How Cognitive Computing Is Revolutionizing The Supply Chain

We sit down with Chris Cameron, Worldwide Sales Leader at IBM


AI is already having a profound effect on industries of every hue. In a recent survey by Teradata Corp, 80% of the 260 enterprises it polled said they are investing in some form of AI, and this number is only set to grow as the technology matures.

One field in which it is having a particularly profound impact is supply chain management. In Gartner’s recent ‘Predicts 2016: Reimagine SCP Capabilities to Survive,’ the research firm said supply chain organizations expected the level of machine automation in their supply chain processes to double in the next five years, with applications ranging from factories to logistics. And the rise of IoT means it will be more important than ever, with machine learning algorithms capable of analyzing the wealth of data so as to provide real-time visibility into the state of their supply chain and recommending or automatically executing actions that can cut costs, prevent accidents, and ensure customer requirements are fulfilled. 

Chris Cameron is Worldwide Sales Leader at IBM. As a digital and physical supply chain professional with more than 25 years experience, he has built supply and demand solutions, along with accompanying processes, in more than 22 countries in all industrial segments. We sat down with him ahead of his presentation at the Supply Chain Innovation Summit in Chicago, where he will discuss the applicability of cognitive technology as a solution and the challenges facing machine learning in the supply chain. 

What do you see as being the major challenges facing supply chain management today?

Agility. The requirements for the organization’s supply chains to be increasingly agile and adaptive is continuing. Changes in end-consumer behavior and enabling demand sensing and capture technology is altering how supply chains need to plan, operate, and optimize. They must adapt sooner and faster in the cycle and improve in a sprinting manner... similar to software development.

All of this is done in an environment where management continues to need margin improvement contributions from the supply chain. This is compared with an imperative for the ability to know where any given customer order is, when it will be delivered, and proactively advise if that delivery promise date is in jeopardy.

How is machine learning helping to tackle these issues? What do you feel the major benefits of the technology are for the supply chain?

Scaling to leverage data. Machine learning and artificial intelligence has several applications in the supply chain. The most obvious ones are the uses of this technology in the operations of tasks, trucks, machinery, auto order, etc. These all benefit in terms of economics and throughput, since they cost less than the human options, make less mistakes and can operate for longer periods.

But the real benefit is in the supply chain management area. This is an area where the same issues happen daily, they just appear differently. For example, delays are fundamentally similar, but where and when they occur is almost random. In addition, the data is now there to predict these, if only we had the human capacity to process that data real time. The major benefit of this technology in the supply chain is the ability to pair it with humans, learn from the human operation and scale that for the next event so the human administrates less and innovates more…meaning the human drives the agility from point number one.

What is IBM, in particular, offering the sector, and what sets it apart from other companies working in the field?

IBM offers many solutions in this area based around our Watson platform. The Watson API’s have been out there for a while and customers have used those to build unique AI applications.

IBM is now using those same API’s to create a supply chain management platform that contains the Watson components. We are training that platform to be ready out of the box with an understanding of the human interactions and a point of view for supply chain. In this way, we can deliver the platform to the clients in a manner that reduces the time to value. The training that a client will then do with the embedded Watson functions will be specific to the client’s business. This acceleration makes AI for business less of a science experiment and more of a ready to go application.

How do you think machine learning will change the role of the supply chain manager in the future? What skills do you believe future supply chain leaders should look to really embrace in order to be successful?

This appears to be evolutionary and the next logical level step up for the supply chain manager. A decade or more ago, there was a big step up for the supply chain manager. This is when optimization tools stepped into the supply chain management arena. In the 2000’s the supply chain manager began to morph into a technology evaluator so that he or she could scale their operations, while not sacrificing the ability to manage their business. They deployed TMS packages, supplier management portals, VMI systems, and B2B platforms. This was the big jump.

For the AI revolution, the supply chain managers seem to be well poised to take these same processes and evolve upwards from the present high-touch toolset to the AI toolset that once trained, does not require the same maintenance. The new skill that the supply chain manager is adding is the critical evaluation skills to be able to dig deeper into what AI is, how it interacts and simplifies data science activities, and how it is applied for their businesses.

What obstacles do you foresee as potentially hindering the adoption of machine learning in the supply chain? What can companies do to overcome these?

The human perception. This is not the worry that the rise of the terminators has begun. The perception that I am referring to is the perception that AI is magic and once plugged in we can operate like we are on the bridge of the Enterprise calling out to the computer. Machine learning and AI require training, like any other employee. However, once trained, they don’t forget, go to sleep, need a day off, etc., unlike employees.

This can be addressed by understanding and evaluating the training curve of an AI application for the business need. This approach, when paired with a commitment to keep the right humans engaged to teach the platform creates the formula for a steep scaling curve and exponential business benefits. 

You can hear more from Chris, as well as other industry leaders, at the Supply Chain Innovation Summit, which takes place in Chicago this November 14-15. View the full agenda here.

BONUS CONTENT: WATCH Bill Guillmart, VP of EPM and IBM Hybrid Cloud at IBM, discuss steering business performance in the cognitive era


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