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DigiTalk Pod

DigiTalk Pod

Chalmers Production Area of Advance

Welcome to DigiTalk podcast, where we talk about digitalization and how it is going to change the way we produce and use things. Produced at Chalmers University of Technology, Sweden.

11 - 11. Knowledge creation for businesses in the digitalization era
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  • 11 - 11. Knowledge creation for businesses in the digitalization era

     

    Guest: Robin Teigland, Professor in Management of Digitalization in the Entrepreneurship and Strategy Division at Chalmers University of Technology, Department of Technology Management and Economics.

    Quote:  “Digitalization challenges many of the basic assumptions around the way business is being conducted for “.

     

    “If you love knowledge, set it free”: this is what Robin and her team are doing with their work, focused on social networks for knowledge creation and knowledge sharing (electronic networks of practice) for tomorrow’s businesses. In fact, you can find lots of material about her projects and lectures on SlideShare! Robin starts the episode explaining that, according to a recent survey she analysed, 63% of executives believes that digitalization will revolutionise their business, but only the 3% adopts some form of AI-related technology. Is a digital disruption happening, then? And how? Robin is one of the main brains involved in the Peniche Ocean Watch Initiative, to rejuvenate coastal communities through enabling a blue circular economy. The initiative is located in Portugal and takes a holistic and integrated perspective on regional empowerment, digital transformation, and ocean initiatives. Reusing ocean waste for producing graphene-reinforced 3D printing material is an example of what the initiative is creating. Plus, those in the coastal community who are involved in the project are paid back in cryptocurrency. Robin is passionate about the ocean also outside her work time: she is a surfer! Robin explains what the challenges of being a multi-disciplinary researcher are, and this is the advice she shares: ensuring between depth and depth, creating a collective competence, adopting consistent language across the teams, understanding what drives the individual contributors.

     

    Check out Robin’s publications:

    Ingram Bogusz, C., Teigland, R., & Vaast, E. (2019). Designed entrepreneurial legitimacy: the case of a Swedish crowdfunding platform. European Journal of Information Systems28(3), 318-335. https://orsociety.tandfonline.com/doi/full/10.1080/0960085X.2018.1534039#.XP168YgzaUk

    Felländer, A., Ingram, C., & Teigland, R. (2015). Sharing economy. In Embracing Change with Caution. Näringspolitiskt Forum Rapport (No. 11) https://www.researchgate.net/profile/Robin_Teigland/publication/278410531_SHARING_ECONOMY_EMBRACING_CHANGE_WITH_CAUTION/links/5580805808aed40dd8cd27ee.pdf

    Wasko, M. M., Faraj, S., & Teigland, R. (2004). Collective action and knowledge contribution in electronic networks of practice. Journal of the Association for Information Systems5(11), 15. https://aisel.aisnet.org/jais/vol5/iss11/15/

    Tue, 11 Jun 2019 - 32min
  • 10 - 10. Smart Manufacturing: a Simulation-based Perspective

     

    Guest: Sanjay Jain, Associate Professor of Decision Sciences at George Washington University, Washington, DC and research associate at NIST (National Institute of Standards and Technology) in the system engineering group.

    “You have to make the [artificial] intelligence accessible and easy to use for the industry”

    We recorded this episode in December 2018 in the venue of the Winter Simulation Conference, which was hosted in Gothenburg, and attracted +1000 between speakers and participants. Our guest, Sanjay Jain, was the program chair of the conference. Sanjay was able to bring a different perspective on industrial digitalization from the perspective brought by our previous guests, who were all based in Europe. In fact, Sanjay is based in the US and works in a multi-faceted environment: academia, a governmental institution, and in a business school, holding MBA classes.

    What does the Industrial Internet of Things mean when put into the context of Smart Manufacturing? Sanjay Jain breaks it down in three pillars, following McKinsey’s classification:  connectivity, intelligence and automation, where all three have to come together and work in unison. Sanjay explains that if the intelligence that smart-manufacturing applications is not accessible and easy to use for the industry, the increased effort for business people in using those applications will make their value plummet, which would cripple the research investment being put into those. This applies to SMEs in particular.

    Among connectivity, intelligence and automation, Sanjay focuses on the intelligence part, applied at a production-system level. In his latest article, Sanjay and his co-authors built a simulation model to quickly estimate cycle times for incoming orders for promising delivery dates. Within the multitude of data-analytics approaches and machine-learning techniques, choosing the best approach/technique to estimate these cycle times is a tough job, with lots of uncertainty. Plus, enough real data from the production system is lacking and it is not enough to feed the algorithms properly. In his paper, two approaches, Neural Networks (NN) and Gaussian Process Regression (GPR), are evaluated using data generated by a manufacturing simulation model itself, skipping the need for the use of lots of real data from the production system. The results showed that the GPR model performed well when trained using limited data and also when the factory is operating under the high load condition.

    Sanjay Jain, Associate Professor of Decision Sciences at George Washington University, Washington, DC and research associate at NIST (National Institute of Standards and Technology) in the system engineering group.

    Check out Sanjay’s publications:

    Jain, S., Anantha Narayanan, A., Yung-Tsun, T.L COMPARISON OF DATA ANALYTICS APPROACHES USING SIMULATION. In Proceedings of the 2018 Winter Simulation Conference. https://www.informs-sim.org/wsc18papers/includes/files/090.pdf

     

    Jain, S., Shao, G. and Shin, S.J., 2017. Manufacturing data analytics using a virtual factory representation. International Journal of Production Research, 55(18), pp.5450-5464. https://doi.org/10.1080/00207543.2017.1321799

     

    Jain, S. and McLean, C.R., 2004. An integrating framework for modeling and simulation for emergency response. National Institute of Standards and Technology https://www.nist.gov/publications/integrating-framework-modeling-and-simulation-emergency-response?pub_id=822205
    Tue, 09 Apr 2019 - 24min
  • 9 - 9. Assessing Smart Maintenance

    Guest: Jon Bokrantz, PhD student, Chalmers University of Technology, Division of Production Systems, Department for Industrial and Materials Science

    “This shared understanding of smart maintenance is rooted in the Swedish manufacturing industry. They really defined what it is”.

    Jon stated: “We cannot build factories that are highly dependent on very advanced manufacturing technology and then allow them to stand still half of the time.” Jon explained the delicate interplay between maintenance management and competitiveness of manufacturing Jon pinpointed the importance of building a unified language to describe the pillar-concepts of smart maintenance. Not only for researchers to work smoothly with practitioners and viceversa. Jon described Smash – assessment of smart maintenance. The aims to SMASh project was to enable digitalization of the Swedish manufacturing industry. Many different management roles were involved in the development of the assessment tool.

    Check out Jon’s publications:

    Bokrantz, J., Skoogh, A., Berlin, C., & Stahre, J. (2017). Maintenance in digitalised manufacturing: Delphi-based scenarios for 2030. International Journal of Production Economics, 191, 154-169. https://doi.org/10.1016/j.ijpe.2017.06.010

    Ylipää, T., Skoogh, A., Bokrantz, J., & Gopalakrishnan, M. (2017). Identification of maintenance improvement potential using OEE assessment. International Journal of Productivity and Performance Management, 66(1), 126-143. https://doi.org/10.1108/IJPPM-01-2016-0028

    Bokrantz, J., Skoogh, A., Berlin, C., & Stahre, J. (2017). Maintenance in digitalised manufacturing: Delphi-based scenarios for 2030. International Journal of Production Economics191, 154-169. https://doi.org/10.1016/j.ijpe.2017.06.010

    Fri, 29 Mar 2019 - 24min
  • 8 - 8. The Digital Twin for Geometrical Variations Management 4.0

    Guest: Professor Rikard Söderberg, Chalmers University of Technology, Head of Department of Industrial and Materials Science and Director of Wingquist Laboratory. 

    “If we feed our simulation algorithms with real-time data, we can compensate [geometrical deviations] and optimize the production”.

    Professor Rikard Söderberg takes us to a journey from the dawn of the engineering discipline of geometrical assurance to the digital twin as key to manage product tolerances and adjust the production according to the varieties of the upcoming products.

    The goal? Reduce costs, waste, and improve quality. Speaking about research utilization and marketable innovation, Rikard invites current budding researchers-entrepreneurs to keep their vision tight and to work hard in a journey that can last for up to 20 years. 

    • Rikard introduces the episode by talking about how his different professional roles act in synergy. He also stressed the importance of focus, vision (personal effectiveness) and elimination of time waste (personal efficiency).

    • Rikard Söderberg continues with the history of geometrical variation and geometrical assurance as engineering discipline in manufacturing/production research. Despite this discipline being quite “traditional”, he stressed the importance of bringing it to the industrial practice in order for companies to achieve cost reduction and value-creation opportunities in terms of quality, both “real” and “perceived”. 

    • Rikard explained the alternatives of securing quality upfront by putting proper tolerances on key features of the product (the stricter tolerances are, the more expensive they will be), as opposed to be “more relaxed” in product design but run higher quality-related and functionality-related risks during the use phase of the product. Software packages can help companies manage this trade off in early product development phase. 

    • Rikard introduced the concept of the digital twin, a digital copy of the real world product. This digital twin of the product “works” through simulation algorithms that are fed with real-time data from the shop floor. The digital twin needs to be precise and accurate. But, again, what’s good enough? Can we scan same takt time as the production line? 

    • Rikard talks about the “reparatory” power of software programs combining analytics and production simulation that compensate in production what goes wrong as the product gets manufactured. However, calculations of these “compensations” need to be made in the same takt time of the production line, and this is a challenge. Opportunities exist, though. Components can be scanned when they leave the supplier’s site. 

    • In relation to the software packages with the capabilities described above, Rikard added that the tougher the competition is in the market for manufacturers, the higher the need for tools for tolerancing is. 

    • Speaking about innovation, Rikard invites current budding researcher-entrepreneurs to keep their vision tight and to work hard. Results can show up in up to 20 years. 

    • A final line from Rikard: “Coming from product development, the final validation is when somebody buys your product”.  

    Check out some of his publications:

    Rikard Söderberg, Kristina Wärmefjord, Julia Madrid, Samuel Lorin, Anders Forslund, Lars Lindkvist. An information and simulation framework for increased quality in welded components. CIRP Annals, Volume 67, Issue 1, 2018

    Edward Morse, Jean-Yves Dantan, Nabil Anwer, Rikard Söderberg, Giovanni Moroni, Ahmed Qureshi, Xiangqian Jiang, Luc Mathieu. Tolerancing: Managing uncertainty from conceptual design to final product. CIRP Annals,Volume 67, Issue 2, 2018, Pages 695-717. 

    Rikard Söderberg, Lars Lindkvist, Kristina Wärmefjord, Johan S. Carlson, Virtual Geometry Assurance Process and Toolbox. Procedia CIRP, Volume 43, 2016, Pages 3-12, 

     

    Wed, 13 Mar 2019 - 26min
  • 7 - 7. Big Data for Big Decisions in Maintenance

    Guest: Mukund Subramaniyan, PhD student, Chalmers University of Technology

    “The data speaks about the behavior of the production system”

    Instead of defining big data in terms of “what” and “how”, Mukund Subramaniyan invites us to asks: “why” big data? In this episode, Mukund Subramaniyan shares his adventure and precious knowledge as cross-disciplinary PhD student, bridging the gap between computer science and production engineering. His motto? Big data for big decisions.

    Mukund sees the potential of data in the production system, and uses his mathematical skills, combined with his knowledge about production systems’ operations, to find the most efficient and effective way to transform data into knowledge. His mission is to help managers and engineers in the production and maintenance departments to make more accurate decisions with higher degree of confidence.

    Mukund’s position in terms of balance between automation and human’s contribution is that algorithms should be giving an augmented intelligence to humans as opposed to be the representatives of an artificial intelligence that does all the job, simply put. Mukund argues that 60-70 % of the work can be done by algorithms, and the remaining part of the work is up to the humans, who judge the results according to their experience, and make the final decision. 

    Check out Mukund’s publications:

    Subramaniyan, M., Skoogh, A., Salomonsson, H., Bangalore, P., Gopalakrishnan, M., & Sheikh Muhammad, A. (2018). Data-driven algorithm for throughput bottleneck analysis of production systems. Production & Manufacturing Research, 6(1), 225-246.

    https://doi.org/10.1080/21693277.2018.1496491

    Subramaniyan, M., Skoogh, A., Gopalakrishnan, M., Salomonsson, H., Hanna, A., & Lämkull, D. (2016). An algorithm for data-driven shifting bottleneck detection. Cogent Engineering, 3(1), 1239516.

    DOI: 10.1080/23311916.2016.1239516

    Thu, 28 Feb 2019 - 25min
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