Automation of Input Data Management - Increasing Efficiency in Simulation of Production Flows

Doctoral thesis, 2011

Production is of significant importance for the social welfare and economic growth in societies worldwide. In Europe, more than 30% of all job opportunities are related to the manufacturing industry. Improvements of material flows in production are of extra importance for reducing the system losses and increasing the robustness of production systems. Unfortunately, the most powerful tools for analyzing dynamic aspects are associated with extensive data requirements and, thus, inefficient procedures for keeping models up-to-date. This thesis addresses the input data management procedure for one such tool, namely discrete event simulation (DES). The purpose of the thesis is to enable daily use of DES to support production engineers in their work with increasing efficiency, sustainability and robustness of production systems. The aim is to reduce the time-consumption for input data management and thereby facilitate the supply of recent production data to DES models. The thesis is divided into two parts, treated as interrelated studies, addressing one research question (RQ) each. Part One (RQ1), mapping the industrial state-of-the-art of input data management, is mainly based on qualitative methods including interviews and questionnaires with DES practitioners. The results show that collection of raw data, identification of available data sources, and data analysis and preparation are the three most time-consuming activities. There is still limited use of automatic support systems and the data are often manually collected, processed and supplied to models by means of spreadsheet interfaces. Findings in Part One also show that automated connections to external databases are important for future sustainability analyses using DES. Part Two (RQ2), proposing and evaluating an approach for automated input data management, is mainly based on the analysis of existing industrial data sources (archive analysis). This review aims to identify the functionalities necessary to automatically transform production data (raw data) to information for a DES model. A demonstrator, called the GDM-Tool, is developed and tested in three independent case studies. The results show that the proposed automated approach reduces the time-consumption for input data management by approximately 75%. There are still difficulties in input data management for DES, partly due to the limited access to detailed production data. Therefore, the author recommends that industrial and academic partners increase efforts necessary to facilitate continuous raw data collection and, by extension, also automated data processing. In cases where enough data are available, the proposed solution (RQ2) enables more frequent updates of DES models and provides production engineers with a powerful tool for increasing efficiency of production systems on a daily basis.