Supply chain process optimisation via the management of variance

Farhad Nabhani, Christian Uhl, Florian Kauf, Alireza Shokri

Research output: Contribution to journalArticlepeer-review

14 Citations (Scopus)
14 Downloads (Pure)


This paper presents a new optimisation approach for variance within a supply chain management process. The approach is presented by the variance cube of purchasing (VCP) that involves a lean method for variance optimisation, namely the cost and variance driver analysis. The approach focuses on the optimisation and the control of existing process variance within the supply chain. The application of the cube is presented by a case study involving a globally acting Tier 1 supplier, who produces steering systems for passenger cars and commercial vehicles. In this case, the sourcing process of this Tier 1 supplier will be analysed, evaluated and optimised regarding variance. The variance is presented in the form of the number of suppliers who are involved in the sourcing process. Unnecessary existing process variance, like an unnecessary huge number of suppliers within the sourcing process, is a type of waste. Time, money, quality and technology can be saved through a greater understanding of the optimal number of suppliers within a sourcing process. The results of the case study led to a generalised method to optimise the existing process variance, present cost improvements as well as optimising the key performance indicator to manage the number of suppliers in the sourcing process. The general approach can be used for other company departments like logistics and for different industries other than automotive. The insights of this article support the operative user and the strategic company management in order to reduce and improve unnecessary variance in different sections. The structured analysis of supply chain process variance via the VCP and the key performance indicator “optimal supplier number per sourcing process” are new to company management.
Original languageEnglish
Pages (from-to)136-153
Number of pages28
JournalJournal of Management Analytics
Issue number2
Early online date29 Jan 2018
Publication statusPublished - Apr 2018

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