Inventory Management, Supply Contracts and Risk Pooling

1. InventoryManagement, Supply Contracts and Risk Pooling Phil Kaminskykaminsky@ieor.berkeley.edu David Simchi-Levi Philip Kaminsky Edith Simchi-Levi

2. Outline of the Presentation • Introduction to Inventory Management • The Effect of Demand Uncertainty • (s,S) Policy • Periodic Review Policy • Supply Contracts • Risk Pooling • Centralized vs. Decentralized Systems • Practical Issues in Inventory Management

3. Customers, demand centers sinks Field Warehouses: stocking points Sources: plants vendors ports Regional Warehouses: stocking points Supply Inventory & warehousing costs Production/ purchase costs Transportation costs Transportation costs Inventory & warehousing costs

4. Inventory • Where do we hold inventory? • Suppliers and manufacturers • warehouses and distribution centers • retailers • Types of Inventory • WIP • raw materials • finished goods • Why do we hold inventory? • Economies of scale • Uncertainty in supply and demand • Lead Time, Capacity limitations

5. Goals: Reduce Cost, Improve Service • By effectively managing inventory: • Xerox eliminated $700 million inventory from its supply chain • Wal-Mart became the largest retail company utilizing efficient inventory management • GM has reduced parts inventory and transportation costs by 26% annually

6. Goals: Reduce Cost, Improve Service • By not managing inventory successfully • In 1994, “IBM continues to struggle with shortages in their ThinkPad line” (WSJ, Oct 7, 1994) • In 1993, “Liz Claiborne said its unexpected earning decline is the consequence of higher than anticipated excess inventory” (WSJ, July 15, 1993) • In 1993, “Dell Computers predicts a loss; Stock plunges. Dell acknowledged that the company was sharply off in its forecast of demand, resulting in inventory write downs” (WSJ, August 1993)

7. Understanding Inventory • The inventory policy is affected by: • Demand Characteristics • Lead Time • Number of Products • Objectives • Service level • Minimize costs • Cost Structure

8. Cost Structure • Order costs • Fixed • Variable • Holding Costs • Insurance • Maintenance and Handling • Taxes • Opportunity Costs • Obsolescence

9. EOQ: A Simple Model* • Book Store Mug Sales • Demand is constant, at 20 units a week • Fixed order cost of $12.00, no lead time • Holding cost of 25% of inventory value annually • Mugs cost $1.00, sell for $5.00 • Question • How many, when to order?

10. EOQ: A View of Inventory* Note: • No Stockouts • Order when no inventory • Order Size determines policy Inventory Order Size Avg. Inven Time

11. EOQ: Calculating Total Cost* • Purchase Cost Constant • Holding Cost: (Avg. Inven) * (Holding Cost) • Ordering (Setup Cost): Number of Orders * Order Cost • Goal: Find the Order Quantity that Minimizes These Costs:

12. EOQ:Total Cost* Total Cost Holding Cost Order Cost

13. EOQ: Optimal Order Quantity* • Optimal Quantity = (2*Demand*Setup Cost)/holding cost • So for our problem, the optimal quantity is 316

14. EOQ: Important Observations* • Tradeoff between set-up costs and holding costs when determining order quantity. In fact, we order so that these costs are equal per unit time • Total Cost is not particularly sensitive to the optimal order quantity

15. The Effect of Demand Uncertainty • Most companies treat the world as if it were predictable: • Production and inventory planning are based on forecasts of demand made far in advance of the selling season • Companies are aware of demand uncertainty when they create a forecast, but they design their planning process as if the forecast truly represents reality • Recent technological advances have increased the level of demand uncertainty: • Short product life cycles • Increasing product variety

16. Demand Forecast • The three principles of all forecasting techniques: • Forecasting is always wrong • The longer the forecast horizon the worst is the forecast • Aggregate forecasts are more accurate

17. SnowTime Sporting Goods • Fashion items have short life cycles, high variety of competitors • SnowTime Sporting Goods • New designs are completed • One production opportunity • Based on past sales, knowledge of the industry, and economic conditions, the marketing department has a probabilistic forecast • The forecast averages about 13,000, but there is a chance that demand will be greater or less than this.

18. Jan 00 Jan 01 Jan 02 Design Production Retailing Feb 00 Sep00 Feb01 Sep01 Production Supply Chain Time Lines

19. SnowTime Demand Scenarios

20. SnowTime Costs • Production cost per unit (C): $80 • Selling price per unit (S): $125 • Salvage value per unit (V): $20 • Fixed production cost (F): $100,000 • Q is production quantity, D demand • Profit = Revenue - Variable Cost - Fixed Cost + Salvage

21. SnowTime Scenarios • Scenario One: • Suppose you make 12,000 jackets and demand ends up being 13,000 jackets. • Profit = 125(12,000) - 80(12,000) - 100,000 = $440,000 • Scenario Two: • Suppose you make 12,000 jackets and demand ends up being 11,000 jackets. • Profit = 125(11,000) - 80(12,000) - 100,000 + 20(1000) = $ 335,000

22. SnowTime Best Solution • Find order quantity that maximizes weighted average profit. • Question: Will this quantity be less than, equal to, or greater than average demand?

23. What to Make? • Question: Will this quantity be less than, equal to, or greater than average demand? • Average demand is 13,100 • Look at marginal cost Vs. marginal profit • if extra jacket sold, profit is 125-80 = 45 • if not sold, cost is 80-20 = 60 • So we will make less than average

24. SnowTime Expected Profit

25. SnowTime Expected Profit

26. SnowTime:Important Observations • Tradeoff between ordering enough to meet demand and ordering too much • Several quantities have the same average profit • Average profit does not tell the whole story • Question: 9000 and 16000 units lead to about the same average profit, so which do we prefer?

27. SnowTime Expected Profit

28. Probability of Outcomes

29. Key Insights from this Model • The optimal order quantity is not necessarily equal to average forecast demand • The optimal quantity depends on the relationship between marginal profit and marginal cost • As order quantity increases, average profit first increases and then decreases • As production quantity increases, risk increases. In other words, the probability of large gains and of large losses increases

30. Fixed Production Cost =$100,000 Variable Production Cost=$35 Selling Price=$125 Salvage Value=$20 Manufacturer DC Manufacturer Retail DC Stores Supply Contracts Wholesale Price =$80

31. Demand Scenarios

32. Distributor Expected Profit

33. Distributor Expected Profit

34. Supply Contracts (cont.) • Distributor optimal order quantity is 12,000 units • Distributor expected profit is $470,000 • Manufacturer profit is $440,000 • Supply Chain Profit is $910,000 • Is there anything that the distributor and manufacturer can do to increase the profit of both?

35. Fixed Production Cost =$100,000 Variable Production Cost=$35 Selling Price=$125 Salvage Value=$20 Manufacturer DC Manufacturer Retail DC Stores Supply Contracts Wholesale Price =$80

36. Retailer Profit (Buy Back=$55)

37. Retailer Profit (Buy Back=$55) $513,800

38. Manufacturer Profit (Buy Back=$55)

39. Manufacturer Profit (Buy Back=$55) $471,900

40. Fixed Production Cost =$100,000 Variable Production Cost=$35 Selling Price=$125 Salvage Value=$20 Manufacturer DC Manufacturer Retail DC Stores Supply Contracts Wholesale Price =$??

41. Retailer Profit (Wholesale Price $70, RS 15%)

42. Retailer Profit (Wholesale Price $70, RS 15%) $504,325

43. Manufacturer Profit (Wholesale Price $70, RS 15%)

44. Manufacturer Profit (Wholesale Price $70, RS 15%) $481,375

45. Supply Contracts

46. Fixed Production Cost =$100,000 Variable Production Cost=$35 Selling Price=$125 Salvage Value=$20 Manufacturer DC Manufacturer Retail DC Stores Supply Contracts Wholesale Price =$80

47. Supply Chain Profit

48. $1,014,500 Supply Chain Profit

49. Supply Contracts

50. Supply Contracts: Key Insights • Effective supply contracts allow supply chain partners to replace sequential optimization by global optimization • Buy Back and Revenue Sharing contracts achieve this objective through risk sharing