Monday, March 25, 2024

g-f(2)2132 Revolutionizing Supply Chains with Machine Learning: A genioux Fact Exploration

 


genioux Fact post by Fernando Machuca and Copilot



Introduction


The article "How Machine Learning Will Transform Supply Chain Management" from Harvard Business Review delves into the transformative potential of Optimal Machine Learning (OML) in revolutionizing supply chain management. The authors, Narendra Agrawal, Morris A. Cohen, Rohan Deshpande, and Vinayak Deshpande, present a compelling case for the application of OML in overcoming the limitations of traditional planning methods and enhancing supply chain performance.



genioux GK Nugget


"Optimal Machine Learning (OML) has the potential to revolutionize supply chain management by enabling better decision-making and improving agility and resilience." — Fernando Machuca and Copilot



genioux Foundational Fact


The article posits that traditional planning methods in supply chain management are flawed, leading to disruptions, delivery delays, and inventory levels that are out of sync with demand. The authors propose OML as a solution, which connects input data directly to supply chain decisions, taking into account a firm's performance priorities. OML also features a "digital twin" representation of the entire supply chain and a data storage system that integrates information throughout the supply chain for quick data access and updating.



The 10 Most Relevant genioux Facts



  1. Optimal Machine Learning (OML) can transform supply chain management.
  2. Traditional planning methods in supply chain management are flawed.
  3. OML connects input data directly to supply chain decisions.
  4. OML takes into account a firm's performance priorities.
  5. OML features a "digital twin" representation of the entire supply chain.
  6. OML includes a data storage system that integrates information throughout the supply chain.
  7. Quick data access and updating are possible with OML.
  8. Two large companies improved their supply chains' performance by implementing OML.
  9. The need for businesses to improve planning for more agility and resilience is highlighted by recent global events.
  10. OML can enhance a company's performance by enabling better decision-making.




Conclusion


The article "How Machine Learning Will Transform Supply Chain Management" presents a compelling argument for the transformative potential of Optimal Machine Learning (OML) in supply chain management. By enabling better decision-making and improving agility and resilience, OML has the potential to revolutionize the way businesses manage their supply chains, ultimately enhancing their performance. As we navigate the complexities of the digital age, such insights underscore the importance of harnessing the power of machine learning to drive innovation and growth.



REFERENCE

The g-f GK Article


Narendra Agrawal, Morris A. Cohen, Rohan Deshpande, and Vinayak DeshpandeHow Machine Learning Will Transform Supply Chain ManagementHarvard Business Review, From the Magazine (March–April 2024).



ABOUT THE AUTHORS


Narendra Agrawal is the Benjamin and Mae Swig Professor of Information Systems and Analytics at Santa Clara University’s Leavey School of Business.

Morris A. Cohen is the Panasonic Professor Emeritus of Manufacturing & Logistics at the University of Pennsylvania’s Wharton School. He is also the founder of AD3 Analytics, a start-up that developed the OML methodology for supply chain management.

Rohan Deshpande is a machine learning scientist at Cerebras Systems and a former chief technology officer at AD3 Analytics.

Vinayak Deshpande is the Mann Family Distinguished Professor of Operations at the University of North Carolina’s Kenan-Flagler Business School.





Classical Summary:


The article "How Machine Learning Will Transform Supply Chain Management" from Harvard Business Review, written by Narendra Agrawal, Morris A. Cohen, Rohan Deshpande, and Vinayak Deshpande, discusses the application of Optimal Machine Learning (OML) in supply chain management¹.


The authors argue that traditional planning methods are flawed, leading to supply chain disruptions, delivery delays, and inventory levels that are out of sync with demand¹. They propose OML as a remedy, which can enable better decisions without the mystery surrounding the planning recommendations produced by current machine-learning models¹.


OML relies on a decision-support engine that connects input data directly to supply chain decisions and takes into account a firm’s performance priorities¹. Other features include a “digital twin” representation of the entire supply chain and a data storage system that integrates information throughout the supply chain and allows for quick data access and updating¹.


The authors provide concrete examples of how two large companies implemented OML and improved their supply chains’ performance¹. They argue that the Covid-19 pandemic, the Russia-Ukraine conflict, trade wars, and other events have highlighted the critical need for businesses to improve planning in order to be more agile and resilient¹.


In summary, the article presents OML as a transformative approach in supply chain management, enabling companies to make better decisions, improve agility and resilience, and ultimately enhance their performance¹.


Source: Conversation with Bing, 3/27/2024

(1) How Machine Learning Will Transform Supply Chain Management. https://hbr.org/2024/03/how-machine-learning-will-transform-supply-chain-management.

(2) How Machine Learning Will Transform Supply Chain Management. https://hbsp.harvard.edu/product/R2402K-PDF-ENG.

(3) Artificial Intelligence/Machine Learning + Supply Chain Planning. https://ctl.mit.edu/sites/ctl.mit.edu/files/2020-07/AI_Machine_Learning_Supply_Chain_Planning_MIT_CTL_Nov_18_RT.pdf.

(4) How AI and Machine Learning are Transforming the Supply Chain. https://www.supplychainbrain.com/articles/36526-how-ai-and-machine-learning-are-transforming-the-supply-chain.

(5) Using Machine Learning to Transform Supply Chain Management. https://www.tcs.com/content/dam/global-tcs/en/pdfs/insights/whitepapers/Using%20Machine%20Learning%20to%20Transform%20Supply%20Chain%20Management.pdf.



Narendra Agrawal


Narendra Agrawal is the Benjamin and Mae Swig Professor of Supply Chain Management and Analytics at Santa Clara University's Leavey School of Business¹. He has been a part of the faculty since 1992¹². He served as the Associate Dean of Faculty from 2010-2015 and Chairman of the Operations Management & Information Systems Department from 2008-2010¹. He was also a visiting professor at The Wharton School in 1999, 2000, and 2010, and at the Indian School of Business, Hyderabad, in 2004, 2006, and 2016¹.


Agrawal holds an undergraduate degree in Mechanical Engineering from the Institute of Technology, B.H.U., India, where he received the Prince of Wales Gold Medal¹. He also holds an M.S. in Management Science from the University of Texas at Dallas, and an M.A. and Ph.D. in Operations and Information Management from The Wharton School of Business, University of Pennsylvania².


His expertise lies in the areas of supply chain management and service operations¹. He has published his research in various journals and is a co-editor of a book titled Retail Supply Chain Management¹. He received the prestigious Fulbright Fellowship in 2020².


Agrawal teaches courses in supply chain management, operations management, computer-based decision models, and service operations in MBA and executive MBA programs¹. He has received numerous teaching awards, including the Dean’s Award for Teaching Excellence every year since 1996, and was voted Professor of the Year by the Executive MBA cohort at Santa Clara University in 2018¹. He also serves as a trustee and Co-Vice Chairman of Give2Asia, a non-profit organization that promotes and facilitates philanthropy to Asia¹.


Source: Conversation with Bing, 3/27/2024

(1) Agrawal, Naren - Leavey School of Business - SCU - Santa Clara University. https://www.scu.edu/business/isa/faculty/agrawal/.

(2) Narendra Agrawal - Santa Clara University. https://www.scu.edu/media/leavey-school-of-business/isa/Agrawal-CV-04272020.pdf.

(3) Naren Agrawal, Ph.D. - Leavey School of Business - SCU. https://www.scu.edu/execed/custom/universitypartnerships/faculty/naren-agrawal-phd/.

(4) Top Stories - Leavey School of Business - SCU - Santa Clara University. https://www.scu.edu/business/news/top-stories/dr-naren-agrawal-named-interim-dean-at-santa-clara-universitys-leavey-school-of-business.html.



Morris A. Cohen


Morris A. Cohen is the Panasonic Professor Emeritus of Manufacturing & Logistics at the University of Pennsylvania’s Wharton School¹². He is also the Co-Director of Wharton’s Fishman-Davidson Center for Service and Operations Management¹². His research interests include service supply chain strategy & solutions, machine-learning applications to supply chain planning, servicization and product-service systems, global operations strategy, bench-marking of manufacturing/logistics systems, performance-based incentives and contracting, service quality measurement, supply chain coordination, and manufacturing/marketing interfaces¹.


Cohen is a Fellow of the Institute for Operations Research and Management Science, and a Senior Fellow of the Manufacturing and Service Operations Management Society¹². He was the founder and chair of the board of MCA Solutions, a software company specializing in after-sales logistics planning systems, which recently merged with PTC¹². He also founded a startup, AD3 Analytics, that applies concepts of machine learning and big data to a new paradigm for supply chain planning and control¹².


He holds a B.A.Sc. in Engineering Sciences from the University of Toronto, as well as an M.S. in Industrial Engineering and a Ph.D. in Operations Research from Northwestern University¹². He has been a policy analyst for the planning branch of the Treasury Board Secretariat of the Government of Canada¹².


Source: Conversation with Bing, 3/27/2024

(1) Morris A. Cohen – Operations, Information and Decisions Department. https://oid.wharton.upenn.edu/profile/cohen/.

(2) Morris Cohen - Knowledge at Wharton. https://knowledge.wharton.upenn.edu/faculty/morris-cohen/.

(3) Morris Cohen - Knowledge at Wharton. https://bing.com/search?q=Morris+A.+Cohen+University+of+Pennsylvania%e2%80%99s+Wharton+School+summary.

(4) MORRIS A. COHEN Panasonic Professor of Manufacturing and Logistics .... https://faculty.wharton.upenn.edu/wp-content/uploads/2014/05/COHEN-CV-Apr-2014.pdf.

(5) undefined. http://opim.wharton.upenn.edu/~cohen/.



Rohan Deshpande


Rohan Deshpande is a machine learning scientist at Cerebras Systems, a company known for accelerating generative AI and creating the world's fastest AI chip². He was also the former Chief Technology Officer at AD3 Analytics, a start-up that developed the Optimal Machine Learning (OML) methodology for supply chain management¹. His work involves applying machine learning techniques to various domains, contributing to the advancement of these fields. His experience in both start-ups and established companies like Cerebras Systems showcases his versatility and expertise in the field of machine learning¹².


Source: Conversation with Bing, 3/27/2024

(1) Cerebras Systems Unveils World’s Fastest AI Chip with Whopping 4 .... https://www.cerebras.net/press-release/cerebras-announces-third-generation-wafer-scale-engine.

(2) How Machine Learning Will Transform Supply Chain Management. https://hbr.org/2024/03/how-machine-learning-will-transform-supply-chain-management.

(3) Cerebras Systems: Achieving Industry Best AI Performance Through A .... https://cerebras.net/wp-content/uploads/2021/04/Cerebras-CS-2-Whitepaper.pdf.



Vinayak Deshpande


Vinayak Deshpande is the Mann Family Distinguished Professor of Operations at the University of North Carolina’s Kenan-Flagler Business School¹. His research interests lie in the area of supply chain collaboration, service parts management, and addressing security and privacy issues in supply chains¹. He conducts his work in various industry sectors, including defense, aviation, airlines, computers, and automobiles¹.


Deshpande has been honored with the best contribution award by the Airline Group of the International Federation of Operational Research Societies (AGIFORS) for his research work focusing on airline flight delays¹. His work with the U.S. Coast Guard on optimizing the supply chain used for aircraft service parts was selected as a finalist for the Edelman Award, and he was honored as an Edelman Award Laureate¹.


Before joining UNC Kenan-Flagler, Deshpande was part of Purdue University’s Krannert School of Management¹. He has consulted with various companies, including Motorola and MCA Solutions¹. His research has been published in premier academic journals such as Management Science and Operations Research¹.


Deshpande received his Ph.D. in operations management from the Wharton School at the University of Pennsylvania. He also holds an M.S. in operations research from Columbia University, an M.S. in industrial engineering from the University of Miami, and a B. Tech. in mechanical engineering from I.I.T., Mumbai¹.


Source: Conversation with Bing, 3/27/2024

(1) Vinayak Deshpande | UNC Kenan-Flagler Business School. https://www.kenan-flagler.unc.edu/faculty/directory/vinayak-deshpande/.

(2) Vinayak Deshpande – Center for the Business of Health. https://cboh.unc.edu/index.php/people/vinayak-deshpande/.

(3) Vinayak Deshpande - University of North Carolina at Chapel Hill. https://kenaninstitute.unc.edu/people/vinayak-deshpande/.



The categorization and citation of the genioux Fact post


Categorization


This genioux Fact post is classified as Bombshell Knowledge which means:  The game-changer that reshapes your perspective, leaving you exclaiming, "Wow, I had no idea!"



Type: Bombshell Knowledge, Free Speech



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References


genioux facts”: The online program on "MASTERING THE BIG PICTURE OF THE DIGITAL AGE”, g-f(2)2132, Fernando Machuca and CopilotMarch 25, 2024, Genioux.com Corporation.
 
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