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 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 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 lead 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 variance cube of purchasing and the key performance indicator "optimal supplier number per sourcing process" are new to company management.

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Purpose -The purpose of this paper is the presentation of a new optimisation approach for product variance from the purchasing perspective. Design/methodology/approach -The research is based on a case study of a collaboration with a global acting automotive Tier 1 supplier, who produces steering systems for cars and commercial vehicles. A total of 116 different variants of three components of a car automotive steering system were analysed and evaluated. The data was gathered from 13 sub suppliers for three different types of a steering system. Findings -Unnecessary time, money, quality and technology can be saved through a greater understanding of such product variances. The results of the case study lead to a general method to optimise existing product variance and present cost improvements and a new key performance indicator to manage product variance out of the purchasing department. Research limitations/implications -The research is based on a purchasing case study at a Tier 1 supplier of the automotive branch. The approach can be used for other company departments like e.g. logistics and for different industries than automotive. Practical implications -A company can be successful and competitive when it meets the customer needs with a maximum on satisfaction without generating of waste. Unnecessary existing product variance is a kind of waste. The insights of this article support the operative user and the strategic company management to reduce and improve unnecessary variance in different sections. Originality/value -The structured analysis of product variance from the purchasing perspective and the key performance indicator "variance share" are new to company management. The research focuses on the management of existing variance out of the purchasing department which is a segment that has received limited academic attention in research to date.

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