The Value of Information

The Value of Information
Chapter 5 The Value of Information

5.1 Introduction Value of using any type of information technology
Potential availability of more and more information throughout the supply chain Implications this availability on effective design and management of the integrated supply chain

Information Types Inventory levels Orders Production Delivery status

More Information Helps reduce variability in the supply chain.
Helps suppliers make better forecasts, accounting for promotions and market changes. Enables the coordination of manufacturing and distribution systems and strategies. Enables retailers to better serve their customers by offering tools for locating desired items. Enables retailers to react and adapt to supply problems more rapidly. Enables lead time reductions.

5.2 Bullwhip Effect While customer demand for specific products does not vary much Inventory and back-order levels fluctuate considerably across their supply chain P&G’s disposable diapers case Sales quite flat Distributor orders fluctuate more than retail sales Supplier orders fluctuate even more

4-Stage Supply Chain FIGURE 5-5: The supply chain

Effect of Order Variability
FIGURE 5-6: The increase in variability in the supply chain

Factors that Contribute to the Variability - Demand Forecasting
Periodic review policy Characterized by a single parameter, the base-stock level. Base-stock level = Average demand during lead time and review period + a multiple of the standard deviation of demand during lead time and review period (safety stock) Estimation of average demand and demand variability done using standard forecast smoothing techniques. Estimates get modified as more data becomes available Safety stock and base-stock level depends on these estimates Order quantities are changed accordingly increasing variability

Factors that Contribute to the Variability – Lead Time
Increase in variability magnified with increasing lead time. Safety stock and base-stock levels have a lead time component in their estimations. With longer lead times: a small change in the estimate of demand variability implies a significant change in safety stock and base-stock level, which implies significant changes in order quantities leads to an increase in variability

Factors that Contribute to the Variability – Batch Ordering
Retailer uses batch ordering, as with a (Q,R) or a min-max policy Wholesaler observes a large order, followed by several periods of no orders, followed by another large order, and so on. Wholesaler sees a distorted and highly variable pattern of orders. Such pattern is also a result of: Transportation discounts with large orders Periodic sales quotas/incentives

Factors that Contribute to the Variability – Price Fluctuations
Retailers often attempt to stock up when prices are lower. Accentuated by promotions and discounts at certain times or for certain quantities. Such Forward Buying results in: Large order during the discounts Relatively small orders at other time periods

Factors that Contribute to the Variability – Inflated Orders
Inflated orders during shortage periods Common when retailers and distributors suspect that a product will be in short supply and therefore anticipate receiving supply proportional to the amount ordered. After period of shortage, retailer goes back to its standard orders leads to all kinds of distortions and variations in demand estimates

Quantifying the Bullwhip
Consider a two-stage supply chain: Retailer who observes customer demand Retailer places an order to a manufacturer. Retailer faces a fixed lead time order placed at the end of period t Order received at the start of period t+L. Retailer follows a simple periodic review policy retailer reviews inventory every period places an order to bring its inventory level up to a target level. the review period is one

Quantifying the Bullwhip
Base-Stock Level = L x AVG + z x STD x √L Order up-to point = If the retailer uses a moving average technique,

Quantifying the Increase in Variability
Var(D), variance of the customer demand seen by the retailer Var(Q), variance of the orders placed by that retailer to the manufacturer When p is large and L is small, the bullwhip effect is negligible. Effect is magnified as we increase the lead time and decrease p.

Lower Bound on the Increase in Variability Given as a Function of p
FIGURE 5-7: A lower bound on the increase in variability given as a f unction of p

Impact of Variability Example
Assume p = 5, L=1 Assume p = 10, L=1 Increasing the number of observations used in the moving average forecast reduces the variability of the retailer order to the manufacturer

Impact of Centralized Information on Bullwhip Effect
Centralize demand information within a supply chain Provide each stage of supply chain with complete information on the actual customer demand Creates more accurate forecasts rather than orders received from the previous stage

Variability with Centralized Information
Var(D), variance of the customer demand seen by the retailer Var(Qk), variance of the orders placed by the kth stage to its Li, lead time between stage i and stage i + 1 Variance of the orders placed by a given stage of a supply chain is an increasing function of the total lead time between that stage and the retailer

Variability with Decentralized Information
Retailer does not make its forecast information available to the remainder of the supply chain Other stages have to use the order information Variance of the orders: becomes larger up the supply chain increases multiplicatively at each stage of the supply chain.

Managerial Insights Variance increases up the supply chain in both centralized and decentralized cases Variance increases: Additively with centralized case Multiplicatively with decentralized case Centralizing demand information can significantly reduce the bullwhip effect Although not eliminate it completely!!

Increase in Variability for Centralized and Decentralized Systems
FIGURE 5-8: Increase in variability for centralized and decentralized systems

Methods for Coping with the Bullwhip
Reducing uncertainty. Centralizing information Reducing variability. Reducing variability inherent in the customer demand process. “Everyday low pricing” (EDLP) strategy.

Methods for Coping with the Bullwhip
Lead-time reduction Lead times magnify the increase in variability due to demand forecasting. Two components of lead times: order lead times [can be reduced through the use of cross-docking] Information lead times [can be reduced through the use of electronic data interchange (EDI).] Strategic partnerships Changing the way information is shared and inventory is managed Vendor managed inventory (VMI) Manufacturer manages the inventory of its product at the retailer outlet VMI the manufacturer does not rely on the orders placed by a retailer, thus avoiding the bullwhip effect entirely.

5.3 Information Sharing And Incentives
Centralizing information will reduce variability Upstream stages would benefit more Unfortunately, information sharing is a problem in many industries Inflated forecasts are a reality Forecast information is inaccurate and distorted Forecasts inflated such that suppliers build capacity Suppliers may ignore the forecasts totally

Contractual Incentives to Get True Forecasts from Buyers
Capacity Reservation Contract Buyer pays to reserve a certain level of capacity at the supplier A menu of prices for different capacity reservations provided by supplier Buyer signals true forecast by reserving a specific capacity level Advance Purchase Contract Supplier charges special price before building capacity When demand is realized, price charged is different Buyer’s commitment to paying the special price reveals the buyer’s true forecast

5.4 Effective Forecasts Retailer forecasts
Typically based on an analysis of previous sales at the retailer. Future customer demand influenced by pricing, promotions, and release of new products. Including such information will make forecasts more accurate. Distributor and manufacturer forecasts Influenced by factors under retailer control. Promotions or pricing. Retailer may introduce new products into the stores Closer to actual sales – may have more information Cooperative forecasting systems Sophisticated information systems iterative forecasting process all participants in the supply chain collaborate to arrive at an agreed-upon forecast All parties share and use the same forecasting tool

5.5 Information for the Coordination of Systems
Many interconnected systems manufacturing, storage, transportation, and retail systems the outputs from one system within the supply chain are the inputs to the next system trying to find the best set of trade-offs for any one stage isn’t sufficient. need to consider the entire system and coordinate decisions Systems are not coordinated each facility in the supply chain does what is best for that facility the result is local optimization.

Global Optimization Issues: Methods to address issues:
Who will optimize? How will the savings obtained through the coordinated strategy be split between the different supply chain facilities? Methods to address issues: Supply contracts Strategic partnerships

5.6 Locating Desired Products
Meet customer demand from available retailer inventory What if the item is not in stock at the retailer? Being able to locate and deliver goods is sometimes as effective as having them in stock If the item is available at the competitor, then this is a problem Other Methods Inventory pooling (Chapter 7) Distributor Integration (Chapter 8)

5.7 Lead-Time Reduction Numerous benefits:
The ability to quickly fill customer orders that can’t be filled from stock. Reduction in the bullwhip effect. More accurate forecasts due to a decreased forecast horizon. Reduction in finished goods inventory levels Many firms actively look for suppliers with shorter lead times Many potential customers consider lead time a very important criterion for vendor selection. Much of the manufacturing revolution of the past 20 years led to reduced lead times Other methods: Distribution network designs (Chapter 6) Effective information systems (e.g., EDI) Strategic partnering (Chapter 8) (Sharing point-of-sale (POS) data with supplier)

5.8 Information and Supply Chain Trade-Offs
Conflicting objectives in the supply chains Designing the supply chain with conflicting goals

Wish-Lists of the Different Stages
Raw material suppliers Stable volume requirements and little variation in mix Flexible delivery times Large volume demands Manufacturing High productivity through production efficiencies and low production costs Known future demand pattern with little variability. Materials, warehousing, and outbound logistics Minimizing transportation costs through: quantity discounts, minimizing inventory levels, quickly replenishing stock. Retailers Short order lead times and efficient, accurate order delivery Customers In-stock items, enormous variety, and low prices.

Trade-Offs: Inventory-Lot Size
Manufacturers would like to have large lot sizes. Per unit setup costs are reduced Manufacturing expertise for a particular product increases Processes are easier to control. Modern practices [Setup time reduction, Kanban and CONWIP] Reduce inventories and improve system responsiveness. Advanced manufacturing systems make it possible for manufacturers to meet shorter lead times and respond more rapidly to customer needs. Manufacturer should have as much time as possible to react to the needs of downstream supply chain members. Distributors/retailers can have factory status and manufacturer inventory data: they can quote lead times to customers more accurately. develops an understanding of, and confidence in, the manufacturers’ ability. allows reduction in inventory in anticipation of manufacturing problems

Trade-offs Inventory-Transportation Costs
Company operates its own fleet of trucks. Fixed cost of operation + variable cost Carrying full truckloads minimizes transportation costs. Outside firm is used for shipping quantity discounts TL shipping cheaper than LTL shipping In many cases demand is much less than TL Items sit for a long time before consumption leading to higher inventory costs. Trade-off can’t be eliminated completely. Use advanced information technology to reduce this effect. Distribution control systems allow combining shipments of different products from warehouses to stores Cross-docking, Decision-support systems allow appropriate balance between transportation and inventory costs

Trade-offs Lead Time-Transportation Costs
Transportation costs lowest when large quantities of items are transported between stages of the supply chain. Hold items to accumulate enough to combine shipments Lead times can be reduced if items are transported immediately after they are manufactured or arrive from suppliers. Cannot be completely eliminated Information can be used to reduce its effect. Control transportation costs reducing the need to hold items until a sufficient number accumulate. Improved forecasting techniques and information systems reduce the other components of lead time may not be essential to reduce the transportation component.

Trade-Offs Product Variety-Inventory
Higher product variety makes supply chain decisions more complex Better for meeting customer demand Typically leads to higher inventories Strategies: Delayed Differentiation (Chapter 6) Ship generic products as far as possible down the supply chain Design for logistics (Chapter 11)

Trade-Offs Cost-Customer Service
Reducing inventories, manufacturing costs, and transportation costs typically comes at the expense of customer service Customer service could mean the ability of a retailer to meet a customer’s demand quickly Strategies: transshipping direct shipping from warehouses to customers Charging price premiums for customized products

5.9 Decreasing Marginal Value of Information
Obtaining and sharing information is not free. Many firms are struggling with exactly how to use the data they collect through loyalty programs, RFID readers, and so on. Cost of exchanging information versus the benefit of doing so. May not be necessary to exchange all of the available information, or to exchange information continuously. Decreasing marginal value of additional information In multi-stage decentralized manufacturing supply chains many of the performance benefits of detailed information sharing can be achieved if only a small amount of information is exchanged between supply chain participants. Exchanging more detailed information or more frequent information is costly. Understand the costs and benefits of particular pieces of information How often this information is collected How much of this information needs to be stored How much of this information needs to be shared In what form it needs to shared

Summary The bullwhip effect suggests that variability in demand increases as one moves up in the supply chain. Increase in variability causes significant operational inefficiencies Specific techniques to “counteract” bullwhip effect Information sharing, i.e., centralized demand information. Incentives to share credible forecasts Alignments of expectations associated with the use of information. Interaction of various supply chain stages. A series of trade-offs both within and between the different stages. Information is the key enabler of integrating the different supply chain stages Information can be used to reduce the necessity of many of these trade-offs

CASE: Reebok NFL Replica Jerseys: A Case for Postponement
Stephen C. Graves, John C. W. Parsons MIT, Cambridge MA, USA McKinsey & Co., Toronto, Ontario, Canada

Stephen C. Graves Copyright 2003. All Rights Reserved
Planning Question How should Reebok plan and manage inventory to manage costs while providing the flexibility required to meet demand for NFL Replica jerseys? Stephen C. Graves Copyright All Rights Reserved

Outline of Case Discussion
Discuss business context, nature of demand, the sales cycle, key success factors, failure modes Discuss supply chain, planning cycle, planning challenges Frame as single-season planning problem; relate to newsvendor model Develop approach and key insights with NE Patriots example Report on findings for NFL Wrap up and summary of learnings Stephen C. Graves Copyright All Rights Reserved

Licensed Apparel Business
Situation Impact Reebok received an NFL exclusive license in 2000 Highly seasonal & very uncertain demand for player jerseys Teams are more predictable, but correlated with success Hot-market players and teams emerge during season High margins, fashion item Demand driven by availability Unsold jerseys can become instantly obsolete – trades; design changes No direct competition for product – 100% market share Demand is concentrated over five month period If product is not quickly available to meet demand the opportunity is lost Lost sales cost more than inventory overstocks, but come with a high risk of obsolescence What are the characteristics of this business? Distinctive elements? How could Reebok lose its license? Stephen C. Graves Copyright All Rights Reserved

Nature of Consumer Demand
Sales are highest at start of season, August – Sept. “Hot market” players and teams emerge over course of season Increase at end of season for contending teams & stars: Christmas, playoffs and Super Bowl Off season is slower, with demand spikes for big-name player movements This reflects sales at retailers Stephen C. Graves Copyright All Rights Reserved

Annual Sales Cycle Jan - Feb May - Aug March - April Sept - Dec Retailers get discount to place pre-season orders for delivery in May Limited ordering by retailers to re-balance stocks; some short LT orders to respond to player movements Retailers order to position stock in their DC’s and stores in anticipation of season, and expect 3 – 4 week delivery LT Retailers order to replenish stores, chase the demand, and expect 1 – 2 week LT for Hot Market items This is Reebok’s sales cycle – sales by Reebok to retailers. Stephen C. Graves Copyright All Rights Reserved

Reebok warehouse in Indy Contract manufacturers – Asia, but more and more are being moved to central America and Caribbean (Honduras, El Salvador, DR) Many contract manufacturers – why? Most sales through large sports retailers, and thru distributors. Stephen C. Graves Copyright All Rights Reserved

Indy has capability to do screen printing – for NFL roughly jerseys per week. Case does not tell us enough about whether or not this is constraining….but does indicate there are options to outsource locally at 10% premium. It’s more expensive to screen print in Indy than at CM Reebok has postponement opportunity -- but it costs more to dress in Indy than at CM, and there can be capacity and/or QR considerations too. Stephen C. Graves Copyright All Rights Reserved

Purchasing Cycle July-Oct Reebok places orders on CMs for April delivery; primarily orders blanks (~20% of annual buy) Reebok places orders for dressed jerseys based on retailers’ advance orders & remaining inventory (~ 15 – 20%) Reebok orders dressed & blank jerseys, based on forecasts and inventory targets Last purchase phase is most challenging Jan-Feb Mar-June Note ordering in July 2005 for 2006 season! During season (Sept. – Nov.) Reebok can expedite jerseys from CMs – by pulling forward the supply; that is in 2005 season, they can pull forward jerseys that have been ordered for 2006 season. This is expensive (air freight, special handling ,etc.) so Reebok does not plan on this – but will do to avoid shortages and lost sales. Stephen C. Graves Copyright All Rights Reserved

Single-Season Planning Problem
What volume and mix of jerseys to purchase during March to June? Planning framework: Given forecasts (and advanced orders) for team and players Decide inventory targets for dressed and blank jerseys for season Place orders guided by these targets Revise forecasts (say) each month based on current information; update targets accordingly How should we set inventory targets? Focus discussion on March to June time window. Framework – how we wish to frame the problem for purposes of building a model? Given framework, how do we set inventory targets? Suggest using newsvendor model, as we have a single period problem, i.e., effectively a single purchase opportunity. This is key to have students think about how they would plan and manage procurement and inventories – with lots of SKUs; multiple CMs with constraints on what they can do; time delays due to shipping; and forecast dynamics as season gets closer in time. Simple approach – view as a single period planning problem; create inventory targets for each player and for each team; place orders to build pipeline of stock, with objective to build to these targets; revise targets as get better forecasts…. Stephen C. Graves Copyright All Rights Reserved

Representative Numbers for Replica Jersey
Suggested Retail Price ---- more than $50 Wholesale Price = $24.00 Blank Cost = $9.50 Cost to dress at CM = + $1.40 Cost to dress at Reebok = + $2.40 Salvage Value for unsold Dressed Jersey = $7 Holding Cost for unsold Blank Jersey = $1.04 Salvage Value for unsold Blank Jersey = $ = $8.46 Representative but not actual numbers; the salvage numbers are for Reebok, not for the retailer. In general, quite difficult to develop estimates for these salvage values. Salvage value of blank jersey is cost of a blank, net of cost to hold in inventory for a year. ($9.50 – = 8.46) --- if I have leftover blanks, Reebok will use next season, assuming no changes in design; but Reebok will incur a holding cost for having the jersey in stock for about one year. For dressed – it can reflect what a jersey could be sold at discount for and/or cost of jersey net of risk-adjusted holding cost (to capture chance of becoming obsolete). The $7 reflects an estimate of what a discounter would pay Reebok to take the leftover dressed jersey. Dressed have lower savage value – because of risk that player retires, gets traded, stinks, etc. Note that at retailer – end of season mark down is about $35; and they can’t return to Reebok. Stephen C. Graves Copyright All Rights Reserved

2003 Forecast – As of March 1, 2003 What should inventory target be for dressed jerseys for each player? And blank jerseys for team? Note that these 6 players could be manufactured to stock, and have quantities that exceed minimum order quantity of 1738 These questions occur for each team – and each team has similar numbers, 5 – 6 players that have significant demand, and then all the others. Developing such a forecast is most difficult – largely judgmental, with some history of past sales; estimates of standard deviations are best guesses of the forecast error, again based on past years. For analysis will assume a normal distribution – which would be a typical, reasonable assumption. Ideally, one would look at forecast errors from prior years and see if this is a good fit. CMs have minimum order quantities of 1728 Stephen C. Graves Copyright All Rights Reserved

What’s the Objective? Expected revenue: $24*E[Dressed_Sold] + 24*E[Blanks_Sold] + $7*E[Dressed_Unsold] + $8.46* E[Blanks_Unsold] Expected Cost: $9.50*Blanks + $10.90*Dressed + $2.40*E[Blanks_Sold] Decisions are what to buy, for dressed and blanks – so how do we evaluate? This presumes no costs for stock outs, beyond lost sales. To compute the number of blanks we dress in Indy --- it’s how many we buy, net of how many we don’t sell. Stephen C. Graves Copyright All Rights Reserved

Model Calculations: Dressed Jerseys
For each star player, we will buy Q dressed jerseys. Assume demand is normally distributed with known mean and sigma. Unmet demand is just partial loss function that we used for fill rate calculations. Stephen C. Graves Copyright All Rights Reserved

Model Approximation: Blank Jerseys
NOTE – one could also just build a simulation to evaluate this; observe demand realization, and then solve simple optimization whereby we apply dressed to the demand, and then satisfy any unmet demand with blanks. For blanks we need approximations --- blanks can be used for other players, plus unmet demand for star players. To model the distribution of demand for blanks, we get expectation by adding in the expected unmet demand for star players. We approximate the sigma – assuming the same coefficient of variation as for the other players. Then rest of equations are the same. At this point – look at spreadsheet and see what students propose….. Stephen C. Graves Copyright All Rights Reserved

Newsvendor-based Approach
Solve newsvendor for entire team to get total quantity of blanks and dressed jerseys to buy, and more importantly: Get service measure for team = probability of not stocking out (critical ratio) Solve newsvendor for each star player to determine how many dressed jerseys to procure from CM, where underage cost reflects option to use blanks Given the dressed jersey quantities, re-solve newsvendor for entire team to find blank jerseys to procure Prior to discussing this approach, the class can discuss how to solve this problem. They should recognize that we can view as a single period problem, and use a newsboy model and/or analyses. One idea would be to solve for a newsboy for each player – e.g., solve the newsboy for Tom Brady, then for Ty Law, etc. this would give the number of dressed jerseys to order from CM for these star players. One need to also order for the “other players;” one idea is to order blanks for these other players, and we could also apply a newsboy model to get this estimate. But then ask how you might do better? Observe that blanks can also be used for the star players – and thus you’d order less dressed jerseys from CM, and postpone the finishing of these jerseys until you see the actual demand over the season. But how can we investigate this postponement opportunity? This is an approximate but pragmatic approach – and relatively easy to explain to managers and students. We first decide how many jerseys to order in total for the team, assuming we’ll have enough blanks to meet demand for any of the players. This analysis gives us the stock out probability for the team. We then consider each star player to determine how many dressed jerseys to procure – where we assume that if we run out of dressed jerseys, we’ll meet shortfall with blanks (if available). We use the team’s stock out probability to estimate the probability that blanks are available. Given the number of dressed jerseys for the star players and the total number of team jerseys, we can then find the actual number of blanks to order. Key assumptions – demands are independent. And the prob. of blanks being available is independent of demand for a star player Stephen C. Graves Copyright All Rights Reserved

Newsvendor Model with Risk Pooling for NE Patriots
Determine total quantity to buy, assuming blank jerseys are the marginal units to buy For blank jerseys: Cost of overage = $9.50 – 8.46 = 1.04 Cost of underage = $24.00 – = 12.10 Prob. of not stocking out of blanks = 0.92 Critical ratio – cost of underage/(cost of overage + cost of underage) = probability of not stocking out. Note – if we just bought blanks, then we would buy enough to cover 92nd percentile of total team demand Stephen C. Graves Copyright All Rights Reserved

Given the stock-out probability for the team: Consider each dressed jersey (i.e. for each star player): Cost of overage = $10.90 – 7.00 = 3.90 Cost of underage if blank available = $1.00 Cost of underage if blank not available = $24.00 – = 13.10 Approx. cost of underage = .92*$ ( )*$13.10=$1.96 Critical ratio = 0.33 Newsvendor purchases dressed jerseys Key insight here is that you have a very low critical ratio for dressed jerseys – you expect to stock out, and will cover shortages with blanks. This is counter to what Reebok was doing. They were primarily buying blanks for the “other players” with low volume demand. Stephen C. Graves Copyright All Rights Reserved

Given the quantities for dressed jerseys, determine demand for blanks: the unmet demand for star players plus demand for the other players Solve newsvendor for blanks: Cost of overage = $9.50 – 8.46 = 1.04 Cost of underage = $24.00 – = 12.10 Prob. of not stocking out of blanks = 0.92 Newsvendor purchases blank jerseys Expected profit is $1.04 M Once we decide the dressed jerseys (inflexible inventory), then we can decide the blanks (flexible inventory) to cover unmet demand, plus the other players. We use an approximation to characterize the distribution of unmet demand, and assume independence (strong assumption) to get a total demand distribution for blanks. Stephen C. Graves Copyright All Rights Reserved

Results: Newsvendor with Risk Pooling Purchase E[sold] E[unsold] E[short] Dressed 51227 44265 6962 Blanks 70932 42712 28221 Total 122159 86976 35183 703 Results: Simple Newsvendor Purchase E[sold] E[unsold] E[short] Dressed 87531 60244 27287 4161 Blanks 38027 22898 15129 377 Total 125558 83142 42416 4537 Use spreadsheet model to get these results. Note that simple newsvendor is just for comparison – suppose we use blanks just for “other players” and then solve independent newsboy problems for popular players to get orders for dressed jerseys and for other players to get order for blanks. This is a proxy for what Reebok had done. Ex Profit is 1.04 million for risk pooling NV; .94 million for newsboy – but this could increase to 1.0 million if unused blanks were used for unmet demand of star players. Stephen C. Graves Copyright All Rights Reserved

NV model with Risk Pooling Naïve NV model Detailed comparison of plans – much different policy being proposed, with significant increase in profit. Note for naïve NB, the expected profit increase to $999K under the assumption that unused blanks would be used to meet unmet demand for popular players – which is a reasonable assumption. In this case the improvement is still 4%. Stephen C. Graves Copyright All Rights Reserved

Observations from Example
Expected profit increases by 5 to 10% over current practice & naïve newsvendor Much different solution strategy: blanks used not just for “other” players but also as postponement option Many more jerseys dressed in Indianapolis Mix of leftovers is largely blanks Value of newsvendor perspective Also note application of newsboy model! Leftover blanks are much less risky that leftover dressed jerseys. Stephen C. Graves Copyright All Rights Reserved

Global Comparison: Model vs. Actual
Ex post analysis of 2003 season using model for 31 teams Applied model using forecast available on Mar. 1, 2003 Only able to observe sales in 2003 and volume “pulled forward” Actual Risk-Pool NV Naïve NV Sales 100 In-stock 85 95 96 Under-stock 15 5 4 Over-stock 27 28 47 Note that model does not incorporate additional information that would be available post March 1. During 2003 season Reebok expedited shipments (cost of $1 per jersey) from purchases placed in summer for 2004 season – pulled this volume forward to 2003! This is what is captured in under-stock figure. The over-stock is the inventory left over as a percentage of actual sales. The in-stock number is the per cent of demand met from planned stock. For NB model we assume we can also pull forward volume to meet unmet demand Stephen C. Graves Copyright All Rights Reserved

Global Comparison: Model vs. Actual
Risk-pool NV increases profits by 6% (naïve NV increases profits by 2%) Plus A less risky mix of remaining jerseys at end of season Over-stock Profile Actual Risk-Pool NV Naïve NV Dressed jerseys 59% 17% 60% Blanks jerseys 41% 83% 40% Total 100% Note that actual and risk-pool NB had about the same amount of inventory leftover; whereas naïve NB had 75%more leftover. This difference in inventory mix was viewed as significant as the profit increase by reebok Stephen C. Graves Copyright All Rights Reserved

Conclusion Context – fashion items, seasonal, high uncertainty in demand Newsvendor with Risk Pooling provides way to plan for and exploit postponement options Results in higher profits, 95% service level, better mix of end-of-year inventory. Results in much different inventory plan – greater use of blanks and local finishing Project resulted in planning tool and new insights for planning for Reebok, and a thesis! A second project focused on forecasting Stephen C. Graves Copyright All Rights Reserved

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