Supply chain simulation in a Big Data context: risks and uncertainty analysis

Due to their complex and dynamic nature, Supply Chains are prone to risks that may occur at any time and place. To tackle this problem, simulation can be used. However, such models should use Big Data technologies, in order to provide the level of data and detail contained in the data sources associated to the business processes. In this regard, this paper considered a real case of an automotive electronics Supply chain. Hence, the purpose of this paper is to propose a simulation tool, which uses real industrial data, provided by a Big Data Warehouse, and use such decision-support artifact to test different types of risks. More concretely, risks in the supply and demand end of the network are analyzed. The presented results also demonstrate the possible benefits that can be achieved by using simulation in the analysis of risks in a Supply Chain.This work has been supported by FCT–Fundação para a Ciência e Tec-nologia within the Project Scope: UID/CEC/00319/2019 and by the Doctoral scholarship PDE/BDE/114566/2016 funded by FCT, the Portuguese Ministry of Science, Technology andHigher Education, through national funds, and co-financed by the European Social Fund(ESF) through the Operational Programme for Human Capital (POCH)

Dynamic temporary blood facility location-allocation during and post-disaster periods

The key objective of this study is to develop a tool (hybridization or integration of different techniques) for locating the temporary blood banks during and post-disaster conditions that could serve the hospitals with minimum response time. We have used temporary blood centers, which must be located in such a way that it is able to serve the demand of hospitals in nearby region within a shorter duration. We are locating the temporary blood centres for which we are minimizing the maximum distance with hospitals. We have used Tabu search heuristic method to calculate the optimal number of temporary blood centres considering cost components. In addition, we employ Bayesian belief network to prioritize the factors for locating the temporary blood facilities. Workability of our model and methodology is illustrated using a case study including blood centres and hospitals surrounding Jamshedpur city. Our results shows that at-least 6 temporary blood facilities are required to satisfy the demand of blood during and post-disaster periods in Jamshedpur. The results also show that that past disaster conditions, response time and convenience for access are the most important factors for locating the temporary blood facilities during and post-disaster periods

Upstream Supply Chain Visibility and Complexity Effect on Focal Company’s Sustainable Performance: Indian Manufacturers’ Perspective

Understanding supply chain sustainability performance is increasingly important for supply chain researchers and managers. Literature has considered supply chain sustainability and the antecedents of performance from a triple bottom line (economic, social, and environmental) perspective. However, the role of supply chain visibility and product complexity contingency in achieving sustainable supply chain performance has not been explored in depth. To address this gap, this study utilizes a contingent resource-based view theory perspective to understand the role of product complexity in shaping the relationship between upstream supply chain visibility (resources and capabilities) and the social, environmental, and economic performance dimensions. We develop and test a theoretical model using survey data gathered from 312 Indian manufacturing organizations. Our findings indicate that supply chain visibility (SCV) has significant influence on social and environmental performance under the moderation effect of product complexity. Hence, the study makes significant contribution to the extant literature by examining the impact of SCV under moderating effect of product complexity on social performance and environmental performance

Manufacturing and Supply Chain Flexibility: Building an Integrative Conceptual Model Through Systematic Literature Review and Bibliometric Analysis

The purpose of this study is twofold: first, to establish the current themes on the topic of manufacturing and supply chain flexibility (MSCF), assess their level of maturity in relation to each other, identify the emerging ones and reflect on how they can inform each other, and second, to develop a conceptual model of MSCF that links different themes connect and highlight future research opportunities. The study builds on a sample of 222 articles published from 1996 to 2018 in international, peer-reviewed journals. The analysis of the sample involves two complementary approaches: the co-word technique to identify the thematic clusters as well as their relative standing and a critical reflection on the papers to explain the intellectual content of these thematic clusters. The results of the co-word analysis show that MSCF is a dynamic topic with a rich and complex structure that comprises five thematic clusters. The value chain, capability and volatility clusters showed research topics that were taking a central role in the discussion on MSCF but were not mature yet. The SC purchasing practices and SC planning clusters involved work that was more focused and could be considered more mature. These clusters were then integrated in a framework that built on the competence–capability perspective and identified the major structural and infrastructural elements of MSCF as well as its antecedents and consequences. This paper proposes an integrative framework helping managers keep track the various decisions they need to make to increase flexibility from the viewpoint of the entire value chain

Analysing the hindrances to the reduction of manufacturing lead-time and their associated environmental pollution

Manufacturing Lead-Time (MLT) is the total time required to process a given product through a plant. Long MLT is the major cause of inefficient manufacturing, since it generates large amount of wastes and creates considerable environmental burden. In the past, a large amount of environmental pollution has been generated by manufacturing industries, most of which come from inefficient practices trough generating various types of wastes. The easiest and probably the least expensive way to cut manufacturing wastes and minimise their consequent environmental impacts is to improve the manufacturing procedures of the companies. A novel approach to do this is to identify and reduce/eliminate the processes and activities which cause the inefficient use of resources. This paper presents the MLT reduction process via identifying the value-adding and non-value adding activities. It also analyses four crucial sources of wastes which generate the greatest environmental pollution and suggestions for improvements are provided

Complex real-life supply chain planning problems

Supply chain planning concerns the selection of strategies and methodologies to facilitate the optimal flow of material from raw material suppliers to end-users through procurement, production and distribution activities. Supply chain (SC) implementation has significant impacts on the financial performance of manufacturing and distribution companies. Developing real-life SC models with centralised planning naturally leads to complex models which are difficult to solve optimally. This chapter firstly presents a comprehensive review on the current literature of SC planning and optimisation and classifies the published models based on their complexity. Next, a mixed-integer non-linear formulation is presented for modelling complex real-life SC planning problems which accommodates the identified gaps in the current literature. Evaluation of the available tools and techniques for the optimisation of the proposed SC model will conclude this chapter. © 2012, IGI Global

Integration in Logistics Planning and Optimization

Logistics planning (LP) is the process of integrating and utilizing suppliers, manufacturers, warehouses, and retailers so that products are produced and delivered at the right quantities and at the right time while minimizing costs and satisfying customer requirements. Implementation of LS has crucial impacts on a company's financial performance and LP optimization is essential to achieve globally optimized operations. The six major cost components that form the overall logistics costs are raw material costs, costs of raw material transportation from vendors to manufacturing plants, production costs at manufacturing plants, transportation costs from plants to warehouses, inventory or storage costs at warehouses, and transportation costs from warehouses to end users. In a logistics optimization model, the overall systemwide costs are to be minimized through effective procurement, production, distribution, and inventory management. It is widely acknowledged that many benefits can be achieved by treating a logistics network as a whole (integration in LS) for optimization purposes, which requires the simultaneous minimization of all systemwide costs. © 2011 Elsevier Inc. All rights reserved

Modeling and Optimization of Aggregate Production Planning- A Genetic Algorithm Approach

The Aggregate Production Plan (APP) is a schedule of the organizations overall operations over a planning horizon to satisfy demand while minimizing costs. It is the baseline for any further planning and formulating the master production scheduling, resources, capacity and raw material planning. This paper presents a methodology to model the Aggregate Production Planning problem, which is combinatorial in nature, when optimized with Genetic Algorithms. This is done considering a multitude of constraints of contradictory nature and the optimization criterion overall cost, made up of costs with production, work force, inventory, and subcontracting. A case study of substantial size, used to develop the model, is presented, along with the genetic operators

An integrated model for the optimisation of a two-echelon supply network

Purpose: The purpose of this paper is to develop a mixed integer formulation that extends the previous production-distribution models by the integration of Aggregate Production Plan and Distribution Plan. Design/methodology/approach: This paper, firstly, presents a comprehensive review and analysis on the proposed production-distribution models and would develop a summary table to describe the main characteristics of the selected models outlining the level of complexity considered at each study. Based on the integration of Aggregate Production Plan and Transportation/Distribution Plan, over the second stage, the paper will develop a mixed integer formulation for a two-echelon supply network. The model incorporates multi-time periods, multi-products, multi-plants, multi-warehouses as well as multi-end users, and considers the real-world variables and constraints. Finally, the developed model will be analyzed in case of a realistic scenario-based production-distribution problem. Findings: This paper developed a mixed integer formulation for the optimization of a two-echelon SN. Considering detailed production cost elements and a realistic range of variables and constraints in the proposed case study indicate the effectiveness of the developed model in the real-world applications. Practical implications: The increasing interest in evaluating the performance of SNs over the last years indicates the need for the development of complex optimization models able to answer unsolved questions in the production-distribution network. Originality/value: Implementation of a supply-chain (SC) system has crucial impacts on a companys financial performance. Overall performance of a Supply Network (SN) is influenced significantly by the decisions taken in its production-distribution plan integrating the decisions in production, transport and warehousing as well as inventory management. Thus, one key issue in the performance evaluation of SNs is the modeling and optimization of production-distribution plan considering its actual complexity