Högskolan i Skövde

Historically, the manufacturing industry is one of the main contributors to the environmental issues. With conservation of the environment becoming more and more critical for survival, it is of importance for the manufacturing industry to take responsibility for minimizing their productions’ environmental impacts. Life cycle assessment has been widely used in the product’s development phase within the manufacturing industry. However, the environmental impacts that come from various dynamic manufacturing processes are only estimated with large uncertainty. Some studies have suggested that the combination of life cycle assessment and production flow simulation is an appropriate approach to address the environmental impacts from the manufacturing processes. Nevertheless, these studies are often limiting their concerns to the limited life cycle phases or certain environmental impacts. This study proposes a framework regarding how to develop a method for evaluating and identifying improvements that help reduce the life-cycle environmental impacts of complex production processes. In addition, this work employs a simplified case study to demonstrate the proposed framework. 

In a knowledge based economy, manufacturing industry has to continuously improve their operations, processes and develop their employees in order to remain competitive in the market.

In this context, the collaboration between industry and universities becomes of vital importance. Universities and industry have traditionally maintained fairly informal or lose ways of cooperation when it comes to education. This article presents a fruitful cooperation which has been established between the University of Skövde, the Industrial Development Center in the region, IDC West Sweden AB, and the manufacturing industry.

The paper describes the development, lessons learned and the outcome of more than 3 years’ experience of close collaboration between the different stakeholders. It presents a methodology, used by the consortium to help manufacturing industries to improve their competiveness using a well defined process including: a company analysis, applied education and long-term coaching. A special focus is put on a long-term commitment by all partners. This alliance has performed more than 140 company analysis, conducted applied education for more than 2500 employees from more than 120 companies and performed coaching of more than 80 companies on site. The trend is that these figures will increase over time.

The established collaboration has been strengthened over this period of time by a number of shared research projects. One of these projects involves an evaluation of the impact that this presented consortium has had on the region´s industry. Lean Learning Academies is another project that has been funded by the European Union within the Lifelong Learning Program, with the aim to increase the competitiveness of European companies and enhance the employability of students.

Despite simulation offers tremendous promise for designing and analyzing complex production systems, manufacturing industry has been less successful in using it as a decision support tool, especially in the early conceptual phase of factory flow design. If simulation is used today for system design, it is more often used in later phases when important design decisions have already been made and costs are locked. With an aim to advocate the use of simulation in early phases of factory design and analysis, this paper introduces FACTS Analyzer, a toolset developed based on the concept of integrating model abstraction, automatic model generation and simulation-based optimization under an innovative Internet-based platform. Specifically, it addresses a novel model aggregation and generation method, which when combined together with other system components, like optimization engines, can synthetically enable simulation to become much easier to use and speed up the time-consuming model building, experimentation and optimization processes, in order to support optimal decision making.

There is an urgent need for the automotive inductry to explore strategies and methods to accelerate the industrial efficiency progress and support decision making in order to regain profitability. At the same time, decision making should not be made strictly from a view of productivity and investment cost. Manufactures worldwide are taking steps towards more sustainable manufacturing. Sustainability, in terms of "Energy Efficiency", "Lean", "Lead Time Efficiency" and other forms of reuse/conservation of resources has become a paramount factor that needs to be considered not only during the operational stage but from the very first day a production system is designed or configured. Therefore, to optimise a manufacturing system today is not only about maximising capacity and minimising costs, it is also about minimising energy use, minimising loss, minimising manufacturing lead time and other sustainability measures. The aim of the presentation is to introduce an innovative simulation-based optimisation and knowledge elicitation methodology for decision-making support within the production systems lifecycle to increase the profitability (increasing cost effectiveness) and simultaneously sustainability (increasing energy efficiency, reducing losses/wastes and shorten Order to Delivery Time) of the Swedish manufacturing industry. The methodology is so called Holistic Simulation Optimisation (HSO) because unlike today's industrial practice that productivity, cost and sustainability are optimised separately, the framework proposed takes into account productivity, cost, and sustainability in a multi-level and multi-objective context. The HSO methodology is realised through the development of a software toolset that synergistically integrates Discrete Event Simulation with the sustainability and cost models that have been developed or extended by industrial companies with state-of-the-art multi-objective optimisation and data mining technologies. The potential benefits of using the HSO methodology on the efficiency of the production systems that are measurable and can be verified quantitatively are: 5-15% increase in productivity; 10-20% reduction in manufacturing lead time; reduction in environmental wastes, in terms of energy use and other forms of losses (10-20%). The paper will present how these estimations are based on the case studies conducted in Swedish automotive industry.

Development of manufacturing systems is dependent on human decision making. One important factor in the decision making process is the organisational ability to transform available information into useful knowledge. The ability is generally limited by the organisation's level of competence and use of methods. However, real systems are not simple and straightforward but dynamically complex and difficult to interpret in order to perform successful change. One tool for diagnosing and solving complex business problems is system dynamics. It is interesting for its capability to acknowledge dynamic complexity.

This paper presents a framework of guidelines that facilitates implementing a system dynamics project for manufacturing systems development. It is the result of industrial case studies, supporting verification of the framework contents. This is presented in order to improve using system dynamics as a decision support in manufacturing. And it may bridge a gap between academic theory and industrial practice.

Lack of time due to daily problems in need of attention restrains proper assessments of improvement opportunities. There is neither proper support at hand to deal with the dynamic complexity of human activity and systems in use. This paper explores if system dynamics simulation can be used to model tooling problems on a management problem level at a manufacturer and evaluates its use. System dynamics is a methodology designed to aid understanding of dynamically complex problems and increases decision making impact. The results focus on the achieved models which prove to have sense behaviour despite lack of thorough data. In conclusion the applied method provides with an analysis of complex problem situations applicable for a decision support, otherwise performed through good guessing. Main characteristics from reality have been included in model and an experimental laboratory to test future policies on achieved.

Computer simulation has been described as the most effective tool for de-signing and analyzing systems in general and discrete-event systems (e.g., production or logistic systems) in particular (De Vin et al. 2004). Historically, the main disadvantage of simulation is that it was not a real optimization tool. Recently, research efforts have been focused on integrating metaheuristic algorithms, such as genetic algorithms (GA) with simulation software so that “optimal” or close to optimal solutions can be found automatically. An optimal solution here means the setting of a set of controllable design variables (also known as decision variables) that can minimize or maximize an objective function. This approach is called simulation optimization or simulation-based optimization (SBO), which is perhaps the most important new simulation technology in the last few years (Law and McComas 2002). In contrast to other optimization problems, it is assumed that the objective function in an SBO problem cannot be evaluated analytically but have to be estimated through deterministic/ stochastic simulation.