Abstract
System dynamics is a method that allows analysts to separate complex social, and behavioral systems into components, to visualize them by reconstructing them as a whole again, and to develop a simulation model. In this chapter, a system dynamics model is developed for a flexible manufacturing system. The case study is developed using Vensim package program. To manage a flexible manufacturing system it is important to determine production schedule rules and examine results. System dynamics is a tool to test these rules without any production loss.
TopShort Literature Rewiev
Olhager and Selldin (2004) investigated supply chain management strategies and practices in a sample of 128 Swedish manufacturing firms. They specifically studied issues related to the supply chain design, integration, planning and control, and communication tools for managing supply chains. The main findings indicate the following. The extent to which suppliers and customers are involved in supply chain planning and control is expected to increase steadily over the next 2 years.
Kehoe and Boughton (2001) studied to describe current research which examines the classification of manufacturing supply chains and positions Internet‐based applications in order to identify the operations management challenges for the next generation of manufacturing planning and control systems.
Lee and Kim (2007) studied a review of the development and use of multi-agent modelling techniques and simulations in the context of manufacturing systems and supply chain management (SCM). The objective of the paper is twofold. First, it presents a comprehensive literature review of current multi-agent systems (MAS) research applications in the field of manufacturing systems and SCM. Second, it aims to identify and evaluate some key issues involved in using MAS methods to model and simulate manufacturing systems.
Ghadge et.al. (2018). studied to assess the impact of additive manufacturing (AM) implementation on aircraft supply chain (SC) networks. Additive and conventional manufacturing spare part inventory control systems are studied and compared, revealing insights into SC performance. The paper offers guidance on the adaption of AM in aircraft SCs and AM’s impact on spare part inventory systems. The study provides robust evidence for making critical managerial decisions on SC re-design driven by a new and disruptive technology. Next-generation SC and logistics will replace the current demand for fulfilling material products by AM machines.
Key Terms in this Chapter
Information Flow: Creation, control and distribution of information is the most significant task of business management. It might be difficult to model the information properly in the business process.
Stocks: The present values of the variables that are formed by the accumulated difference between the inflow and outflow.
System Dynamic Language: This language consists of four components: stocks, flows, decision functions and information flow.
Flow: It is defined as the increase or decrease in the unit time interval in stocks and denoted by f ( t ).
Material Delays: In this structure, the material moves forward from the beginning to the end of the pipeline without any change during a period of time, similar to a water running through a pipeline.
Material Flow: It includes stocks and flow rates of physical goods such as raw materials, inventories in the process or finished goods.
System Dynamics: The basis of system dynamics is to understand how system structures cause system behavior and system events
Decision Functions: States of the policy that determines how to convert available information in stock into a decision.
Delays: A characteristic of dynamic systems and affect both material and information flow.
Information Delay: They are usually caused by the transfer of information and the delay in the process of taking an action after the information is received. Information delays could occur in vertical hierarchical administrations.