الگوبرداری مراکز توزیع با استفاده از تجزیه و تحلیل مولفه اصلی و تحلیل پوششی داده ها : یک مطالعه موردی از صربستان

The efficiency of distribution systems is largely affected by the performances of distribution centres. The main objective of this paper is to develop and propose a DEA model for distribution centres efficiency measuring that can help managers in decision making and improving the efficiency. Due to numerous indicators that describe DCs operating, the main problem is indicators selection. In order to improve discriminatory power of classical DEA models PCA–DEA approach is used. This paper analysis the efficiency of distribution centres of one trading company in Serbia. Proposed models integrate operational, quality, energy, utilisation and equipment warehouse and transport indicators. Several hypotheses are tested in this paper. The results showed that small distribution centres are more efficient than large.

In order to survive in the market and achieve profitability, the companies need to perform their activities in an efficient way. Efficiency is a very important indicator of companies’ operations analysis, and it is one of the basic and the most frequently used performances. Measuring, monitoring and improving efficiency are the main tasks for companies in the 21st century. The importance of efficiency measuring in logistics has been recognised in literature (Chow et al., 1994, Hackman et al., 2001 and Min and Joo, 2006). This process is a very complicated one due to the complex structure of logistics systems. Distribution centres (DCs) are complex logistics systems which connect producers with other participants in the chain, including end-users. DCs of trading companies and DCs in general represent complex logistics systems with a very important place and role in the supply chains. In literature little has been done for the performance measurement of the distribution side of the supply chain. This paper analyses in more detail the efficiency of DCs of the trading company that operates in the region of Serbia. “Single ratio” indicators have been used for estimating the efficiency of DCs for a long time. These indicators do not provide enough information about the system operating. Recently, an increasing number of authors have advocated the use of approaches such as the Data Envelopment Analysis (DEA) method (Min and Joo, 2006 and Toloo and Nalchigar, 2011). Adler and Golany, 2001 and Adler and Golany, 2002 have suggested using the Principal Component Analysis (PCA), a methodology that produces uncorrelated linear combinations of original inputs and outputs, to improve discrimination in the DEA with a minimal loss of information. The DEA models often fail when there are an excessive number of inputs and outputs in relation to the number of decision making units (DMUs). DC’s operating describes a large number of different indicators, and the problem is how to select relevant indicators which describe DC operating in the best way. Variables selection problem is recognized in literature (Boussofiane, Dyson, & Thanassoulis, 1991). Various indicators with different effect on systems, subsystems, processes and activities further complicate the selection of variables. The main objective of this paper is to develop a model for measuring efficiency of DCs of one trading company. Information obtained from the company management and the author’s experience is used in the process of model development. Next section gives a review of indicators used for measuring efficiency in logistics. The third section describes the PCA–DEA approach. Efficiency evaluation system of observed DCs is given in the fourth section. In section five the results of the proposed model are described. Several hypotheses are also tested in section five. At the end of the paper, the concluding remarks and directions of future research are presented.

Measuring, monitoring and improving efficiency affect market success. In this paper a model for measuring DC’s efficiency, based on different indicators, is developed. The main problem in this paper is how to select, from a large number of indicators those that best describe the DC operating. PCA is used for improving discriminatory power of the model. The observed company monitors a number of different indicators. They are divided into six different groups. The classical DEA model does not give good results, so PCA–DEA approach is used. Additional restrictions are set in accordance with the opinion of the DC’s management, as well as the author’s opinion and experience. Model results show remarkable importance of additional constraints. Relationship between weight coefficients assigned to PCs from different groups greatly affect final results. Different models that favour operational, utilisation and quality indicators are tested on the observed set. The highest level of discrimination is achieved with Model V in which the emphasis is on quality indicators. The quality of the service affects both customer satisfaction and the company’s revenues. The results show that for efficiency evaluation operational indicators are more important than utilisation indicators. Management pays more attention to operational indicators than utilisation, but less than quality indicators. This paper investigates the influence of different factors on the efficiency scores. Three hypotheses are set in this paper. The influence of the “peak months” on DC’s efficiencies was examined in the first hypothesis. This hypothesis is rejected. In the second hypothesis, there was no significant difference in the efficiency scores of DCs located in large and small cities. The last hypothesis confirmed assumption from the literature. Namely, there is difference in efficiency scores of small and large DCs. In literature, there is a lack of case studies that test the PCA–DEA approach on real logistics systems. This fact indicates the insufficient amount of research in this area. This paper shows how a theoretical model can be applied in practice. The model proposed in this paper corresponds to a real situation of the observed trading company. Proposed methodology represents support in the decision making process. Models presented in this paper, with minor adjustments, can be used for measuring and improving the efficiency of providers, warehouses, suppliers, etc. Presented models are a good basis for development of future models. In the future research, models should include environmental and other quality indicators.