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Sourcing and Procurement Cost Allocation in Multi‐Division Firms
Author(s) -
Fang Fang,
Natarajan Harihara Prasad
Publication year - 2020
Publication title -
production and operations management
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.279
H-Index - 110
eISSN - 1937-5956
pISSN - 1059-1478
DOI - 10.1111/poms.13139
Subject(s) - procurement , leverage (statistics) , computer science , context (archaeology) , operations research , business , vendor , strategic sourcing , supply chain , insourcing , industrial organization , operations management , outsourcing , economics , strategic planning , marketing , paleontology , strategic financial management , machine learning , engineering , biology
Through Central Procurement Organizations ( CPO s), large firms with multiple divisions have begun adopting a center‐led sourcing approach that allows firms to centralize strategic sourcing activities, while permitting decentralized execution by divisions, allowing the firm to leverage large purchase volumes with vendors. This new center‐led procurement environment has brought a new decision requirement: How should a CPO select vendors for each division's requirements to minimize the firm's total procurement cost and simultaneously develop a fair and alignment‐inducing mechanism to allocate the costs (and savings) of company‐wide procurement to the divisions? Past research and current practice have not addressed this linkage between vendor selection and cost allocation in multi‐division firms. This work models this sourcing and procurement cost allocation ( SPC ) problem facing CPO s of large firms as a mixed‐integer optimization problem. This model is flexible and can incorporate several commonly‐used cost allocation rules. We show that the SPC problem is NP ‐hard. Therefore, to support practical decision‐making in this context, we develop a tailored solution approach for the SPC problem. Our approach enhances the base model by adding strong valid inequalities. We tested our model and its enhancements in an extensive numerical study, solving over 160 instances. Results reveal that (a) the CPO can ensure fair cost allocation at a modest cost (relative to the centralized solution) with our model, and (b) the proposed tailored approach is effective and necessary in solving a wide variety of instances.