The Impact of the Internet on Supply Chain Management Including some excerpts form David Simchi-Levi Professor of Engineering Systems Massachusetts Institute of Technology Move the push-pull boundary to reduce costs while improving cycle times. Move the push-pull boundary to reduce costs while improving cycle times.

HEC MONTRÉAL – MBA E-Commerce Jacques Robert & Jean Talbot, HEC Montréal Supply Chain Management Definition: Definition: Supply Chain Management is primarily concerned with the efficient integration of suppliers, factories, warehouses and stores so that merchandise is produced and distributed in the right quantities, to the right locations and at the right time, and so as to minimize total system cost subject to satisfying service requirements. Notice: Notice: – Who is involved – Cost and Service Level – It is all about integration Excerpt form David Simchi-Levi Professor of Engineering Systems Massachusetts Institute of Technology

HEC MONTRÉAL – MBA E-Commerce Jacques Robert & Jean Talbot, HEC Montréal The Business Model e- e-Business is the process of redefining old business models, using Internet technology, so as to improve the extended enterprise performance e-Business is the process of redefining old business models, using Internet technology, so as to improve the extended enterprise performance e-commerce is part of e-Businesse-commerce is part of e-Business Internet technology is the driver of the business changeInternet technology is the driver of the business change The focus is on the extended enterprise:The focus is on the extended enterprise: Intra-organizational Intra-organizational Business to Consumer (B2C) Business to Consumer (B2C) Business to Business (B2B) Business to Business (B2B) Excerpt form David Simchi-Levi Professor of Engineering Systems Massachusetts Institute of Technology

HEC MONTRÉAL – MBA E-Commerce Jacques Robert & Jean Talbot, HEC Montréal Conflicting Objectives in the Supply Chain 1. Purchasing Stable volume requirements Stable volume requirements Flexible delivery time Flexible delivery time Little variation in mix Little variation in mix Large quantities Large quantities 2. Manufacturing Long run production Long run production High quality High quality High productivity High productivity Low production cost Low production cost 3. Warehousing Low inventory Low inventory Reduced transportation costs Reduced transportation costs Quick replenishment capability Quick replenishment capability 4. Customers Short order lead time Short order lead time High in stock High in stock Enormous variety of products Enormous variety of products Low prices Low prices Excerpt form David Simchi-Levi Professor of Engineering Systems Massachusetts Institute of Technology

HEC MONTRÉAL – MBA E-Commerce Jacques Robert & Jean Talbot, HEC Montréal A new Supply Chain Paradigm A shift from a Push System...A shift from a Push System... –Production decisions (assembly) are based on forecast –Goods are produced, stored in retail inventory –Shocks in demand lead to stock out or unsold products …to a Push-Pull System …to a Push-Pull System –Part of the production decisions (assembly) are performed on demand –Parts inventory is replenished based on forecasts –Customizing of products Example: DellExample: Dell Excerpt form David Simchi-Levi Professor of Engineering Systems Massachusetts Institute of Technology

HEC MONTRÉAL – MBA E-Commerce Jacques Robert & Jean Talbot, HEC Montréal From Make-to-Stock Model…. Excerpt form David Simchi-Levi Professor of Engineering Systems Massachusetts Institute of Technology

HEC MONTRÉAL – MBA E-Commerce Jacques Robert & Jean Talbot, HEC Montréal ….to Assemble-to-Order Model Excerpt form David Simchi-Levi Professor of Engineering Systems Massachusetts Institute of Technology

HEC MONTRÉAL – MBA E-Commerce Jacques Robert & Jean Talbot, HEC Montréal Pickup sandwiches Sandwiches made on demand –More choice / –Longer preparation time –Piecework –No unsold stock Sandwiches prepared in advance –More limited choice –No delay of preparation / faster service –Cheaper production line –unsold stock

HEC MONTRÉAL – MBA E-Commerce Jacques Robert & Jean Talbot, HEC Montréal Choice criteria Diversity of taste / willingness to satisfy heterogeneity of tastes and preferencesDiversity of taste / willingness to satisfy heterogeneity of tastes and preferences Added cost of piece productionAdded cost of piece production Impatience of customersImpatience of customers Incertitude in demand.Incertitude in demand. Cost of unsold stocksCost of unsold stocks On demand Prepared in advance

HEC MONTRÉAL – MBA E-Commerce Jacques Robert & Jean Talbot, HEC Montréal Excerpt form David Simchi-Levi Professor of Engineering Systems Massachusetts Institute of Technology

HEC MONTRÉAL – MBA E-Commerce Jacques Robert & Jean Talbot, HEC Montréal Demand Forecast The three principles of all forecasting techniques: – Forecasts are always wrong –The longer the forecast horizon the worst is the forecast – Aggregate forecasts are more accurate - The Risk Pooling Concept Excerpt form David Simchi-Levi Professor of Engineering Systems Massachusetts Institute of Technology

HEC MONTRÉAL – MBA E-Commerce Jacques Robert & Jean Talbot, HEC Montréal Risk pooling: numerical example Many stores selling a good,Many stores selling a good, –2 consumers / store A consumer has a chance to desire the good.A consumer has a chance to desire the good. Value of a sale=30$, cost of inventory 10$/unityValue of a sale=30$, cost of inventory 10$/unity Examine of the possible inventory policiesExamine of the possible inventory policies

HEC MONTRÉAL – MBA E-Commerce Jacques Robert & Jean Talbot, HEC Montréal Profits under full information (maximum possible profits) EventProbabilityGain No consumer wants the good 25%0$ One consumer wants the good 50%+20$ Two consumers want the good 25%+40$ Expected gain = 20 $ Average gain per consumer: 10$

HEC MONTRÉAL – MBA E-Commerce Jacques Robert & Jean Talbot, HEC Montréal I- One unit per Store EventProbabilityGain One unit unsold 25%-10$ One unit sold 50%+20$ One lost sale (stockout) 25%+20$ Expected gain = 12,5 $ Average gain per consumer: 6,25$

HEC MONTRÉAL – MBA E-Commerce Jacques Robert & Jean Talbot, HEC Montréal II- Two units per store EventProbabilityGain Two units unsold 25%-20$ One unsold unit & One sold unit 50%+10$ Two units sold 25%+40$ Expected gain = 10 $ Average gain per consumer=5$

HEC MONTRÉAL – MBA E-Commerce Jacques Robert & Jean Talbot, HEC Montréal 4 clients / store 2 units in inventory SaleProbabilityGain 0 unit 1/16-20$ 1 unit ¼+10$ 2 units 3/8 + ¼ +1/16= 11/16 +40$ Expected gains=460/16 = 28,7$ Average gain per consumer = 7,18$ Higher average gain with 4 clients than with 2.

HEC MONTRÉAL – MBA E-Commerce Jacques Robert & Jean Talbot, HEC Montréal n clients / store s units in inventory Probability that x consumers wants the good Total expect profits Optimal inventory s. Marginal benefit of adding one extra unit in inventory become nil.

HEC MONTRÉAL – MBA E-Commerce Jacques Robert & Jean Talbot, HEC Montréal Effect of pooling risk Number of consumers n Optimal inventory s Profits per consumers 647, , , , ,827

HEC MONTRÉAL – MBA E-Commerce Jacques Robert & Jean Talbot, HEC Montréal Pooling risk

HEC MONTRÉAL – MBA E-Commerce Jacques Robert & Jean Talbot, HEC Montréal How to pool risk Pool consumers across time by having stocking for a longer periodPool consumers across time by having stocking for a longer period –Increase stocking time –Use stock as a buffer Pool consumers geographically by having large stores serving more customersPool consumers geographically by having large stores serving more customers –Increase transportation costs if there are economies of scale in transportation –Greater delays for consumers –Last mile

HEC MONTRÉAL – MBA E-Commerce Jacques Robert & Jean Talbot, HEC Montréal How to pool risk Delaying assembly: pool consumers across products by using the same resources for different productsDelaying assembly: pool consumers across products by using the same resources for different products –Requires manufacturing flexibility –Greater delays for consumers

HEC MONTRÉAL – MBA E-Commerce Jacques Robert & Jean Talbot, HEC Montréal Matching Supply Chain Strategies with Products Excerpt form David Simchi-Levi Professor of Engineering Systems Massachusetts Institute of Technology

HEC MONTRÉAL – MBA E-Commerce Jacques Robert & Jean Talbot, HEC Montréal Locating the Push-Pull Boundary Excerpt form David Simchi-Levi Professor of Engineering Systems Massachusetts Institute of Technology

HEC MONTRÉAL – MBA E-Commerce Jacques Robert & Jean Talbot, HEC Montréal Business models in the Book Industry From Push Systems... From Push Systems... – Barnes and Noble...To Pull Systems...To Pull Systems – Amazon.com, And, finally to Push-Pull Systems And, finally to Push-Pull Systems – Amazon.com, 1999-present – 7 warehouses, 3M sq. ft., Excerpt form David Simchi-Levi Professor of Engineering Systems Massachusetts Institute of Technology

HEC MONTRÉAL – MBA E-Commerce Jacques Robert & Jean Talbot, HEC Montréal e-Business in the Retail Industry Brick-&-Mortar companies establish Virtual retail stores Wal-Mart, K-Mart, Barnes and NobleWal-Mart, K-Mart, Barnes and Noble Use a hybrid approach in stocking High volume/fast moving products for local storageHigh volume/fast moving products for local storage Low volume/slow moving products for browsing and purchase on lineLow volume/slow moving products for browsing and purchase on line Channel Conflict Issues Excerpt form David Simchi-Levi Professor of Engineering Systems Massachusetts Institute of Technology

HEC MONTRÉAL – MBA E-Commerce Jacques Robert & Jean Talbot, HEC Montréal e-Business Opportunities Reduce Facility CostsReduce Facility Costs –Eliminate retail –distributor sites Reduce Inventory CostsReduce Inventory Costs –Apply the risk- pooling concept Centralized stockingCentralized stocking Postponement of product differentiationPostponement of product differentiation e-Business Challenges Manage channel conflict issuesManage channel conflict issues Automated fulfillmentAutomated fulfillment Use and share optimally information as soon as availableUse and share optimally information as soon as available Speed up assemblySpeed up assembly Speed up deliverySpeed up delivery Manage the “last mile”.Manage the “last mile”.