Chantal Bisson-Krol, VP AI & ML Solutions at Kinaxis, lays out five ways to successfully augment your supply chain with artificial intelligence.

Supply chains have never needed more help – from geopolitical conflicts to extreme weather, the challenges facing supply chains across every industry are becoming much more pronounced. As with almost every other industry, AI technology could be perfectly poised to support supply chain professionals to overcome these challenges – but how?

AI technology isn’t simply a plug-and-play solution, however. To get the most out of AI, supply chain professionals need to abide by a few guiding principles.

1. AI should augment humans

First thing’s first: the achievements of AI in the past couple of years are nothing short of incredible, which is why it’s easy to forget the things that machines cannot provide, which I call the three C’s: context, collaboration and conscience. Models cannot derive meaning from context, critical in so many areas of the supply chain, nor can they work together to solve problems, including addressing issues like sustainability or human rights in supply chains.

This is why AI should augment humans. The most powerful combination is for humans and AI to work together, a belief reflected in a Workday survey of decision-makers, 93% of whom believe in the importance of keeping the human in the loop when AI is making significant decisions.

2. AI needs to fuse with heuristics and optimisation

AI can model problems at scale to produce more precise recommendations, such as greater demand forecast accuracy or better predictions of on-time delivery. Precision is also a benefit of optimisation, a field of AI familiar to many in supply chains for its ability to make the best use of resources within constraints to specific objectives, such as cost minimisation. Scale, though, can be a challenge: optimising a supply network can involve 200 million interdependent variables, slowing down even the fastest optimisation solver. Instead, some turn to heuristics, a problem-solving model that utilises a practical solution, or best practice, to produce a quick and feasible course of action good enough for the situation.

A fusion of the heuristics and AI can “warm start” an optimisation model, creatively combining the strengths of each approach to achieve an equilibrium of speed, precision and cost-effectiveness. Supply chain professionals should keep their hands on the wheel and remember that the most elegant solution is one that uses the right model for the right problem at the right time, no more, no less.

3. Concurrency and AI can transform supply chain management

Supply chains connect many functions across a company and beyond, which is why optimising one link doesn’t optimise the entire chain. For example, AI can greatly increase the accuracy of forecasts, but we want more than highly-efficient silos. The power of AI on its own is not enough.

The real breakthrough is not from AI but with concurrency, which integrates AI in the workflow to align decision-making across the supply chain for faster response. We want AI for its ability to predict with greater precision, speed, and elegance, and we need concurrency to connect supply chains for better, faster response, no matter what the conditions are. The bottom line is that AI embedded in concurrency leverages predictions while absorbing the volatility we cannot predict from the inevitable disruptions our supply chains will always face.

4. Democratise the power of AI

For AI to realise its potential, everyone must be able to use it. We will always need experts to explore new ways to apply AI, but empowering supply chain practitioners to adopt it themselves is crucial to realising its true power within the supply chain industry. For this reason, the best solutions are the ones which don’t require technical proficiency in AI or data science in order to use in your day-to-day role.

If solutions are designed for someone with supply chain context and business knowledge, they can “consume” the results of a model without knowing how to build it. Democratising AI in this way ensures its use, so choose to work with a provider who allows you to start from where you are and evolve.

5. Build Trust in Your AI

Many AI solutions come in a black box that even data scientists struggle to unpack. This is bad for visibility, but it can also be bad for adoption; supply chain professionals are ultimately responsible for their forecasts and, if they can’t explain how an AI platform is helping them to make their forecasts, they might think twice before trusting it. In fact, researchers have found that humans are more forgiving of what they perceive to be error on the part of fellow humans than they are from machines, a trait that can lead to them to develop “algorithm aversion.”

One approach to overcoming this aversion is state-of-the-art techniques that make black box AI models more understandable. For example, explainability techniques such as feature attribution methods can be used in demand sensing to help a planner see how adding a signal like weather affects predictions. Creating AI solutions that we can understand goes hand in hand with democratisation and, ultimately, will help improve adoption across the supply chain industry.

It’s clear that AI is transformative for the supply chain, and it’s fascinating to envision an industry augmented by this exciting technology. As we ramp up our use of AI, though, we need to remember that the trick to getting the most out of it is by adopting a human-centred approach. When AI is embedded across the end-to-end supply chain, expertly fusing the best techniques available, we can reimagine what is possible in our supply chains.

  • AI in Supply Chain

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