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Secure collaborative supply chain planning and inverse optimization - The JELS model

TitleSecure collaborative supply chain planning and inverse optimization - The JELS model
Publication TypeJournal Article
Year of Publication2011
AuthorsPibernik, R, Zhang, Y, Kerschbaum, F, Schröpfer, A
JournalEuropean Journal of Operations Research
Pagination75 - 85
Date Published01/2011
Keywordscollaboration, information sharing, secure multi-party computation, SMC, supplychain management

It is a well-acknowledged fact that collaboration between different members of a supplychain yields a significant potential to increase overall supplychain performance. Sharing private information has been identified as prerequisite for collaboration and, at the same time, as one of its major obstacles. One potential avenue for overcoming this obstacle is Secure Multi-Party Computation (SMC). SMC is a cryptographic technique that enables the computation of any (well-defined) mathematical function by a number of parties without any party having to disclose its input to another party. In this paper, we show how SMC can be successfully employed to enable joint decision-making and benefit sharing in a simple supplychain setting. We develop secure protocols for implementing the well-known “Joint Economic Lot Size (JELS) Model” with benefit sharing in such a way that none of the parties involved has to disclose any private (cost and capacity) data. Thereupon, we show that although computation of the model’s outputs can be performed securely, the approach still faces practical limitations. These limitations are caused by the potential of “inverseoptimization”, i.e., a party can infer another party’s private data from the output of a collaborativeplanning scheme even if the computation is performed in a secure fashion. We provide a detailed analysis of “inverseoptimization” potentials and introduce the notion of “stochastic security”, a novel approach to assess the additional information a party may learn from joint computation and benefit sharing. Based on our definition of “stochastic security” we propose a stochastic benefit sharing rule, develop a secure protocol for this benefit sharing rule, and assess under which conditions stochastic benefit sharing can guarantee secure collaboration.