The Performance Evaluation of third-party logistics customer service based on Fuzzy Comprehensive Evaluation

Abstract

According to the features of the customer service of third-party logistics, setting up its KPI system of customer service, and then use the fuzzy comprehensive evaluation to evaluate the service performance of third-party logistics enterprises based on the evaluation system, and finally use example to apply the Fuzzy Comprehensive Evaluation Model to the reality of the work of the enterprises, reflecting the usefulness of this method. Introduction Nowadays, the market competition has become more and more intense, in order to improve the core competitiveness of enterprises, the non-core business will be outsourced to those professional companies, thus forming the third-party logistics enterprises. In this article, we will use the fuzzy comprehensive evaluation method to evaluate the performance of third-party logistics. KPI system of service performance for third-party logistics enterprises A. KPI System of customer service or third-party logistics enterprises [1] According to services content of third-party logistics, we can divide the KPI system of customer service for third-party logistics into the following three areas: warehousing management, transportation management, financial settlement. 1) Warehousing management Through the understanding of all the links in warehouse operations, we can know that the indicator system of warehouse management can be evaluated in four aspects: inventory accuracy, warehousing accuracy, outputting accuracy, cargo damage rate. 2) Transportation management This indicator can be defined in six aspects: delivery timeliness, arrival timeliness, customer complaints, customer satisfaction, breakage rate, order fulfillment rate. 3) Financial settlement The financial settlement for other companies to evaluate their business service is an important indicator, which includes: settlement timeliness, settlement accuracy. Fig1 KPI system of quality of service The Establishment of Fuzzy Comprehensive Evaluation Model 2nd International Conference on Science and Social Research (ICSSR 2013) © 2013. The authors Published by Atlantis Press 703 A. The Definition of Fuzzy Comprehensive Evaluation Fuzzy comprehensive evaluation method is a comprehensive evaluation method based on fuzzy mathematics, changing the qualitative evaluation into quantitative evaluation, which uses fuzzy mathematics to make an overall assessment for those objects restricted by many factors. . B. Establish Evaluation Indicator System Set M [3] In the service performance system of third-party logistics, we set its evaluation index system as below: M=(M1,M2,M3) M1: warehouse management ;M2:transportation management ;M3:financial settlement. And each indicator also contains second-level indicators: M1={M11(inventory accuracy),M12=(warehousing accuracy),M13= (outputting accuracy) ,M14= (cargo damage rate)}; M2={M21(delivery timeliness),M22(arrival timeliness),M23(customer complaints),M24(customer satisfaction) ,M25(breakage rate),M26(order fulfillment rate)}; M3= {M31(settlement timeliness),M32(settlement accuracy)}. C. Establish Evaluation Set N [4] It’s difficult to quantify the pollution levels, so we need to set up a group of fuzzy evaluation results for each second-level evaluation. For example, we can set up four fuzzy evaluation results for each evaluation: N1: A (excellent), N2: B (good), N3: C (average), N4: D (poor). D. To Determine the Weight Vector of each Indicator 1) The Weight Vector of First-Level Indicators Calculate the coefficient of the first-level indicators by the score, which makes up the first-level indicator vector, denoted by U={U1,U2, ,Um} ,Ui is the weight vector of first-level indicator Mi. U ∑ ∑ ∑ ∑ ∑ , Ui , ∑ U =1,i=1,2, ,m (1) 2) The Weight Vector of Second-Level Indicators Similarly, all the coefficients of second-level indicators make up the vector, denoted by Uij={Ui1, Ui2, ,Uin},Uij is the weight vector of second-level indicator Mij. U ∑ ∑ ∑ , Uij 0,∑ U =1,i=1,2, ,n (2) E. Establish Fuzzy Comprehensive Evaluation Matrix [5] After collecting large amount of questionnaires to their long-term business cooperation enterprises, getting the second-level fuzzy evaluation matrix Pk as follows: P a ⋯ a ⋮ ⋱ ⋮ a ⋯ a 0 a 1, ∑ a =1,i=1, 2, ,n. (3) a = (4) Getting the weight coefficient vector Uij of second-level indicators according to the formula, as Ni= U •Pk ,getting result vector: Ni= {bi1,bi2, ,bin} The matrix which was consisted of all the second-level result vectors is just the fuzzy evaluation matrix P: P= b ⋯ b ⋮ ⋱ ⋮ b ⋯ b Similarly, according to the fuzzy transformation formula N=Mi•Pi, getting the first-level indicators evaluation results vector N, denoted as N={b1,b2, ,bn}. Analysis of Numerical examples There is a thirdparty logistics company which wants to conduct an internal assessment of customer service, they conduct a survey for those 200 companies they have long-term cooperation, the findings are as follows: