Inventory Levels Units or dollars subcontracted, backordered, or lost Inputs and Outputs to APP Figure 9.3

  11. Adjusting Capacity to Meet Demand Producing at a constant rate and using inventory to absorb fluctuations in demand (level production) Hiring and firing workers to match demand (chase demand) Maintaining resources for high demand levels Increase or decrease working hours (overtime and undertime) Subcontracting work to other firms Using part-time workers Providing the service or product at a later time period (backordering)

  12. Strategy Details • Level production - produce at constant rate & use inventory as needed to meet demand • Chase demand - change workforce levels so that production matches demand • Maintaining resources for high demand levels - ensures high levels of customer service

  13. Strategy Details • Overtime & undertime - common when demand fluctuations are not extreme • Subcontracting - useful if supplier meets quality & time requirements • Part-time workers - feasible for unskilled jobs or if labor pool exists • Backordering - only works if customer is willing to wait for product/services

  14. Level Production

  15. Demand Production Units Time Level Production Figure 9.4 (a)

  16. Demand Production Units Time Chase Demand Figure 9.4 (b)

  17. QUARTER SALES FORECAST (LB) Spring 80,000 Summer 50,000 Fall 120,000 Winter 150,000 APP Using Pure Strategies Hiring cost = $100 per worker Firing cost = $500 per worker Inventory carrying cost = $0.50 pound per quarter Production per employee = 1,000 pounds per quarter Beginning work force = 100 workers Example 9.1

  18. QUARTER SALES FORECAST (LB) Spring 80,000 Summer 50,000 Fall 120,000 Winter 150,000 Level production (50,000 + 120,000 + 150,000 + 80,000) 4 = 100,000 pounds APP Using Pure Strategies Hiring cost = $100 per worker Firing cost = $500 per worker Inventory carrying cost = $0.50 pound per quarter Production per employee = 1,000 pounds per quarter Beginning work force = 100 workers Example 9.1

  19. SALES PRODUCTION QUARTER FORECAST PLAN INVENTORY Spring 80,000 100,000 20,000 Summer 50,000 100,000 70,000 Fall 120,000 100,000 50,000 Winter 150,000 100,000 0 400,000 140,000 Cost = 140,000 pounds x 0.50 per pound = $70,000 Level Production Strategy Example 9.1

  20. SALES PRODUCTION WORKERS WORKERS WORKERS QUARTER FORECAST PLAN NEEDED HIRED FIRED Spring 80,000 80,000 80 0 20 Summer 50,000 50,000 50 0 30 Fall 120,000 120,000 120 70 0 Winter 150,000 150,000 150 30 0 100 50 Chase Demand Strategy Cost = (100 workers hired x $100) + (50 workers fired x $500) = $10,000 + 25,000 = $35,000 Example 9.1

  21. MONTH DEMAND (CASES) MONTH DEMAND (CASES) January 1000 July 500 February 400 August 500 March 400 September 1000 April 400 October 1500 May 400 November 2500 June 400 December 3000 APP Using Mixed Strategies Production per employee = 100 cases per month Wage rate = $10 per case for regular production = $15 per case for overtime = $25 for subcontracting Hiring cost = $1000 per worker Firing cost = $500 per worker Inventory carrying cost = $1.00 case per month Beginning work force = 10 workers Example 9.2

  22. APP by Linear Programming Minimize Z = $100 (H1 + H2 + H3 + H4) + $500 (F1 + F2 + F3 + F4) + $0.50 (I1 + I2 + I3 + I4) Subject to P1 - I1 = 80,000 (1) Demand I1 + P2 - I2 = 50,000 (2) constraints I2 + P3 - I3 = 120,000 (3) I3 + P4 - I4 = 150,000 (4) Production 1000 W1 = P1 (5) constraints 1000 W2 = P2 (6) 1000 W3 = P3 (7) 1000 W4 = P4 (8) 100 + H1 - F1 = W1 (9) Work force W1 + H2 - F2 = W2 (10) constraints W2 + H3 - F3 = W3 (11) W3 + H4 - F4 = W4 (12) where Ht = # hired for period t Ft = # fired for period t It = inventory at end of period t Pt = units produced in periodt Wt = workforce size for periodt Example 9.3

  23. EXPECTED REGULAR OVERTIME SUBCONTRACT QUARTER DEMAND CAPACITY CAPACITY CAPACITY 1 900 1000 100 500 2 1500 1200 150 500 3 1600 1300 200 500 4 3000 1300 200 500 Regular production cost per unit $20 Overtime production cost per unit $25 Subcontracting cost per unit $28 Inventory holding cost per unit per period $3 Beginning inventory 300 units APP by the Transportation Method Example 9.4

  24. PERIOD OF USE Unused PERIOD OF PRODUCTION 1 2 3 4 Capacity Capacity 1 2 3 4 Beginning 0 3 6 9 Inventory 300 — — — 300 Regular 600 300 100 — 1000 Overtime 100 100 Subcontract 500 Regular 1200 — — 1200 Overtime 150 150 Subcontract 250 250 500 Regular 1300 — 1300 Overtime 200 — 200 Subcontract 500 500 Regular 1300 1300 Overtime 200 200 Subcontract 500 500 Demand 900 1500 1600 3000 250 20 23 26 29 25 28 31 34 28 31 34 37 20 23 26 25 28 31 28 31 34 20 23 25 28 28 31 20 25 28 The Transportation Tableau Table 9.2

  25. REGULAR SUB- ENDING PERIOD DEMAND PRODUCTION OVERTIME CONTRACT INVENTORY 1 900 1000 100 0 500 2 1500 1200 150 250 600 3 1600 1300 200 500 1000 4 3000 1300 200 500 0 Total 7000 4800 650 1250 2100 Burruss’ Production Plan Table 9.3

  26. Other Quantitative Techniques • Linear decision rule (LDR) • Search decision rule (SDR) • Management coefficients model

  27. Demand Management • Shift demand into other periods • Incentives, sales promotions, advertising campaigns • Offer product or services with countercyclical demand patterns • Partnering with suppliers to reduce information distortion along the supply chain

  28. Demand Distortion along the Supply Chain

  29. Hierarchical Planning Process

  30. Production Planning Capacity Planning Resource Level Items Aggregate production plan Resource requirements plan Product lines or families Plants Master production schedule Rough-cut capacity plan Critical work centers Individual products Material requirements plan Capacity requirements plan All work centers Components Shop floor schedule Input/ output control Manufacturing operations Individual machines Hierarchical Planning Process Figure 9.5

  31. PERIOD ON-HAND = 50 1 2 3 4 5 6 PERIOD ON-HAND = 50 1 2 3 4 5 6 Forecast 100 100 100 100 100 100 Customer orders Master production schedule 200 200 200 Available to promise Forecast 100 100 100 100 100 100 Customer orders 90 120 130 70 20 10 Master production schedule 200 200 200 Available to promise 40 0 170 Available-to-Promise ATP in period 1 = (50 + 200) - (90 + 120) = 40 ATP in period 3 = 200 - (130 + 70) = 0 ATP in period 5 = 200 - (20 + 10) = 170 Example 9.5

  32. Available-to-Promise

  33. Product Request Is an alternative product available at an alternate location? Yes Yes Is the product available at this location? Available-to-promise No No Allocate inventory Capable-to-promise date Yes Is an alternative product available at this location? Available-to-promise Is the customer willing to wait for the product? No Yes Allocate inventory Revise master schedule Is this product available at a different location? Yes No Trigger production Lose sale No Available-to-Promise Figure 9.6

  34. Aggregate Planning for Services Most services can’t be inventoried Demand for services is difficult to predict Capacity is also difficult to predict Service capacity must be provided at the appropriate place and time Labor is usually the most constraining resource for services

  35. Cu Cu + Co P(n < x)  Yield Management where n = number of no-shows x = number of rooms or seats overbooked Cu = cost of underbooking; i.e., lost sale Co = cost of overbooking; i.e., replacement cost P = probability

  36. Yield Management NO-SHOWS PROBABILITY 0 .15 1 .25 2 .30 3 .30 Example 9.4

  37. NO-SHOWS PROBABILITY P(N < X) 0 .15 .00 1 .25 .15 2 .30 .40 3 .30 .70 .517 Expected number of no shows 0(.15) + 1(.25) + 2(.30) + 3(.30) = 1.75 Optimal probability of no-shows P(n < x)  = = .517 Cu Cu + Co 75 75 + 70 Yield Management Example 9.4

  38. NO-SHOWS PROBABILITY P(N < X) 0 .15 .00 1 .25 .15 2 .30 .40 3 .30 .70 Cost of overbooking [2(.15) + 1(.25)]$70 = $38.50 Cost of bumping customers (.30)$75 = $22.50 Lost revenue from no-shows $61.00 Total cost of overbooking by 2 rooms .517 Expected number of no shows 0(.15) + 1(.25) + 2(.30) + 3(.30) = 1.75 Optimal probability of no-shows P(n < x)  = = .517 Expected savings = ($131.225 - $61) = $70.25 a night Cu Cu + Co 75 75 + 70 Yield Management Example 9.4

More Related