Innovation and Strategies in Supply Chain Management David Simchi-Levi Professor of Engineering Systems Massachusetts Institute of Technology Tel: 617-253-6160 E-mail: dslevi@mit.edu

Outline of the Presentation Introduction Push-Pull Systems Supply Contracts ©Copyright 2004 D. Simchi-Levi

Today’s Supply Chain Pitfalls Long Lead Times Uncertain Demand Complex Product Offering Component Availability System Variation Over Time ©Copyright 2004 D. Simchi-Levi

The Bullwhip Effect and its Impact on the Supply Chain Consider the order pattern of a single color television model sold by a large electronics manufacturer to one of its accounts, a national retailer. Figure 1. Order Stream Huang at el. (1996), Working paper, Philips Lab ©Copyright 2004 D. Simchi-Levi

The Bullwhip Effect and its Impact on the Supply Chain Figure 2. Point-of-sales Data-Original Figure 3. POS Data After Removing Promotions ©Copyright 2004 D. Simchi-Levi

The Bullwhip Effect and its Impact on the Supply Chain Figure 4. POS Data After Removing Promotion & Trend ©Copyright 2004 D. Simchi-Levi

©Copyright 2004 D. Simchi-Levi Higher Variability in Orders Placed by Computer Retailer to Manufacturer Than Actual Sales Lee, H, P. Padmanabhan and S. Wang (1997), Sloan Management Review ©Copyright 2004 D. Simchi-Levi

Increasing Variability of Orders Up the Supply Chain Lee, H, P. Padmanabhan and S. Wang (1997), Sloan Management Review ©Copyright 2004 D. Simchi-Levi

©Copyright 2004 D. Simchi-Levi We Conclude …. Order Variability is amplified up the supply chain; upstream echelons face higher variability. What you see is not what they face. ©Copyright 2004 D. Simchi-Levi

©Copyright 2004 D. Simchi-Levi The Bullwhip Effect Retailers P&G Customers ©Copyright 2004 D. Simchi-Levi

©Copyright 2004 D. Simchi-Levi What are the Causes…. Promotional sales Volume and Transportation Discounts Inflated orders - IBM Aptiva orders increased by 2-3 times when retailers thought that IBM would be out of stock over Christmas - Same with Motorola’s Cellular phones ©Copyright 2004 D. Simchi-Levi

©Copyright 2004 D. Simchi-Levi What are the Causes…. Single retailer, single manufacturer. Retailer observes customer demand, Dt. Retailer orders qt from manufacturer. Dt qt Retailer Manufacturer L ©Copyright 2004 D. Simchi-Levi

©Copyright 2004 D. Simchi-Levi What are the Causes…. Promotional sales Volume and Transportation Discounts Inflated orders Demand Forecast Long cycle times ©Copyright 2004 D. Simchi-Levi

©Copyright 2004 D. Simchi-Levi Consequences…. Increased safety stock Reduced service level ©Copyright 2004 D. Simchi-Levi

©Copyright 2004 D. Simchi-Levi Consequences…. Single retailer, single manufacturer. Retailer observes customer demand, Dt. Retailer orders qt from manufacturer. Dt qt Retailer Manufacturer L ©Copyright 2004 D. Simchi-Levi

©Copyright 2004 D. Simchi-Levi Consequences…. Increased safety stock Reduced service level Inefficient allocation of resources Increased transportation costs ©Copyright 2004 D. Simchi-Levi

Multi-Stage Supply Chains Consider a multi-stage supply chain: Stage i places order qi to stage i+1. Li is lead time between stage i and i+1. qo=D q1 Retailer Stage 1 Manufacturer Stage 2 q2 Supplier Stage 3 L1 L2 ©Copyright 2004 D. Simchi-Levi

©Copyright 2004 D. Simchi-Levi What are the Causes…. Promotional sales Volume and Transportation Discounts Inflated orders Demand Forecast Long cycle times Luck of centralized demand information ©Copyright 2004 D. Simchi-Levi

Example: Automotive Supply Chain Custom order takes 60-70 days Many different products High level of demand uncertainty Dealers’ inventory does not capture demand accurately GM estimates: “Research shows we lose 10% to 11% of sales because the car is not available” ©Copyright 2004 D. Simchi-Levi

Supply Chain Strategies Achieving Global Optimization Managing Uncertainty Risk Pooling Risk Sharing ©Copyright 2004 D. Simchi-Levi

Sequential Optimization vs. Global Optimization Procurement Planning Manufacturing Distribution Demand Sequential Optimization Supply Contracts/Collaboration/Integration/DSS Procurement Planning Manufacturing Distribution Demand Global Optimization Source: Duncan McFarlane ©Copyright 2004 D. Simchi-Levi

A new Supply Chain Paradigm A shift from a Push System... Production decisions are based on forecast …to a Push-Pull System ©Copyright 2004 D. Simchi-Levi

From Make-to-Stock Model…. Suppliers Configuration Assembly ©Copyright 2004 D. Simchi-Levi

©Copyright 2004 D. Simchi-Levi 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 Risk Pooling ©Copyright 2004 D. Simchi-Levi

A new Supply Chain Paradigm A shift from a Push System... Production decisions are based on forecast …to a Push-Pull System ©Copyright 2004 D. Simchi-Levi

Push-Pull Supply Chains The Supply Chain Time Line Push-Pull Boundary PUSH STRATEGY PULL STRATEGY Customers Suppliers Low Uncertainty High Uncertainty ©Copyright 2004 D. Simchi-Levi

A new Supply Chain Paradigm A shift from a Push System... Production decisions are based on forecast …to a Push-Pull System Parts inventory is replenished based on forecasts Assembly is based on accurate customer demand ©Copyright 2004 D. Simchi-Levi

….to Assemble-to-Order Model Suppliers Configuration Assembly ©Copyright 2004 D. Simchi-Levi

©Copyright 2004 D. Simchi-Levi 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 Risk Pooling ©Copyright 2004 D. Simchi-Levi

Business models in the Book Industry From Push Systems... Barnes and Noble ...To Pull Systems Amazon.com, 1996-1999 And, finally to Push-Pull Systems Amazon.com, 1999-present 7 warehouses, 3M sq. ft., ©Copyright 2004 D. Simchi-Levi

Direct-to-Consumer:Cost Trade-Off

Business models in the Grocery Industry From Push Systems... Supermarket supply chain ...To Pull Systems Peapod, 1989-1999 Stock outs 8% to 10% And, finally to Push-Pull Systems Peapod, 1999-present Dedicated warehouses Stock outs less than 2% ©Copyright 2004 D. Simchi-Levi

Business models in the Grocery Industry Key Challenges for e-grocer: Transportation cost Density of customers Very short order cycle times Less than 12 hours ©Copyright 2004 D. Simchi-Levi

e-Business in the Retail Industry Brick-&-Mortar companies establish Virtual retail stores Wal-Mart, K-Mart, Barnes and Noble Use a hybrid approach in stocking Fast moving/High volume products for local storage Slow moving/Low volume products for on-line purchase Channel Conflict Issues ©Copyright 2004 D. Simchi-Levi

Matching Supply Chain Strategies with Products Demand uncertainty (C.V.) Delivery cost Unit price L H H L Economies of Scale Pull Push Pull Push I Computer II IV III ©Copyright 2004 D. Simchi-Levi

Shifting the Push-Pull Boundary: A Case Study Manufacturer of circuit boards and other high-tech products Sells customized products with high value and short life cycles Multi-stage BOM e.g., copper & fiberglass  circuit board  enclosure  processor Case study concerns one of 27,000 SKUs ©Copyright 2004 D. Simchi-Levi

©Copyright 2004 D. Simchi-Levi

©Copyright 2004 D. Simchi-Levi

©Copyright 2004 D. Simchi-Levi

Comparison of Performance Measures ©Copyright 2004 D. Simchi-Levi

©Copyright 2004 D. Simchi-Levi

©Copyright 2004 D. Simchi-Levi

©Copyright 2004 D. Simchi-Levi Comparison of Performance Measures ©Copyright 2004 D. Simchi-Levi

Safety Stock vs. Quoted Lead Time For a given lead-time, the optimized supply chain provides reduced costs For a given cost, the optimized supply chain provides better lead-times ©Copyright 2004 D. Simchi-Levi

Outline of the Presentation Introduction Push-Pull Systems Supply Contracts ©Copyright 2004 D. Simchi-Levi

©Copyright 2004 D. Simchi-Levi Supply Contracts Fashion items short life cycles High product variety One production opportunity Simple supply chain structure High demand uncertainty ©Copyright 2004 D. Simchi-Levi

©Copyright 2004 D. Simchi-Levi Supply Contracts Manufacturer Manufacturer DC Retail DC Stores Fixed Production Cost =$100,000 Variable Production Cost=$35 Wholesale Price =$80 Selling Price=$125 Salvage Value=$20 ©Copyright 2004 D. Simchi-Levi

©Copyright 2004 D. Simchi-Levi Demand Scenarios ©Copyright 2004 D. Simchi-Levi

Summary of Retailer Information Wholesale cost per unit (C): $80 Selling price per unit (S): $125 Salvage value per unit (V): $20 Average demand = 13,000 units Should the retailer order more than average demand, less than average demand or exactly average demand? ©Copyright 2004 D. Simchi-Levi

©Copyright 2004 D. Simchi-Levi Scenario Analysis Scenario One: Suppose you make 12,000 jackets and demand ends up being 13,000 jackets. Profit = 125(12,000) - 80(12,000) = $540,000 Scenario Two: Suppose you make 12,000 jackets and demand ends up being 11,000 jackets. Profit = 125(11,000) - 80(12,000) + 20(1000) = $435,000 ©Copyright 2004 D. Simchi-Levi

Distributor Expected Profit ©Copyright 2004 D. Simchi-Levi

Distributor Expected Profit ©Copyright 2004 D. Simchi-Levi

Supply Contracts (cont.) Distributor optimal order quantity is 12,000 units Distributor expected profit is $470,000 Manufacturer profit is $440,000 Supply Chain Profit is $910,000 IS there anything that the distributor and manufacturer can do to increase the profit of both? ©Copyright 2004 D. Simchi-Levi

©Copyright 2004 D. Simchi-Levi Supply Contracts Manufacturer Manufacturer DC Retail DC Stores Fixed Production Cost =$100,000 Variable Production Cost=$35 Wholesale Price =$80 Selling Price=$125 Salvage Value=$20 ©Copyright 2004 D. Simchi-Levi

Retailer Profit (Buy Back=$55) ©Copyright 2004 D. Simchi-Levi

Retailer Profit (Buy Back=$55) $513,800 ©Copyright 2004 D. Simchi-Levi

Manufacturer Profit (Buy Back=$55) ©Copyright 2004 D. Simchi-Levi

Manufacturer Profit (Buy Back=$55) $471,900 ©Copyright 2004 D. Simchi-Levi

©Copyright 2004 D. Simchi-Levi Supply Contracts Manufacturer Manufacturer DC Retail DC Stores Fixed Production Cost =$100,000 Variable Production Cost=$35 Wholesale Price =$80 Selling Price=$125 Salvage Value=$20 ©Copyright 2004 D. Simchi-Levi

Retailer Profit (Wholesale Price $70, RS 15%) ©Copyright 2004 D. Simchi-Levi

Retailer Profit (Wholesale Price $70, RS 15%) $504,325 ©Copyright 2004 D. Simchi-Levi

Manufacturer Profit (Wholesale Price $70, RS 15%) ©Copyright 2004 D. Simchi-Levi

Manufacturer Profit (Wholesale Price $70, RS 15%) $481,375 ©Copyright 2004 D. Simchi-Levi

©Copyright 2004 D. Simchi-Levi Supply Contracts ©Copyright 2004 D. Simchi-Levi

©Copyright 2004 D. Simchi-Levi Supply Contracts Manufacturer Manufacturer DC Retail DC Stores Fixed Production Cost =$100,000 Variable Production Cost=$35 Wholesale Price =$80 Selling Price=$125 Salvage Value=$20 ©Copyright 2004 D. Simchi-Levi

©Copyright 2004 D. Simchi-Levi Supply Chain Profit ©Copyright 2004 D. Simchi-Levi

©Copyright 2004 D. Simchi-Levi Supply Chain Profit $1,014,500 ©Copyright 2004 D. Simchi-Levi

©Copyright 2004 D. Simchi-Levi Supply Contracts ©Copyright 2004 D. Simchi-Levi

Supply Contracts: Key Insights Effective supply contracts allow supply chain partners to replace sequential optimization by global optimization Buy Back and Revenue Sharing contracts achieve this objective through risk sharing ©Copyright 2004 D. Simchi-Levi

Supply Contracts: Case Study Example: Demand for a movie newly released video cassette typically starts high and decreases rapidly Peak demand last about 10 weeks Blockbuster purchases a copy from a studio for $65 and rent for $3 Hence, retailer must rent the tape at least 22 times before earning profit Retailers cannot justify purchasing enough to cover the peak demand In 1998, 20% of surveyed customers reported that they could not rent the movie they wanted ©Copyright 2004 D. Simchi-Levi

Supply Contracts: Case Study Starting in 1998 Blockbuster entered a revenue sharing agreement with the major studios Studio charges $8 per copy Blockbuster pays 30-45% of its rental income Even if Blockbuster keeps only half of the rental income, the breakeven point is 6 rental per copy The impact of revenue sharing on Blockbuster was dramatic Rentals increased by 75% in test markets Market share increased from 25% to 31% (The 2nd largest retailer, Hollywood Entertainment Corp has 5% market share) ©Copyright 2004 D. Simchi-Levi

What are the drawbacks of RS? Administrative Cost Lawsuit brought by three independent video retailers who complained that they had been excluded from receiving the benefits of revenue sharing was dismissed (June 2002) The Walt Disney Company has sued Blockbuster accusing them of cheating its video unit of approximately $120 million under a four year revenue sharing agreement (January 2003) Impact on sales effort Retailers have incentive to push products with higher profit margins Automotive industry: automobile sales depends on retail effort ©Copyright 2004 D. Simchi-Levi

What are the drawbacks of RS? Retailer may carry substitute or complementary products from other suppliers One supplier offers revenue sharing while the other does not Substitute products: retail will push the product with high margin Complementary products: retailer may discount the product offered under revenue sharing to motivate sales of the other product ©Copyright 2004 D. Simchi-Levi

©Copyright 2004 D. Simchi-Levi Other Contracts Quantity Flexibility Contracts Supplier provides full refund for returned items as long as the number of returns is no larger than a certain quantity Sales Rebate Contracts Supplier provides direct incentive for the retailer to increase sales by means of a rebate paid by the supplier for any item sold above a certain quantity ©Copyright 2004 D. Simchi-Levi