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Impact of Returns on Supply Chain Coordination. Ana Muriel Department of Mechanical and Industrial Engineering, University of Massachusetts In collaboration with Rocio Ruiz-Benitez. Outline . Motivation Model Analysis Computational Study Conclusions. Motivation.

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impact of returns on supply chain coordination

Impact of Returns on Supply Chain Coordination

Ana Muriel

Department of Mechanical and Industrial Engineering, University of Massachusetts

In collaboration with Rocio Ruiz-Benitez

outline
Outline
  • Motivation
  • Model
  • Analysis
  • Computational Study
  • Conclusions
motivation
Motivation
  • The value of commercial product returns now exceeds $100 billion annually in the US (Stock, Speck and Shear (2002))
    • Commercial product returns: Products returned for any reason within 90 days of purchase.
    • Hewlett Packard recently estimated the cost of consumer returns for North America exceeded 2% of their total outbound sales revenue.
    • Returns ~ 6% of sales
  • Ferguson, Guide and Souza (2005)
motivation4
Motivation
  • Policy of most US retailers:

Full returns no question asked!!

  • Return rates: 6% to 15% (Dekker and Van der Laan(2003))
    • Mail order companies and e-tailers: as high as 35%
  • Largely ignored in supply chain coordination and contracts literature
      • Most research on consumer returns concerns inventory policies, production planning and reverse logistics (Fleischmann and Kuik (2003), Kiesmuller (2003))
literature review
Literature Review
  • Wood (2001), “Remote Purchase Environments: The influence of Return Policy Leniency on Two-Stage Decision Processes”, Journal of Marketing Research 38, 157-169.
  • Dekker and Van der Laan (2003), “Inventory control in reverse logistics”, chapter in Business Aspects of Closed-Loop Supply Chains, V.D. Guide Jr., L.N. Van Wassenhove, editors. Carnegie Mellon University Press, Pittsburgh, PA
  • Fleischmann M. and Kuik R. (2003), “On optimal inventory control with independent stochastic items returns”, European Journal of Operational Research 151, 25-37
  • Kiesmuller, G.P. (2003), “Optimal control of a one product recovery system with leadtimes”, International journal of Production Economics 81-82, 333-340
  • Ferguson, Guide and Souza (2005), “Supply Chain Coordination for False Failure Returns”, working paper. Georgia Institute of Technology.
  • Souza, Guide, van Wassenhove and Blackburn (2005), “Time Value of Commercial Product Returns”, working paper. University of Maryland.
research questions
Research Questions:
  • What is the profit impact of incorporating consumer returns in our decision models?
    • Centralized system
    • Decentralized system
  • How does it affect retail prices and quantities ordered?
  • How does this depend on
    • the magnitude of logistics costs?
    • the relative share between retailer and manufacturer?
    • the proportion of product that is returned?
classical model
Classical Model

SalesS = min(y,Q)

  • Two-echelon supply chain
  • Stochastic and price dependent demand y
  • Manufacturer’s decision variables: wholesale price w

repurchase price s

  • Retailer’s decision variables: order quantity Q

selling price r

  • Single replenishment opportunity

cQ

wQ

rS

Manufacturer

Retailer

s(Q-S)

returns model
Returns Model

vR

l1R

l2R

  • A percentage of sales is returned Returns R = aS
  • Manufacturer’s returns logistics cost: l1
  • Retailer’s returns handling cost: l2
    • This costs include inspection, shipping, sorting, repackaging, remanufacturing, disposal
    • Average salvage value of returned item v

wQ

rS

cQ

Manufacturer

Retailer

s(Q-S)

wR

rR

costs associated with returns
Costs Associated with Returns
  • System costs:  = r - v + l
  • Manufacturer costs 1 = w - v + l1
  • Retailer costs 2 = r – w + l2
slide10

Demand Distribution

y = stochastic and price dependent demand faced by the retailer:

y=xD(r)

x= positive r. v. with mean 1 and density function f()

D(r) = expected demand quantity, decreasing in retail price

Demand density function

slide11

Profit Functions and Optimal Decision Variables:

  • Decentralized System
  • T = R + M
    • Retailer
    • R = rS +s(Q-S)– wQ – 2R
    • Manufacturer
    • M = (w-c)Q – s(Q-S) –1R
  • Centralized System
  • C = rS – cQ – R
slide12

Analysis

Objective: Compare the following decision rules

  • Policy IR:
    • Ignores customer returns when optimizing
    • QIR, rIR,wIR, sIR
    • Customer returns considered a posteriori, to calculate respective profits
    • Expected profit: PIR
  • Policy CR:
    • Considers customer returns when optimizing
  • QCR, rCR,wCR, sCR
  • Expected profit: PCR
analysis centralized system
Analysis: Centralized System
  • Proposition:Under deterministic and price dependent demand, the optimal retail price increases and the order quantity decreases when considering consumer returns. That is,

QCR< QIR and rCR> rIR

    • Intuitive since the profit margin is reduced by consumer returns.
analysis centralized system14
Analysis: Centralized System
  • Theorem:Under stochastic and price dependent demand we have that
      • For fixed r, QCR(r)< QIR(r)
      • For fixed Q, rCR(Q)> rIR(Q)
      • Under mild conditions,

QCR< QIR and rCR> rIR

        • C1: For all r> rIR,QIR(r) QIR(rIR)
        • C2: For all Q<QIR, rIR(Q) rIR(QIR)
analysis decentralized system
Analysis: Decentralized System
  • Corollary:Given w, the retailer’s optimal decisions satisfy:
      • For fixed r, QCR(r)< QIR(r)
      • For fixed Q, rCR(Q)> rIR(Q)
      • Under mild conditions,

QCR< QIR and rCR> rIR

        • C1: For all r> rIR,QIR(r) QIR(rIR)
        • C2: For all Q<QIR, rIR(Q) rIR(QIR)
question
Question
  • Will consumer returns always result in higher prices and lower quantities in a decentralized supply chain?
analysis system coordination under buy back contracts
Analysis: System CoordinationUnder Buy-Back Contracts
  • Theorem: Under consumer returns, a policy that allows for unlimited returns at a partial credit s will lead to supply chain coordination for appropriate values of s and w. In particular,
  • Allowing no returns is system suboptimal

Extension of Pasternack(1985), demand is not price dependent

slide18

Computational Study

  • Assumptions:
    • f(x) ~ uniform distribution in [0,2]
    • Linear demand model
    • D(r)=b(r-k)
    • where b<0 and k>0 constants
    • b=-3, k=5
    • (Emmons and Gilbert (1998))
slide19

Centralized System

l=1

CR

l=2

IR

l=3

  • We observe: QCR < QIRand rCR > rIR
  • QCR decreases as l increases
  • Profit difference increases with l and a
  • 10% returns and l=1, the difference is 6.33%
  • Percent improvement increases with  and l
decentralized system
We observe:

QCR < QIR

rCR > rIR

For fixed value of w,

RCR >RIR

But for optimal w, RIR >RCR

Decentralized System

CR

Q*

IR

r*

Manuf.

Retail.

Total

slide21

Profit Functions at optimal w

CR

Manuf.

Retail.

IR

Total

Percent Savings

Manufacturer: up to 10%

Retailer: 9% to 66%

Total: 6% to 37%

sensitivity analysis
Sensitivity Analysis

With respect to:

1) Share of logistic cost faced by

retailer ()

2) Percentage of consumer returns ()

slide23
Under policy IR…

QIR, rIR and wIR constant

logistics costs do not intervene in the decision making process

Under policy CR…

QCR and rCR increase with ;

Manufacturer decreases wCR

as incentive for retailer to increase order quantity

Ends up bearing all logistics cost

If  > 70% => RIR* < RCR*

Q*

CR

r*

IR

w*

Manuf.

Retail.

Total

slide24

a=.06

a=.2

a=.35

CR

IR

conclusions
Conclusions

When considering returns …

  • Centralized system:

1) Lower quantities and higher retail prices

2) Significant profit increase

  • Decentralized system:

1) Lower quantities and higher retail prices

2) Poor coordination of the supply chain

        • All members worse off in general
        • Ignoring returns reduces double marginalization

3) The manufacturer bears the returns logistics costs:

Higher percentage manufacturer decreases

incurred by retailer wholesale price to compensate