The Value of Information Phil Kaminsky David Simchi-Levi Philip Kaminsky Edith Simchi-Levi

McGraw-Hill/Irwin © 2003 Simchi-Levi, Kaminsky, Simchi-Levi Value of Information  “In modern supply chains, information replaces inventory” –Why is this true? –Why is this false?  Information is always better than no information. Why?  Information –Helps reduce variability –Helps improve forecasts –Enables coordination of systems and strategies –Improves customer service –Facilitates lead time reductions –Enables firms to react more quickly to changing market conditions.

McGraw-Hill/Irwin © 2003 Simchi-Levi, Kaminsky, 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

McGraw-Hill/Irwin © 2003 Simchi-Levi, Kaminsky, Simchi-Levi Figure 2. Point-of-sales Data-Original Figure 3. POS Data After Removing Promotions The Bullwhip Effect and its Impact on the Supply Chain

McGraw-Hill/Irwin © 2003 Simchi-Levi, Kaminsky, Simchi-Levi Figure 4. POS Data After Removing Promotion & Trend The Bullwhip Effect and its Impact on the Supply Chain

McGraw-Hill/Irwin © 2003 Simchi-Levi, Kaminsky, 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

McGraw-Hill/Irwin © 2003 Simchi-Levi, Kaminsky, Simchi-Levi Increasing Variability of Orders Up the Supply Chain Lee, H, P. Padmanabhan and S. Wang (1997), Sloan Management Review

McGraw-Hill/Irwin © 2003 Simchi-Levi, Kaminsky, 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.

McGraw-Hill/Irwin © 2003 Simchi-Levi, Kaminsky, Simchi-Levi What are the Causes….  Promotional sales –Forward buying  Volume and transportation discounts –Batching  Inflated orders –IBM Aptiva orders increased by 2-3 times when retailers thought that IBM would be out of stock over Christmas –Motorola cell phones

McGraw-Hill/Irwin © 2003 Simchi-Levi, Kaminsky, Simchi-Levi What are the Causes….  Single retailer, single manufacturer. –Retailer observes customer demand, Dt. –Retailer orders q t from manufacturer. RetailerManufacturer DtDt qtqt L

McGraw-Hill/Irwin © 2003 Simchi-Levi, Kaminsky, Simchi-Levi What are the Causes….  Promotional sales  Volume and transportation discounts  Inflated orders  Demand forecasting –Order-up-to points are modified as forecasts change – orders increase more than forecasts  Long cycle times –Long lead times magnify this effect

McGraw-Hill/Irwin © 2003 Simchi-Levi, Kaminsky, Simchi-Levi What are the Causes….  Single retailer, single manufacturer. –Retailer observes customer demand, Dt. –Retailer orders q t from manufacturer. RetailerManufacturer DtDt qtqt L

McGraw-Hill/Irwin © 2003 Simchi-Levi, Kaminsky, Simchi-Levi How big is the increase?  Suppose a P period moving average is used.

McGraw-Hill/Irwin © 2003 Simchi-Levi, Kaminsky, Simchi-Levi Var(q)/Var(D): For Various Lead Times L=5 L=3 L= L=5 L=3 L=1

McGraw-Hill/Irwin © 2003 Simchi-Levi, Kaminsky, Simchi-Levi Consequences….  Increased safety stock  Reduced service level  Inefficient allocation of resources  Increased transportation costs

McGraw-Hill/Irwin © 2003 Simchi-Levi, Kaminsky, Simchi-Levi Multi-Stage Supply Chains  Consider a multi-stage supply chain: –Stage i places order q i to stage i+1. –L i is lead time between stage i and i+1. Retailer Stage 1 Manufacturer Stage 2 Supplier Stage 3 q o =D q1q1 q2q2 L1L1 L2L2

McGraw-Hill/Irwin © 2003 Simchi-Levi, Kaminsky, Simchi-Levi Multi stage systems  Centralized: each stage bases orders on retailer’s forecast demand.  Decentralized: each stage bases orders on previous stage’s demand

McGraw-Hill/Irwin © 2003 Simchi-Levi, Kaminsky, Simchi-Levi Multi-Stage Systems:Var(q k )/Var(D) Dec, k=5 Cen, k=5 Dec, k=3 Cen, k=3 k=1

McGraw-Hill/Irwin © 2003 Simchi-Levi, Kaminsky, Simchi-Levi The Bullwhip Effect: Managerial Insights  Exists, in part, due to the retailer’s need to estimate the mean and variance of demand.  The increase in variability is an increasing function of the lead time.  The more complicated the demand models and the forecasting techniques, the greater the increase.  Centralized demand information can significantly reduce the bullwhip effect, but will not eliminate it.

McGraw-Hill/Irwin © 2003 Simchi-Levi, Kaminsky, Simchi-Levi Coping with the Bullwhip Effect in Leading Companies  Reduce uncertainty –POS –Sharing information –Sharing forecasts and policies  Reduce variability –Eliminate promotions –Year-round low pricing  Reduce lead times –EDI –Cross docking  Strategic partnerships –Vendor managed inventory –Data sharing

McGraw-Hill/Irwin © 2003 Simchi-Levi, Kaminsky, Simchi-Levi Example: Quick Response at Benetton  Benetton, the Italian sportswear manufacturer, was founded in In 1975 Benetton had 200 stores across Italy.  Ten years later, the company expanded to the U.S., Japan and Eastern Europe. Sales in 1991 reached 2 trillion.  Many attribute Benetton’s success to successful use of communication and information technologies.

McGraw-Hill/Irwin © 2003 Simchi-Levi, Kaminsky, Simchi-Levi Example: Quick Response at Benetton  Benetton uses an effective strategy, referred to as Quick Response, in which manufacturing, warehousing, sales and retailers are linked together. In this strategy a Benetton retailer reorders a product through a direct link with Benetton’s mainframe computer in Italy.  Using this strategy, Benetton is capable of shipping a new order in only four weeks, several week earlier than most of its competitors.

McGraw-Hill/Irwin © 2003 Simchi-Levi, Kaminsky, Simchi-Levi How Does Benetton Cope with the Bullwhip Effect? 1. Integrated Information Systems Global EDI network that links agents with production and inventory information EDI order transmission to HQ EDI linkage with air carriers Data linked to manufacturing 2. Coordinated Planning Frequent review allows fast reaction Integrated distribution strategy

McGraw-Hill/Irwin © 2003 Simchi-Levi, Kaminsky, Simchi-Levi Information for Effective Forecasts  Pricing, promotion, new products –Different parties have this information –Retailers may set pricing or promotion without telling distributor –Distributor/Manufacturer might have new product or availability information  Collaborative Forecasting addresses these issues.

McGraw-Hill/Irwin © 2003 Simchi-Levi, Kaminsky, Simchi-Levi Information for Coordination of Systems  Information is required to move from local to global optimization  Questions: –Who will optimize? –How will savings be split?  Information is needed : –Production status and costs –Transportation availability and costs –Inventory information –Capacity information –Demand information

McGraw-Hill/Irwin © 2003 Simchi-Levi, Kaminsky, Simchi-Levi Locating Desired Products  How can demand be met if products are not in inventory? –Locating products at other stores –What about at other dealers?  What level of customer service will be perceived?

McGraw-Hill/Irwin © 2003 Simchi-Levi, Kaminsky, Simchi-Levi Lead-Time Reduction  Why? –Customer orders are filled quickly –Bullwhip effect is reduced –Forecasts are more accurate –Inventory levels are reduced  How? –EDI –POS data leading to anticipating incoming orders.

McGraw-Hill/Irwin © 2003 Simchi-Levi, Kaminsky, Simchi-Levi Information to Address Conflicts  Lot Size – Inventory: –Advanced manufacturing systems –POS data for advance warnings  Inventory -- Transportation: –Lead time reduction for batching –Information systems for combining shipments –Cross docking –Advanced DSS  Lead Time – Transportation: –Lower transportation costs –Improved forecasting –Lower order lead times  Product Variety – Inventory: –Delayed differentiation  Cost – Customer Service: –Transshipment