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Manufacturing systems design is a complex task and is crucial to the future of a company. Computer simulation provides an efficient and visual method for system designers. The popularity of simulation is owing to its ability to model systems in a fast and cost‐effective way, its flexibility and its ability to model the time dynamic behavior of systems. This paper demonstrates the use of computer simulation as a tool for assisting managers and engineers in the design and analysis of a new manufacturing system by presenting a case study. The case study is a study of proposed instrument panel/crashpad production and assembly work‐cells in an automotive industry. The emphasis on the case study was to evaluate alternative designs, predict system performance, detect any potential problems, experiment with system parameters and determine the sensitivity of the system to these parameters. The case study results show that computer simulation can be a useful decision‐making support tool for the analysis of the system performance and the selection of the design alternatives before the system is actually implemented.

Discusses computer modelling and simulation as a decision‐making technique frequently used by all genres of manager in a wide range of disciplines and in all types of organizations. Suggests that for auditors to do an effective job – a competent, quality audit in a timely and efficient way – they must familiarize themselves with the technique, know how it is used in the decision process and be aware of its shortcomings. Explores the many ways computer modelling and simulation are used in the decision process and discusses how the technique was actually used to facilitate several audits. Describes the use of computer modelling and simulation during an audit to validate an auditee‐developed model, simulate the outcome of an auditee′s plans, test an auditee′s in‐place system and, finally, help enhance the credibility of auditor recommendations.

Most manufacturing processes tend to involve more than one level of detail at the design phase. These often consist of a higher level that represents the building‐blocks of the firm and a lower level that represents a more detailed structure of the process. When designing such processes, this type of structure is difficult to capture without some form of modelling. In such cases simulation can be used to help overcome this problem. This paper presents an investigation of simulation packages.

These simulation packages were investigated regarding their abilities to model business processes related to manufacturing systems.

The research findings suggest that no one simulation package currently available can alone offer sufficiently flexible facilities for the variable detailed modelling of manufacturing systems design.

The paper relates to one specific design framework called manufacturing system design (MSD). It defines the higher level of detail as the conceptual modelling level and the lower level as the detailed design level. A four‐step framework is proposed, and it is argued that this may better deal with problems of detail variability.

Previous research suggests that developing dynamic models of business processes prior to their radical change could increase the success of BPR projects. Identifies barriers encountered in existing business processes and presents an overview of business process modelling methods that can be used to identify ways of eliminating these barriers. A case study is used to demonstrate how simulation modelling can be used to effectively re‐engineer manufacturing processes. The developed model is then manipulated, with results being generated to discover the possibilities of increasing the through‐put of the system. The usability of simulation modelling for evaluating alternative business process strategies is then investigated. Guidelines for achieving more widespread use of business process simulation are then proposed.

Complex system modeling requires not only understanding of modeling framework but also domain knowledge of the system. The purpose of this paper is to present an approach which separates the domain knowledge from the modeling framework with different views.

By establishing the mechanism of association and fusion among the views, the description and characterization of system from different aspect and point of view can form a complete system model. Based on the approach, a modeling and simulation (M&S) platform named SimFaster is developed. Modeling environment and simulation engine are the most important parts of the platform. The modeling environment provides multi‐views and multi‐layers to help the developers to modeling the structure, layers, composition, behavior, and interactions of an application system. The simulation engine provides mechanism of integration and interaction for components and objects, and provides runtime support for the concepts and terms from modeling environment. The simulation engine organizes the objects in the memory of distributed system as reflective object database system, so it is repository centered architecturally.

Based on the approach of multi‐views modeling, the platform is a flexible framework and supports top‐down design, model reuse and interoperation, dynamic refinement of models, corporative design among different users in different stages, and the rebuilt of application rapidly.

This paper deals with high‐level models of the complex systems.

This platform helps to design, modeling, and simulation complex system (especially for weapon combat system). It can participate into all the stages of the development of complex product/system, and can support the validation, refinement, optimization of models, and systems.

This paper presents a multi‐views modeling approach for the modeling of complex system.

Simulation is one of the most widely used tools within management science. The teaching of simulation has traditionally involved theory and practical model development. With the advent of modern software, practical model development can be undertaken with very little knowledge of simulation theory. This enables students who are more able in model building to develop their capabilities in this area and use their strengths to help develop the theoretical knowledge as they progress. This paper demonstrates how a little knowledge of the principles of simulation has been used to help students to develop working models by prototyping.

Describes the development of an adaptive simulation model for a keyboard assembly cell for real‐time decision support. Discusses the architecture of the modelling and control system, including the movement of entities and conveyors, describing how up to four different keyboard types may be modelled, with a PC cell controller continually monitoring the state changes of the assembly line, passing the data captured to the simulation model created in ARENA.

The purpose of this paper is to analyse, in a finite element simulation, the failure of a multilayer printed circuit board (PCB), exposed to an impact load, to better evaluate the reliability and lifetime. Thereby the focus was set on failures in the outermost epoxy layer.

The fracture behaviour of the affected material was characterized. The parameters of a cohesive zone law were determined by performing a double cantilever beam test and a corresponding simulation. The cohesive zone law was used in an enriched finite element local simulation model to predict the crack initiation and crack propagation. Using the determined location of the initial crack, the energy release rate at the crack tip was calculated, allowing an evaluation of the local loading situation.

A good concurrence between the simulated and the experimentally observed failure pattern was observed. Calculating the energy release rate of two example PCBs, the significant influence of the chosen type on the local failure behaviour was proven.

The work presented in this paper allows for the simulation and evaluation of failure in the outermost epoxy layers of printed circuit boards due to impact loads.

The purpose of this paper is to deal with the application of the stochastic inventory model to the three‐tier supply chain and verify the values obtained by mathematical model in physical simulation.

The paper investigates three‐stage serial supply chain with stochastic demand and fixed replenishment lead‐time. Inventory holding costs are charged at each stage, and each stage may incur a consumer backorder penalty cost charged by primary supplier to secondary supplier. The customer‐demand follows Poisson distribution. The base stock model is implemented for inventory control at both suppliers. Physical simulation is then designed in such a way that it satisfies all the assumptions for mathematical model. Simulation is run to verify the values obtained from mathematical model.

Computer simulation is designed to include all the assumptions made by mathematical model. Hence, mathematical base stock model and computer simulation model are comparable. Demand follows Poisson distribution in both cases. The backorder cost and inventory holding cost are calculated in each phase of simulation and summarized. The paper infers that the total inventory cost is optimum in phase II, in which reorder point is same as that calculated by mathematical model. In phase I, total inventory cost is more than that of phase II because of backorders. In phase III, excess inventory increased the total cost. Thus, the values obtained from mathematical model produce optimal inventory cost. Base stock model is effective when the demand is not deterministic and service factor assumed in mathematical model is 0.9, which is quite acceptable. Base stock model assumes replenishment order quantity as 1 and the total inventory cost decreases with replenishment lead time. Base stock model is beneficial for supply chains having short replenishment lead time. Computer simulation results indicate that discrete event simulations can be used to model stochastic systems like organizational supply chains and to validate the results from mathematical models.

The paper offers a review of simulation work aiming to support improvement of agility in the supply chain.

In recent years, computer simulation has become a mainstream decision support tool in manufacturing industry. In order to maximise the benefits of using simulation within businesses simulation models should be designed, developed and deployed in a shorter time span. A number of factors, such as excessive model details, inefficient data collection, lengthy model documentation and poorly planned experiments, increase the overall lead time of simulation projects. Among these factors, input data modelling is seen as a major obstacle. Input data identification, collection, validation, and analysis, typically take more than one‐third of project time. This paper presents a IDEF (Integrated computer‐aided manufacturing DEFinition) based approach to accelerate identification and collection of input data. The use of the methodology is presented through its application in batch manufacturing environments. A functional module library and a reference data model, both developed using the IDEF family of constructs, are the core elements of the methodology. The paper also identifies the major causes behind the inefficient collection of data.