Simulation in the supply chain context: a survey

Owing to its inherent modeling flexibility, simulation is often regarded as the proper means for supporting decision making on supply chain design. The ultimate success of supply chain simulation, however, is determined by a combination of the analyst's skills, the chain members' involvement, and the modeling capabilities of the simulation tool. This combination should provide the basis for a realistic simulation model, which is both transparent and complete. The need for transparency is especially strong for supply chains as they involve (semi)autonomous parties each having their own objectives. Mutual trust and model effectiveness are strongly influenced by the degree of completeness of each party's insight into the key decision variables. Ideally, visual interactive simulation models present an important communicative means for realizing the required overview and insight. Unfortunately, most models strongly focus on physical transactions, leaving key decision variables implicit for some or all of the parties involved. This especially applies to control structures, that is, the managers or systems responsible for control, their activities and their mutual attuning of these activities. Control elements are, for example, dispersed over the model, are not visualized, or form part of the time-indexed scheduling of events. In this article, we propose an alternative approach that explicitly addresses the modeling of control structures. First, we will conduct a literature survey with the aim of listing simulation model qualities essential for supporting successful decision making on supply chain design. Next, we use this insight to define an object-oriented modeling framework that facilitates supply chain simulation in a more realistic manner. This framework is meant to contribute to improved decision making in terms of recognizing and understanding opportunities for improved supply chain design. Finally, the use of the framework is illustrated by a case example concerning a supply chain for chilled salads.

A modeling framework for supply chain simulation: opportunities for improved decision-making

A review and critique on integrated production–distribution planning models and techniques

A supply chain is defined as an integrated system that coordinates a series of inter-connected business processes in order to: (1) obtain raw materials and parts; (2) convert these raw materials and parts into finished products; (3) distribute these products to retailers or consumers; (5) expedite information flow among various business entities; (e.g. suppliers, manufacturers, distributers, 3PLs, and consumers). The major objective of it is to enhance the operational productivity, profitability, and competitiveness of whole supply chain members. There are various classification schemes to categorize supply chain models respect to a wide spectrum of the supply chain concept. Min and Zhou (2002) classified supply chain models to four categories: (1) deterministic (non-probabilistic); (2) stochastic (probabilistic); (3) hybrid; (4) IT-driven. Deterministic models assume that all the model parameters are certainly known and fixed, while stochastic models take into account the uncertain and random parameters.

Quantitative Models in Supply Chain Management

A simulation-based multi-objective genetic algorithm approach for networked enterprises optimization

Supply Chain management, the management of the flow of goods or services from materials stage to the end user, is a complex process because of the level of uncertainty at each stage of the supply chain. Computer simulation, because it can be applied to operational problems that are too difficult to model and solve analytically, is an especially effective tool to help analyze supply chain logistical issues.While most engineers have had some exposure to the tools and technology of computer modeling and simulation, the use of simulation for supply chain analysis has not been prevalent until recently. The software tool, Supply Solver, was developed in an effort to provide supply chain solutions using simulation as the foundation. In this paper, the goal will be to show how discrete-event simulation is used to analyze supply chain processes. This paper will also demonstrate what some of the considerations are in using Supply Solver to help solve supply chain design problems.

Using simulation to analyze supply chains

With new technologies emerging daily, one of the largest problems companies currently face is the challenge of upgrading slow, outdated systems. While it is common to consider things like cost efficiency and automation in new systems, companies often overlook the human element in their system redesign. By factoring in the human element, companies can avoid having to make costly adjustments to their system because of unexpected human error. Task network modeling is one approach to modeling human performance in complex systems. Micro Saint is a modeling environment that supports task network modeling and human performance modeling. This paper will discuss how to model the human element using Micro Saint along with a brief case study.

Micro Saint: Micro Saint modeling and the human element

For the past nineteen years, Micro Saint simulation software has been helping the military and other commercial companies answer questions on how to improve performance and utilization for their various processes. Recently, Micro Saint has been redesigned to be faster, modular and more powerful. Because these changes represent such a major change from the original Micro Saint, we are releasing a brand new tool called Micro Saint Sharp. Micro Saint Sharp is still a general purpose tool that can be used to provide solutions ranging from queuing problems involving hospital waiting rooms to complex human decision processes involving future command and control systems. This paper will provide an overview of Micro Saint Sharp and present some of its new modeling capabilities.

Micro saint sharp simulation software: micro saint sharp simulation software

Micro Saint is a discrete-event simulation software package for building models that simulate real-life processes. With Micro Saint models, users can gain useful information about processes that might be too expensive or time-consuming to test in the real world. Some common application areas for simulation modeling include the following:• Modeling manufacturing processes, such as production lines, to examine resource utilization, efficiency, and cost.• Modeling transportation systems to examine issues such as scheduling and resource requirements.• Modeling service systems to optimize procedures, staffing and other logistical considerations.• Modeling training systems and their effectiveness over time.• Modeling human operator performance and interaction under changing conditions.Simulation is a cost-effective way to help show decision-makers the most cost-efficient alternatives to any problem.

Micro saint: modeling with the Micro Saint simulation package

With new technologies emerging daily, one of the largest problems companies currently face is the challenge of upgrading slow, outdated systems While it is common to consider things like cost efficiency and automation in new systems, companies often overlook the human element in their system redesign By factoring in the human element, companies can avoid having to make costly adjustments to their system because of unexpected human error Task network modeling is one approach to modeling human performance in complex systems Micro Saint is a modeling environment that supports task network modeling and human performance modeling This paper discusses how to model the human element using Micro Saint along with a brief case study

http://ieeexplore.ieee.org/iel5/8343/26360/01172883.pdf

Daniel Schunk

Papers

Using simulation to analyze supply chains

Micro Saint: Micro Saint modeling and the human element

Micro saint sharp simulation software: micro saint sharp simulation software

Micro saint: modeling with the Micro Saint simulation package

Micro Saint modeling and the human element