The Value of Information Designing & Managing the Supply Chain Chapter 4 Byung-Hyun Ha

Outline  Barilla SpA  Introduction  The Bullwhip Effect  Effective Forecast  Information for the Coordination of Systems

Barilla SpA  Barilla SpA is the world’s largest pasta manufacturer  The company sells to a wide range of Italian retailers, primarily through third party distributors  During the late 1980s, Barilla suffered increasing operational inefficiencies and cost penalties that resulted from large week-to-week variations in its distributors’ order patterns

Distribution Channels

Weekly Demand for Barilla Dry Products

Demand Fluctuations  The extreme fluctuation is truly remarkable when one considers the underlying aggregate demand for pasta in Italy  Causes of Demand Fluctuations  Transportation discounts  Volume discount  Promotional activity  No minimum or maximum order quantities  Product proliferation  Long order lead times  Poor customer service rates  Poor communication

Demand Fluctuations  Impact of demand fluctuation  Inefficient production or excess finished goods inventory  Utilization of central distribution is low Workers Equipment  Transportation costs are higher than necessary

Just-in-Time Distribution (JITD) Program  JITD proposal  Decision-making authority for determining shipments from Barilla to a distributor would transfer from the distributor to Barilla  Rather than simply filling orders specified by the distributor, Barilla would monitor the flow of its product through the distributor’s warehouse, and then decide what to ship to the distributor and when to ship it  Evaluation of the proposal  JITD proposal as a mechanism for reducing these costs?  Why should this work?  How does it work?  What makes Barilla think that it can do a better job of determining a good product/delivery sequence than its distributors?

JITD Program  Resistance from the Distributors  “Managing stock is my job; I don’t need you to see my warehouse or my figures.”  “I could improve my inventory and service level myself if you would deliver my orders more quickly; I would place my order and you would deliver within 36 hours.”  “We would be giving Barilla the power to push products into our warehouse just so that Barilla can reduce its costs.”  Resistance from Sales and Marketing  “Our sales levels would flatten if we put this program in place.”  “How can we get the trade to push Barilla product to retailers if we don’t offer some sort of incentive?”  “If space is freed up in our distributors’ warehouses…the distributors would then push our competitors’ product more than ours.”  “…the distribution organization is not yet ready to handle such a sophisticated relationship.”

Introduction  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

Increasing Variability of Orders  Lee, Padmanabhan, Wang (1997)

Bullwhip Effect  Order variability is amplified up the supply chain; upstream echelons face higher variability  Main factors contributing to increase in variability  Demand forecasting  Lead time  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

Impact of Promotional Sales  Order pattern of a single color television model sold by a large electronics manufacturer to one of its accounts, a national retailer order stream

Impact of Promotional Sales Point-of-sales Data-Original POS Data After Removing Promotions

Demand Forecasting & Lead Time  Single retailer, single manufacturer  Retailer observes customer demand, D t  Retailer orders q t from manufacturer  Suppose a P period moving average forecasting is used RetailerManufacturer DtDt qtqt L Chen et al. 2000

Demand Forecasting & Lead Time  Var(q)/Var(D) for various lead times L=5 L=3 L= L=5 L=3 L=1 P Var(q) Var(D)

Demand Forecasting & Lead Time  Multi-stage supply chains  Stage i places order q i to stage i+1  L i is lead time between stage i and i+1  Centralized: each stage bases orders on retailer’s forecast demand  Decentralized: each stage bases orders on previous stage’s demand Retailer Stage 1 Manufacturer Stage 2 Supplier Stage 3 q o =D q1q1 q2q2 L1L1 L2L2

Demand Forecasting & Lead Time  Var(q k )/Var(D) with regard to stages Var(q k ) Var(D) P

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

Coping with the Bullwhip Effect  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

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

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

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?

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.

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