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  • 1.
    Birkie, Seyoum Eshetu
    KTH, School of Industrial Engineering and Management (ITM), Applied Mechanical Engineering (KTH Södertälje).
    Exploring business model innovation for sustainable production: lessons from Swedish manufacturers2018In: Procedia Manufacturing, E-ISSN 2351-9789, Vol. 25, p. 247-254Article in journal (Refereed)
    Abstract [en]

    Businesses globally are facing changes these days. To remain viable, business model innovation provides a strategic renewal mechanism. However, there are only few studies that investigated such challenging undertaking in light of changes for sustainable manufacturing systems. This study aims to explore practices towards establishing more sustainable production systems of the future. The investigation is based on publicly available data from selected Swedish manufacturers. Closeness of innovations towards some business model archetypes is discussed using the aspects of value proposition, value creation and delivery, and value capture. The findings indicate that a more structured and integrative way of understanding business model innovation for sustainable production is needed. They also imply that the value capture aspect of business models needs to address how different stakeholders, not just the business firm, capture different forms of value. The learning from this study could be used for designing more structured and large scale future research in the subject matter.

  • 2.
    Etemady Qeshmy, Danial
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Industrial Economics and Management (Dept.).
    Makdisi, Jacob
    KTH, School of Industrial Engineering and Management (ITM), Industrial Economics and Management (Dept.).
    Ribeiro da Silva, Elias
    KTH, School of Industrial Engineering and Management (ITM), Industrial Economics and Management (Dept.). Pontifical Catholic Univeristy of Parana, Imaculada Conceição, 1155, 80215-901, Curitiba, Brazil.
    Angelis, Jannis
    KTH, School of Industrial Engineering and Management (ITM), Industrial Economics and Management (Dept.), Industrial Management. Research Institute of Industrial Economics, Grevgatan 34, SE-10215, Stockholm, Sweden.
    Managing Human Errors: Augmented Reality systems as a tool in the quality journey2018In: Procedia Manufacturing, E-ISSN 2351-9789, Vol. 28, p. 24-30Article in journal (Refereed)
    Abstract [en]

    The manufacturing industry is shifting, entering a new era with smart and connected devices. The fourth industrial revolution is promising increased growth and productivity by the Smart Factory and within the enabling technologies is Augmented Reality (AR). At the same time as the technology is introduced, errors in manufacturing are a problem which is affecting the productivity and the quality. This research aims to find the main causes of human errors in assembly lines and thereafter explores whether augmented reality is an appropriate tool to be used in order to address those issues. Based on a literature review that identified and characterized a preliminary set of root causes for human errors in assembly lines, these causes were empirically studied in an exercise that covered an in-depth case study in a multinational automotive company. Data in form of interviews and deviation- reports have been used to identify the causing factors and the result showed that the main causes of human errors are the amount of thinking, deciding and searching for information which affected the cognitive load of the operator and in result their performance. Several interviews with experts in augmented reality allowed to verify if AR technology would be feasible to solve or mitigate the found causes. Besides that, in repetitive manual assembly operations, AR is better used showing the process in order to train new operators. At the same time for experienced operators, AR should show information only when an error occurs and when there is a need of taking an active choice. Nevertheless, while theoretically able to managing human error when fully developed, the desired application makes the augmentation of visual objects redundant and increasingly complex for solving the identified causes of errors which questions the appropriateness of using AR systems. Furthermore, the empirical findings showed that for managing human errors, the main bottleneck of an AR system is the systems artificial intelligence capabilities.

  • 3.
    Fratini, Livan
    et al.
    University of Palermo, Palermo, Italy.
    Ragai, Ihab
    Penn State University, PA, United States.
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering, Production Systems.
    Preface2019In: Procedia Manufacturing, E-ISSN 2351-9789, Vol. 34, p. 1-2Article in journal (Refereed)
  • 4.
    Fratini, Livan
    et al.
    University of Palermo, Palermo, Italy.
    Ragai, Ihab
    Penn State University, PA, United States.
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering, Production Systems.
    Special issue of journal of manufacturing processes on new trends in manufacturing processes research2019In: Procedia Manufacturing, E-ISSN 2351-9789, Vol. 34, p. 8-9Article in journal (Refereed)
  • 5.
    Fratini, Livan
    et al.
    University of Palermo, Palermo, Italy.
    Ragai, Ihab
    Penn State University, PA, United States.
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering, Production Systems.
    Special issue of journal of manufacturing systems on new trends in manufacturing systems research2019In: Procedia Manufacturing, E-ISSN 2351-9789, Vol. 34, p. 6-7Article in journal (Refereed)
  • 6.
    Urciuoli, Luca
    KTH, School of Industrial Engineering and Management (ITM).
    An algorithm for improved ETAs estimations and potential impacts on supply chain decision making2018In: Procedia Manufacturing, E-ISSN 2351-9789, Vol. 25, p. 185-193Article in journal (Refereed)
    Abstract [en]

    Latest years have seen companies in the supply chain dealing with increasing larger amount of data. The ultimate goal is to develop digital ecosystems combined with sensor data to allow companies, including suppliers, logistics service providers, transport carriers, freight forwarders, manufactures to jointly and (almost) openly share data, improve visibility, and optimize operations. In this paper we conceptualize an algorithm that collects maritime transport data, and thereby computes more accurate ETAs. Thereafter, we discuss implications for data-driven management across several functions of a supply chain, e.g. purchasing, marketing, inventory policies, transport synchronization and adaptive process planning.

  • 7.
    Wang, Lihui
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Fratini, L.
    Shih, A. J.
    Preface2018In: Procedia Manufacturing, E-ISSN 2351-9789, Vol. 26, p. 1-2Article in journal (Refereed)
  • 8.
    Wang, Lihui
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    Fratini, L.
    Shih, A. J.
    Special Issue of Journal of Manufacturing Processes on Advancing Manufacturing Processes Research at NAMRC 462018In: Procedia Manufacturing, E-ISSN 2351-9789, Vol. 26, p. 8-9Article in journal (Refereed)
  • 9. Yamaguchi, H.
    et al.
    Shih, A. J.
    Wang, Lihui
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering, Production Systems.
    Dods, B. G.
    Bartles, D. L.
    Wavering, A. J.
    Fratini, L.
    Ragai, I.
    Mears, L.
    Moylan, S. P.
    Paul, B.
    Mullany, B.
    Budzinski, J.
    History of NAMRI and NAMRC2019In: Procedia Manufacturing, E-ISSN 2351-9789, Vol. 34, p. 3-5Article in journal (Refereed)
1 - 9 of 9
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