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Quick Response in Manufacturer-Retailer Channels Ananth V. Iyer Mark E. Bergen Presented By Evren Körpeoğlu

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Quick Response Quick Response is a strategy focusing on providing shorter lead times. It enables orders to be placed closer to the start of the selling season.

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Quick Response & Apparel Industry Used mainly in Apparel Industry, because of; Long lead times between order placement and delivery of product.

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Quick Response To Reduce the lead times; Information Sharing using EDI Logistics improvements Increased use of Air Freight Point of sale scanners and barcoding Improved manufacturing methods such as laser fabric cutting or modular sewing cells

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Is it Pareto Improving? Do both the manufacturer and the retailer benefit from QR? This paper investigates the effect of QR and how to make it become Pareto improving.

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Model Structure Old System: L = Lead Time (5 to 8 months) QR System: L 2 = Lead Time (2 to 5 months) L 2 ≤L L 2 = L – L 1

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Demand Structure We have two levels of demand uncertainity: Uncertainity of the number of people who come to the store and their propensity to buy Uncertainity about mean demand

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Demand Structure before QR N(θ,σ 2 ): distribution of demand during season assuming that we know meand demand N(μ,τ 2 ): mean demand estimation m(x)~N( μ,τ 2 +σ 2 ) → demand distribution at time 0

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Demand Structure after QR Data collected during period L 1 is used to estimate the demand of the season. If the demand in period L 1 is d 1, then: m(x|d 1 )~N(μ(d 1 ), σ 2 + 1/Ρ)

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QR and the Channel Model Parameters: c: Cost per unit to buy product from manufacturer ∏: Goodwill cost per unit h: holding cost per unit of product left over at the the end of the season w: production cost per unit r: retailer’s revenue per unit

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The Old System Holding CostsGoodwill Costs Expected Revenues

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The Old System Optimum initial inventory: Optimal service level (s):

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The Old System Maximum Expected Profit:

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The Old System Expected Quantity Sold: Expected Quantity Left over:

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QR System Retailer’s inventory choice involves two steps: 1.Observe the demand during L 1 and use it to estimate seasonal demand 2.Choose optimal inventory to maximize expected profits with estimated demand. The demand before season,d 1, directly affects this choice.

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QR System Optimum initial inventory: Maximum Expected Profit:

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QR System Expected Quantity Sold: Expected Quantity Left over:

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Old System vs QR System Since: EI QR-sold ≥ EI old-sold EI QR-left ≤ EI old-left

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Old System vs QR System Expected Manufacturer Profit in old system: Expected Manufacturer Profit in QR system:

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When is QR Pareto Improving? Lemma 1: If s≤0.5 then under QR we have an increase in manufacturer and retailer expected profits because Z(s) < 0 Thus, under low service levels, QR is pareto improving.

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When is QR Pareto Improving? In the case that manufacturer pays a salvage credit per unit, if this credit is large enough, then QR will be pareto improving even if s≥0.5

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When is QR Pareto Improving? Lemma 2: If s≥0.5 then under QR we have an increase in expected retailer profits but a decrease in expected manufacturer profits

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Other Methods... Commitments using service level Commitments regarding the wholesale price Volume commitments across products

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Commitments using service level After QR, retailer may use a higher service level but it is not beneficial for the manufacturer Since the increase in service level can not be shown in the model, the goodwill costs are changed.

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Commitments using service level Theorem 1. If s≥0.5, d=σ/τ and then increasing ∏ to ∏’=(s’(r+h)+c-r)/(1-s’) and s’ such that Z(s’)=Z(s)/y makes QR Pareto improving.

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Commitments using service level Lemma 3. If s≥0.5, d=σ/τ and then increasing ∏ to ∏’=(s’(r+h)+c-r)/(1-s’) and s’ such that Z(s’)=Z(s)/y, results in EI QR-sold (s’) ≥ I old-sold and EI QR-left (s’)≤ I old-left

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Commitments using service level Other ways in using service level; Contractual commitment between manufacturer and retailer to provide high service level Use of cooperative advertising that uses service level guarantee Increasing the goodwill cost

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Wholesale Price Quantity Discounts: If manufacturer forces retailer pay c 1 >c if the quantity purchased is smaller than the I old, and c if it is larger than I old, manufacturer may benefit from QR. Lemma 4: There exists a quantity discount scheme that makes QR Pareto improving

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Wholesale Price Lemma 5: There exists a wholesale price c* that depends on system parameters such that if c≥c*, then there exists no flat wholeslae price commitments that can make QR Pareto improving.

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Volume Commitments across Products Assuming we have M products, the buyer should have a commitment that the total amount of purchased products will be as much as before the QR.

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Volume Commitments across Products Lemma 6: The impact of a volume commitment of I(M), which is initial inventory across M products, on expected profit for the retailer is given by Thus a volume commitment of M(μ+Z s √σ 2 + τ 2 ) across M products makes QR Pareto improving.

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Volume Commitments across Products Volume commitment maintains manufacturer’s profit but genrate an expected service level which is below that is required for service level commitments

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Volume Commitments across Products Lemma 7: The expected service level s(M) under a volume commitment of MI old across M products, s(M), is s≤S(M)≤s’, where is s’ is defined as in Theorem 1

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Conclusion Impact of a channel view on Quick Response for fashion apparel industry is examined. Classical inventory model are used to show these effects. Three ways for Pareto improving are consider: service level, price and volume commitments.

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THANK YOU FOR LISTENING!... Comments & Questions

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