Supply Chain Modeling in the Aftermath of a Disaster: A System Dynamics Approach in Housing Recovery

2020 ◽  
Vol 67 (3) ◽  
pp. 531-544
Author(s):  
Rafael Diaz ◽  
Joshua G. Behr ◽  
Francesco Longo ◽  
Antonio Padovano
2008 ◽  
Vol 27 (1) ◽  
pp. 3-13
Author(s):  
Charu Chandra ◽  
Jānis Grabis

Multiple interrelated decision-making models are frequently used in supply chain modeling. Model integration is a precondition for efficient development and utilization of these models. This paper discusses use of modern information technology (IT) techniques and methods for integration of supply chain decision-making models. The overall approach to using IT at various stages of model development is presented. Data and process modeling techniques are used to developed semi-formalized representation of integrated models. These models support integration of decision-making components with other parts of supply chain information system. Process modeling is also used to describe interrelationships among multiple decision-making models. This representation is used as the basis for implementation of integrated models. The service-oriented architecture is proposed as an implementation platform. The presented discussion serves as the basis for further developments in developing integrated supply chain decision-making models.


2021 ◽  
pp. 1-14
Author(s):  
Katayoun Naderi ◽  
Roya M. Ahari ◽  
Javid Jouzdani ◽  
Atefeh Amindoust

Fierce competition in the global markets forced companies to improve the design and management of supply chains, because companies are always looking for more profit and higher customer satisfaction. The emergence of the green supply chain is one of the most important developments of the last decade. It provides an opportunity for companies to adjust their supply chains according to environmental goals and sustainability. The integrated production-inventory-routing is a new field that aims to optimize these three decision-making levels. It can be described as follow: a factory produces one or more products, and sells them to several customers (by direct delivery or a specific customer chain). The current study aims to model a production-inventory-routing system using a system dynamics approach to design a green supply chain under uncertain conditions. For this purpose, first, the association between selected variables was determined. Then, the proposed model was validated. Finally, to identify variables with the highest influence, four scenarios were developed. The results indicated that minimum total transportation cost, the total warehouse capacity of the supply chain, and the maximum production rate are the most influential strategies to achieve ideal condition.


2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jagan Mohan Reddy K. ◽  
Neelakanteswara Rao A. ◽  
Krishnanand Lanka ◽  
PRC Gopal

Purpose Pull production systems have received much attention in the supply chain management environment. The number of Kanbans is a key decision variable in the pull production system as it affects the finished goods inventory (FGI) and backorders of the system. The purpose of this study is to compare the performance of the fixed and dynamic Kanban systems in terms of operational metrics (FGI and backorders) under the demand uncertainty. Design/methodology/approach In this paper, the system dynamics (SD) approach was used to model the performance of fixed and dynamic Kanban based production systems. SD approach has enabled the feedback mechanism and is an appropriate tool to incorporate the dynamic control during the simulation. Initially, a simple Kanban based production system was developed and then compared the performance of production systems with fixed and dynamic controlled Kanbans at the various demand scenarios. Findings From the present study, it is observed that the dynamic Kanban system has advantages over the fixed Kanban system and also observed that the variation in the backorders with respect to the demand uncertainty under the dynamic Kanban system is negligible. Research limitations/implications In a just-in-time production system, the number of Kanbans is a key decision variable. The number of Kanbans is mainly depended on the demand, cycle time, safety stock factor (SSF) and container size. However, this study considered only demand uncertainty to compare the fixed and dynamic Kanban systems. This paper further recommends researchers to consider other control variables which may influence the number of Kanbans such as cycle time, SSF and container size. Originality/value This study will be useful to decision-makers and production managers in the selection of the Kanban systems in uncertain demand applications.


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