Assessing Dynamic Efficiency of Machine-made Carpet Industry by Network DEA Technique

The results of dynamic efficiency evaluation not only help managers to realize their business' position in competitive market, but also enable them to compare current company’s performance with previous periods and do strategic planning properly. For doing so, network data envelopment analysis is a logical approach. Hence, the main objective of this illustration is to measure dynamic efficiency by means of network data envelopment analysis technique. Although different approaches in network DEA are introduced recently, the need for a comprehensive methodology in this area is remained because of the defects of previous methodologies. Consequently, a novel approach based on multi-objective optimization is introduced in this paper in order to measure the efficiency of a network structure. Finally, the case of Machine Made Carpet Industry (MMCI) is used and the dynamic performance of MMCI's companies in the period of four years is measured. Efficiency results of case data showed that the methodology proposed in this paper is able to eliminate defects of previous approaches and evaluate both total and annual efficiency simultaneously Introduction: Dynamic efficiency assessment is so crucial for managers to watch out their business performance by passing the time. In a competitive market, understanding whether the company is performing in an efficient manner or not, in comparison to their rivals, is so important for managers. In this regard, assessing dynamic efficiency is the objective of this research and Machine-made Carpet Industry (MMCI) is taken into account as the case study. So, the companies producing machine made carpets are considered as the Decision Making Units (DMUs). Therefore, the main purpose of this research is to assess the dynamic efficiency of MMCI’s companies during a four-year period. The methodology used for assessing dynamic efficiency is network data envelopment analysis. Materials and Methods: The main purpose of this research is to assess the dynamic efficiency of MMCI’s companies during a four-year period by means of network data envelopment analysis technique. For doing so, five different approaches are used; while, four of this approaches include ‘Standard DEA approach, Separation approach, Average approach and Relational analysis approach’ are in the literature and the last approach, named as ‘Max-min approach’ is developed for the first time in this paper. All the first four approaches are used for assessing the efficiency of this research’s network structure and the disadvantages of all four approaches were highlighted by details. Finally, this paper introduces a multi-objective optimization method named as max-min approach for assessing total and partial efficiency of the network structure simultaneously. This new approach is able to eliminate the defeats of the previous ones and bring a comprehensive methodology for assessing the dynamic efficiency of DMUs. Results and Discussion: In this article, firstly, the weaknesses of the available methodologies in the literature for assessing the dynamic efficiency of a network structure by means of network data envelopment analysis are illustrated. Then, a new approach based on multi-objective optimization technique is proposed in order to assess dynamic efficiency of a four-stage network structure with extra inputs and outputs. In more details, this new approach has the ability to eliminate the defeats of the methodologies available in the literature which can briefly be named as the disability in measuring total and partial efficiency simultaneously, being biased in giving importance to some sub-processes, lack of discrimination and disability in assessing unique efficiency scores for sub-processes. This paper’s novel approach is named as max-min optimization approach and is able to assess the unique and unbiased efficiency scores in a network structure for both total and partial efficiency simultaneously. To be more accurate, the efficiency assessment which are obtained by the methodology of this paper is unique. In addition, decision makers’ point of view plays no role in giving the priority to any sub-process and all the stages have the same importance in measuring the efficiency of a network structure. Last but not the least is that, since these sub-processes are connected, efficiency assessment should be done in a manner that takes into account the role of intermediate parameters and this consideration is done appropriately in this paper. Conclusion: In this paper, dynamic efficiency assessment of MMCI’s companies is measured by means of network data envelopment analysis. Since the approaches presented in literature have some weaknesses, this paper aims to develop a comprehensive network data envelopment analysis approach which is able to measure dynamic efficiency of DMUs in an appropriate manner. To do so, this research develops a novel methodology based on network data envelopment analysis. This approach is a multi-objective programming technique that measure total and partial efficiency of a network structure simultaneously in a unique and unbiased manner and is named as max-min approach. Finally, the max-min approach presented in this investigation is a proper methodology in assessing dynamic efficiency of a network structure in a period of time. References Cook, W. D., Zhu, J., Bi, G., & Yang, F. (2010). Network DEA: Additive efficiency decomposition. European Journal of Operational Research, 207(2), 1122-1129. KAO, C. (2016). Efficiency decomposition and aggregation in network data envelopment analysis. European Journal of Operational Research, 255, 778-786. TONE, K. & TSUTSUI, M. )2014(. Dynamic DEA with network structure: A slacks-based measure approach. Omega, 42, 124-131.