TY - JOUR
T1 - Benchmarking company performance from economic and environmental perspectives
T2 - Time series analysis for motor vehicle manufacturers
AU - Zeng, Qinqin
AU - Beelaerts van Blokland, Wouter
AU - Santema, Sicco
AU - Lodewijks, Gabriel
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
KW - Benchmarking
KW - Motor vehicle manufacturer
KW - Performance measure
KW - Time series forecasting
UR - http://www.scopus.com/inward/record.url?scp=85077547827&partnerID=8YFLogxK
U2 - 10.1108/BIJ-05-2019-0223
DO - 10.1108/BIJ-05-2019-0223
M3 - Article
AN - SCOPUS:85077547827
VL - 27 (2019)
SP - 1127
EP - 1158
JO - Benchmarking: an international journal
JF - Benchmarking: an international journal
SN - 1463-5771
IS - 3
ER -