Data-driven Services and Servitization in Manufacturing: Innovation, Engineering, Transformation, and Management

Permanent URI for this collectionhttps://hdl.handle.net/10125/107423

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    AI-Driven Comprehension of Autonomous Construction Equipment Behavior for Improved PSS Development
    (2024-01-03) Aeddula, Omsri; Ruvald, Ryan; Wall, Johan; Larsson, Tobias
    This paper presents an approach that utilizes artificial intelligence techniques to identify autonomous machine behavior patterns. The context for investigation involves a fleet of prototype autonomous haulers as part of a Product Service System solution under development in the construction and mining industry. The approach involves using deep learning-based object detection and computer vision to understand how prototype machines operate in different situations. The trained model accurately predicts and tracks the loaded and unloaded machines and helps to identify the data patterns such as course deviations, machine failures, unexpected slowdowns, battery life, machine activity, number of cycles per charge, and speed. PSS solutions hinge on efficiently allocating resources to meet the required site-level output. Solution providers can make more informed decisions at the earlier stages of development by using the AI techniques outlined in the paper, considering asset management and reallocation of resources to account for unplanned stoppages or unexpected slowdowns. Understanding machine behavioral aspects in early-stage PSS development could enable more efficient and customized PSS solutions.
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    From Scarcity to Abundance: Expansion Manufacturing Data through Limited Defect Images
    (2024-01-03) Moon, Junhyung; Yang, Minyeol; Park, Songmi; Jeong, Jongpil
    The increasing adoption of IoT sensors, communication capabilities, and software applications in manufacturing environments has led to a growing demand for handling diverse large-scale manufacturing data. This trend indicates that AI is being researched and developed as an essential tool for improving cost-effectiveness and efficiency. Recently, there has been a significant increase in demand for process improvement using deep learning technology in smart manufacturing processes. However, obtaining a sufficient amount of training data in real industrial environments is challenging due to security and cost concerns for companies. Therefore, we propose utilizing generative artificial intelligence to efficiently expand manufacturing datasets. For data augmentation, we use a model that combines Stable Diffusion and LoRA fine tuning, and apply the text generation approach of BLIP. We anticipate that these data augmentation will help to improve the performance of artificial intelligence in the manufacturing field while reducing the cost of data collection.
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    Design Principles for Product-Service-Software-System Innovation for Healthcare Manufacturers
    (2024-01-03) Adler, Leon; Ebel, Martin; Gebauer, Heiko; Rathi, Dipti
    Healthcare equipment manufacturers face the challenge of service innovation while simultaneously ensuring profitability and sustainability. However, service innovation fostered by digitalization seems promising to overcome these challenges through an innovative service offering leading towards Product-Service-Software-Systems (PSSS). In addition, the role of service design could be leveraged to create these new services successfully. Thus, this study contributes by further complementing service design and service innovation research with a design science approach. We present how a healthcare manufacturer can drive a transition to digital servitization by utilizing six design principles to offer PSSS innovation.
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    Exploring Capabilities for the Smart Service Transformation in Manufacturing: Insights from Theory and Practice
    (2024-01-03) Koldewey, Christian; Fichtler, Timm; Scholtysik, Michel; Biehler, Jan; Schreiner, Nick; Sommer, Franziska; Schacht, Maximilian; Kaufmann, Jonas; Rabe, Martin; Sedlmeier, Joachim; Dumitrescu, Roman
    Digital Servitization is one of the significant trends affecting the manufacturing industry. Companies try to tackle challenges regarding their differentiation and profitability using digital services. One specific type of digital services are smart services, which are digital services built on data from smart products. Introducing these kinds of offerings into the portfolio of manufacturing companies is not trivial. Moreover, they require conscious action to align all relevant capabilities to realize the respective business goals. However, what capabilities are generally relevant for smart services remains opaque. We conducted a systematic literature review to identify them and extended the results through an interview study. Our analysis results in 78 capabilities clustered among 12 principles and six dimensions. These results provide significant support for the smart service transformation of manufacturing companies and for structuring the research field of smart services.
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