Deprecated: Function add_option was called with an argument that is deprecated since version 2.3.0 with no alternative available. in /users/tgh1653/public_html/wp-includes/functions.php on line 6078
IBM-SMU AI-based Supply Chain Risk Optimisation Models Saves Millions of Dollars - TGH Technology and Business Portal/Blog

IBM (NYSE: IBM) Manufacturing Solutions Pte. Ltd. and the Singapore Management University (SMU) recently announced that their successful IBM-SMU AI-based Supply Chain Risk Optimisation Models Saves Millions of Dollars. This risk-constrained materials requirements allocation research project yielded USD$35 million in savings for IBM Infrastructure’s supply chain globally.

What this project is all about

This project has helped procurement professionals in IBM to identify potential risks, including delays, quality and pricing, and mitigate their impact on businesses, with the accurate quantification of risk and cost-effective decision-making.

The accomplishment was also recognised by the prestigious Manufacturing Leadership Council, a division of the National Association of Manufacturers, based in the United States of America. The Council named IBM the global winner of the 2023 Manufacturing Leadership award under the Digital Supply Chains category. This shows IBM as one of the few world-class manufacturing leaders that have demonstrated efficiency and value in creating resilient and responsive supply chain networks, and highlights the importance of SMU’s research to facilitate this essential process.

Where it all began

The collaboration between IBM and SMU began in 2021 when the ongoing pandemic highlighted the vulnerabilities in supply chains and the urgent need to innovate and achieve supply chain resilience. As part of the agreement, SMU leveraged its expertise in supply chain logistics, together with multidimensional data provided by IBM, to build a new risk optimisation approach that informs decisions on emerging risks in procurement and logistics. IBM ILOG CPLEX Optimizer Studio was also applied to enable rapid development and deployment of decision optimisation models using data science, machine learning and AI capabilities.

The successful deployment of the source code developed by SMU’s School of Computing and Information Systems initially led to USD$1.8 million in hard annual savings for IBM’s hard disk drive (HDD) commodity category in one year, with a projected USD$35 million in hard annual savings realisable across the breadth of IBM Infrastructure globally. The outcomes from this first phase of collaboration were also integrated into IBM’s Cognitive Supply Chain Advisor 360 solution, potentially allowing organisations around the world to leverage this innovation to optimise their supply chains.

“The complexity and fragility of the global supply chain is impacting everything, from the speed and efficiency of operations to the ability to meet consumer expectations. ASEAN is no exception. Organisations need to start building resilient and sustainable supply chains to future-proof unanticipated disruptions,” said Matthias Graefe, Director of Supply Chain Transformation, IBM. “This trailblazing supply chain resiliency optimisation project with SMU is a great example of how academics and businesses can collaborate to develop new solutions for the world’s most pressing problems. We look forward to bringing this innovation and best practice to benefit organisations across the globe while continuing to support SMU as they work towards the next breakthrough.”

SMU Professor of Computer Science Lau Hoong Chuin, the Principal Investigator for the project, said: “Working with IBM’s supply chain and data science teams, we applied data-driven optimisation models and methods to help planners make more risk-tolerant, cost-effective global supply chain decisions.” He added: “In the next phase, we look to leverage the potential of quantum computing to improve the computational performance needed for augmenting supply chain decision-making capabilities in an age of heightened risks and uncertainty.”

* Information courtesy of IBM Singapore *

Leave a Reply

Your email address will not be published. Required fields are marked *