10. Smart Manufacturing: a Simulation-based Perspective
Release Date: 04/09/2019
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: 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