Backup Agreements in Fashion Buying-The Value of Upstream Flexibility Eppen and Iyer (1997)

1. Backup Agreements in Fashion Buying-The Value of Upstream FlexibilityEppen and Iyer (1997) Presented By: Hakan Umit 21 April 2003

2. Scope • Address Backup Agreements between a catalog company and manufacturers • Present a systematic action to provide upstream sourcing flexibility

3. A Backup Agreement is… • commitment of the catalog company for a certain number of units to be purchased for the season long before the it starts

4. Characteristics of the System • The manufacturer holds back a constant fraction of the commitment and delivers the remaining units before the start of the fashion season • The catalog company can order up to this backup quantity for the original purchase cost and receive quick delivery but will pay a penalty cost for any backup of the units it does not buy

5. Problems Associated with the System • Long lead times • High levels of uncertainty • Role of returns • Limited opportunities to adjust buying decisions

6. Decisions to be made by customers • Commitment quantity • Number of units to be taken from the backup

7. This paper provides… • Theoretical and Applied Results: • Stochastic dynamic programming model for the backup agreement • Parallel retrospective study that compares the performance of a catalog company and proposed model’s results • Extensive test results to evaluate the impact of changes in contract conditions

8. The Process Flow • : commitment for the season • : percentage of units of y to be hold by the manufacturer • : penalty cost to be paid by the customer if it does not take from backup • : cost of a unit product to the catalog co. • : price of a unit product at the catalog co.

9. : Random demand in P-I • : Unit holding cost in P-I • : Unit cost for unsatisfied demand • : Percentage of returned sales in P-I • : Percentage ofthat arrives in time to satisfy demand for P-II

10. Two weeks Period-II Period-I units on hand and 1 units in returns Product is offered for a price of r per unit units are delivered for a cost of c per unit or 0 units on hand and returned units units are held at the manufacturer Random demand, , occurs uv(sales) arrive on time to satisfy demand in P-II v(sales) returned Net Revenue:

11. The Demand Process • Demand is assumed to be generated by pure demand processes • A pure demand process provides a probability distribution of demand for P-I, P-II and for the season • Which pure demand process will generate the demands is uncertain, thus prior probabilities are assigned to these processes by the buyer

12. Defining the Demand Model consists of two steps.. • Specify a set of pure demand processes • Select a set of prior probabilities at the start of 1st period that each pure demand processes will be the ones that actually produces the demand Cumulative Demand Distributions Probability Density Functions Let P1i be the prior probability for pure demand process i

13. The Dynamic Programming Model • f1(0): optimal expected profit for the two period problem assuming that 0 items are on hand at the beginning of P-I. Then G1(y) Density function of demand in P-I Maximum Expected Profit in P-II if y items are committed

14. The Maximization Problem for is: Where, Conditional Density for ,the demand in period 2 given

15. Results

16. A Retrospective Parallel Test • Data Set of CATCO (for years 1990-1993) • Sales estimates when purchase decisions were made • the quantity ordered • the actual demand during the year • the purchase cost and selling price • the return and cancel rates • backup agreements

17. The Demand Process: • Demand was segregated into intervals then a histogram was plotted: the data fell into 4 nonoverlapping regions • In each of these regions the data were generated by random draws from a negative binomial distribution • Specific model was developed for the pure processes that consists of 4 negative binomial distributions

18. The Demand Process: • From smaller through greater expected demand, the processes are referred as Dogs, Crawlers, Walkers and Runners • Planned classification by the buyer are used to create priors for the items

19. Retrospective Study • Optimal value of y is determined • Amount to be taken from backup is not available, therefore given a value of the model evaluates and uses an order-up-to policy to choose this amount • Expected profit, Expected purchases are determined

20. Results • Buyers at Catco took advantage of the backup opportunity to improve their net profit • The model improved expected profit to $50,433 • Expected dollar-weighted cancel rate decreased • Revenues increased • Purchases decreased • Sales to the outlet store decreased

21. The Impact of b and on Expected Profit

22. Because it is cheaper to buy an item and have it on hand than it is to pay the penalty for not taking it from backup.

23. The Impact of b and on Commitment

24. If it is unlikely that we run out in the first period using the commitment that is optimal when no backup is available, then the optimal commitment with backup is greater than the optimal commitment when no backup is offered

25. The Impact of Commitment on Expected Profit

26. The Impact of the Structure of Demand on the Value of Backup Contracts

27. The Impact on the Manufacturer

28. Conclusions • Backup is an important practice in merchandising of fashion goods that can benefit both the retailer and the manufacturer • A retrospective parallel test established the potential impact of the model at Catco • Adjusting the order commitment in response to the offered can have significant impact on the expected profit

29. Thank You