A decomposition approach for the performance analysis of a serial multi-echelon supply chain

Abstract

Over the last decade, supply chain coordination has attracted much attention in the context of industrial logistics systems, because of the role played in production support logistics. There is a growing interest in the analysis and measurement of performances, because performance analysis allows us to build supporting decision tools for the design, planning and management of the supply chain. This paper presents an analytical approach for performance analysis based on a decomposition principle. The approach is applied to a serial supply chain that consists of two levels of suppliers, a production plant and a distribution centre, and operates as a pull system. This configuration is typical for bulk materials such as grain. The method allows us to approximately calculate performance indicators (such as customer satisfaction and inventory levels) taking into account all the interactions between the different echelons of the supply chain. The accuracy of the approximations is confirmed by the results obtained via a simulation model. Numerical analysis allows us to outline interesting and non-intuitive behaviour of the performance indicators for different stages of the supply chain.

Warehouse performance measurement: a literature review

“…These new services require additional indicators to measure the related performance. Oftentimes, the indicators Measures the accuracy (by location and units) of the physical inventory compared to the reported inventory Kiefer and Novack (1999), Rimiene (2008), Wang, Chen, and Xie (2010), Yang and Chen (2012), Ramaa, Subramanya, and Rangaswamy (2012) Picking accuracy b Accuracy of the orders picking process where errors may be caught prior to shipment such as during packaging Rimiene (2008), Saetta et al (2012), Yang and Chen (2012) Storage accuracy Storing products in proper locations Voss, Calantone, and Keller (2005) are complex; either the equations are not available or they are too difficult to calculate. The warehouse capability (Sohn, Han, and Jeon 2007), the supervisory coaching behaviour (Ellinger, Ellinger, and Keller 2003), Table 10.…”

“…In this review, we propose a sub-category, called the labour productivity and Banaszewska et al (2012) appears in this category (see Table A2). Another example is the article by Saetta et al (2012), where the authors measure the customer satisfaction as 'the percentage of orders on time' and we classify the article under a broader indicator which is the 'on time delivery' (see Table A2). The results from this analysis are given in Table 5, which demonstrates all performance indicators comprised in a dimension.…”

Modeling the Multi-channel Section in the Supply Chain System using the Multiserver Queue Analogy

“…It can be identified as a flow line system if the echelons are seen as workstations. This is seen in Karaman and Altiok [2], Saetta et al [3], and Murdapa et al [4].…”

The Supply Chain Design of Biomass Energy Plants: A Simulation Approach

“…These static models are not able to describe the dynamic behavior and the complexity related to a biomass supply chain, that can be achieved only adopting simulation approaches [11]. Simulation approaches have been also utilized to validate analytical models developed for multi-echelon supply chains of bulk materials such as grain [12]. Among the simulation frameworks, the Integrated Biomass Supply Analysis and Logistics (IBSAL) model [11] considers various stages of the agricultural biomass logistics system.…”