7. Big Data for Big Decisions in Maintenance
Release Date: 02/28/2019
Robin Teigland explains how digitalization is changing the competitive paradigm of “traditional” businesses, where multinational corporations perform on the basis on size, structure and ownership of resources. Robin gives us many examples of how the digital disruption can start actually small, based on principles of resource sharing and open innovation. Robin Teigland is a Professor in Management of Digitalization in the Entrepreneurship and Strategy Division at Chalmers University of Technology.info_outline 10. Smart Manufacturing: a Simulation-based Perspective
What does the Industrial Internet of Things mean when put into the context of Smart Manufacturing? Sanjay Jain from George Washington University, Washington, DC, breaks it down in three pillars: connectivity, intelligence and automation. Sanjay focuses on the intelligence part mainly. He shows how synthetic data generated by manufacturing simulation models, when feeding data analytics techniques, helps us to predict cycle times and delivery rates accurately, even with a limited training dataset.info_outline 9. Assessing Smart Maintenance
Jon Bokrantz, Chalmers University of Technology, explains: This shared understanding of smart maintenance is rooted in the Swedish manufacturing industry. They really defined what it is. He also talks about "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.info_outline 8. The Digital Twin for Geometrical Variations Management 4.0
Professor Rikard Söderberg, Chalmers University of Technology, 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.info_outline 7. Big Data for Big Decisions in Maintenance
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, Chalmers University of Technology, shares his adventure and precious knowledge as cross-disciplinary PhD student, bridging the gap between computer science and production engineering.info_outline 6. Optimal Factory Layout thanks to Virtual Reality
“With a realistic model of the factory [in VR], actors affected by changes in factory layout are actively involved in the planning process”, says Liang Gong. He is a PhD student at Chalmers University of Technology (Production Systems), and is the expert of Virtual Reality technologies adoption in his research group. He talks about why computer aided design (CAD) and simulation tools are great for estimating quantitative measures in factory layout planning.info_outline 5. Utilizing Data from the Production System
This is a special episode, because Maja Bärring and Daniel Nåfors are the first PhD students who have been interviewed in DigiTalk Pod. Maja’s research focuses on understanding the value of data brought by digital technologies in production, such as the 5G and blockchain, whereas Daniel’s research focuses on supporting layout planning in factories via 3D laser scanner technology applied to virtual reality.info_outline 4. The Future of Maintenance
How to achieve a failure-free production? Listen to Anders Skoogh, Associate Professor and research group leader for Production Service Systems & Maintenance at Chalmers. Anders brings a perspective that combines technology, management and strategy and that transforms the concept of maintenance from reactive and insular to proactive and collaborative. Anders, being the Director of the Production Engineering Master Programme, describes how digitalization has been brought into curricula.info_outline 3. The Digital Factory
Professor Björn Johansson talks about virtual production and digital twins, and shares with us how digital tools can help improve the sustainability performance of factories.info_outline 2. Sustainable Manufacturing
Assistant Professor Mélanie Despeisse takes us to a journey starting from the quintessence of manufacturing – using valuable resources to produce value for society – to sustainable manufacturing and related concepts.info_outline
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.
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.