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utdallas.edu /~metin 1 Optimal Level of Product Availability Chapter 12 of Chopra

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utdallas.edu /~metin 2 Outline u Determining optimal level of product availability –Single order in a season –Continuously stocked items u Ordering under capacity constraints u Levers to improve supply chain profitability

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utdallas.edu /~metin 3 Motivating News Article: Mattel, Inc. & Toys “R” Us Mattel [who introduced Barbie in 1959 and run a stock out for several years then on] 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

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utdallas.edu /~metin 4 Key Questions u How much should Toys R Us order given demand uncertainty? u How much should Mattel order? u Will Mattel’s action help or hurt profitability? u What actions can improve supply chain profitability?

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utdallas.edu /~metin 5 Another Example: Apparel Industry How much to order? Parkas at L.L. Bean Expected demand is 1,026 parkas.

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utdallas.edu /~metin 6 Parkas 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 per parka = s = 50-10=$40 Profit from selling parka = p-c = 100-45 = $55 Cost of overstocking = c-s = 45-40 = $5

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utdallas.edu /~metin 7 Optimal level of product availability p = sale price; s = outlet or salvage price; c = purchase price CSL = Probability that demand will be at or below reorder point Raising the order size if the order size is already optimal Expected Marginal Benefit = =P(Demand is above stock)*(Profit from sales)=(1-CSL)(p - c) Expected Marginal Cost = =P(Demand is below stock)*(Loss from discounting)=CSL(c - s) Define C o = c-s; C u =p-c (1-CSL)C u = CSL C o CSL= C u / (C u + C o )

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utdallas.edu /~metin 8 Order Quantity for a Single Order C o = Cost of overstocking = $5 C u = Cost of understocking = $55 Q * = Optimal order size

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utdallas.edu /~metin 9 Optimal Order Quantity Optimal Order Quantity = 13(‘00) 0.917

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utdallas.edu /~metin 10 Parkas at L.L. Bean u Expected demand = 10 (‘00) parkas u Expected profit from ordering 10 (‘00) parkas = $499 u Approximate Expected profit from ordering 1(‘00) extra parkas if 10(’00) are already ordered = 100. 55.P(D>=1100) - 100. 5.P(D<1100)

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utdallas.edu /~metin 11 Parkas at L.L. Bean

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utdallas.edu /~metin 12 Revisit L.L. Bean as a Newsvendor Problem u Total cost by ordering Q units: –C(Q) = overstocking cost + understocking cost Marginal cost of raising Q* - Marginal cost of decreasing Q* = 0 Show Excel to compute expected single-period cost curve.

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utdallas.edu /~metin 13 Ordering Women’s Designer Boots Under Capacity Constraints Available Store Capacity = 1,500.

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utdallas.edu /~metin 14 Assuming No Capacity Constraints Storage capacity is not sufficient to keep all models!

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utdallas.edu /~metin 15 Algorithm for Ordering Under Capacity Constraints {Initialization} ForAll products, Q i := 0. Remaining_capacity:=Total_capacity. {Iterative step} While Remaining_capacity > 0 do ForAll products, Compute the marginal contribution of increasing Q i by 1 If all marginal contributions <=0, STOP {Order sizes are already sufficiently large for all products} else Find the product with the largest marginal contribution, call it j {Priority given to the most profitable product} Q j := Q j +1 and Remaining_capacity=Remaining_capacity-1 {Order more of the most profitable product}

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utdallas.edu /~metin 16 Marginal Contribution=(p-c)P(D>Q)-(c-s)P(D
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Q)-(c-s)P(D

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utdallas.edu /~metin 17 Optimal Safety Inventory and Order Levels: (ROP,Q) ordering model Lead Times time inventory Shortage An inventory cycle ROP Q

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utdallas.edu /~metin 18 A Cost minimization approach as opposed to the last chapter’s service based approach u Fixed ordering cost = S R / Q u Holding cost = h C (Q/2+ss) where ss = ROP – L R u Backordering cost (based on per unit backordered), with f(.), the distribution of the demand during the lead time, u Total cost per time

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utdallas.edu /~metin 19 Optimal Q (for high service level) and ROP u Q*=Optimal lot size u ROP*=Optimal reorder point u A cost / benefit analysis to obtain CSL: –(1-CSL)bR/Q= per time benefit of increasing ROP by 1 »(1-CSL)b= per cycle benefit of increasing ROP by 1 –hC= per time cost of increasing ROP by 1 –(1-CSL)bR/Q=hC gives the optimality equation for ROP

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utdallas.edu /~metin 20 Imputed Cost of Backordering R = 100 gallons/week; R = 20; H=hC= $0.6/gal./year L = 2 weeks; Q = 400; ROP = 300. What is the “imputed cost” of backordering? Let us use a week as time unit. H=0.6/52 per gal per week. Recall the formula CSL = 1-HQ * /bR

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utdallas.edu /~metin 21 Levers for Increasing Supply Chain Profitability u Increase salvage value »Obermeyer sells winter clothing in south America during the summer. »Sell the Xmas trees to Orthodox Christians after Xmas. »Buyback contracts, to be discussed. u Decrease the margin lost from a stock out –Pooling: »Between the retailers of the same company. u Ex. Volvo trucks. »Between franchises/competitors. u Franchises: Car part suppliers, McMaster-Carr and Grainger, are competitors but they buy from each other to satisfy the customer demand during a stock out. u Competitors: BMW dealers in the metroplex: Richardson, Dallas, Arlington, Forth Worth –Dallas competes with Richardson so no pooling between them –Dallas pools inventory with the rest –Transportation cost of pooling a car from another dealer $1,500 –Rebalancing: No transportation cost if cars are switched in the ship in the Atlantic u Improve forecasting to lower uncertainty u Quick response by decreasing replenishment lead time which leads to a larger number of orders per season u Postponement of product differentiation u Tailored (dual) sourcing

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utdallas.edu /~metin 22 Impact of Improving Forecasts EX: Demand is Normally distributed with a mean of R = 350 and standard deviation of R = 150 Purchase price = $100, Retail price = $250 Disposal value = $85, Holding cost for season = $5 How many units should be ordered as R changes? Price=p=250; Salvage value=s=85-5=80; Cost=c=100 Understocking cost=p-c=250-100=$150, Overstocking cost=c-s=100-80=$20 Critical ratio=150/(150+20)=0.88 Optimal order quantity=Norminv(0.88,350,150)=526 units Expected profit? Expected profit differs from the expected cost by a constant.

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utdallas.edu /~metin 23 Computing the Expected Profit with Normal Demands

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utdallas.edu /~metin 24 Impact of Improving Forecasts Where is the trade off? Expected overstock vs. Expected understock. Expected profit vs. ?????

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utdallas.edu /~metin 25 Cost or Profit; Does it matter?

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utdallas.edu /~metin 26 Quick Response: Multiple Orders per Season u Ordering shawls at a department store –Selling season = 14 weeks (from 1 Oct to 1 Jan) –Cost per shawl = $40 –Sale price = $150 –Disposal price = $30 –Holding cost = $2 per week u Expected weekly demand = 20 u StDev of weekly demand = 15 u Understocking cost=150-40=$110 per shawl u Overstocking cost=40-30+(14)2=$38 per shawl u Critical ratio=110/(110+38)=0.743=CSL

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utdallas.edu /~metin 27 Ordering Twice as Opposed to Once u The second order can be used to correct the demand supply mismatch in the first order –At the time of placing the second order, take out the on- hand inventory from the demand the second order is supposed to satisfy. This is a simple inventory correction idea. u Between the times the first and the second orders are placed, more information becomes available to demand forecasters. The second order is typically made against less uncertainty than the first order is.

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utdallas.edu /~metin 28 Impact of Quick Response Correcting the mismatch with the second order OUL: Ideal O rder U p to L evel of inventory at the beginning of a cycle Average total order approximately = OUL 1 +OUL 2 -Ending Inventory As we decrease CSL, profit first increases, then decreases and finally increases again. The profits are computed via simulation.

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utdallas.edu /~metin 29 Forecasts Improve for the Second Order Uncertainty reduction from SD=15 to 3 With two orders retailer buys less, supplier sells less. Why should the supplier reduce its replenishment lead time?

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utdallas.edu /~metin 30 Postponement is a cheaper way of providing product variety u Dell delivers customized PC in a few days u Electronic products are customized according to their distribution channels u Toyota is promising to build cars to customer specifications and deliver them in a few days u Increased product variety makes forecasts for individual products inaccurate –Lee and Billington (1994) reports 400% forecast errors for high technology products –Demand supply mismatch is a problem »Huge end-of-the season inventory write-offs. Johnson and Anderson (2000) estimates the cost of inventory holding in PC business 50% per year. u Not providing product flexibility leads to market loss. –An American tool manufacturer failed to provide product variety and lost market share to a Japanese competitor. Details in McCutcheon et. al. (1994). u Postponement: Delaying the commitment of the work-in-process inventory to a particular product, a.k.a. end of line configuration, late point differentiation, delayed product differentiation.

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utdallas.edu /~metin 31 Postponement u Postponement is delaying customization step as much as possible u Need: –Indistinguishable products before customization –Customization step is high value added –Unpredictable demand –Negatively correlated product demands –Flexible SC to allow for any choice of customization step

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utdallas.edu /~metin 32 Forms of Postponement by Zinn and Bowersox (1988) u Labeling postponement: Standard product is labeled differently based on the realized demand. –HP printer division places labels in appropriate language on to printers after the demand is observed. u Packaging postponement: Packaging performed at the distribution center. –In electronics manufacturing, semi-finished goods are transported from SE Asia to North America and Europe where they are localized according to local language and power supply u Assembly and manufacturing postponement: Assembly or manufacturing is done after observing the demand. –McDonalds assembles meal menus after customer order.

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utdallas.edu /~metin 33 Examples of Postponement u HP DeskJet Printers –Printers localized with power supply module, power cord terminators, manuals u Assembly of IBM RS/6000 Server –50-75 end products differentiated by 10 features or components. Assembly used to start from scratch after customer order. Takes too long. –Instead IBM stocks semi finished RS/6000 called vanilla boxes. Vanilla boxes are customized according to customer specification. u Xilinx Integrated Circuits –Semi-finished products, called dies, are held in the inventory. For easily/fast customizable products, customization starts from dies and no finished goods inventory is held. For more complicated products finished goods inventory is held and is supplied from the dies inventory. –New programmable logic devices which can be customized by the customer using a specific software. u Motorola cell phones –Distribution centers have the cell phones, phone service provider logos and service provider literature. The product is customized for different service providers after demand is materialized.

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utdallas.edu /~metin 34 Postponement u Saves Inventory holding cost by reducing safety stock –Inventory pooling –Resolution of uncertainty u Saves Obsolescence cost u Increases Sales u Stretches the Supply Chain –Suppliers –Production facilities, redesigns for component commonality –Warehouses

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utdallas.edu /~metin 35 Value of Postponement: Benetton case u For each color, 20 weeks in advance forecasts –Mean demand= 1,000; Standard Deviation= 500 u For each garment –Sale price = $50 –Salvage value = $10 –Production cost using option 1 (long lead time) = $20 »Dye the thread and then knit the garment –Production cost using option 2 (short lead time) = $22 »Knit the garment and then dye the garment u What is the value of postponement? –p=50; s=10; c=20 or c=22

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utdallas.edu /~metin 36 Value of Postponement: Benetton case u CSL=(p-c)/(p-c+c-s)=30/40=0.75 u Q=norminv(0.75,1000,500)=1,337 u Expected profit by using option 1 for all products 4 x 23,644=$94,576

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utdallas.edu /~metin 37 Apply option 2 to all products: Benetton case u CSL=(p-c)/(p-c+c-s)=28/40=0.70 u Demand is normal with mean 4 x 1000 and st.dev sqrt(4) x 500 u Q=norminv(0.75,4000,1000)=4524 u Expected profit by using option 2 for all products=$98,902

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utdallas.edu /~metin 38 Postponement Downside u By postponing all three garment types, production cost of each product goes up u When this increase is substantial or a single product’s demand dominates all other’s (causing limited uncertainty reduction via aggregation), a partial postponement scheme is preferable to full postponement.

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utdallas.edu /~metin 39 Partial Postponement: Dominating Demand u Color with dominant demand: Mean = 3,100, SD = 800 u Other three colors: Mean = 300, SD = 200 u Expected profit without postponement = $102,205 u Expected profit with postponement = $99,872 u Are these cases comparable? –Total expected demand is the same=4000 –Total variance originally = 4*250,000=1,000,000 –Total variance now=800*800+3(200*200)=640,000+120,00=760,000 u Dominating demand yields less profit even with less total variance. Postponement can not be any better with more variance.

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utdallas.edu /~metin 40 Partial Postponement: Benetton case u For each product a part of the demand is aggregated, the rest is not u Produce Q 1 units for each color using Option 1 and Q A units (aggregate) using Option 2, results from simulation: Q 1 for eachQAQA Profit 13370$94,576 04524$98,092 1100550$99,180 1000850$100,312 8001550$104,603

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utdallas.edu /~metin 41 Tailored (Dual) Sourcing u Tailored sourcing does not mean buying from two arbitrary sources. These two sources must be complementary: –Primary source: Low cost, long lead time supplier »Cost = $245, Lead time = 9 weeks –Complementary source: High cost, short lead time supplier »Cost = $250, Lead time = 1 week u An example CWP (Crafted With Pride) of apparel industry bringing out competitive advantages of buying from domestic suppliers vs international suppliers. u Another example is Benetton’s practice of using international suppliers as primary and domestic (Italian) suppliers as complementary sources.

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utdallas.edu /~metin 42 Tailored Sourcing: Multiple Sourcing Sites

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utdallas.edu /~metin 43 Dual Sourcing Strategies from the Semiconductor Industry

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utdallas.edu /~metin 44 Learning Objectives u Optimal order quantities are obtained by trading off cost of lost sales and cost of excess stock u Levers for improving profitability –Increase salvage value and decrease cost of stockout –Improved forecasting –Quick response with multiple orders –Postponement –Tailored sourcing

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