| 1. |
- Bokrantz, Jon, 1988, et al.
(författare)
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A Methodology for Continuous Quality Assurance of Production Data
- 2016
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Ingår i: Proceedings - Winter Simulation Conference. - 0891-7736. ; 2016-February, s. 2088-2099
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Konferensbidrag (refereegranskat)abstract
- High quality input data is a necessity for successful Discrete Event Simulation (DES) applications, and there are available methodologies for data collection in DES projects. However, in contrast to standalone projects, using DES as a day-to-day engineering tool requires high quality production data to be constantly available. Unfortunately, there are no detailed guidelines that describes how to achieve this. Therefore, this paper presents such a methodology, based on three concurrent engineering projects within the automotive industry. The methodology explains the necessary roles, responsibilities, meetings, and documents to achieve a continuous quality assurance of production data. It also specifies an approach to input data management for DES using the Generic Data Management Tool (GDM-Tool). The expected effects are increased availability of high quality production data and reduced lead time of input data management, especially valuable in manufacturing companies having advanced automated data collection methods and using DES on a daily basis.
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| 2. |
- Bokrantz, Jon, 1988, et al.
(författare)
-
Data Quality Problems in Discrete Event Simulation of Manufacturing Operations
- 2018
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Ingår i: Simulation. - : SAGE Publications. - 1741-3133 .- 0037-5497. ; 94:11, s. 1009-1025
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Tidskriftsartikel (refereegranskat)abstract
- High-quality input data are a necessity for successful discrete event simulation (DES) applications, and there are available methodologies for data collection in DES projects. However, in contrast to standalone projects, using DES as a daily manufacturing engineering tool requires high-quality production data to be constantly available. In fact, there has been a major shift in the application of DES in manufacturing from production system design to daily operations, accompanied by a stream of research on automation of input data management and interoperability between data sources and simula- tion models. Unfortunately, this research stream rests on the assumption that the collected data are already of high qual- ity, and there is a lack of in-depth understanding of simulation data quality problems from a practitioners’ perspective. Therefore, a multiple-case study within the automotive industry was used to provide empirical descriptions of simulation data quality problems, data production processes, and relations between these processes and simulation data quality problems. These empirical descriptions are necessary to extend the present knowledge on data quality in DES in a prac- tical real-world manufacturing context, which is a prerequisite for developing practical solutions for solving data quality problems such as limited accessibility, lack of data on minor stoppages, and data sources not being designed for simula- tion. Further, the empirical and theoretical knowledge gained throughout the study was used to propose a set of practi- cal guidelines that can support manufacturing companies in improving data quality in DES.
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| 3. |
- Ng, Amos, et al.
(författare)
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Optimal Maintenance Resources Allocation Using Automated Simulation-based Optimisation and Data Management
- 2015
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Ingår i: ASIM - Simulation in Production and Logistics. - 9783839609361
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Konferensbidrag (refereegranskat)abstract
- This paper introduces a Streamlined Modeling and Decision Support (StreaMod) approach in which input data management, simulation model generation/update and simulation-based optimization are synergistically integrated into a largely automated process. The aim of this automated process is to support decision making related to the optimal maintenance resources allocation that could improve the performance of production/logistic systems.
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| 4. |
- Subramaniyan, Mukund, 1989, et al.
(författare)
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An algorithm for data-driven shifting bottleneck detection
- 2016
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Ingår i: Cogent Engineering. - : Informa UK Limited. - 2331-1916. ; 3:1, s. 1-19
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Tidskriftsartikel (refereegranskat)abstract
- Manufacturing companies continuously capture shop floor information using sensors technologies, Manufacturing Execution Systems (MES), Enterprise Resource Planning systems. The volumes of data collected by these technologies are growing and the pace of that growth is accelerating. Manufacturing data is constantly changing but immediately relevant. Collecting and analysing them on a real-time basis can lead to increased productivity. Particularly, prioritising improvement activities such as cycle time improvement, setup time reduction and maintenance activities on bottleneck machines is an important part of the operations management process on the shop floor to improve productivity. The first step in that process is the identification of bottlenecks. This paper introduces a purely data-driven shifting bottleneck detection algorithm to identify the bottlenecks from the real-time data of the machines as captured by MES. The developed algorithm detects the current bottleneck at any given time, the average and the non-bottlenecks over a time interval. The algorithm has been tested over real-world MES data sets of two manufacturing companies, identifying the potentials and the prerequisites of the data-driven method. The main prerequisite of the proposed data-driven method is that all the states of the machine should be monitored by MES during the production run.
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