A conic optimization approach for SKU rationalization

Expanding variety and the number of offered products is attractive for a firm to fit customer needs. Nevertheless, the greater complexity and the proliferation of stock-keeping units (SKUs) without substantial differentiation may not substantially improve customer satisfaction while raising costs. Based on the principle that the product-line size involves operational implications and particularly manufacturing and holding costs, this paper develops a mixed-integer nonlinear mathematical program (MINLP) to support efficient product portfolio reductions. Basically, the fixed costs elimination and the risk-pooling effects must balance the demand contraction due to customer dissatisfaction. Off-the-shelve Mixed-Integer Quadratic Problem (MIQP) solver provides optimal solution to the proposed conic quadratic reformulation, and a real-life industrial case illustrates the program and the algorithm efficiency. Findings show that our mathematical programming subject to various assumptions and estimations is efficient to rationalize portfolios up to at least 400 SKUs.

[1]  François Maniquet,et al.  Equality Of Opportunity: The Economics Of Responsibility , 2012 .

[2]  Pierre Pestieau,et al.  Aging, Social Security Design, and Capital Accumulation , 2015, SSRN Electronic Journal.

[3]  Luc BAUWENS,et al.  Handbook of Volatility Models and Their Applications , 2012 .

[4]  Thierry Bréchet,et al.  Modeling of environmental adaptation versus pollution mitigation , 2014 .

[5]  Pierre Pestieau,et al.  Lobbying, Family Concerns, and the Lack of Political Support for Estate Taxation , 2015 .

[6]  Charles J. Corbett,et al.  THE VALUE OF SKU RATIONALIZATION IN PRACTICE (THE POOLING EFFECT UNDER SUBOPTIMAL INVENTORY POLICIES AND NONNORMAL DEMAND) , 2009 .

[7]  Nigar Hashimzade,et al.  Solutions Manual to Accompany Intermediate Public Economics , 2006 .

[8]  Kosuke Ishii,et al.  Design for Variety , 1998 .

[9]  Isabelle Thomas,et al.  Atlas de Belgique. Partie 4 : Habitat. , 2011 .

[10]  Xavier Wauthy,et al.  From Bertrand to Cournot via Kreps and Scheinkman: a hazardous journey , 2014 .

[11]  Nigar Hashimzade,et al.  Solutions Manual to Accompany Intermediate Public Economics 2nd edition , 2013 .

[12]  J. Park,et al.  Development of a production cost estimation framework to support product family design , 2005 .

[13]  Zuo-Jun Max Shen,et al.  An Inventory-Location Model: Formulation, Solution Algorithm and Computational Results , 2002, Ann. Oper. Res..

[14]  G. Dobson,et al.  Heuristics for pricing and positioning a product-line using conjoint and cost data , 1993 .

[15]  Fred S. Zufryden,et al.  An Integer Programming Approach to the Optimal Product Line Selection Problem , 1988 .

[16]  Jacques-François Thisse,et al.  Economic Geography and the Unequal Development of Regions , 2012 .

[17]  Marshall L. Fisher,et al.  OR FORUM-Rocket Science Retailing: The 2006 Philip McCord Morse Lecture , 2009, Oper. Res..

[18]  Alper Atamtürk,et al.  A Conic Integer Programming Approach to Stochastic Joint Location-Inventory Problems , 2012, Oper. Res..

[19]  James M. Utterback,et al.  The product family and the dynamics of core capability , 1992 .

[20]  Kamalini Ramdas.  MANAGING PRODUCT VARIETY: AN INTEGRATIVE REVIEW AND RESEARCH DIRECTIONS , 2003 .

[21]  Gregory Dobson,et al.  Positioning and Pricing a Product Line , 1988 .

[22]  E. Schokkaert,et al.  Empirical Social Choice , 2011 .

[23]  Zuo-Jun Max Shen,et al.  A Joint Location - Inventory Model , 2003, Transp. Sci..

[24]  R. Amir.  Supermodularity and Complementarity in Economics: An Elementary Survey , 2003 .