Previously published as: Work Study
Online from: 2004
Subject Area: Performance Management and Measurement
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|Title:||The role of performance measurement systems to support quality improvement initiatives at supply chain level|
|Author(s):||Luca Cagnazzo, (Department of Industrial Engineering, University of Perugia, Perugia, Italy), Paolo Taticchi, (Department of Electronic and Information Engineering, University of Perugia, Perugia, Italy), Alessandro Brun, (Department of Management, Economics and Industrial Engineering, Politecnico di Milano, Milan, Italy)|
|Citation:||Luca Cagnazzo, Paolo Taticchi, Alessandro Brun, (2010) "The role of performance measurement systems to support quality improvement initiatives at supply chain level", International Journal of Productivity and Performance Management, Vol. 59 Iss: 2, pp.163 - 185|
|Keywords:||Critical success factors, Performance measures, Qualitative methods, Supply chain management|
|Article type:||Research paper|
|DOI:||10.1108/17410401011014249 (Permanent URL)|
|Publisher:||Emerald Group Publishing Limited|
Purpose – The purpose of this paper is to discuss which are the most important critical success factors (CSFs) to be attained for determining whether a group of companies in a supply chain (SC) is prepared to undergo a qualitative improvement initiative (QII). By answering four critical research questions, the paper focuses on the role of performance measurement systems (PMSs) as a CSF to support QII and discusses which PMS is more appropriate for the different SC context existing in the new economy.
Design/methodology/approach – The methodology adopted in this research relies on different approaches. In order to evaluate the readiness of a company in the SC to start a QII, the authors propose a checklist of CSFs defined through a literature review. Further, to evaluate the impact of PMSs to support QII at the SC level, the authors review PMS literature and discuss the characteristics of the various PMS available. Last, in order to select the best PMS to support the QII of companies belonging to different SC, the authors propose a classification matrix based on the SC characteristics.
Findings – The main findings of this research is the understanding of the PMS role in supporting QII at SC level.
Research limitations/implications – The research presented in this paper needs to be validated through the use of case studies, in order to optimize the methodology proposed.
Practical implications – This paper offers to academics, managers and practitioners a structured methodology to select the right PMS to support QII in specific SC contexts.
Originality/value – Few researches are available exploring the correlation between PMS and QII, especially in particular collaborative environments such as SCs. As a consequence, the methodology and findings of this paper add value to the existing body of knowledge and offer good insights for addressing future research.
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