OPSM 501: Operations Management

1. Koç University Graduate School of Business MBA Program OPSM 501: Operations Management Week 10: Supply Chain contracts Newsvendor Zeynep Aksin zaksin@ku.edu.tr

2. Hamptonshire Express • Anna has a degree from journalism & operations research • She has started a daily newspaper in her hometown • She used a leased PC: lease cost $10 per day • A local printer prints newspapers at 0.20 per copy • Sales the next day between 6 am and 10 am • Newsstand rental $30 per day • Express sold to customers at $1 per copy • Copies not sold by 10 am are discarded • Anna estimates daily demand to be distributed N(500,100)

3. Question 1 • Optimal stocking quantity? • Profit at this stocking quantity?

4. Ordering Level and Profits in Vertically Integrated Channel h=1; Anna sells to market directly: i* = 584; E[Profit] = $331.33; Fill rate 98%

5. Improving demand through effort • After 6 weeks of operation, Anna thinks she can improve demand by adding a profile section • Experiments indicate that demand is a function of time she invests in preparing the section • She thinks D=500 +50

6. Question 2 • How many hours should she invest daily in the creation of the profile section? Assume the opportunity cost of her time is $10 per hour. • Compare optimal profits to previous scenario

7. Optimal Level of Effort in Vertically Integrated Channel • Demand potential increases by 50 • Expected profit increases by 0.8*50 i* = 684 E[Profit] = 371.33

8. Delegating sales to Ralph • Anna is really busy, so asks Ralph to take-over the retailing portion of her job. • Ralph agrees to run the newsstand from 6 AM to 10 AM and pay the daily rent of $30 • He estimates demand the next day based on viewing a copy of the paper the previous night at 10 PM • He buys the papers from Anna at $0.8 per copy • Ralph is responsible for unsold copies at the end of the day

9. Question 3 • Assuming h=4 try to determine the optimal stocking quantity for Ralph? • Why is this quantity different than the one in Question 2? • Now vary h in spreadsheet 3c which calculates the optimal newsboy quantity for the differentiated channel, i.e. to maximize Ralph’s profit. • How would changing the transfer price from the current value of 0.8 impact Ann’s effort level and Ralph’s stocking decision? (Spreadsheet 3d) • Compare an integrated (centralized) firm to a differentiated (decentralized) one.

10. Ordering Level and Profits in Differentiated Channel Case 1. h=4; Anna sells to market directly: i* = 684; E[Profit] = $371.33; Fill rate 98% Case 2. h=4; Anna sells thru Ralph: i* = 516; E[Total Profit] = $322 Anna makes $260 Ralph makes $ 62 Fill rate 84% Why??

11. Effect of Transfer (Wholesale) Price in Differentiated Channel

12. Optimal Effort in Decentralized Channel Optimal effort level for Anna is h=2 (and not 4). h=2h=4 Stocking quantity: $487 $516 Anna’s profit: $262 $260 Ralph’s profit: $56 $ 62 Total profit: $318 $322 Fill rate: 83% 84% Why??

13. Inefficiencies in a Differentiated Channel • Supplier chooses w, retailer chooses i* • Retail ignores +ve effect of stocking one more unit on supplier • Supplier ignores +ve effect of cutting wholesale price/increasing effort on retailer • Supplier prices above marginal cost/exerts low effort • Retailer stocks less • Supply chain profits shrink

14. Contracts • Specifies the parameters within which a buyer places orders and a supplier fulfills them • Example parameters: quantity, price, time, quality • Double marginalization: buyer and seller make decisions acting independently instead of acting together; both of them make a margin on the same sale – gap between potential total supply chain profits and actual supply chain profits results • Buyback contracts can be offered that will increase total supply chain profit

15. Returns policies • Rationale: set buyback price b so that (retailer cost structure = supply cost structure) • Supplier can use both w and b • Supplier is bundling insurance with the good

16. Example

17. Reasons for return policies • Supplier is less risk averse than retailers • Supplier has a higher salvage value • Safeguarding the brand • Signalling information • Avoiding brand switching

18. Costs of Return Policies • Extra transportation and handling • Extra depreciation • Getting the return rate wrong • Retailer incentives

19. The case of books • Returns as in Hamptonshire Express… • …However publisher has no control of return quantities • No control of inventory-shelf arrangements • No control over private-label • No control of retail price • Key difference: power has shifted from publisher to retailer

20. Video sales • Hollywood: video rentals and sales $20B business, and largest source of revenue • Rentals slipping • Competition from direct services • Customer dissatisfaction (20% cannot rent video they want on a typical trip) • What’s the problem? Bad forecasting? Inefficient replenishment?

21. Revenue Sharing • Reduce wholesale price in return for a share of revenues • Encourages retailers to stock more • $60 a tape • $3/rental – rent each tape 20 times to break even • $9 a tape, studio receives half revenue • $3/rental – rent each tape 6 times to break even • Retailer stocks more

22. Revenue sharing • When does it work? • marginal cost of increasing inventory low • administrative burden low • for price-sensitive products

23. The Impact of Revenue Sharing • Blockbuster Instituted the “Go Home Happy” marketing initiative • Results • Store traffic went up • Market share 4th quarter 98 = 26% • Market share 2nd quarter 99 = 31% • Revenue in 2nd quarter 99: +17% from 98 • Cash flow in 2nd quarter 99: +61% from 98

24. 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

25. Supply Chain Management: the challenge • Global optimization • Conflicting Objectives • Complex network of facilities • System Variations over time • Managing uncertainty • Matching Supply and Demand • Demand is not the only source of uncertainty

26. The newsvendor is all around us • Newspaper • Apparel industry • The flu shot

27. Recall Marks & Spencer Perfect forecast Excess demand Excess stock Expected demand Actual demand

28. Recall Zara’a Approach to Demand uncertainty Excess stock and unmet demand are avoided by stopping production when market saturates Expected demand Actual demand Small batches

29. Flu vaccine example • Each year’s flu vaccine is different: can’t produce ahead or keep from last year • Flu vaccine production requires growing strains: there is a lead time • Factories have limited capacity • Demand is uncertain • Need to commit to production before flu season starts • Result: frequent shortage of vaccine or left overs at the end of the season

30. The Newsvendor Model Develop a Forecast: How did Anna come up with N(500, 100) for example? 11-30

31. O’Neill’s Hammer 3/2 wetsuit

32. Historical forecast performance at O’Neill Forecasts and actual demand for surf wet-suits from the previous season

33. Empirical distribution of forecast accuracy

34. Normal distribution tutorial • All normal distributions are characterized by two parameters, mean = m and standard deviation = s • All normal distributions are related to the standard normal that has mean = 0 and standard deviation = 1. • For example: • Let Q be the order quantity, and (m, s) the parameters of the normal demand forecast. • Prob{demand is Q or lower} = Prob{the outcome of a standard normal is z or lower}, where • (The above are two ways to write the same equation, the first allows you to calculate z from Q and the second lets you calculate Q from z.) • Look up Prob{the outcome of a standard normal is z or lower} in the Standard Normal Distribution Function Table. 11-34

35. Converting between Normal distributions • Start with • = 100, • = 25, Q = 125 Center the distribution over 0 by subtracting the mean Rescale the x and y axes by dividing by the standard deviation 11-35

36. Using historical A/F ratios to choose a Normal distribution for the demand forecast • Start with an initial forecast generated from hunches, guesses, etc. • O’Neill’s initial forecast for the Hammer 3/2 = 3200 units. • Evaluate the A/F ratios of the historical data: • Set the mean of the normal distribution to • Set the standard deviation of the normal distribution to 11-36

37. O’Neill’s Hammer 3/2 normal distribution forecast • O’Neill should choose a normal distribution with mean 3192 and standard deviation 1181 to represent demand for the Hammer 3/2 during the Spring season. 11-37

38. Empirical vs normal demand distribution Empirical distribution function (diamonds) and normal distribution function with mean 3192 and standard deviation 1181 (solid line) 11-38

39. Demand Scenarios for a Jacket

40. 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

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

42. 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

43. 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

44. Scenarios and their probabilities Demand Expected Profit Production quantity

45. Expected Profit

46. Expected Profit

47. Expected Profit

48. 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?

49. Probability of Outcomes

50. 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 • Fixed cost has no impact on production quantity, only on whether to produce or not • 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