Proceedings of the 2nd Conference on Production Systems and Logistics (CPSL 2021)

Proceedings of the CPSL 202

Proceedings of the Conference on Production Systems and Logistics: CPSL 2022

[no abstract available

Controlling a Vacuum Suction Cup Cluster using Simulation-Trained Reinforcement Learning Agents

Using compressed air in industrial processes is often accompanied by a poor cost-benefit ratio and a negative impact on the environmental footprint due to usual distribution inefficiencies. Compressed air-based systems are expensive regarding installation and lead to high running costs due to pricey maintenance requirements and low energy efficiency due to leakage. However, compressed air-based systems are indispensable for various industrial processes, like handling parts with Class A surface requirements such as outer skin sheets in automobile production. Most of those outer skin parts are solely handled by vacuum-based grippers to minimize any visible effect on the finished car. Fulfilling customer expectations and simultaneously reducing the running costs of decisive systems requires finding innovative strategies focused on using the precious resource of compressed air as efficiently as possible. This work presents a sim2real reinforcement learning approach to efficiently hold a workpiece attached to a vacuum suction cup cluster. In addition to pure energy-saving, reinforcement learning enables those agents to be trained without collecting extensive data beforehand. Furthermore, the sim2real approach makes it easy and parallelizable to examine numerous agents by training them in a simulation of the testing rig rather than at the testing rig itself. The possibility to train various agents fast additionally facilitates focusing on the robustness and simplicity of the found agents instead of only searching for strategies that work, making training an intelligent system scalable and effective. The resulting agents reduce the amount of energy necessary to hold the workpiece attached by more than 15% compared to a reference strategy without machine learning and by more than 99% compared to a conventional strategy

Enhancing Decision-Making In SCM: Investigating The Status Quo And Obstacles Of Advanced Analytics In Austrian Companies

Over the past few years, the stability and predictability of logistics and supply chain networks have significantly decreased. This has led to higher risks and increased uncertainty in decision-making within supply chain management (SCM). Fortunately, the abundance of available data presents a tremendous opportunity to alleviate this uncertainty. However, realizing the full potential of advanced analytics, such as predictive and prescriptive analytics, is hindered by a lack of knowledge regarding their practical applications and performance benefits, as well as a deficiency in implementation expertise. This research paper examines the current state of advanced analytics applications and the primary challenges faced by Austrian companies in this domain. The findings reveal a distinct pattern: although the literature highlights numerous performance advantages, the practical utilization of advanced analytics remains at a rudimentary stage and is primarily confined to isolated departments. While demand management, procurement, and transport planning have shown some initial success in their implementation, other areas like production planning and, particularly, warehouse management lag. The primary challenges observed in practice include a limited understanding of the potential of advanced analytics, lack of transparency and data quality issues, difficulties in internal marketing, and inadequate organizational integration. These challenges, along with potential courses of action, serve as a starting point for other companies aiming to address similar issues. The significance of this work lies not only in its theoretical contribution to existing research on advanced analytics in SCM but also as one of the few studies that delve into the practical implementation and specific application domains of advanced analytics in Austria

Developing A Key Performance Indicator System To Integrate Sustainable Corporate Objectives Into Maintenance Using The Analytic Hierarchy Process

Maintenance in a manufacturing company is a key function for maintaining or restoring the functional condition of the production equipment and machinery and thus for maintaining the overall efficiency of the company. Because of this role, maintenance is often considered "sustainable". As a result of regulatory requirements as well as stakeholder demands, companies are under pressure to specify their sustainability strategies. However, due to a lack of knowledge about the sustainability potential of this function, the identification of clear objectives for sustainable maintenance is often neglected. Therefore, this paper presents a performance indicator system. 133 performance indicators in the three dimensions of sustainability (economic, environmental and social) were identified in a systematic literature review. In a qualitative content analysis and inductive categorisation, these were then assigned to 20 different categories. The hierarchical arrangement as well as the derivation of sustainable maintenance objectives from the corporate strategy enables companies to rank the performance indicators with the help of AHP (Analytic Hierarchy Process), a tool of MCDM (Multi Criteria Decision Making). This leads to a system of performance indicators based on a company's sustainability strategy, which strengthens the focus on sustainability in maintenance functions

The Contribution of new Production Technologies and Circular Economy Towards meeting the Future Demand of Proton-exchange Membrane Fuel Cells – A Literature Review

The energy and mobility sectors contribute significantly towards the global CO2 emissions. The proton-exchange membrane fuel cell finds application in both sectors and represents a possible green and sustainable technology for electricity generation. Current production rates do not satisfy the predicted demand for proton-exchange membrane fuel cells as the diffusion of this technology keeps increasing. Nor does the per-part cost guarantee a globally sufficiently broad application. The industry must overcome technological and economic obstacles to enable higher production rates at a lower cost per unit. This research gives an overview of current proton-exchange membrane fuel cell production and stacking technologies and provides an outlook on processes that need to be improved to enable faster and lower-cost production. Additionally, the impact of remanufacturing as an end of life option on the circular economy, production, and ecological impact of proton-exchange membrane fuel cells is examined. The knowledge generated by this research shall support increasing proton-exchange membrane fuel cell production rates to catch up with the predicted demand. Since current research on proton-exchange membrane fuel cell remanufacturing is rare, findings on this topic will support the industry in preparing for circular production processes in the future. Results of the present work include an overview of the current state of production for proton-exchange membrane fuel cells, the areas that need improvement, and the role of a circular economy

Derivation of MTM-UAS® analyses from virtual reality tools using MTMmotion®

Designing human work in a productive way plays a vital role in maintaining competitiveness of industrial companies. In order to fulfil this task, practitioners can use different methods to describe, analyse and design work processes. One widely used method is the process building block system MTM-UAS. Analysing the work processes manually (with or MTM-UAS or other methods) requires manual effort for data collection and interpretation of the method user. Due to this fact, not every industrial company has the capacities to design work productively. One possibility to reduce this effort is the automatic interpretation of digitized human motion data. Motion data describes human movements and includes, for example, distances covered, joint positions or object interactions. The technology virtual reality is capable to generate this movement data. Thanks to the advances in the technology in recent years, it can be used in a large number of workplaces. This article presents an approach how one can use the motion data of virtual reality tools to derive an automated MTM-UAS analysis. The technology thus reduces the analysis effort significantly and offers the possibility of expanding the areas of application. First, the article describes a workplace example, that was modelled in a virtual reality tool. It then explains the data that is needed to automatically generate MTM-UAS analyses and that can be generated by the virtual reality tool. It then shows how the data is translated into a valid MTM-UAS analysis. The article concludes with an outlook on how this approach can be transferred to other technologies like human simulation. With such a transfer it is conceivable to design a higher number of workplaces in a productive way and thus to maintain the competitiveness of industrial companies

MakerSpaces and Value Creation in Start-ups in Germany

Initiatives and projects such as the “Excellence Start-up Center.NRW” aim to increase the competitiveness of Germany through startups and has the explicit goal of creating new and sustainable jobs. In addition, so- called MakerSpaces are being created in parallel in many areas, which are considered as creative areas and are intended to support the construction of prototypes and the testing of hypotheses to create value for potential start-ups and to establish a valid business model. The question here is whether these initiatives and projects provide support for industrial value creation in Germany. This would require production and logistics to be considered when creating and developing new business models. Established methods of production research (e.g. simultaneous engineering) and logistics (e.g. supply chain management) should be taken into account. The results of a short survey – by questioning potential startups and advisors – show whether production and logistics are already considered in the consulting by the MakerSpaces or if there are further unmet needs

Methodical Approach for Detailed Planning of Services to offer Product Service Systems

The transformation of current business models towards offering product service systems (PSS) provides manufacturing companies numerous opportunities to consolidate or even expand their competitive position. Companies are confronted with the challenge of successfully designing this transformation process simultaneously. In order to approach the development of new business models and the transformation process, business model patterns and best practices provide a good first orientation for companies. However, these are designed to be industry-neutral and rather abstract when considering the actual processes. Thus, they do not offer any individual support to companies in the specific development of a business model and its required service delivery processes. Service delivery processes are part of a business model and describe activities that take place to provide services. Small and medium-sized enterprises (SMEs) in particular do not have the necessary time, technical and methodological resources to manage a transfer from abstract business model examples to an individual business model. This barrier often leads SME to remain with their traditional business model. Therefore, this paper presents a methodology for the detailed planning of service delivery processes. The designed methodology supports the phases design and implementation, which are part of the business model development. The methodology describes a structured procedure, in which relevant services first have to be identified. These services are then broken down into individual process modules on a second level. The modules are elements that can get combined to services. On a third level there are explicit process models. The process models are assigned to the modules and define the respective process steps and the requirements for the implementation. The approach is designed to support companies successfully transform to new business models for PSS by applying the detailed planning for services with specific modules that contain detailed process models and requirements

Modelling The Digital Twin For Data-Driven Product Development - A Literature Review

Due to advanced connectivity and increasing distribution of product-service, more and more data is available from the products used and produced. Scientific publications often describe that this product data can be applied in product development to make it more efficient and that the digital twin can play a central role in data provision and interoperability. However, less attention is paid to how the digital twin should be designed for this purpose and how it should be adequately modelled for these use cases. Therefore, this paper presents a structured literature review to analyse which methods are already described in science to model digital twins in a target-oriented way for use cases of data-driven product development. Not only are the procedures interesting, but also the type of digital twin for which they are intended and whether they describe the procedure at the level of a rough macrostructure or detailed microstructure