Company performance measurement is fundamental for decision-makers to monitor a company's performance and to solve management problems. The evolution of company performance measurement tools started from a pure financial-biased framework. The first generation of company performance measurement tools was achieved through supplementing the traditional financial measures with non-financial measures. The second generation addressed the dynamic of value creation by investigating transformations of resources. Both the first and the second generation showed appropriateness in how they reflect the realities in companies. The third generation emphasized the business-oriented methodology to real free cash flow activities. This dissertation, that will present a fourth generation company performance measurement tool, has a focus on motor vehicle manufacturers (MVMs) due to its economic significance and its environmental impact during vehicles' production.@en

Purpose: Current literature presents limited measurement methods of quantifying manufacturers' performance with environmental concerns. The purpose of this paper is to construct a company performance index for benchmarking motor vehicle manufacturers (MVMs) with environmental concerns. Design/methodology/approach: Methods of constructing the index include regression analysis, a modified linear method for normalizing variables and a geometric mean for aggregating variables into a single index I_MVM (index for MVMs). A case study is conducted in 12 MVMs from 2008 to 2017. A sensitivity analysis with the simple additive weighting method is performed to analyze how different aggregation methods affect the final value. The index I_MVM is assessed through a benchmark with three existing indices. Findings: Three realistic considerations are identified from MVMs, based on which proper and transparent methods are chosen to construct the I_MVM. The construction of the index I_MVM has been assessed through a benchmark against the methodologies of three other indices. The results indicate that the new measurement is feasible and effective for MVMs to measure their company performance from an environmental perspective. Practical implications: The construction of the index I_MVM can support policymakers with accurate statistics for decision-making. As a response to current imperative climate policies, this paper raises awareness of CO₂ emissions in vehicles' production. For statistical organizations and stakeholders in the investment world, this paper provides available and reliable statistics for trend analysis of different MVMs. Originality/value: A new method is designed for constructing a company performance index for MVMs. Three environmental variables are identified based on literature, their environmental impact as well as their data availability from public documents. A ranking by manufacturer with environmental concerns is generated. This index can contribute with available statistics and useful insights toward decision-making.

@en

Composite indicators (CIs) are needed for decision makers to effectively benchmark holistic company performance. Composite indicators at macro levels are inappropriate to be implemented at the company level. By a literature survey, this article identified 29 individual methods for constructing CIs, 17 specific business sectors where CIs have been utilized in practice, and the motor vehicle manufacturing sector as the most studied sector. This article identified nine problems and provided four recommendations for future research.

@en

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 I_MVM 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 I_MVM. 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 I_MVM is benchmarked against three frameworks by six benchmark metrics. The I_MVM 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 I_MVM 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.

@en

Current research in the field of performance measurement hasn't presented a rigorous composite indicator for quantifying company performance, with environmental indicators for automobile companies. This paper aims to construct this missing composite indicator. A new approach is developed, including techniques of fuzzy logic, analytic network process, the entropy theory and a geometric mean with unequal weights. The method is transparent, and the composite indicator derived can serve as a statistical tool for benchmarking. A case study is conducted in six leading automobile companies with data from the fiscal year 2016.

@en

This paper aims to develop a quantitative model of company performance from an inventory perspective for truck manufacturers. With the inventory performance as a new dimension, fourteen indicators are identified to form a conceptual framework for truck manufacturers to measure their company performance. Accordingly, techniques of the fuzzy logic and the analytic network process (ANP) are used to generate the quantitative model, considering the interdependency between the indicators and the uncertainty arising from human qualitative judgments. A case study is conducted in nine truck manufacturers, with time series data from the fiscal year 2004 to 2015. The ranking result out of the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is used as a validation, which proves the higher accuracy of the model developed.@en