Development of key performance selection index model

Abstract

Purpose: The main idea of this paper is to introduce the refined model for selection of the Key performance indicators (KPI). The KPI selection model can be considered as a tool for analysis of the enterprise, which should be able to simplify the choice of the right metrics for the company, where study has been conducted. The Enterprise analysis model (EAM) will provide the information regarding weak spots on the production and provide further steps to the management. Those actions will save time and reduce resources that are necessary to implement metrics in company. Design/methodology/approach: Main activities performed include: optimization of EAM; Fuzzy AHP and SMARTER criteria’s for ranking the KPIs; reliability analysis and weights appointment to questions and KPIs. In addition, the expert group has participated in the analysis of this work and has made a high impact on the results. Findings: The main result of this work is the final version of the KPI selection model. Research limitations/implications: The future research should be focused on optimization of the model and in adding additional module for automatic data collection. The Production Monitoring System (PMS) that should help to collect data about the status of the machine park, taking into account the downtime, overall equipment efficiency (OEE) and etc. Practical implications: The proposed model can be used in SME (small and medium enterprises) in order to improve the productivity. The concept was tested in particular company. Originality/value: The KPI selection model combine different methodologies into one general approach. Due to this fact, the process of finding right metrics can be reduced significantly. The proposed approach allows saving resources for the research of metrics.

Authors and Affiliations

S. Kaganski

Keywords

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  • EP ID EP199524
  • DOI 10.5604/01.3001.0010.2077
  • Views 143
  • Downloads 0

How To Cite

S. Kaganski (2017). Development of key performance selection index model. Journal of Achievements in Materials and Manufacturing Engineering, 1(82), 33-40. https://europub.co.uk/articles/-A-199524