یک چارچوب تعیین معیار بر اساس دلفی-AHP-TOPSIS برای بهبود عملکرد زنجیره سرد
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|1002||2011||13 صفحه PDF||سفارش دهید||محاسبه نشده|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Expert Systems with Applications, Volume 38, Issue 8, August 2011, Pages 10170–10182
This paper aims to develop a benchmarking framework that evaluates the cold chain performance of a company, reveals its strengths and weaknesses and finally identifies and prioritizes potential alternatives for continuous improvement. A Delphi-AHP-TOPSIS based methodology has divided the whole benchmarking into three stages. The first stage is Delphi method, where identification, synthesis and prioritization of key performance factors and sub-factors are done and a novel consistent measurement scale is developed. The second stage is Analytic Hierarchy Process (AHP) based cold chain performance evaluation of a selected company against its competitors, so as to observe cold chain performance of individual factors and sub-factors, as well as overall performance index. And, the third stage is Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) based assessment of possible alternatives for the continuous improvement of the company’s cold chain performance. Finally a demonstration of proposed methodology in a retail industry is presented for better understanding. The proposed framework can assist managers to comprehend the present strengths and weaknesses of their cold. They can identify good practices from the market leader and can benchmark them for improving weaknesses keeping in view the current operational conditions and strategies of the company. This framework also facilitates the decision makers to better understand the complex relationships of the relevant cold chain performance factors in decision-making.
A ‘cold chain’ is comprises of equipments and processes that keeps perishable products under conditioned environment. These perishable products can be categorized into two types: living products (fruits, vegetables, live seafood, flowers, etc.) and non-living products (meat, dairy products, processed food products, medicines, blood, frozen products, etc.), which all require appropriate atmosphere to defy microbial spoilage (Donselaar, Woensel, Broekmeulen, & Fransoo, 2006). In today’s competitive environment, perishable products are one of the main drivers through which retailers are attaching additional customers to increase profitability. Heller has stated that the quality of perishable goods assortment is becoming the core reason many customers choose one retailer over another (as cited in Thron, Nagy, & Wassan, 2007). The global market for perishable goods such as cooled products and processed foods is growing due to changing lifestyles and overall declining prices. Retailers who are in the business of perishables can find a direct correlation between the cold chain performance and the quality delivered to the customer. Any disorder in time–distance or temperature in the cold chain could hamper the net present value of the activities and adversely influence the over all performance (Bogataj, Bogataj, & Vodopivec, 2005). Since long time, the cold chain data has been under utilized and used solely for the purposes of evaluating the integrity of individual shipments (i.e. facilitating the accept or reject decisions). These data can be gathered to measure performance of the cold chain, which in turn can identify flaws and weaknesses in the processes. Benchmarking is a well-defined tool for improving these weaknesses through improvement processes in which a company measures its performance against that of market leaders, finds how market leaders have achieved their performance levels, and prudently uses this knowledge to improve its own performance (Saunders, Mann, & Smith, 2007). A well defined performance measurement system (PMS) aims to support the setting of objectives, evaluating performance, and determining future courses of action on a strategic, tactical and operational level (Gunasekaran, Patel, & Tirtiroglu, 2001). PMS allows comparison of planned and actual parameter values and taking certain reactive measures in order to improve performance or re-align the monitored value to the defined value (Beamon, 1999).
نتیجه گیری انگلیسی
Measuring performance of a cold chain is difficult, because it has many characteristics that set it apart from other types of supply-chains. This paper has attempted to present a benchmarking framework that simplifies and reduces the complexities of cold chain evaluation with a view to helping focus the practicing mangers’ efforts towards improvement. The evaluator can judge the performance of the cold chain in a better way, as data collection is relatively easy and accessible due to the consistent measuring system defined for all the qualitative and quantitative factors. The consistent measuring system has facilitated to evaluate performance more reliably for each factor and sub-factor. With the proposed Delphi-AHP-TOPSIS framework managers can easily understand the present strengths and weaknesses of their companies as compared to market leaders. They can identify the good practices from the market leader and can benchmark them for improving the weaknesses. Managers can further analyze the effectiveness of the potential improvement opportunities as per the current operational requirements and strategies of the company. This framework also facilitates the decision makers to better understand the complex relationships of the relevant attributes in the decision-making, which may consequently improve the accuracy of the decision. Indeed, gathering information from the direct competitors is not an easy task. In a partnering, ethical and legal manner the information gathering should be done. Competitor’s information can be collected even without directly contacting the competitor from public domain published by various research agencies and economy monitoring institutes like Gartner, CMIE, companies’ annual reports, etc. Also, it can be observed from the list of factors and sub-factors that nowhere strategic information is tried to acquire or required for hurting competitors’ competitive advantage. Furthermore, the company needs not just copy the practices learnt from the competitors, it can adapt and go beyond the learning and use innovative means to create what is the most relevant as per its operational strategy. And in this way they can develop competitive advantage. One of the limitations of the framework can be viewed as the subjectivity of rating and evaluation standard of the measuring system. But, at the same time its flexibility can be viewed as the strength also as the standardizations of the rating and evaluation values are not restricted. Depending upon the particular requirement, these standards can be modified for better results. Also, as the pairwise comparison of factors and sub-factors are derived using Delphi method, can be changed in the preference of current company requirements. In addressing the same decision problem, many people will have different ideas concerning the rating of one attribute over another. Sensitivity analysis addresses this issue of variation in judgment from person to person or for the same person from time to time. Since the selection of the best alternative depends upon the pairwise comparison of factors set by experts, thorough sensitivity analysis is important to foresee the impact of changes in these in a comprehensible way. In the present framework sensitivity analysis can be done to find out the changes in the final alternative selection with variation in the pairwise comparison taken from expert opinion on Satty scale (1/9 to 9). Then the variation effects on the final selection of improvement alternative can be judged. Sensitivity analysis sees the robustness of proposed framework due to variation in the experts’ opinion in assigning the influence during comparison. A suggestion for future research could be the use of proposed framework to other sectors with small alterations. Different companies can choose their own factors and sub-factors with different values of relative weights, as per their own goals and business strategies.