Chapter 12: Determining the Optimal Level of Product Availability

Chapter 12: Determining the Optimal Level of Product Availability
Part IV: Planning and Managing Inventories in A Supply Chain Chapter 12: Determining the Optimal Level of Product Availability Cost of holding inventory increases in a non linear fashion with an increased product availability. The marginal cost of increased product availability is increasing. Thus it is important to obtain the appropriate level of product availability. © Chopra / OPNS 455 / Optimal Availability

Mattel, Inc. & Toys “R” Us Mattel was hurt last year by inventory cutbacks at Toys “R” Us, and officials are also eager to avoid a repeat of the 1998 Thanksgiving weekend. Mattel had expected to ship a lot of merchandise after the weekend, but retailers, wary of excess inventory, stopped ordering from Mattel. That led the company to report a $500 million sales shortfall in the last weeks of the year ... For the crucial holiday selling season this year, Mattel said it will require retailers to place their full orders before Thanksgiving. And, for the first time, the company will no longer take reorders in December, Ms. Barad said. This will enable Mattel to tailor production more closely to demand and avoid building inventory for orders that don't come. - Wall Street Journal, Feb. 18, 1999 We want to determine whether Mattel made the right decision. For this we need to understand a variety of decisions and their impact on profits. © Chopra / OPNS 455 / Optimal Availability

Key Questions How much should Toys R Us order given demand uncertainty? How much should Mattel order? Will Mattel’s action help or hurt profitability? What actions can improve supply chain profitability? Supplier Stage Supplier Stage Retailer Stage Retailer sets: Retail price Level of availability at store Supplier sets: Wholesale price schedule Available quantity/capacity Supply/allocation rules © Chopra / OPNS 455 / Optimal Availability Any change in supplier settings will impact retailer setting. This, in turn, ends up impacting supplier profits. Thus, Supplier must take all this into account when making decisions. All answers are based on understanding how the Retailer makes the level of availability decision.

Drivers of Supply Chain Performance
Efficiency Responsiveness Supply chain structure Facilities Transportation Inventory Seasonal Inventory Cycle Inventory Safety Inventory Level of Product Availability Newsboy tradeoff for Seasonal items; continuously stocked items; multiple products under capacity constraints Levers to improve supply chain profits and decrease seasonal inventory So far: we have focused on comp. strategy => gives implied demand uncertainty and SC strategy => desired SC capabilities and how much responsiveness SC should exhibit as a whole. This raises the question: How does a supply chain manager achieve strategic fit? These four drivers characterize the supply chain (structure and operation) and its performance in terms of responsiveness/efficiency, and the degree of strategic fit, i.e., consistency with targeted customer needs (across SC.) (Think of race car analogy.) How do we build the SC? Note: Responsiveness spectrum is 1-dimensional. Convenient to summarize the level of SC responsiveness and for comparisons. But for design need to disaggregate. (E.g.: Dell vs. steel mill). Transp./Inv./Facility: How do McMaster and Grainger provide a high variety of products (over 200,000) quickly to customers? They use inventory and responsive transportation to provide same day to 2-day delivery to their customers. Grainger further increases responsiveness by having about 370 storefronts (facilities) where they provide same day delivery. Inventory: How does Amazon provide higher variety than Borders? Amazon provides high product variety by consolidating inventory into a few storage locations. Facility: Toyota has decided to increase responsiveness by making each production facility flexible enough to be able to supply a primary and a secondary market. Until 1998 their strategy was to have production facilities focused on only the local market. Information: Dell and Wal-Mart have shared sales and inventory information with their suppliers to improve supply chain responsiveness and eliminate waste. Note: The tradeoffs are qualitatively the same in each industry, but they play out differently depending on magnitudes of the different effects – that’s the hard and fascinating part of SCM! Note: We said course is about managing flows. Drivers & flows closely linked. Flow routes determined by facility and customer locations (where); flow rates & patterns by facility & transportation capacities, demand and push-pull boundary; flow accumulation = inventory location and push/pull boundary; flow and buffer content (location of transformation). If time: course outline. © Chopra / OPNS 455 / Optimal Availability

Estimating Optimal Level of Product Availability Buyers’ Estimate of Demand Distribution at L.L. Bean Mention that L.L. Bean is a mail order company deciding on the number of units of a Fall jacket to order. An estimate of demand using past information and expertise of buyers is given here. What should the appropriate order quantity be? In general distribution may not be known. Discuss methodology used in the Matching supply and demand article as a possibility in deciding on demand uncertainty (distribution). Expected Demand = 1,026 Parkas © Chopra / OPNS 455 / Optimal Availability

Estimating Optimal Level of Product Availability Cost of Over- and Understocking at L.L Bean
Cost per parka = c = $45 Sale price per parka = p = $100 Discount price per parka = $50 Holding and transportation cost = $10 Salvage value = s = $50-$10 = $40 Profit from selling parka = Cu = p-c = $100-$45 = $55 Cost of overstocking = Co = c-s = $45+$10-$50 = $5 What information is required to make the ordering decision? Stress cost of understocking and overstocking. How to evaluate these costs for this example? © Chopra / OPNS 455 / Optimal Availability

Order Quantity = 1000 Parkas (Expected Demand = 1,026)
Estimating Optimal Level of Product Availability Profit from Ordering the Expected Demand at L.L. Bean Order Quantity = 1000 Parkas (Expected Demand = 1,026) Probability Demand Sold Overstocked Understocked Profit 0.01 400 600 $ 19,000 0.02 500 $ 25,000 0.04 $ 31,000 0.08 700 300 $ 37,000 0.09 800 200 $ 43,000 0.11 900 100 $ 49,000 0.16 1,000 $ 55,000 0.20 1,100 1,200 0.10 1,300 1,400 1,500 1,600 1,700 Expected: 1,026 915 85 111 $ 49,900 Show detailed calculation of expected profit. © Chopra / OPNS 455 / Optimal Availability

Estimating Optimal Level of Product Availability Expected Marginal Contribution of Increasing Order Size by 100 units at L.L. Bean If we order 1,000, the CSL=probability(demand ≤ 1,000) = 0.51 Additional 100 units sell with probability 1-CSL = 0.49. We earn margin Cu=p-c = $55 / unit. Additional 100 units do not sell with probability CSL = 0.51. We lose Co= c-s = $5 per unit. Expected marginal contribution of an additional 100 units = 0.49 x 100 x $ x 100 x $5 = $2,440 © Chopra / OPNS 455 / Optimal Availability

Estimating Optimal Level of Product Availability Expected Marginal Contributions as Availability is Increased Discuss marginal benefit and marginal cost of each jacket. sells with 1-p => Cu does not sell with p => Co We keep increasing order size as long as expected benefit exceeds expected cost. Optimal Order Quantity = 1,300 Parkas Expected Profit = $ 54,160 Service level = 92% © Chopra / OPNS 455 / Optimal Availability

Estimating Optimal Level of Product Availability Seasonal Items with a Single Order in a Season: Summary p = sale price s = outlet or salvage price c = purchase price O* = optimal order size CSL* = optimal cycle service level = probability (demand ≤ O*) At the optimal cycle service level CSL* and order size O*: Expected marginal profit from raising the order size by one unit to O*+ 1 ≤ 0 Expected Marginal Revenue = probability the unit sells  Cu = (1-CSL*) Cu Expected Marginal Cost = probability the unit does not sell  Co = CSL* Co Therefore: (1-CSL*)  Cu ≤ CSL* Co Optimal Cycle Service Level: CSL* ≥ Cu / (Cu + Co ) = (p-c) / (p-s) Cu = p-c Co = c-s Notes: Key message: In the presence of variability, ordering the optimal amount rather than the expected amount can have a significant impact on profits. Optimal quantity will be based on a tradeoff between lost margin from under stocking and cost of overstocking. Optimal CSL = increasing in Cu, decreasing in Co. How can we increase salvage value => reduce Co? E.g. SO: south america. Outlets: increases availability in original market! Effect of Co: CSL goes up Expected marginal contribution is higher for each CSL Same margin (Cu), lower overstocking cost (Co) Higher profits, higher sales. How can we decrease margin lost (cost of understocking). Backup sourcing Rain check, discounts. In practice the larger Cu rel to Co, the higher the CSL: Nordstrom: high margin, high reputation for availability Critical fractile © Chopra / OPNS 455 / Optimal Availability

Product Availability for Continuous Distributions: Example
Motown studios is deciding on the number of copies of a CD to have manufactured. The manufacturer currently charges $2 for each CD. Motown sells each CD for $12 and currently places only one order for the CD before its release. Unsold CDs must be trashed. Demand for the CD has been forecast to be normally distributed with a mean of 30,000 and a standard deviation of 15,000. How many CDs should Motown order? Cu = 12 – 2 = $10 Co = 2 – 0 = $2 Optimal cycle service level, CSL = Cu/(Cu+Co) = 10/12 = 0.833 Optimal order = NORMINV(.833, 30000, 15000) = 44,511 Expected profit = $255,027 Expected profit from ordering 30,000 units = 228,190 © Chopra / OPNS 455 / Optimal Availability

Evaluating Expected Profits, Overstock, and Understock
Expected profits = (p-s) NORMDIST((O - )/, 0, 1, 1) - (p-s) NORMDIST((O- )/, 0, 1, 0) – O(c-s) NORMDIST(O, , , 1) + O (p-c) [1 - NORMDIST(O, , , 1)] (12.3) Expected overstock = (O - )NORMDIST((O - )/, 0, 1, 1) +  NORMDIST((O - )/, 0, 1, 0) (12.4) Show results using quantity discount spreadsheet for the given example. Optimal order quantity = 44,511 If the optimal quantity Is ordered Expected Profit = 255,027 Expected Overstock = 15,841 Probability of overstock = 0.83 Expected overstock / overstock = 19,008 Expected understock = 1329 Probability of understock = 0.17 Expected understock / understock = 7975 Expected understock = ( - O)[1- NORMDIST((O - )/, 0, 1, 1)] +  NORMDIST((O - )/, 0, 1, 0) (12.5) © Chopra / OPNS 455 / Optimal Availability

Product Availability for Continuous Distributions Under Quantity Discounts
Motown studios is deciding on the number of copies of a CD to have manufactured. The manufacturer currently charges $2 for each CD. Motown sells each CD for $12 and currently places only one order for the CD before its release. Demand for the CD has been forecast to be normally distributed with a mean of 30,000 and a standard deviation of 15,000. How many CDs should Motown order? What is the expected profit? What is the expected overstock? What is the expected understock? The manufacturer now offers a price of $1.95 for orders of at least 50,000 CDs and a price of $1.90 for orders of at least 60,000 CDs. How should Motown respond? Profit from ordering 44,511 units at $2 per unit = 255,027 Prob(overstock) = 0.83 Expected overstock = 15,841 Expected overstock/overstock = 15,841/0.83 = 19,008 Prob(understock) = 0.17 Expected understock = 1,329 Expected understock/understock = 1,329/0.17 = 7,975 Profit from ordering 50,000 units at $1.95 per unit = This may be worthwhile if the higher level of service (over 90%) can help draw additional demand Profit from ordering 60,000 units at $1.90 per unit = 244,472 This will not be worthwhile because expected losses are large. © Chopra / OPNS 455 / Optimal Availability

Efficiency Responsiveness Supply chain structure Facilities Transportation Inventory So far: we have focused on comp. strategy => gives implied demand uncertainty and SC strategy => desired SC capabilities and how much responsiveness SC should exhibit as a whole. This raises the question: How does a supply chain manager achieve strategic fit? These four drivers characterize the supply chain (structure and operation) and its performance in terms of responsiveness/efficiency, and the degree of strategic fit, i.e., consistency with targeted customer needs (across SC.) (Think of race car analogy.) How do we build the SC? Note: Responsiveness spectrum is 1-dimensional. Convenient to summarize the level of SC responsiveness and for comparisons. But for design need to disaggregate. (E.g.: Dell vs. steel mill). Transp./Inv./Facility: How do McMaster and Grainger provide a high variety of products (over 200,000) quickly to customers? They use inventory and responsive transportation to provide same day to 2-day delivery to their customers. Grainger further increases responsiveness by having about 370 storefronts (facilities) where they provide same day delivery. Inventory: How does Amazon provide higher variety than Borders? Amazon provides high product variety by consolidating inventory into a few storage locations. Facility: Toyota has decided to increase responsiveness by making each production facility flexible enough to be able to supply a primary and a secondary market. Until 1998 their strategy was to have production facilities focused on only the local market. Information: Dell and Wal-Mart have shared sales and inventory information with their suppliers to improve supply chain responsiveness and eliminate waste. Note: The tradeoffs are qualitatively the same in each industry, but they play out differently depending on magnitudes of the different effects – that’s the hard and fascinating part of SCM! Note: We said course is about managing flows. Drivers & flows closely linked. Flow routes determined by facility and customer locations (where); flow rates & patterns by facility & transportation capacities, demand and push-pull boundary; flow accumulation = inventory location and push/pull boundary; flow and buffer content (location of transformation). If time: course outline. Seasonal Inventory Cycle Inventory Safety Inventory Level of Product Availability Newsboy tradeoff for Seasonal items; continuously stocked items © Chopra / OPNS 455 / Optimal Availability

Estimating Optimal Level of Product Availability Continuously Stocked Items
Given CSL = probability of not stocking out in a cycle with current level of safety stock = Cycle Service Level H = cost of holding one unit for one year D = Annual demand Q = Replenishment lot size Basic tradeoff Benefit from increasing safety inventory (additional sales if demand is high) versus cost of increasing safety inventory (holding cost of one unit) Walmart selling detergent – Cu = discount = backlogging cost. © Chopra / OPNS 455 / Optimal Availability

Benefit from increasing safety inventory by one unit = (1- CSL*) Cu
Estimating Optimal Level of Product Availability Continuously Stocked Items Benefit from increasing safety inventory by one unit = (1- CSL*) Cu Cost of increasing safety inventory by one unit = HQ/D Equating the two gives optimal level of product availability CSL* = 1-HQ/(CuD) The higher Q the larger fill rate, since we have fewer cycles, but same expected shortage per cycle. higher holding cost per cycle, but stockout probability is unchanged => is worth reducing the CSL! Other way to see this: The lower the quantity backlogged since fill rate goes up. Can reduce safety stock Often cost of understocking is not known. Another way to go: given what we are doing, for what Cu would this be appropriate? Here: we are likely holding way too much. Note: Safety inventory = 3.5 SD above the mean of lead time demand! © Chopra / OPNS 455 / Optimal Availability

Estimating Cost of Understocking for Continuously Stocked Items
Data D = 100 gallons/week; D= 20; H = $0.6/gal./year L = 2 weeks; Q = 400; ROP = 300. What is the implied cost of stocking out? Safety Inventory = ROP – D*L = 100 Standard deviation of lead time demand: 20*sqrt(2)=28.3 With given policy, CSL=NORMSDIST(100/28.3)=0.9998 Implied cost of stocking out: Cu= HQ / (1-CSL) / D = 0.6*400/ / 5,200 = $230.8 Source: Example 12.3 in C & M © Chopra / OPNS 455 / Optimal Availability

Efficiency Responsiveness Supply chain structure Facilities Transportation Inventory So far: we have focused on comp. strategy => gives implied demand uncertainty and SC strategy => desired SC capabilities and how much responsiveness SC should exhibit as a whole. This raises the question: How does a supply chain manager achieve strategic fit? These four drivers characterize the supply chain (structure and operation) and its performance in terms of responsiveness/efficiency, and the degree of strategic fit, i.e., consistency with targeted customer needs (across SC.) (Think of race car analogy.) How do we build the SC? Note: Responsiveness spectrum is 1-dimensional. Convenient to summarize the level of SC responsiveness and for comparisons. But for design need to disaggregate. (E.g.: Dell vs. steel mill). Transp./Inv./Facility: How do McMaster and Grainger provide a high variety of products (over 200,000) quickly to customers? They use inventory and responsive transportation to provide same day to 2-day delivery to their customers. Grainger further increases responsiveness by having about 370 storefronts (facilities) where they provide same day delivery. Inventory: How does Amazon provide higher variety than Borders? Amazon provides high product variety by consolidating inventory into a few storage locations. Facility: Toyota has decided to increase responsiveness by making each production facility flexible enough to be able to supply a primary and a secondary market. Until 1998 their strategy was to have production facilities focused on only the local market. Information: Dell and Wal-Mart have shared sales and inventory information with their suppliers to improve supply chain responsiveness and eliminate waste. Note: The tradeoffs are qualitatively the same in each industry, but they play out differently depending on magnitudes of the different effects – that’s the hard and fascinating part of SCM! Note: We said course is about managing flows. Drivers & flows closely linked. Flow routes determined by facility and customer locations (where); flow rates & patterns by facility & transportation capacities, demand and push-pull boundary; flow accumulation = inventory location and push/pull boundary; flow and buffer content (location of transformation). If time: course outline. Seasonal Inventory Cycle Inventory Safety Inventory Level of Product Availability Newsboy tradeoff for Seasonal items; continuously stocked items; multiple products under capacity constraints © Chopra / OPNS 455 / Optimal Availability

Optimal Availability for Multiple Products Under Capacity Constraint
Available Capacity = 3,000. © Chopra / OPNS 455 / Optimal Availability

Optimal Availability Assuming No Capacity Constraint
The sum of all optimal quantities equals 2,164 Opti Total Order Quantity = 3,674 > 3,000 © Chopra / OPNS 455 / Optimal Availability

Optimal Availability Under Capacity Constraint Optimal Ordering Policy
High End Mid Range At optimality, expected marginal contribution of each item ordered is equal Optimal Order Ratio 0.55 0.94 0.93 Optimal Order to Mean 0.74 1.00 1.04 Ratio of CSL 0.53 0.68 0.75 © Chopra / OPNS 455 / Optimal Availability

Optimal Availability Under Capacity Constraint Optimal Ordering Policy
Step 1: Set order quantity Qi = 0 for all products i. Step 2: For all products i, compute/update the expected marginal contribution at the current order quantity Step 3: For the product j with the largest positive expected marginal contribution, increase order quantity Qj by the minimum increment. This is equivalent to auctioning off capacity one unit at a time and assigning it to the highest bidder (the one with the largest expected marginal contribution) Step 4: If there is still capacity available and there is some product with a positive expected marginal contribution, return to Step 2, else stop. This procedure can also be thought of as each unit of capacity being auctioned to the highest bidder. As bidders get more capacity, their expected valuation of further capacity declines because the probability of the unit selling declines. The procedure is continued until all capacity is sold. © Chopra / OPNS 455 / Optimal Availability

Efficiency Responsiveness Supply chain structure Facilities Transportation Inventory Seasonal Inventory Cycle Inventory Safety Inventory Level of Product Availability Newsboy tradeoff for Seasonal items; continuously stocked items; multiple products under capacity constraints Levers to improve supply chain profits and decrease seasonal inventory So far: we have focused on comp. strategy => gives implied demand uncertainty and SC strategy => desired SC capabilities and how much responsiveness SC should exhibit as a whole. This raises the question: How does a supply chain manager achieve strategic fit? These four drivers characterize the supply chain (structure and operation) and its performance in terms of responsiveness/efficiency, and the degree of strategic fit, i.e., consistency with targeted customer needs (across SC.) (Think of race car analogy.) How do we build the SC? Note: Responsiveness spectrum is 1-dimensional. Convenient to summarize the level of SC responsiveness and for comparisons. But for design need to disaggregate. (E.g.: Dell vs. steel mill). Transp./Inv./Facility: How do McMaster and Grainger provide a high variety of products (over 200,000) quickly to customers? They use inventory and responsive transportation to provide same day to 2-day delivery to their customers. Grainger further increases responsiveness by having about 370 storefronts (facilities) where they provide same day delivery. Inventory: How does Amazon provide higher variety than Borders? Amazon provides high product variety by consolidating inventory into a few storage locations. Facility: Toyota has decided to increase responsiveness by making each production facility flexible enough to be able to supply a primary and a secondary market. Until 1998 their strategy was to have production facilities focused on only the local market. Information: Dell and Wal-Mart have shared sales and inventory information with their suppliers to improve supply chain responsiveness and eliminate waste. Note: The tradeoffs are qualitatively the same in each industry, but they play out differently depending on magnitudes of the different effects – that’s the hard and fascinating part of SCM! Note: We said course is about managing flows. Drivers & flows closely linked. Flow routes determined by facility and customer locations (where); flow rates & patterns by facility & transportation capacities, demand and push-pull boundary; flow accumulation = inventory location and push/pull boundary; flow and buffer content (location of transformation). If time: course outline. © Chopra / OPNS 455 / Optimal Availability

Levers to Improve SC profits and decrease Seasonal Inventory
Increase salvage value (over stock outlets) Decrease cost of under stocking (substitution) Improve forecasts Multiple orders in a season Postponement What happens to profits, understock, and overstock in each case? © Chopra / OPNS 455 / Optimal Availability

Levers to Improve SC profits and decrease Seasonal Inventory: Postponement of Product Differentiation Supply Chain Flows Without Postponement Supply Chain Flows With Component Commonality and Postponement © Chopra / OPNS 455 / Optimal Availability

Value of Postponement: Benetton
Data Demands (uncorrelated) Each color: Mean = 1,000; SD = 500 Aggregate: Mean = 4,000, SD = 1000 For each garment Sale price = $50 Salvage value = $10 Production cost using option 1 (long lead time) = $20 Production cost using option 1 (greige thread) = $22 What is the effect of postponement? Expected overstock and under stock reduced What is the value of postponement? Expected profit increases from $94,576 to $98,092 Introduce postponement (see notes) Delay differentiation of products closer to time of sale. In push phase: long lag time to demand, aggregate inventories (and processes) In pull phase: short lag time, disaggregate. Value: match demand and supply Reduction of uncertainty => safety inventory lower, lower understock: higher profits Cost: hence need to evaluate tradeoff Paint, Benetton (dying knitted fabric is higher cost by 10%), HP. Magnitude of value high uncertainty (CV), low correlation. drops with numbers aggregated (Dell example) Low if dissimilar Tailored postponement: best of both worlds No postponement on fraction of demand that is stable Postponement on fraction of demand the is volatile: high benefits. Benetton story Forecast 20 weeks ahead. Option1: buy for each color. Option 2: buy uncolored thread, differentiation after demand is known. © Chopra / OPNS 455 / Optimal Availability

Value of Postponement: Benetton
How does the value of postponement change as the demand uncertainty increases/decreases? Which components are better postponed – most or least expensive? Short or long lead times? How does value change as number of colors postponed increases/decreases? With a process that allows postponement do you want to sell more or fewer colors? © Chopra / OPNS 455 / Optimal Availability

Value of Postponement with Dominant Product
Demand Color with dominant demand: Mean = 3,100, SD = 800 Other three colors: Mean = 300, SD = 200 Aggregate: Mean = 4,000, SD = 872 Expected profit without postponement = $102,205 Expected profit with postponement = $99,872 Why is postponement not valuable with a dominant product? How should we react? CV=0.26 CV=0.67 CV=0.22 Why does this happen? Large fraction can already be matched (demand and supply) fairly well: gain in matching low, but higher cost lowers margin. Smaller colors do get benefit, but the gain on these is not high enough to offset loss. What could we do? Just postpone small ones. Small positive benefit. Next slide: is complete postponement optimal? © Chopra / OPNS 455 / Optimal Availability

Tailored Postponement: Benetton
4 colors, for each: mean demand = 1,000, SD=500 Produce Q1 units for each color, and QA units undyed Q1 (colored) QA(neutral) Total Aver. Profit Aver. Overstock Aver. Understock 4,524 $ ,092 715 190 1337 5,348 $ ,576 1648 300 700 1,850 4,650 $ ,730 308 168 800 1,550 4,750 $ ,603 427 170 900 950 4,550 $ ,326 607 266 1,050 $ ,647 664 230 1000 850 4,850 $ ,312 815 195 4,950 $ ,951 803 149 1100 550 $ ,180 1026 211 650 5,050 $ ,510 1008 185 © Chopra / OPNS 455 / Optimal Availability

Cautions in Implementing Postponement
The cost of postponement Postponement often increases the manufacturing cost Design and production costs can only be justified over a family of products Value of postponement is larger the more uncertain and the less correlated the individual product demands Cautions End products must look suitably different to the consumer Do a small set of products provide most of the sales? Do products have low uncertainty? Tailored postponement Higher manufacturing cost is justified only for uncertain portion of demand. Consider more efficient process for stable portion of demand. Make link to tailored centralization © Chopra / OPNS 455 / Optimal Availability

Managing Inventories and Uncertainty in the Supply Chain: Summary of Lessons
Push Pull Cycle Inventory Safety Inventory Seasonal Inventory Aggregation Volume based discounts over rolling horizon EDLP, promote to limit forward buy Quick response Reduce uncertainty Reduce lead time Reduce lead time variability Increase reorder frequency Accurate response by pooling Tailored pooling based on Demand correlation Coefficient of variation Value of product Level of service Holding cost Increase salvage value and decrease lost margin Shorten lead time to reduce uncertainty Multiple orders in season. Tailored postponement © Chopra / OPNS 455 / Optimal Availability

Chapter 12: Determining the Optimal Level of Product Availability

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