《供应链系统设计与管理》课程教学课件（讲稿）Lecture 5（Chapter 2）Risk pooling（centralized system）

Lecture 5Risk Pooling(Centralized system)王长琼武汉理工大学2017.5

Lecture 5 Risk Pooling (Centralized system) 1 王长琼 武汉理工大学 2017,5

ObjectivesUnderstandinginventoryina supplychainWhyinventoryisimportant?How to manage it?SingleStageInventoryControl- Economic Lot Size Model-Demand Uncertainty/MultipleOrderOpportunitiesRisk Pooling

Objectives • Understanding inventory in a supply chain Why inventory is important? How to manage it? • Single Stage Inventory Control - Economic Lot Size Model - Demand Uncertainty Multiple Order Opportunities Risk Pooling 2

2.3RiskPooling· Aggregated forecast is more accurateDemand variability is reduced if one aggregatesdemand across locations.- High demand from one customer may be offset bylow demand from another.Reduction invariabilityallowsadecreaseinsafetystock and therefore reduces average inventory3

2.3 Risk Pooling • Aggregated forecast is more accurate • Demand variability is reduced if one aggregates demand across locations. - High demand from one customer may be offset by low demand from another. • Reduction in variability allows a decrease in safety stock and therefore reduces average inventory. 3

Demand Variation MeasuresStandard deviation, measures how muchdemand tends to vary around the average-anabsolute measureofthevariability Coefficient of variation, cv = /μ-a relative measure of the variability, relative to theaverage demand

Demand Variation Measures • Standard deviation,  measures how much demand tends to vary around the average – an absolute measure of the variability • Coefficient of variation, cv =  / – a relative measure of the variability, relative to the average demand 4

AcME:RiskPoolingCaseElectronic equipment manufacturer and distributor2 warehouses for distribution in Massachusetts andNew Jersey (partitioning the northeast market intotwo regions)Each retailer (Customer) is assigned a warehouseWarehouses receive material from ChicagoCurrent rule:97%servicelevel(3%probabilityofstock-out5

 ACME: Risk Pooling Case • Electronic equipment manufacturer and distributor • 2 warehouses for distribution in Massachusetts and New Jersey (partitioning the northeast market into two regions) • Each retailer (Customer) is assigned a warehouse • Warehouses receive material from Chicago • Current rule: 97 % service level (3 % probability of stock-out) 5

Currentdistribution systems:WarehouseOneMarket OneSupplierMassachusettsWarehouse TwoMarket Two·ChicagoNewJersey

Market Two • Current distribution systems: Supplier Warehouse One Warehouse Two Market One •Chicago Massachusetts New Jersey

Current distribution systems: New idea: aggregating warehouses into oneMarketOneWarehouseSupplierMarket TwoCentralizedsystemSomesuitableplace

• Current distribution systems: Market Two Supplier Warehouse Market One • New idea: aggregating warehouses into one Centralized system Some suitable place

Worse?Better?New ldea:Replace the 2 warehouses with a singlewarehouse (located some suitable place) and tryto implement the same service level 97 %DeliveryleadtimesmayincreaseBut maydecreasetotal inventory considerablyWhy?Compare the total inventories of two systems8

 New Idea：Better？Worse？ • Replace the 2 warehouses with a single warehouse (located some suitable place) and try to implement the same service level 97 % • Delivery lead times may increase • But may decrease total inventory considerably. • Why? Compare the total inventories of two systems 8

(1)Historical DataPRODUCTA12346758Week3733453855301858Massachusetts3526464140481855NewJersey79807878817836113TotalPRODUCTB12345678Week33001300Massachusetts30240310NewJersey26303230Total

(1) Historical Data PRODUCT A Week 1 2 3 4 5 6 7 8 Massachusetts 33 45 37 38 55 30 18 58 New Jersey 46 35 41 40 26 48 18 55 Total 79 80 78 78 81 78 36 113 PRODUCT B Week 1 2 3 4 5 6 7 8 Massachusetts 0 3 3 0 0 1 3 0 New Jersey 2 4 3 0 3 1 0 0 Total 2 6 3 0 3 2 3 0

(2) S Summary of Historical DataStatisticsProductStandardCoefficientAverageDemandDeviationofofVariationDemandAMassachusetts39.313.20.34BMassachusetts1.361.211.125ANewJersey38.612.00.31BNewJersey1.251.581.26ATotal (aggreg.)77.920.710.27BTotal (aggreg.)1.90.812.375

(2) Summary of Historical Data Statistics Product Average Demand Standard Deviation of Demand Coefficient of Variation Massachusetts A 39.3 13.2 0.34 Massachusetts B 1.125 1.36 1.21 New Jersey A 38.6 12.0 0.31 New Jersey B 1.25 1.58 1.26 Total (aggreg.) A 77.9 20.71 0.27 Total (aggreg.) B 2.375 1.9 0.81