Benchmarking company performance from economic and environmental perspectives: Time series analysis for motor vehicle manufacturers

Qinqin Zeng*, Wouter Beelaerts van Blokland, Sicco Santema, Gabriel Lodewijks

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

3 Citations (Scopus)


Purpose: The purpose of this paper is to develop an approach to measuring the performance of motor vehicle manufacturers (MVMs) from economic and environmental (E&E) perspectives. Design/methodology/approach: Eight measures are identified for benchmarking the performance from E&E perspectives. A new company performance index IMVM is constructed to quantitatively generate the historical data of MVMs’ company performance. Autoregressive integrated moving average (ARIMA) models are built to generate the forecast data of the IMVM. The minimum Akaike information criteria value is used to identify the model of the best fit. Forecast accuracy of the ARIMA models is tested by the mean absolute percentage error. Findings: The construction of the index IMVM is benchmarked against three frameworks by six benchmark metrics. The IMVM satisfies all of its applicable metrics while the three frameworks are incapable to satisfy their applicable metrics. Out of 15, 4 MVMs are excluded for benchmarking future performance due to their non-stationary time series data. Based on the forecast IMVM data, GM is the best performer among the 15 samples in the FY2018. Originality/value: This research highlights the environmental perspective during vehicles’ production. The development of this approach is based on publicly available data and transparent about the methods it used. The data out of the approach can benefit stakeholders with insights by benchmarking the historical performance of MVMs as well as their future performance.

Original languageEnglish
Pages (from-to)1127-1158
Volume27 (2019)
Issue number3
Publication statusPublished - 2020


  • Benchmarking
  • Motor vehicle manufacturer
  • Performance measure
  • Time series forecasting


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