An integrated framework for continuous assessment and improvement of manufacturing systems

Abstract

This paper presents an integrated framework for assessment and ranking of manufacturing systems based on management and organizational performance indicators. The integrated approach of this paper is based on principal component analysis. The validity of the model is verified and validated by numerical taxonomy and clustering analysis approach. Furthermore, the non-parametric correlation methods, namely, Spearman and Kendall-Tau correlation experiments should show high level of correlation between the findings of PCA and taxonomy. To achieve the objectives of this study, a comprehensive study was conducted to locate all economic and technical indicators which influence organizational performance. Sixty one indicators were identified and classified in five categories, namely, (1) financial, (2) customer satisfaction, (3) process innovation, (4) production process and (5) organizational learning and growth. These indicators are related to organizational and managerial productivity and efficiency. Two actual test problems and a random sample of 12 indicators were selected to show the applicability of the integrated approach. The results of PCA showed the weak and strong points of each sector in regard to the selected indicators. Furthermore, it identifies which indicators have the major impacts on the overall performance of industrial sectors. The modeling approach of this paper could be easily utilized for managerial and organizational ranking and analysis of other sectors. The results of such studies would help top managers to have better understanding and improve existing systems with respect to managerial and organizational performance.

Introduction

Major factors influencing the overall productivity of an industrial organization are identified as technology, machinery, management, personnel and rules and procedures [1], [2], [3]. Organizational and management factors plays an important role in the overall performance of manufacturing systems. In fact, managerial and organizational productivity is correlated with the overall performance of a manufacturing system. Furthermore, the overall performance of an industrial organization is often assessed by managerial and organizational productivity. The need for an integrated approach for continuous assessment and improvement of manufacturing systems based on management performance has become essential. Continuous assessment requires manufacturing classifications and taxonomy to be introduced to enhance knowledge and understanding about the behavior of manufacturing systems [4], [5], [6], [7], [8]. Consequently, it will enable predictions to be made about organizational system behavior. In selecting a performance measure or indicator, it is important to consider the measure’s suitability to the control system’s objectives, the measure invasiveness and its complexity [9]. In selecting an appropriate range of performance measures it will be necessary to balance them to make sure various dimensions of manufacturing performance is considered [10], [11]. Furthermore, we need to make sure that one or more dimensions of performance are not stressed to detriment of others.

This study has identified major productivity indicators, which affect management performance in industrial organizations. An integrated study must consider not only the traditional productivity view but also it must consider other views such as efficiency, effectiveness and profitability. Effectiveness is defined as actual output to planned output, efficiency is defined as actual output to actual input and profitability is defined as total revenue to total cost. Furthermore, this study considers the four views of management and organization productivity, which are: (1) traditional productivity, (2) efficiency, (3) effectiveness and (4) profitability. In this study, all of the four views are referred to as management and organization productivity. By consolidating a set of management and organization productivity indicators, the telecommunication sectors may be ranked and analyzed by principal component analysis (PCA). Also, the validity and credibility of the PCA may be verified and validated by numerical taxonomy (NT) approach and non-parametric correlation experiments. It should be mentioned that data envelopment analysis (DEA) was first selected as the verification tool, but several indexes could not be considered due to the unique structure of DEA. Based on examination of 64 plants in Germany, it was concluded that machinery and training play the most important role in productivity improvement of industrial organizations [12]. Hong and colleagues showed that data envelopment analysis (DEA) can be used to evaluate the efficiency of system integration projects and proposed a methodology to overcome the limitations of DEA by utilizing DEA along with machine learning [13]. Multivariate analysis were used with the purpose of identifying critical export marketing success factors by a survey of 134 export activities of manufacturing firms in Denmark [14]. A multivariate analysis was used to test whether there is any relationship between airline flight delays and the financial situation of an airline [15].

Multivariate analysis was used to identify valuation of farmland in Spain [16]. Multivariate analysis was performed with the purpose of identifying critical export marketing success factors [17]. The relative position of United Kingdom car market was assessed with the aid of multivariate statistical analysis [18]. Other researchers used a multivariate linear statistical model to investigate the effects of speed, travel distance and part weight on robot repeatability and accuracy [19]. A fuzzy clustering and classification model for productivity analysis of machinery industry is discussed by Chen and colleagues [20]. A multivariate approach was used among 128 manufacturing organization to indicate that man–machine interfaces are significant contributors to reducing the negative effect of system complexity [21]. Three performance measures, namely, customer satisfaction, productivity and technological competitiveness were collected from a large sample of manufacturing sites in Australia and New Zealand and analyzed by multivariate analysis technique [22]. Application of multivariate techniques including PCA and neural networks in a pulp mill factory is proposed and discussed by Kumar [23]. There are other studies, which show the applications of multivariate analysis in various settings [24], [25].