05/14/2003 By Çağrı LATİFOĞLU

1. Value of Information Sharing in Multi-Retailer Setting with Inter-Correlated and Auto-Correlated Demands 05/14/2003 By Çağrı LATİFOĞLU

2. Outline • Introduction • Value of Information Sharing • Impact of Demand Correlation • Joint Effect & Game Theory Extensions • Conclusion

3. 1 w/h – 1 retailer case 1 w/h – N retailer case Introduction W/H W/H Retailer Retailer Retailer ................... Demand Demand

4. Introduction • Our aim is to find/construct a demand model for the quantifying the value of information sharing in a multi-retailer setting with auto-correlated and inter- correlated demands.

5. Information Sharing • The retailer shares • Demand • Inventory • Inventory policy • Promotion plan • The manufacturer shares • Inventory • Capacity

6. Information Sharing-Benefits • Helps manufacturer in ordering process and allocation process • Reduced variance to the manufacturer which leads to: • Reduced safety stock at the manufacturer • Reduced flexibility need at the manufacturer • Reduced smoothing costs at the manufacturer

7. Information Sharing-Incentives • No immediate benefits to retailer if infinite capacity at the retailer is assumed. There should be an arrangement between the manufacturer and retailers. • Examples: • Use of vendor managed inventory to save retailer’s overhead and processing costs • offering discounts to retailer • reducing lead time

8. Information Sharing-Examples • Lee at al.(2000) • Value of Information Sharing in a Two-level Supply Chain • AR(1) demand process • Inventory Reduction and Cost Reduction • More valuable when: • long lead times • high demand variance within periods • high auto-correlation over time

9. Information Sharing-Examples • Cachon & Fisher(2000) • Supply Chain Inventory Management and Value of Shared Information • Stationary Stochastic Demand Process • Inventory Reduction and Cost Reduction • More valuable when: • Different Retailers • Unknown Demand

10. Information Sharing-Examples • Improving supply-chain performance by sharingadvance demandinformation (2001) U.W. Thonemann • Expected cost is concave decreasing in the number of customers who share ADI. • If the cost ofobtaining ADI is also concave in the number of customers who share ADI, then either none or all customersshare ADI in an optimal solution. • We showed that all members of a supply chain benefit fromsharing ADI. • The manufacturer benefit from reduced cost • customers benefit from lower prices orhigher service levels • It introduces variation in the base-stocklevels and increases the variability of the production quantities.

11. Information Sharing-References • Additional References: • Benefits of information sharing with supply chain partnerships (2001) Yu ZX, Yan H, Cheng TCE • Forecasting errors and the value of information sharing in a supply chain (2002) Zhao XD, Xie JX • Information sharing in a supply chain (2000) Lee HL, Whang SJ • Modeling the benefits of information sharing-based partnerships in a two-level supply chain (2002) Yu Z, Yan H, Cheng TCE • Leveraging information in multi-echelon inventory systems (2002) Mitra et. al.

12. Demand Correlation • Two types of demand correlation to be considered: • Auto-correlation: That is the correlation of the demand with itself in a time period. Ex: You are less likely to buy a car tomorrow if you bought one today. • Inter-correlation(or cross-correlation): That is the correlation of demands that are realized by different retailers. Ex:If you buy your car from one retailer,that means you won’t buy from another one in close future assuming you want to but only one car.

13. Demand Correlation-Examples • Multistage Safety Stock Planning with Item Demands Correlated Across Products and Through Time (1995) Indefurth • Autocorrelation of demands brings a tendency to hold SS at end item-level • Intercorrelation of demands brings a tendency to hold SS at upper levels. • An optimization approach for AR(1) is introduced • An optimization approach for jointly correlated demands is also introduced

14. Demand Correlation-References • Flexible service capacity: Optimal investment and the impact of demand correlationNetessine et. al. (2002) • Time-dependent demand in requirements planning: An exploratory assessment of the effects of serially correlated demand sequences on lot-sizing performance Raiszadeh et. al. (2002) • Impacts of buyers' order batching on the supplier's demand correlation and capacity utilization in a branching supply chainJung et. al.(1999) • Coordinated replenishments in inventory systems with correlated demands (2000) Liu et. al. • Plus the paper’s that will be included in the joint-effect

15. Joint Effect • A two-echelon allocation model and the value of information under correlated forecasts and demands. (1996) Güllü • Impact of Demand Correlation on the value of and incentives for information sharing in a supply chain (2001) Raghunathan

16. Joint Effect-Examples • In Güllü (1996) the depot-retailes environment considered in Eppen-Schrage(1981) is used. • It extends Eppen & Schrage model to incorporate forecasts (for many periods and retailers) to be a part of the state of the system. • Forecasts for future periods are updated in each period according to an evolution model • Evolution model allows the incorporation of correlation of demands(both auto- and cross-) in the model.

17. Joint Effect-Examples • Demand model: Dnj= (dn,nj , dn,n+1j ,..., dn,n+M-1j , µj , µj ,...) • Where N is number of retailers, • j=1,2,..,N • dn,nj =demand realization of retailer j in period n and dn,n+kj = demand forecast made in period n for period n + k • µj =meand demand of retailer j

18. Joint Effect-Examples • dn+1,n+M-1j = dn,n+M-1j + εn,M-1j • έj =(έn1, έn2,..., έnN) • έj ~ zero mean, multi-variate distribution • Cross-correlations are allowed.

19. Joint Effect-Examples • Results: • As fraction of variability leraned by keeping track of forecasts increases, the difference between ore-up-to-levels increases. • Forecasts and demand across retailers become more negatively correlted as difference gets larger. • Imbalance between forecasts and demands are captred progressively and total system stock is reduced. • If correlation increases order-up-to-levels increase and difference(between consequent values of S) gets smaller. • Forecasters are confident about the total mean to be observed but unsure about which retailer will receive what portion of demand.

20. Joint Effect-Examples • Raghunathan (2001) • Prior studies have shown manufacturer directly effected but the retailer’s won’t participate unless they receive some prize. • Shapley value concept from Game Theory is used to distribute the surplus generated from information sharing.(a value employed frequently in n-person cooperative games) • This paper is an extension of Lee at. al.(2000) Single retailer model is extended to N retailer model • Retailers share their forecast and demand information with manufacturer.

21. Joint Effect-Examples • Demand Model: • DitF retailer i’s forecast of period t using actual demand during Dit-1period t-1. • DitF = d + ρ Dit-1+ εitwhere d >0, 1> ρ>-1, i € [1,2,...N] • εit~ Normal(0, σ2). εitis correlated with εjtwith coefficient pr • YitF = d + ρ Yit-1+ δit => Manufacturer’s demand

22. Joint Effect-Examples • Ordering decisions with and without information sharing is compared • It is observed that if manufacturer’s service level is sufiiciently high, the benefit comes from primarily inventory reduction. • Variance of manufacturer’s forecast is higher when • More retailers share information, • Correlation across time or retailers is higher, • Variance of demand is higher.

23. Joint Effect-Examples • Observations made: • Value of information sharing is higher when cross-correlation and auto-correlation is high. • When correlation is sufficiently high, marginal value of addition of a retailer to the coalition decreases • In the case of negative correlation or independent demands, addition of a retailer to coalition members realize increasingly larger incremental value.

24. Joint Effect-Examples • Under high correlation, retailers receive less • Information sharing partnerships are to be formed withh less retailers under high correlation • Accelarating physical flow of goods is more valuable than expanding the flow of information when capacity of manufacturer is limited. • Higher correlation increases manufacturer surplus but the marginal value of manufacturer surplus decreases as number of retailers increase

25. Joint Effect-Examples • Allocation of the surplus • The members of the coalition do not compete rather colloborate to gain even more surplus when demands are independent • When retailers are substituable, manufacturer’s bargaining power increase as retailer’s decrease.

26. Game Theoretic Extensions • How reatilers behave when cross correlation and autocorrelation exists is an important issue for both • Deciding the incentive to apply • Deciding the structure of partnership • So we can extend the subject by considering game theoretic approach

27. Game Theoretic Extensions • Benefits of cooperation in a production distribution environment (1999) Gavirneni • Grouping customers for better allocation of resources to serve correlated demands (1999) Tyagi et. al. • Information sharing in a supply chain with horizontal competition (2002) Li LD • Decentralization and Collusion (1998) Baliga et. al. • Market collusion and the politics of protection (2001) Ludema • Distributional assumptions in the theory of oligopoly information exchange (1998) Malueg et. al. • Information sharing between heterogenous uncertain reasoning models in a multi-agent environement: a case study (2001) Luo et. al. • Information disaggregating and incentives for non-collusiveinformation sharing (1998) Novshek et. al.

28. Q & A Thanks for listening!!!