presented by A ğ cagül YILMAZ

1. Supply Chain Inventory Management and the Value of Shared InformationGerard P.Cachon* Marshall Fisher presented by Ağcagül YILMAZ

2. Content • Aim • Literature • Assumptions • Policies • Traditional Information Policy • Lower Bound • Full Information Policy • Numerical Study • Results • Discussion

3. Aim... • How information technology improves supply chain performance? • Ex.Barilla (5 days lead time reduction). • Under Traditional Information Policy, Lower Bound,Full Information Policy w/ different parameters such as # of retailers,batch size,lead time,penalty cost,holding cost,demand distribution • Better supplier replenishments and better allocation to retailers.

4. Literature • *** studies on: • -Sharing other parameters of retailer • -Forecast Sharing for future demand • -Perfect source of inventory • -Limited supplier capacity • -Nonstationary retailers demand • -Sharing information w/different allocation rules • -no outside inventory source • -local information get retailers but not suppliers • -lower bound for multiple retailer order over all feasible policies

5. Assumptions • 1 Supplier • N identical Retailers • Stationary Stochastic Consumer Demand w/known distribution. • Multiple of base batch quantity as shipment quantity • Fixed transportation times btw locations • Holding cost at all levels,backorder penalty cost at retailers • 1 Product under constant pricing condition • No capacity constraint

6. Assumptions • No incentive conflicts among firms • Rational ordering policies in firms • Perfect Information Sharing • Nodiversion of stock among retailers • Replenishment delays due to stock-outs at supplier • Periodic inventory review within each period the following sequence of events occur: retailers order,supplier orders,inventory shipments are received&released,inventory holding and backorders are charged. • Not perfect source of inventory • Sharing local information of retailer by supplier

7. POLICIES TRADITIONAL INFORMATION POLICY LOWER BOUND FULL INFORMATION POLICY

8. TRADITIONAL INFORMATION POLICY • Supplier knows only the retailer`s orders. • (Rr, nQr) reorder point policy for retailers • (Rs,nQs) reorder point policy for suppliers • Batch priority allocation as supplier`s allocation policy(first-in-first-out)

9. LOWER BOUND • Simulation based lower bound over all feasible policies. • Independent of the level of the information sharing. • Two steps: • Division of the supply chain cost into two parts • Lower bound for each components

10. LOWER BOUND • Division of the Supply Chain Cost • - Retailer Charged &Supplier Charged ( in fact actual cost in infinite horizon) • Evaluation of the Lower Bound • - Uniformly distributed reorder point policy for retailer charge calculation - Location constraint is relaxed for supplier charges -How inventory allocated btw retailers ,Myopic Policy Calculation of the cost done by simulation due to large state space and difficulty in steady state distribution of IP Confidence intervals for each cost found in simulations is given.

11. FULL INFORMATIONPOLICY • Full information provides the supplier w/data to • - improve its order quantity decisions • - improve its allocation decisions • Allocation based on • -retailer`s inventory positions rather than the number batches they order • -the allocation of a batch based on retailer`s inventory positions in the period the batch is shipped rather than ordered. • -Order quantity by using the lower bound expression • - Under traditional R* found and put in lower bound information policy expression. • Simulation is necessary .

12. FULL INFORMATION POLICY • Out of balance in retailer`s inventory position is less likely • when; • -Qr is increased, • -Consumer demand variability is decreased, • -Ls is decreased.

13. Numerical Study • 768 scenarios w/combination of following parameters • N € (4,16) ; Qr € (2,4,8,16) ; Lr € (1,5) ; hr =1-hs ; pr € (5,25) ; • σr € (0.36,1) ; Qs € (1,4,16); Ls € (1,5) ; hs (0.5,1) µr=1 • For each one traditional information policy, 40 simulations for each scenario over 10 supplier order cycles for lower bound and full information policy • When lower bound greater than full inf. Policy cost 90 additional simulations per scenario. • 95% confidence intervals for lower bound and full inf. Case.

14. RESULTS • On the average cost reduction 2.2% lower in full inf.policy than traditional inf. policy.12.1% is the max. difference. • On the average cost reduction 3.4% lower in lower bound than traditional inf. policy.13.8% is the max. difference. • 21% reduction in cost by cutting lead times nearly half. • 22% reduction in cost by cutting batches nearly half.

15. DISCUSSION • - Demand information is most valuable when IP of the retailers approaches R, in an unknown environment • - Use information technology to accelerate the physical flow of goods rather than expanding the flow of information to get higher cost reduction in supply chain. • - Full information policy is close to optimal. • - Full information policy is better than traditional as expected. .

16. THANKS!