Socio-technical requirements for production planning and control systems

Due to increasing customer requirements and intensifying competition, manufacturing companies are facing growing challenges in the successful order handling. As a result, employees are forced to make increasingly complex decisions in the shortest possible time. At this, the tasks of production planning and control (PPC) are particularly affected. In response to the increasing complexity of tasks, companies rely more than ever on the potential of socio-technical systems, rendered possible by the integration of information systems (IS) in the daily decision-making process. However, due to the increasing complexity of systems used, many users are not capable to raise the potential of information systems acquired, which is why the benefits of IS implementation often fall short of expectations. The following paper thus analyses and structures potential decisive factors causing the lack of problem solving capability in context of using PPC systems. Based on findings from acceptance research, socio-technical influencing factors for the targeted handling of information systems are determined. The developed requirement framework is furthermore compared with current IS implementation strategies to derive future research needs

Concept for the cost prognosis in the industrialization of highly iteratively developed physical products

With the ongoing technological progress and increasing global competition, companies are facing a continuously changing market environment. Due to the volatility of the market, rapid product adjustments and shorter product life cycles are required. Changing customer requirements are rarely taken into account, leading to inventions that do not make the transition to innovations. Highly iterative product development poses a possibility to integrate the customer voice into the development process and thus shorten the time-to-market and enable companies to respond to changes in requirements. Within the scope of highly iterative product development methods, cost analysis remains one of the main challenges for companies. Since the scope of development is not known at the beginning of a project, neither development nor industrialization costs can be specified. This, however, is essential for product and process development to meet cost-related customer requirements and for forecasting the production and investment budgets. With existing methods, it is either possible to agree to a fixed development budget and target price or to enable the customer to make changes during development. The concept presented in this paper aims to counteract this challenge. Therefore, existing approaches are analyzed with regard to derived requirements for the transfer from highly iterative and integrated product and process development to agile cost analysis. Influencing factors on product and production process costs are identified based on findings from literature. By aligning the influencing factors and requirements, dependencies between target costs of a product and degrees of freedom of highly iterative product and production process development can be derived and used for the development of a framework for iterative cost analysis. In conclusion, a concept for an agile cost prognosis for the industrialization of highly iterative developed physical products is presented

Towards a Methodology for the Economic Performance Increase of Production Lines using Reinforcement Learning

The increasing number of variants in product portfolios contributes to the challenge of efficient manufacturing on production lines due to the resulting small batch sizes and thus frequent product changes that lower the average overall plant effectiveness. Especially for companies that manufacture at high speed on production lines, such as in the Fast Moving Consumer Good (FMCG) industry, it is a central task of operational management to increase the performance of production lines. Due to the multitude of different adjustment levers at several interdependent machines, the identification of efficient actions and their combination into economic improvement trajectories is challenging. There is a variety of approaches to address this challenge, e.g. simulation-based heuristics. However, these approaches mostly focus on details instead of giving a holistic perspective of the possibilities to improve a production line or are limited in practical application. In other areas of application, reinforcement learning has shown remarkable success in recent years. The principle feasibility of using reinforcement learning in this application context has been demonstrated as well. However, it became apparent that the integration of expert knowledge throughout the improvement process is necessary. For this reason this paper transforms five modules defined from an engineering point of view into the mathematical scheme of a markov decision problem, a default framework for reinforcement learning. This provides the foundation for applying reinforcement learning in combination with expert knowledge from an engineering perspective

Data-based identification of knowledge transfer needs in global production networks

Manufacturing companies’ value chains are increasingly distributed globally, which presents companies with the challenge of coordinating complex production networks. In general, these production networks grew historically rather than having been continuously planned, leading to heterogeneous production structures with many tangible and intangible flows to be coordinated. Thereby, many authors claim that the knowledge flow is one of the most important flows and the source of competitive advantage. However, today’s managers face major challenges in transferring production knowledge, especially across globally distributed production sites. The first obstacle to a successful knowledge transfer is to identify what kind of knowledge should be transferred between whom and at what time. This process can take months of information collection and evaluation and is often too time-consuming and costly. Thus, this paper presents an approach to automatically identify at what point knowledge should be transferred. In order to achieve this, the company's raw data is being used to identify which employees work on similar production processes and how these processes perform. Therefore, production processes, which can be compared with each other, need to be formed, even though these processes may be performed at different production sites. Still, not every defined cluster of production processes necessarily requires the initiation of knowledge transfer since performing a knowledge transfer always entails considerable effort and some processes might already be aligned with each other. Consequently, in a next step it is analyzed how these comparable production processes differ from each other by taking into account their performances by means of feedback data. As a result, trigger points for knowledge transfer initiation can be determined

Software-based Identification of Adaptation Needs in Global Production Networks

Internal and external influencing factors force companies to adapt their production networks to changing conditions, which entails a high level of complexity. To be competitive in the future, manufacturing companies have to minimize the required adaptation time between the occurrence of a change and the implementation of an adaptation. While some approaches deal with modelling and evaluating network configuration, there is a lack in identifying the need for adaptation. In practice, the creation of scenarios is often based on the experience and knowledge of the network designer. This paper presents an approach to systematically link perceived key figure changes to possible adaptation alternatives in network configuration. For this purpose, the relevant objects for network adaptations are first defined and adaptation alternatives are systematically described. Subsequently, these are combined with a set of key figures to derive suitable adaptation alternatives depending on their development. The approach is further implemented in a software-based prototype that enables the automated generation of adaptation alternatives in response to perceived changes and provides the user with a listing of possible alternatives prioritized by their utility. The validation with company data demonstrates that by earlier and automated identification of possible configuration adaptations, the adaptation time to changes can be reduced and the generated scenarios are less dependent on the individual experience of the user