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Performance measurement systems in supply chains

Author

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  • Richard Cuthbertson
  • Wojciech Piotrowicz

Abstract

Purpose - The purpose of this article is to propose a common framework for the empirical analysis of supply chain performance measurement systems used in different supply chain contexts. Design/methodology/approach - This is a conceptual paper, which includes an extensive literature review and an illustrative case study. The content, context, process framework is applied to structure the body of knowledge and the case study. Findings - Supply chain performance measurement is a context‐dependent process, tailored to specific supply chain requirements. To understand how a performance measurement system in a supply chain has developed and is used there is a need to capture its context, process and content. Research limitations/implications - The framework is illustrated by a single case study. Further empirical research is required to fully appreciate the breadth of application of this framework. Practical implications - The proposed framework can help to develop performance measurement systems that are suitable for certain organisational and supply chain contexts in which a company operates, as well as to compare different systems used across different supply chains. Originality/value - The paper demonstrates an approach for analysing existing supply chain performance measurement systems that can be applied across different supply chains and sectors. This will create an opportunity to use a consistent data collection process across a variety of supply chain situations and thus generate data for further theory development.

Suggested Citation

  • Richard Cuthbertson & Wojciech Piotrowicz, 2011. "Performance measurement systems in supply chains," International Journal of Productivity and Performance Management, Emerald Group Publishing Limited, vol. 60(6), pages 583-602, July.
    • Handle: RePEc:eme:ijppmp:v:60:y:2011:i:6:p:583-602
    DOI: 10.1108/17410401111150760
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    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
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    Cited by:

    1. Lima-Junior, Francisco Rodrigues & Carpinetti, Luiz Cesar Ribeiro, 2019. "Predicting supply chain performance based on SCOR® metrics and multilayer perceptron neural networks," International Journal of Production Economics, Elsevier, vol. 212(C), pages 19-38.
    2. Maestrini, Vieri & Luzzini, Davide & Maccarrone, Paolo & Caniato, Federico, 2017. "Supply chain performance measurement systems: A systematic review and research agenda," International Journal of Production Economics, Elsevier, vol. 183(PA), pages 299-315.
    3. Dennis Vegter & Jos van Hillegersberg & Matthias Olthaar, 2021. "Performance Measurement Systems for Circular Supply Chain Management: Current State of Development," Sustainability, MDPI, vol. 13(21), pages 1-18, November.
    4. Sajal Kabiraj & Dwarika Prasad Uniyal, 2012. "A Coevolutionary Model as a Collaborative Mechanism for the Business Exchange in the High-technology Industrial Value Chain," Jindal Journal of Business Research, , vol. 1(2), pages 193-207, December.
    5. Cory Searcy, 2016. "Measuring Enterprise Sustainability," Business Strategy and the Environment, Wiley Blackwell, vol. 25(2), pages 120-133, February.
    6. Chen, Sihua & Du, Jiangze & He, Wei & Siponen, Mikko, 2022. "Supply chain finance platform evaluation based on acceptability analysis," International Journal of Production Economics, Elsevier, vol. 243(C).

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