Delphi Study on evaluating information in simulation studies for manufacturing and logistics planning

Abel, Dennis; Jessen, Ulrich; Wenzel, Sigrid

doi:10.1057/jos.2013.7

Cited by 6 publications

(3 citation statements)

References 6 publications

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“…Effectively improving business operations is achieved by first identifying critical stations (e.g. machines, people) that directly impact throughput (production), quality, delivery, cost, and morale (Adel et al, 2013;El-Khalil, 2009;Gunasekaran and McGaughey, 2003;Kumar and Phrommathed, 2006). The next step after identification of critical operations is to determine the characteristic and to generate analytical data for each operation that can support proper system analysis (Anand and Kodali, 2009;Kumar and Phrommathed, 2006).…”

Section: Literature Reviewmentioning

confidence: 99%

“…Based on a study conducted by El-Khalil ( 2009) manufacturing companies such as Toyota and the Big Three have been utilizing discrete computer modeling and simulation for issues such as design, analyzing behavior, predicting performance (at different range of variables), recommending system changes, studying labor, and machine changes on throughput within the manufacturing process. The following are some of the advantages of utilizing discrete-event simulation (Adel et al, 2013;Anand and Kodali, 2009;EL-Khalil, 2013;Liker, 2004;Law, 2007;Zulch and Becker, 2010):…”

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confidence: 99%

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Simulation analysis for managing and improving productivity

El-Khalil

2015

Journal of Manufacturing Technology Management

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Purpose – 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. Design/methodology/approach – 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. Findings – 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. Research limitations/implications – 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. Practical implications – 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. Originality/value – 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.

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Section: Literature Reviewmentioning

confidence: 99%

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confidence: 99%

Simulation analysis for managing and improving productivity

El-Khalil

2015

Journal of Manufacturing Technology Management

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“…The scholastic researches in [5] demonstrated that various simulation approaches could be used for various educational aims. DES is capable of mitigating operational issues whereby strategic problems are incorporated in model dynamics.…”

Section: Introductionmentioning

confidence: 99%

An Architecture of Procedures in Modeling for Manufacturing Logistics

Mangalika

2021

International Journal of Advanced Information and Communication Technology

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The concepts of Modeling and Simulation (M&S) are gradually becoming a fundamental sector which attracts the attention of researchers in the modern age. Operational systems in Small and Medium-sized Enterprises (SMEs) have to be investigated using these concepts to achieve quality results. Until now, many scholastic contributions have been published regarding the various perspectives and aspects of M&S. However, there are gaps in the discussion of the complete cycle of modeling and simulation which incorporates the verification and development of an architecture that can be used in manufacturing logistics for Product Development Schemes (PDS). In that case, this paper presents the Simulation Procedures (SP) and architecture can be used to comprehend the systems for product development. This contribution focusses on the validation and verification of the architecture as well. As such, this contribution fills the prevailing gap between the architectural design/development and the validation/verification of modeling and simulation procedures. These are the fundamentals in the application of the M&S approach to comprehend the systems of product development in SMEs.

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