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|کد مقاله||سال انتشار||تعداد صفحات مقاله انگلیسی||ترجمه فارسی|
|1375||2010||14 صفحه PDF||سفارش دهید|
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Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Decision Support Systems, Volume 48, Issue 4, March 2010, Pages 568–581
In the last decade, the growing economy in Taiwan has brought about rapid growth in the logistics demands of enterprises. An important goal in the field of third party logistics (3PLs) is to improve the performance of logistics activities to enhance operation efficiency and enterprise competency. However, the employee performance must be determined in order to improve the activity performance of 3PLs. Thus, the aim of this research is to develop an employee performance estimation (EPE) model that includes three modules: direct performance determination (DPD), indirect performance determination (IPD), and performance score analysis (PSA). Moreover, a web-based logistics information management (LIM) platform was established via the EPE model in order to assist the managers in collecting and maintaining shop-floor operation data and to identify low-performance logistics tasks as well as inexperienced employees. In addition, a real-world case was used to demonstrate applicability of the proposed model and platform. As a whole, this paper presents an integrated model with the aims to more accurately calculate employee performance and significantly reduce the workload of 3PL decision makers.
As the economy continues to grow in Taiwan, enterprises require more cooperation with professional logistics service providers in order to accomplish logistics activities since the complexity of logistics activities (e.g., distribution or warehousing) has gradually increased. This has resulted in a drastic increase in the number of third party logistics (3PLs) established for the purpose of fulfilling the logistics demands of enterprises. In order to enhance operation competency and efficiency, some 3PLs have utilized a variety of automated techniques and management strategies to improve the performance of logistical tasks. Although conventional 3PLs invest a large amount of money and time in their logistic operations, operation competency and efficiency has not shown significant improvement because managers cannot systematically recognize either low-performance logistics tasks or inexperienced employees. Logistics managers do not take a systematic approach for determining the performance of operators. In addition, logistics-related data (e.g., operation time) from the shop floor cannot be accurately gathered and imported into a logistics database and thus, they cannot be employed for operator performance evaluation. Under such circumstances, 3PL managers have difficulties reusing and analyzing logistics-related data. To overcome these problems, this research proposes a model aimed at determining the performance of different types of employees by utilizing the shop floor data of logistics activities. With regard to employee performance calculation, this research uses quantitative factors to estimate the operational performance of direct workers and indirect managers. Two performance reasoning modules are developed in this study: • Direct Performance Determination: Used to determine the Real Performance (RP), Effective Performance (EP) and Derived Performance (DP) of direct workers. • Indirect Performance Determination: Used to determine the Verification Performance (VP), Assessment Performance (AP) and Inference Performance (IP) of indirect managers. The two modules can be combined to generate an integrated employee performance estimation model. In the proposed performance estimation model, the RP may first be calculated via the duration and quantitative outputs of logistical tasks. Subsequently, several quality indices (e.g., the operator trend index and operator idle index) can be formulated to determine the EP and DP. The team-level trend index, team-level quality index, schedule index and budget index can be formulated to estimate the VP, AP and IP. The operator and manager performance indices (i.e., RP, EP, DP, VP, AP and IP) can be given to logistics managers in order to identify both low-performance logistics tasks and inexperienced employees. In summary, the proposed performance estimation approach can be used in the logistics management systems of 3PLs to produce an automatic determination of employee performance in a logistics center. By estimating the performances of all levels of employee, low-performance logistics tasks as well as inexperienced employees can be determined so that the demands of 3PLs for improvement in operational competency and efficiency can be fulfilled.
نتیجه گیری انگلیسی
To accurately estimate the employee performance in a DC, this paper developed an integrated model to calculate the employee performance based on the logistics operation data and business information. In the proposed model, three critical modules including DPD, IPD and PSA were developed to estimate the logistics performance of employees at distinct levels in a DC organization hierarchy. According to the proposed model, this paper also established a web-based LIM platform to reduce the workload of DC decision makers, to assist in the management of logistics operation data and to support the bottleneck analysis of employees and logistics tasks. In addition, the Nung Hsueh DC in Taiwan was used to analyze applicability of the EPE model and the LIM platform. According to analysis results of the random approach, EPE approach and expert evaluation, the proposed model and platform can effectively assist the inexperienced DC managers to acquire the employee performance accepted by the experienced DC managers. As a whole, this paper presents a feasible approach for LSPs to accurately determine employee performance. However, the applications (e.g., identification of employees with significant performance increase or decrease) of employee performance are not investigated in this paper. In order to assist the DC managers to manage the employees and logistics tasks, the future research can focus on applying the employee performance generated via the EPE model to identify the employees with the significant performance increase or decrease and that might be beneficial to enhance the operation efficiency.