Burggräf, Peter
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Publication Access status: Metadata only , Bridging human expertise and machine learning in production management: a case study on ML-based decision support systems to prevent missing parts at assemblyIn the field of production management, decision support systems (DSS) equipped with machine learning (ML) have significantly advanced production planning and control within manufacturing companies. These systems are crucial, particularly in the machinery industry, for predicting shortages such as missing parts at the start of assembly. However, current ML-based DSS typically focus solely on predicting occurring problems or suggesting options for simplified scenarios, often missing the critical integration of human operators in the decision-making loop. This study introduces an advanced DSS that integrates ML to predict a missing part for the assembly start and to automatically provide a clear indication of the causes behind predicted shortages. This is achieved by employing shapley additive explanations (SHAP) to the respective ML-based prediction model. Thus, this analysis enables production controllers to initiate both proactive and/or reactive actions by detailed insights into the system’s predictions, and fostering a more dynamic interaction providing between human decision-makers and automated systems. This integration reduces the reliance on time-consuming manual analyses and enhances transparency in decision-making processes. The efficacy of the integrated approach is demonstrated by a case study conducted at a German machinery manufacturer, specialized in low-volume, high-variety production. The findings of this case study confirm that the DSS is efficacious in supporting complex decision-making processes, making it a valuable tool for modern production environments.Source Type:Article3 - Some of the metrics are blocked by yourconsent settings
Publication Access status: Metadata only , Hybrid intelligence – systematic approach and framework to determine the level of Human-AI collaboration for production management use cases(2024); Artificial intelligence (AI) and machine learning (ML) have emerged as transformative technologies in production management, enhancing decision-making and operational efficiency across a range of use cases. The challenge of determining the optimal level of Human-AI collaboration in decision-making processes persists for many organizations, despite the potential benefits of such integration. However, the current systematic approaches frequently lack a structured approach to determine the level of Human-AI collaboration in production management use cases. This results in inconsistent applications and suboptimal outcomes. This study presents a first and superior systematic approach for the systematic evaluation, development and implementation of AI in production management and introduces a structured framework that can be employed to assess and determine the optimal level of Human-AI collaboration for a range of production use cases. The framework classifies use cases based on critical factors such as data availability, process variability, error susceptibility, and decision complexity. This classification assists managers in calibrating the balance between automation and human intervention. The application of this framework could enhance the efficacy, productivity, and accuracy of Human-AI collaboration in production management. Furthermore, the systematic approach incorporates continuous evaluation and adaptation, ensuring long-term success in dynamic production environments. These findings provide a practical tool for managers to strategically implement AI, improving decision-making processes and operational outcomes. The proposed framework offers immediate opportunities to enhance production management through a structured, scalable, and human-centered approach to AI integration.Source Type:Article1 - Some of the metrics are blocked by yourconsent settings
Publication Access status: Metadata only , Machine learning implementation in small and medium-sized enterprises: insights and recommendations from a quantitative studyMachine learning (ML) offers high potential in manufacturing industry; moreover, for example the effectiveness of quality prediction and evaluation can be greatly improved using Machine Learning, which can generate significant competitive advantages. However, the potentials of ML are not fully exploited by small and medium-sized enterprises. A qualitative empirical study was conducted with 60 companies from different industry sectors to determine when SMEs are more likely to use ML. Here, it is shown that the willingness to invest in applications is substantial for the implementation of ML. Also, the availability of sufficient qualitative data within the SME is imperative for applying ML. Furthermore, recommendations for action for SMEs are established to close the technology adoption gap in SMEs and to leverage the benefits of ML.Source Type:Article2 - Some of the metrics are blocked by yourconsent settings
Publication Access status: Metadata only , Performance improvements in horizontally integrated production networks through real-time reschedulingin the event of disruptionsRescheduling is a frequently used reactive strategy in order to limit the effects of disruptions on throughput times in multi-stage production processes. However, organizational deficits often cause delays in the information on disruptions, so rescheduling cannot limit disruption effects on throughput times optimally. Our approach strives for an investigation of possible performance improvements in multi-stage production processes enabled by realtime rescheduling in the event of disruptions. We developed a methodology whereby we could measure these possible performance improvements. For this purpose, we created and implemented a simulation model of a multi-stage production process. We defined system parameters and varied factors according to our experiment design, such as information delay, lot sizes and disruption durations. The simulation results were plotted and evaluated using DoE methodology. Dependent on the factor settings, we were able to prove large improvements by real-time rescheduling regarding the absorption of disruption effects in our experiments.Source Type:Article3
