Kinaxis introduces Planning.AI to help speed decisions in demand and supply chain planning

Kinaxis today launches Planning.AI, a new analytics platform that automates much of the groundwork involved in answering complex planning questions in demand and supply chain. Timed to coincide with the opening of its annual Kinexions22 event today, the new offering harnesses machine learning and advanced data analytics with the aim of helping planners reach faster, more accurate decisions. At a time of growing disruption to both demand and supply, this kind of analysis is needed to help companies interpret and act on real-time data, as Anne Robinson, Chief Strategy Officer at Kinaxis, explains:

Those techniques ... are much better at dealing with a lot of that richness of data in-the-moment, as opposed to time-series historical information. We want to be able to marry those both together.

Those outside-in signals become of critical importance when solving some of these, 'I want to understand my plan, I need to understand it right now,' because things are changing so rapidly.

Fusion of techniques

An add-on to the core Kinaxis RapidResponse demand and supply chain planning system, Planning.AI uses a combination of analytics approaches that planners might otherwise need to do in separate tools or even manually. It combines machine learning for analysis of large volumes of data with the use of heuristics, a rules-based approach to decision-making that narrows the scope of an issue, and feeds the results into an optimizer or solver that logically determines the best or fastest result. Using machine learning to help build the optimization model reduces the volume of data that needs to be analyzed. This dramatically reduces the time it takes to get an answer, while still preserving the required accuracy. A company can then respond much faster to market disruptions. As Robinson explains:

There's sometimes a trade-off. How can I get to a faster decision? Or how can I get to the best decision? What we're saying here, we're bringing these techniques ... together so that we can make the best decision with the amount of precision needed in the fastest amount of time possible. What makes us different is the fusion of all these techniques together.

There are two applications available at launch that harness the capabilities of Planning.AI. One is focused on demand planning. Demand.AI promises to broaden the internal and external data that can be analyzed, while improving sensing and forecasting across either short or long-term horizons. The other, Supply.AI, focuses on supply chain planning, and is intended to help planners balance trade-offs across factors such as cost, revenue, on-time delivery, capacity, and other factors. Robinson says:

The supply variability these days is nearly as bad or worse than what historical demand variability has been. So that's where we're focusing our efforts, on looking at that complex supply network. I think people are having these aha moments that, 'Oh, we need to make sure we have some level of transparency to second, third, fourth-tier suppliers, and what does that jumbly mess look like? Then you want to optimize that to a certain degree, so that as things shift, as things change, you can make the best decisions. That's really where Supply.AI is being able to add a new level of intelligence.

Responding to disruption

One example of the kind of issue where Supply.AI might help is a contract manufacturer that has pre-purchased components and sub-assemblies and then finds it has more stock than it needs to meet forecast production volumes. It might then want to figure out the minimum purchase of other components to combine with those leftover items to produce the most revenue from finished goods it can sell — but without taking a hit on its sustainability metrics. Robinson sums up:

We don't want to charge for obsolescence. We don't want to be the company that wastes from a sustainability perspective. We would like to make some more revenue. But we also need to buy additional components to be able to make use of those components before we eliminate them. So you're thinking, bottom line, top line and green, simultaneously. Clearly, they're not all going to be perfect, so how do you make the best decisions, given all of those key outcomes, with the myriad of decisions underneath?

This example is typical of the expectations on supply chain professionals today, she adds, which has only been exacerbated by the uncharacteristic disruption brought on by events such as the COVID-19 pandemic and the Ukraine conflict. She says:

If you're trying to talk about disruptions and people having to manage their supply networks, there is a real change in expectation on the supply chain leader, to ensure the solvency and success and, quite frankly, to survive during these disruptive times, that is placed on their shoulders. Oh, by the way, let's throw sustainability on there. Let's throw caution against cyber issues.

As you start to ladle this on, that supply network has a lot of constraints and considerations that need to be evaluated, as you're making those trade-off decisions. Oh, by the way, you need to make them in-the-moment, so good luck with that!

In a separate product announcement, Kinaxis has added new workflow automation capabilities to the Command & Control Center first released last year. This provides a centralized console for monitoring intelligence signals from their digital demand and supply chains. It's another example of how companies can ensure they're ready to respond to the unexpected in these uncertain times. Robinson adds:

Never has it been more critical [to] stress test your supply chain. Stress test it ahead of time, so that when in the moment, you've actually already been through these exercises and you're prepared in real time to be able to make those critical decisions. So important right now. That's a key element, I would say, in really creating a resilient supply chain, but one that's agile enough to respond.

My take

Agile analysis has become more crucial for planners during the multiple disruptions to both demand and supply that the world has experienced in the past two years, and the need is not going away any time soon. Indeed, the response to disruption of previously highly streamlined supply chains has been to diversify sourcing and thus introduce even more complexity. Faced with these challenges, it makes sense to apply machine learning to automate more of the heavy lifting and free up planners to make better decisions.

[Updated with the correct product name Supply.AI. When originally published, this was incorrectly given as Supplier.AI.]

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