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To develop a methodology to augment enterprise resource planning (ERP) systems with the discrete event simulation's inherent ability to handle the uncertainties.

The ERP system still contains and uses the material requirements planning (MRP) logic as its central planning function. As a result, the ERP system inherits a number of shortcomings associated with the MRP system, including unrealistic lead‐time determination. The developed methodology employs bi‐directional feedback between the non‐stochastic ERP system and the discrete event simulation model until a set of converged lead times is determined.

An example of determining realistic production lead‐time data in the ERP system is presented to illustrate how such a marriage can be achieved.

The research demonstrates that the limited planning functionality of the ERP system can be complemented by external system such as discrete event simulation models. The specific steps developed for this research can be adopted for other enhancements in different but comparable situations.

The organizations who have been using the discrete event simulation in their planning and decision‐making processes can integrate their simulation models and the ERP system following the steps presented in this paper. The ideas in this paper can be used to look for automatic data collection process to update or build the simulation models.

The ERP implementation is a significant investment for any corporation. Once the ERP implementation is completed successfully, the corporations must look for ways to maximally return on their investment. The research results may be used to enhance the implemented ERP systems or to fully utilize the capabilities in a corporation.

Describes the motivation, the initial implementation and tests carried out during the development of a combined discrete‐time and continuous‐simulation system intended primarily for the simulation of hybrid systems in manufacturing processes. The system, which is still under development, is the product of fusing two existing and independently developed packages, and the current implementation serves as a pilot design for evaluating the issues involved, both in terms of product development and in terms of the theoretical difficulties in combining discrete‐event and continuous‐time systems. The initial design has already been implemented and test simulations performed. Provides a superficial description of the system interface which is predominantly based on the existing interface of the discrete‐event simulator.

The current economic crisis increased the demand on management to improve process efficiency. The purpose of this paper is to identify and resolve inefficiencies within the car assembly system utilizing discrete simulation modeling and analysis in order to improve productivity at one of the original equipment manufacturers (OEM) body shops in North America.

This research was driven by a manager’s recommendation from one of the Big Three (GM, Ford, Chrysler LLC) companies in order to improve operational performance. The data utilized in creating the simulation model was obtained from one of the assembly facilities that produce three different vehicles over a period of one year. All model simulation, inputs and outputs were discussed and agreed upon by facility management.

The established base model was verified and validated to mimic the actual facility outputs indicating all process bottlenecks. Two model scenarios were considered: the first scenario focussed on the top bottleneck processes flexibility with a ROI of 497 percent, while the second considered changing the model mix percentage leading to a cost improvement of $1.6 million/annually.

The model only considered management decision on buffer sizes, batch size and the top bottleneck station alternatives to make improvements. Simulating improvements in labor efficiency, robots uptime, scrap root cause, and maintenance response to downtime where not considered.

This paper indicated the importance of discrete simulation modeling in providing alternatives for improving process efficiency under certain financial limitations. Given the similarity of the automotive manufacturing processes among the various companies, the findings for this particular facility remain valid for other facilities.

Investment cost and process improvement are currently the two biggest challenges facing operations managers in the manufacturing industry. This study allows managers to gain a broader perspective on discrete simulation ability to simulate complicated systems and present different process improvement alternatives.

Lean implementation is vastly incorporated in core manufacturing processes; however, its applicability in the supply chain and service industry is still in its infancy. To acquire performance excellence and thrive in the global competitive market, many firms are adopting newer methodologies. But, there is a stringent need for production simulation systems to analyze supply chains both inbound and outbound. The era of face validation is slowly disappearing. Lean tools and procedures that provide future state assumptions need advanced tools and techniques to measure, quantify, analyze and validate them. The purpose of this study is to enable dynamic quantification and visualization of the future state of a warehouse supply chain value stream map using discrete event simulation (DES) technique.

This study aimed to apply an integrated approach of the value stream mapping (VSM) and DES in a Malaysian pharmaceutical production warehouse. The main focus is diverted towards reducing the warehouse supply chain lead time by initially constructing a supply chain value stream map (both present state and future state) and integrating its data in a DES modelling and simulation software to dynamically visualize the changes in future state value stream map.

The DES simulation was able to mimic the future state lead time reductions successfully, which assists in better decision-making. Improvements were seen related to total lead time, process time, value and non-value-added percentage. Warehouse performance metrics such as receiving, put away and storage rates were substantially improved along with pallet processing time, worker and forklift throughput usage percentage. Detailed findings are clearly stated at the end of this paper.

This study is limited to the warehouse environment and further additional process models and functional upgrades in the DES software systems are very much needed to directly visualize and quantify all the possible Lean assumptions such as radio frequency image identification/Andon (Jidoka), 5S, Kanban, Just-In-Time and Heijunka. However, DES has a leading edge in extracting dynamic characteristics out of a static VSM timeline and capture details on discrete events precisely by picturizing facility modification and lead time related to it.

This paper includes all the fundamental pharmaceutical warehouse supply chain processes and the simulations of the future state VSM in a real-life context by successfully reducing supply chain lead time and allowing managers in inculcating near-optimal decision-making, controlling and coordinating warehouse supply chain activities as a whole.

This integrated approach of DES and VSM can involve managers and top management to support the adoption of anticipated changes. This study also has the potential to engage practitioners, researchers and decision-makers in the warehouse industry.

This study involves a powerful DES software package that can mimic the real situation as a virtual simulation and all the data and model building are based on a real warehouse scenario in the pharmaceutical industry.

This study aims to seek to advance a system dynamics-discrete event hybrid simulation modelling concept useful for taking improvement decisions where one needs to consider the interactions between human factors and process flow elements in lean manufacturing systems.

A unique approach is taken to hybrid simulation modelling where the whole problem situation is first conceptualized using a causal loop diagram and stock and flow diagram, before transmitting to a hybrid simulation model. The concept is intended to simplify the simulation modelling process and make the concept pliable for use in various types of lean manufacturing problem situations.

The hybrid simulation modelling concept was applied to a lean manufacturing case where quality performance was sporadic mainly because of production pressures. The hybrid modelling concept revealed a solution that advanced full compliance with lean and one that required changes in job scheduling policies to promote both continuous improvement and throughput increases.

Because non-tangible aspects of lean were objectively assessed using the hybrid modelling concept, the study is an advancement towards establishing a credible link between human resource aspects of lean and the performance of an organization.

The applied hybrid model enabled managers in the plant navigate the trade-off decision they often face when choosing to advance production output ahead of continuous improvement practices.

System dynamics-discrete event hybrid simulation modelling is a rarity in lean manufacturing systems.

Construction‐oriented discrete‐event simulation often faces the problem of defining uncertain information input, such as subjectivity in selecting probability distributions that result from insufficient or lack of site productivity data. This paper proposes incorporation of fuzzy set theory with discrete‐event simulation to handle the vagueness, imprecision and subjectivity in the estimation of activity duration, especially when insufficient or no sample data are available. Based upon an improved activity scanning simulation algorithm, a fuzzy distance ranking measure is adopted in fuzzy simulation time advancement and event selection for simulation experimentation. The uses of the fuzzy activity duration and the probability distribution‐modeled duration are compared through a series of simulation experiments. It is observed that the fuzzy simulation outputs are arrived at through only one cycle of fuzzy discrete‐event simulation, still they contain all the statistical information that are produced through multiple cycles of simulation experiments when the probability distribution approach is adopted.

Several different simulation techniques, such as discrete event simulation (DES), system dynamics (SD) and agent-based modelling (ABM), have been used to model complex construction systems such as construction processes and project management practices; however, these techniques do not take into account the subjective uncertainties that exist in many construction systems. Integrating fuzzy logic with simulation techniques enhances the capabilities of those simulation techniques, and the resultant fuzzy simulation models are then capable of handling subjective uncertainties in complex construction systems. The objectives of this chapter are to show how to integrate fuzzy logic and simulation techniques in construction modelling and to provide methodologies for the development of fuzzy simulation models in construction. In this chapter, an overview of simulation techniques that are used in construction is presented. Next, the advancements that have been made by integrating fuzzy logic and simulation techniques are introduced. Methodologies for developing fuzzy simulation models are then proposed. Finally, the process of selecting a suitable simulation technique for each particular aspect of construction modelling is discussed.

Sound design, planning and monitoring is critical to the operational and financial success of today′s sophisticated manufacturing and non‐manufacturing systems. Owing to their increasing complexity, discrete‐event simulation is becoming the most acceptable tool to aid planning the design and management of production and operations. This growing acceptance has led to the development of many simulators. Two fundamental criteria enable assessment of the suitability of these simulations in complex environments. First, the sophistication of their modelling capability to handle a wide range of problematic situations and second, ease of use. These two considerations, however, tend to conflict; resulting in flexible simulators being difficult to use and vice versa. DSSL II is an advanced simulation methodology with a well defined and user‐friendly modelling strategy. It has been devised to offer a versatile approach in modelling today′s sophisticated systems and policies. Features incorporated include a schematic modelling concept to represent the operational logic of systems, a set of software modules and an associated logical structure. Using the logical structure, the modules are combined to transform the concept of the schematic model into a representative computer program. Using DSSL II, models of manufacturing and non‐manufacturing systems are constructed readily to provide accurate and trustworthy answers to essential “what‐if” questions posed by decision makers, to determine which out of several scenarios would be the most appropriate. The purpose of this paper is to present the concepts and techniques employed by DSSL II. A simple case study and an example of a real industrial application are given in order to demonstrate its features and potential.

Focuses on the integrated use of simulation tools, particularly discrete‐event simulation, in the design and development of manufacturing systems in Japanese industry. The results are based on questionnaires and visits to seven large Japanese manufacturers and show that most of the visited companies do not use simulation to any large extent, particularly not discrete‐event simulation. Some of the reasons for this are general, while others are specific for Japan. However, the use of simulation is believed to increase in Japanese industry. Furthermore, argues that there is a large potential for increased use of advanced simulation techniques in Japanese manufacturing companies, mainly for two reasons. This would result in improved communication, reduced time‐to‐market and higher flexibility in volume and product‐mix.

This paper aims to compare the prognostic and visualisation capabilities of all the three simulation paradigms to identify their suitability and rigor in eliminating weaknesses and bottlenecks in a Lean Six Sigma (LSS) project.

The paper uses an light-emitting diode (LED) factory as a business case to show the differences and advantages of using three different simulation techniques to solve a manufacturing problem.

Even though this is only one business case that shows how system dynamics (SD) can be effective in a Six Sigma manufacturing project, more examples are needed to validate this hypothesis within Six Sigma and Lean manufacturing fields. Even though, discrete-events (DE) and agent-based (AB) models are both equally well suited to develop the manufacturing processes and the choice of what to use may be arbitrarily dependent on the available software or the preference of the modeller, hybrid models seem to become extremely powerful. Therefore, more hybrid models need to be constructed within LSS (especially when a flowchart and a SIPOC ((Suppliers, Inputs, Process, Outputs and Customers) table are combined to develop a hybrid model) to achieve the most accurate results with accurate representation of reality.

There are three commonly used simulation techniques, DE, AB and SD, but choosing the right simulation methodology has often been a challenge.