Johan Stahre

Simulation-based sustainable manufacturing system design

Manufacturing simulation and digital engineering tools and procedures have had a positive impact on the manufacturing industry. However, to design a sustainable manufacturing system, a multitude of system dimensions must be jointly optimized. This paper proposes an integrated simulation tool helping to maximize production efficiency and balance environmental constraints already in the system design phase. Lean manufacturing, identification and elimination of waste and production losses, and environmental considerations are all needed during development of a sustainable manufacturing system. Engineers designing the manufacturing system need decision support, otherwise sub-optimization is more likely to occur. We present methods for calculating energy efficiency, CO2 emissions and other environmental impacts integrated into factory simulation software.

Information enabling production ramp-up

Purpose – This paper seeks to identify information enabling and supporting production ramp-up processes, by exploring critical events and the role of information in such events. Design/methodology/approach – The research approach was based on empirical and theoretical investigations. A selection of 30 events, considered the most critical for production ramp-up realization and/or performance at one Swedish automotive company, were categorized and constituted the base for the analysis which focused information types and sources enabling event handling. Findings – Information enabling event handling is a balanced combination of problem and domain information, regardless of event category. However, a differentiation concerning preference and usage of information types between experienced and less experienced personnel is identified. Problem-solving information has the character of pragmatic information, composed of complementary parts of confirmation and novelty in terms of domain and problem information. The preferred information source in all event categories was “other people”. Research limitations/implications – The study focuses on the application of information in relation to critical events during production ramp-up. General information theory is not addressed in depth. Practical implications – Information type and information source are not dependent on certain event categories, which allows a general information strategy enabling production ramp-up. To facilitate production ramp-up and event handling managers and key personnel need to apply a holistic perspective and need to be updated on domain information of the products, the equipment, and the production process during production ramp-up. Originality/value – The originality is in the focus and role of information to achieve an efficient production ramp-up performance. A supporting model is developed which describes the structure of pragmatic information for personnel with various levels of experience, regardless of event category.

Empirically testing the impact of manufacturing system complexity on performance

As the increase in manufacturing competitiveness forces organizations to use more sophisticated and complex software, system performance depends on clever systems design, efficient planning and scheduling of the related processes. For these advanced manufacturing systems the dependence on human competence is greater. However, previous studies indicate that the human aspects for successfully implementing such systems have been neglected. The objective here is to test the hypotheses that system complexity is inversely related to performance, and that training of system operators, and the quality of the man/machine interface reduces the negative impact of system complexity. A sample of discreet manufacturing systems from 128 organizations was used to test these hypotheses empirically. Moderated multivariate regression indicates that man/machine interfaces are significant contributors to reducing the negative effect of systems complexity. With a lower level of significance, operator training has a similar impact. For complex manufacturing systems software, it behoves managers to insure that the man/machine interface provides the desirable features outlined in this study.

Measuring and analysing Levels of Automation in an assembly system

The level of automation employed in semi-automated assembly systems is crucial, both to system performance and cost. This paper presents a methodology to enable selection of the right Level of Automation. The method thoroughly maps existing product and information flows as well as the automation level in separate parts of the system. It then analyses and identifies future automation possibilities, i.e. the automation potential seen from an industrial perspective. Further development of the method is based on validations and industrial case studies.

Evaluating human/machine interaction problems in advanced manufacturing

This paper describes a cross-disciplinary research project called ‘Humanufacturing’. In the project, theoretical models for operator roles and human behaviour levels have been combined to evaluate human/machine interaction in modern manufacturing systems. A data collection method, used for case studies in Swedish industry, is described. Also, a prototype decision-support tool based on expert system technology is presented. The conclusion is that theoretical models, previously used in continuous process industry, appear also to be useful in discrete parts manufacturing. Methods used seem to be industrially applicable, and have resulted in specifications for operator training, decision support and education.

COGNITIVE AUTOMATION STRATEGY -FOR RECONFIGURABLE AND SUSTAINABLE ASSEMBLY SYSTEMS

Purpose – The paper aims to discuss the importance of considering both the physical and cognitive automation when aiming for a flexible or reconfigurable assembly system. This is done in order to handle the increased demand for mass customized production and to maintain or improve the social sustainability within the company.Design/methodology/approach – The methodologies used in this paper are a theoretical review about task allocation and levels of automation and a methodology called DYNAMO++ for the industrial case studies.Findings – The paper provides both theoretical and empirical insights about the importance of considering both the cognitive and physical automation when aiming for a reconfigurable assembly system.Research limitations/implications – The paper will only discuss the cognitive strategy from a social sustainability perspective and not from an economical or environmental angle.Practical implications – The paper presents data from three industrial case studies, mostly in the automotive in...

Control System Approaches for Sustainable Development and Instability Management in the Globalization Age

Abstract Mankind civilization in the globalization age depends heavily on advanced information technologies resulting from automation of control and decision expertise and their respective scientific disciplines. These have a multitude of impacts on development of national economies within the global economy. The broad area of social systems, being essentially human centred systems, is a cross-, inter- and multi-disciplinary challenge to control community. Social systems in modern civilization, currently undergoing globalization, are reviewed from the systems science viewpoint and on the grounds of recent developments in control science and technology. Recent developments put new emphasis on the social responsibility of the control and automation field during the ongoing changes from the cold-war bipolar world to a unipolar one on the way to mankinds multi-polar world of the future. The focus should be on innovative systems approaches, employing new paradigms, to combined knowledge and technology transfer world-wide, that may remedy some of the negative aspects of globalization.

Measuring complexity in mixed-model assembly workstations

In an effort to maintain or increase their market share and at the same time prevent costs from escalating, manufacturing organisations are increasingly using their current manufacturing system to produce custom output. As a consequence, the large number of product variants increases significantly the complexity of manufacturing systems, both for the operators as for the support services. This is especially true in automotive industry, where customisation is increasing at a rapid pace. To counter the ensuing loss of productivity, a more fundamental approach to dealing with this complexity in manufacturing processes is required. In order to investigate the impact of complexity on production performance, one must first delineate the concept and then identify as unambiguously as possible highly complex workstations. This article defines complexity at the workstation level and proposes a complexity measure for mixed-model assembly workstations. Based on data from several leading automotive companies from Belgium and Sweden, some statistical models are proposed to characterise workstations complexity. The models are described and their validity and accuracy are discussed.

Automation strategies: existing theory or ad hoc decisions?

Automating manufacturing systems potentially improves competitiveness. Empirical studies show that the most successful result is achieved when decisions concerning automation are linked to the manufacturing strategies and competitive priorities of the company. It is suggested that automation is regarded as a separate decision group, within the manufacturing strategy content field.

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.