Data quality problems in discrete event simulation of manufacturing operations

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 simulation models. Unfortunately, this research stream rests on the assumption that the collected data are already of high quality,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 practical 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 simulation. Further, the empirical and theoretical knowledge gained throughout the study was used to propose a set of practical guidelines that can support manufacturing companies in improving data quality in DES

Framework for the usage of data from real-time indoor localization systems to derive inputs for manufacturing simulation

Discrete event simulation is becoming increasingly important in the planning and operation of complex manufacturing systems. A major problem with today’s approach to manufacturing simulation studies is the collection and processing of data from heterogeneous sources, because the data is often of poor quality and does not contain all the necessary information for a simulation. This work introduces a framework that uses a real-time indoor localization systems (RTILS) as a central main data harmonizer, that is designed to feed production data into a manufacturing simulation from a single source of truth. It is shown, based on different data quality dimensions, how this contributes to a better overall data quality in manufacturing simulation. Furthermore, a detailed overview on which simulation inputs can be derived from the RTILS data is given

Are simulation tools ready for big data? Computational experiments with supply chain models developed in Simio

Peer-review under responsibility of the scientific committee of the International Conference on Industry 4.0 and Smart Manufacturing. The need and potential benefits for the combined use of Simulation and Big Data in Supply Chains (SCs) has been widely recognized. Having worked on such project, some simulation experiments of the modelled SC system were conducted in SIMIO. Different circumstances were tested, including running the model based on the stored data, on statistical distributions and considering risk situations. Thus, this paper aimed to evaluate such experiments, to evaluate the performance of simulations in these contexts. After analyzing the obtained results, it was found that whilst running the model based on the real data required considerable amounts of computer memory, running the model based on statistical distributions reduced such values, albeit required considerable higher time to run a single replication. In all the tested experiments, the simulation took considerable time to run and was not smooth, which can reduce the stakeholders' interest in the developed tool, despite its benefits for the decision-making process. For future researches, it would be beneficial to test other simulation tools and other strategies and compare those results to the ones provided in this paper.This work has been supported by national funds through FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2019 and by the Doctoral scholarship PDE/BDE/114566/2016 funded by FCT, the Portuguese Ministry of Science, Technology and Higher Education, through national funds, and co-financed by the European Social Fund (ESF) through the Operational Programme for Human Capital (POCH)

Bypassing data issues of a supply chain simulation model in a big data context

Peer-review under responsibility of the scientific committee of the International Conference on Industry 4.0 and Smart Manufacturing. Supply Chains (SCs) are complex and dynamic networks, where certain events may cause severe problems. To avoid them, simulation can be used, allowing the uncertainty of these systems to be considered. Furthermore, the data that is generated at increasingly high volumes, velocities and varieties by relevant data sources allow, on one hand, the simulation model to capture all the relevant elements. While developing such solution, due to the inherent use of simulation, several data issues were identified and bypassed, so that the incorporated elements comprise a coherent SC simulation model. Thus, the purpose of this paper is to present the main issues that were faced, and discuss how these were bypassed, while working on a SC simulation model in a Big Data context and using real industrial data from an automotive electronics SC. This paper highlights the role of simulation in this task, since it worked as a semantic validator of the data. Moreover, this paper also presents the results that can be obtained from the developed model.This work has been supported by national funds through FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2019 and by the Doctoral scholarship PDE/BDE/114566/2016 funded by FCT, the Portuguese Ministry of Science, Technology and Higher Education, through national funds, and co-financed by the European Social Fund (ESF) through the Operational Programme for Human Capital (POCH)

A digital twin framework for the simulation and optimization of production systems

Industry 4.0 has raised the expectations on productivity, automation, and resource efficiency of manufacturing systems. This paper proposes a digital twin framework for the simulation and optimization of production lines and cells that can be used in the design and operation stages. The framework is supported by an architecture that connects manufacturing and machine tool data (digital shadow), the discrete event simulation model and the optimization engine, allowing for a variety of functionalities to plan and manage the production system. A use case is provided to demonstrate this framework, implemented in an automated line for the manufacturing of railway axles

On the use of simulation as a Big Data semantic validator for supply chain management

Simulation stands out as an appropriate method for the Supply Chain Management (SCM) field. Nevertheless, to produce accurate simulations of Supply Chains (SCs), several business processes must be considered. Thus, when using real data in these simulation models, Big Data concepts and technologies become necessary, as the involved data sources generate data at increasing volume, velocity and variety, in what is known as a Big Data context. While developing such solution, several data issues were found, with simulation proving to be more efficient than traditional data profiling techniques in identifying them. Thus, this paper proposes the use of simulation as a semantic validator of the data, proposed a classification for such issues and quantified their impact in the volume of data used in the final achieved solution. This paper concluded that, while SC simulations using Big Data concepts and technologies are within the grasp of organizations, their data models still require considerable improvements, in order to produce perfect mimics of their SCs. In fact, it was also found that simulation can help in identifying and bypassing some of these issues.This work has been supported by FCT (Fundacao para a Ciencia e Tecnologia) within the Project Scope: UID/CEC/00319/2019 and by the Doctoral scholarship PDE/BDE/114566/2016 funded by FCT, the Portuguese Ministry of Science, Technology and Higher Education, through national funds, and co-financed by the European Social Fund (ESF) through the Operational Programme for Human Capital (POCH)

A data-driven approach to diagnosing throughput bottlenecks from a maintenance perspective

Prioritising maintenance activities in throughput bottlenecks increases the throughput from the production system. To facilitate the planning and execution of maintenance activities, throughput bottlenecks in the production system must be identified and diagnosed. Various research efforts have developed data-driven approaches using real-time machine data to identify throughput bottlenecks in the system. However, these efforts have mainly focused on identifying bottlenecks and only offer limited maintenance-related diagnostics for them. Moreover, these research efforts have been proposed from an academic perspective using rigorous scientific methods. A number of challenges must be addressed, if existing data-driven approaches are to be adapted to real-world practice. These include identifying relevant data types, data pre-processing and data modelling. Such challenges can be better addressed by including maintenance-practitioner input when developing data-driven approaches. The aim of this paper is therefore to demonstrate a data-driven approach to diagnosing throughput bottlenecks, using the combined knowledge of the maintenance and data-science domains. Diagnostic insights into throughput bottlenecks are obtained using unsupervised machine-learning techniques. The demonstration uses real-world machine datasets extracted from the production line. The novelty of the research presented in this paper is that it shows how inputs from maintenance practitioners can be used to develop data-driven approaches for diagnosing throughput bottlenecks having more practical relevance. By gaining these diagnostic insights, maintenance practitioners can better understand shop-floor throughput bottleneck behaviours from a maintenance perspective and thus prioritise various maintenance actions

Simulation-based impact analysis for sustainable manufacturing design and management

This research focuses on effective decision-making for sustainable manufacturing design and management. The research contributes to the decision-making tools that can enable sustainability analysts to capture the aspects of the economic, environmental and social dimensions into a common framework. The framework will enable the practitioners to conduct a sustainability impact analysis of a real or proposed manufacturing system and use the outcome to support sustainability decision. In the past, the industries had focused more on the economic aspects in gaining and sustaining their competitive positions; this has changed in the recent years following the Brundtland report which centred on incorporating the sustainability of the future generations into our decision for meeting today’s needs (Brundtland, 1987). The government regulations and legislation, coupled with the changes in consumers’ preference for ethical and environmentally friendly products are other factors that are challenging and changing the way companies, and organisations perceive and drive their competitive goals (Gu et al., 2015). Another challenge is the lack of adequate tools to address the dynamism of the manufacturing environment and the need to balance the business’ competitive goal with sustainability requirements. The launch of the Life Cycle Sustainability Analysis (LCSA) framework further emphasised the needs for the integration and analysis of the interdependencies of the three dimensions for effective decision-making and the control of unintended consequences (UNEP, 2011). Various studies have also demonstrated the importance of interdependence impact analysis and integration of the three sustainability dimensions of the product, process and system levels of sustainability (Jayal et al., 2010; Valdivia et al., 2013; Eastwood and Haapala, 2015). Although there are tools capable of assessing the performance of either one or two of the three sustainability dimensions, the tools have not adequately integrated the three dimensions or address the holistic sustainability issues. Hence, this research proposes an approach to provide a solution for successful interdependence impact analysis and trade-off amongst the three sustainability dimensions and enable support for effective decision-making in a manufacturing environment. This novel approach explores and integrates the concepts and principles of the existing sustainability methodologies and frameworks and the simulation modelling construction process into a common descriptive framework for process level assessment. The thesis deploys Delphi study to verify and validate the descriptive framework and demonstrates its applicability in a case study of a real manufacturing system. The results of the research demonstrate the completeness, conciseness, correctness, clarity and applicability of the descriptive framework. Thus, the outcome of this research is a simulation-based impact analysis framework which provides a new way for sustainability practitioners to build an integrated and holistic computer simulation model of a real system, capable of assessing both production and sustainability performance of a dynamic manufacturing system

Digital Twins of production systems - Automated validation and update of material flow simulation models with real data

Um eine gute Wirtschaftlichkeit und Nachhaltigkeit zu erzielen, müssen Produktionssysteme über lange Zeiträume mit einer hohen Produktivität betrieben werden. Dies stellt produzierende Unternehmen insbesondere in Zeiten gesteigerter Volatilität, die z.B. durch technologische Umbrüche in der Mobilität, sowie politischen und gesellschaftlichen Wandel ausgelöst wird, vor große Herausforderungen, da sich die Anforderungen an das Produktionssystem ständig verändern. Die Frequenz von notwendigen Anpassungsentscheidungen und folgenden Optimierungsmaßnahmen steigt, sodass der Bedarf nach Bewertungsmöglichkeiten von Szenarien und möglichen Systemkonfigurationen zunimmt. Ein mächtiges Werkzeug hierzu ist die Materialflusssimulation, deren Einsatz aktuell jedoch durch ihre aufwändige manuelle Erstellung und ihre zeitlich begrenzte, projektbasierte Nutzung eingeschränkt wird. Einer längerfristigen, lebenszyklusbegleitenden Nutzung steht momentan die arbeitsintensive Pflege des Simulationsmodells, d.h. die manuelle Anpassung des Modells bei Veränderungen am Realsystem, im Wege. Das Ziel der vorliegenden Arbeit ist die Entwicklung und Umsetzung eines Konzeptes inkl. der benötigten Methoden, die Pflege und Anpassung des Simulationsmodells an die Realität zu automatisieren. Hierzu werden die zur Verfügung stehenden Realdaten genutzt, die aufgrund von Trends wie Industrie 4.0 und allgemeiner Digitalisierung verstärkt vorliegen. Die verfolgte Vision der Arbeit ist ein Digitaler Zwilling des Produktionssystems, der durch den Dateninput zu jedem Zeitpunkt ein realitätsnahes Abbild des Systems darstellt und zur realistischen Bewertung von Szenarien verwendet werden kann. Hierfür wurde das benötigte Gesamtkonzept entworfen und die Mechanismen zur automatischen Validierung und Aktualisierung des Modells entwickelt. Im Fokus standen dabei unter anderem die Entwicklung von Algorithmen zur Erkennung von Veränderungen in der Struktur und den Abläufen im Produktionssystem, sowie die Untersuchung des Einflusses der zur Verfügung stehenden Daten. Die entwickelten Komponenten konnten an einem realen Anwendungsfall der Robert Bosch GmbH erfolgreich eingesetzt werden und führten zu einer Steigerung der Realitätsnähe des Digitalen Zwillings, der erfolgreich zur Produktionsplanung und -optimierung eingesetzt werden konnte. Das Potential von Lokalisierungsdaten für die Erstellung von Digitalen Zwillingen von Produktionssystem konnte anhand der Versuchsumgebung der Lernfabrik des wbk Instituts für Produktionstechnik demonstriert werden

Implementation Strategies for Modeling and Simulation in Military Organizations

Some IT project managers working for U.S. military organizations are struggling to implement modern modeling and simulation (M&S) technology. Implementation strategies are needed to help IT practitioners deliver meaningful simulations and models that ultimately help senior leaders make logical and science-based decisions. Grounded in the extended technology acceptance model, the purpose of this qualitative multiple-case study was to explore strategies some IT project managers supporting U.S. military organizations use to implement modern M&S technology. The participants included 10 civil servants who successfully implemented modeling and simulation technology for military organizations located in the United States eastern region. Data was collected from one-on-one semistructured interviews (n = 10) and internal and external organizational documents (n = 12) provided by the participants. Data were analyzed using thematic analysis. Three major themes emerged: understand the true M&S requirements, incorporate subject matter experts throughout implementation, and anticipate and overcome persistent challenges. One recommendation is for practitioners to develop tasks and milestones to address these challenges at the beginning of implementation and add them to the project schedule. The implications for positive social change include the potential for successful implementation of models and military organizations\u27 simulations to safeguard human lives