Anders Skoogh

A methodology for input data management in discrete event simulation projects

Discrete event simulation (DES) projects rely heavily on high input data quality. Therefore, the input data management process is very important and, thus, consumes an extensive amount of time. To secure quality and increase rapidity in DES projects, there are well structured methodologies to follow, but a detailed guideline for how to perform the crucial process of handling input data, is missing. This paper presents such a structured methodology, including description of 13 activities and their internal connections. Having this kind of methodology available, our hypothesis is that the structured way to work increases rapidity for input data management and, consequently, also for entire DES projects. The improvement is expected to be larger in companies with low or medium experience in DES.

A test implementation of the core manufacturing simulation data specification

This paper describes an effort of testing the Core Manufacturing Simulation Data (CMSD) information model as a neutral data interface for a discrete event simulation model developed using Enterprise Dynamics. The implementation is based upon a model of a paint shop at a Volvo Car Corporation plant in Sweden. The model is built for a Swedish research project (FACTS), which focuses on the work procedure of developing new and modified production systems. FACTS has found standardized simulation data structures to be of high interest to achieve efficient data collection in conceptual stages of production development programs. For the CMSD-development team, implementations serve as an approach to validate the structures in CMSD and to gather requirements for future enhancements. CMSD was originally developed to support job shops, but the results of this implementation indicate a good possibility to extend CMSD to also support flow shops.

Discrete event simulation to generate requirements specification for sustainable manufacturing systems design

A sustainable manufacturing systems design using processes, methodologies, and technologies that are energy efficient and environmental friendly is desirable and essential for sustainable development of products and services. Efforts must be made to create and maintain such sustainable manufacturing systems. Discrete Event Simulation (DES) in combination with Life Cycle Assessment (LCA) system can be utilized to evaluate a manufacturing system performance taking into account environmental measures before actual construction or use of the manufacturing system. In this paper, we present a case study to show how DES can be utilized to generate requirements specification for manufacturing systems in the early stages of the design phase. Requirement specification denotes the description of the behavior of the system to be developed. The case study incorporates use of LCA data in combination with DES. Data for the model in the case study is partly provided through the format supported by the Core Manufacturing Simulation Data (CMSD) standardization effort. The case study develops a prototype paint shop model, and incorporates alternate decisions on energy use, choice of machines, and environmental bottleneck detection. The study results indicate the potential use of utilizing DES in combination with LCA data to generate requirements specification for designing sustainable manufacturing systems.

Input data management methodology for discrete event simulation

Input Data Management (IDM) is a time consuming and costly process for Discrete Event Simulation (DES) projects. In this paper, a methodology for IDM in DES projects is described. The approach is to use a methodology to identify and collect data, then use an IDM software to extract and process the data. The IDM software will structure and present the data in Core Manufacturing Simulation Data (CMSD) format, which is aimed to be a standard data format for any DES software. The IDM methodology was previously developed and tested by Chalmers University of Technology in a case study in the automotive industry. This paper presents a second test implementation in a project at the National Institute of Standards and Technology (NIST) in collaboration with an aerospace industry partner.

Input data management in simulation - Industrial practices and future trends

Discrete Event Simulation has been acknowledged as a strategically important tool in the development and improvement of production systems. However, it appears that companies are failing to reap full benefits of this powerful technology as the maintenance of simulation models has become very time-consuming, particularly due to vast amounts of data to be handled. Hence, an increased level of automation of input data handling is highly desirable. This paper presents the current practices relating to input data management and identifies further research and development required to achieve high levels of automation. A survey of simulation users shows that there has been a progress in the use of automated solutions compared to a similar study presented by Robertson and Perera in 2002. The results, however, reveal that around 80% of the users still rely on highly manual work procedures in input data management.

Simulation data architecture for sustainable development

Reducing costs, improving quality, shortening the time-to-market, and at the same time act and think sustainable are major challenges for manufacturing industries. To strive towards these objectives, discrete event simulation (DES) has proven to be an effective tool for production system decision support.

Automated input data management: evaluation of a concept for reduced time consumption in discrete event simulation

Input data management is a crucial and time-consuming part of a simulation project. Consequently, improvement of this process has substantial potential to increase the rapidity of simulation projects, thus enabling more detailed analyses in design and development of production systems. This paper presents the development of a concept and an associated software demonstrator called the Generic Data Management Tool (GDM-Tool), which automates several critical and time-consuming data input activities. More specifically, raw data are extracted from multiple sources, with different data structures, and transformed to simulation input through data cleaning, calculations and distribution fitting, all done in one automated process. Finally, the simulation input is presented in the Core Manufacturing Simulation Data format, for further use in simulation applications. As a first step towards validation, the GDM-Tool was evaluated during a case study in the automotive industry. The results show that the time needed for input data management was reduced by 78%, as relative to a traditional manual approach.

Environmental activity based cost using discrete event simulation

Discrete event simulation (DES) provides engineers with a flexible modeling capability for extensive analysis of a production flow and its dynamic behavior. Activity based costing (ABC) modeling can provide additional knowledge about the monetary costs related to the manufacturing processes in DES. In addition, ABC modeling has been proposed as a tool for environmental impact analysis. Thus, previous studies have separately brought ABC into DES and ABC into environmental impact analysis. Bringing all three areas together, an ABC environmental simulation could provide deeper understanding about environmental impacts in the manufacturing processes than a regular Life Cycle Assessment (LCA) analysis. This paper proposes to use ABC modeling in conjunction with DES to perform a more detailed economic and environmental impact cost analysis. It is emphasized that the time to perform both analysis in one simulation is shorter or equal to perform them separately. Moreover, the approach can resolve some LCA problems.

Towards continuously updated simulation models: combining automated raw data collection and automated data processing

Discrete Event Simulation (DES) is a powerful tool for efficiency improvements in production. However, instead of integrating the tool in the daily work of production engineers, companies apply it mostly in single-purpose studies such as major investment projects. One significant reason is the extensive time-consumption for input data management, which has to be performed for every simulation analysis to avoid making decisions based upon obsolete facts. This paper presents an approach that combines automated raw data collection and automated processing of raw data to simulation information. MTConnect is used for collection of raw data and the GDM-Tool is applied for data processing. The purpose is to enable efficient reuse of DES models by reducing the time-consumption for input data management. Furthermore, the approach is evaluated using production data from the aerospace industry.

Simulation-based planning of maintenance activities by a shifting priority method

Machine failures are major causes of direct downtime as well as system losses (blocked and idle times) in production flows. A previous case study shows that prioritizing bottleneck machines over others has the potential to increase the throughput by about 5%. However, the bottleneck machine in a production system is not static throughout the process of production but shifts from time to time. The approach for this paper is to integrate dynamic maintenance strategies into scheduling of reactive maintenance using Discrete Event Simulation. The aim of the paper is to investigate how a shifting priority strategy could be integrated into the scheduling of reactive maintenance. The approach is applied to and evaluated in an automotive case-study, using simulation for decision support. This shows how to shift prioritization by tracking the momentary bottleneck of the system. The effect of shifting priorities for planning maintenance activities and its specific limitations is discussed.