JDA’s Exciting New Supply Chain Product Strategy

Originally published by Logics Viewpoints, May 1, 2017
Written by Steve Banker

JDA Software is a leading provider of supply chain software. Last week, JDA’s user conference – JDA Focus – took place. Usually, the first thing I do when I get to a big supply chain show like this is pull out the agenda and look for well-known companies that are speaking on how they are using supply chain solutions to improve their capabilities.

There was no shortage of such companies at Focus. But I didn’t attend any of their presentations. The reason? The new product strategy was so compelling that all I attended were product demos and presentations where the new strategy was described in detail.

JDA is working toward providing off the shelf solutions that allow companies to use SNEW data – social media, news, event, and weather data – to improve their supply chain capabilities. In short, they are working to leverage Internet of Things (IoT) to improve supply chain management. JDA admits they are not there yet. But they are much further along than their competitors. The reason for this is that they have built partnerships with leading providers of IoT content and supporting workflows to take advantage of this data. Their most important IoT partner is TransVoyant.

TransVoyant appears to be a very mature provider of IoT services. The company has been providing data and analysis to US security services for years. TransVoyant takes a multitude of signals – satellite, port congestion, air cargo status updates, weather, and many other data feeds – and then uses machine learning to predict when “enemy” nations and terrorist groups may be about to engage in injurious actions.

JDA is looking to use SNEW data in three ways in their applications. First, existing forecasts can be improved with new data sources. For example, their demand forecasting applications will potentially be able to make more accurate forecasts based on new data sources. In the short term, weather and event data will be most helpful. In the longer term, social media data might be tapped.

Secondly, the planning to execution handoffs can be better optimized. For example, a transportation management system (TMS) grabs orders and creates optimized plans of how shipments should be routed in the coming days. Eventually, these plans are executed. Now JDA can take all the JDA TMS planned moves and send them to TransVoyant. The TransVoyant application can view origins, routes, and destinations and predict that certain loads will not arrive on time because of things like road construction, a very large sporting event, or weather. A large shipper might send TransVoyant thousands of shipments for a planning horizon and perhaps only one or two percent are flagged as being at risk. The JDA TMS solution can then dynamically reoptimize those loads. This is where the JDA/TransVoyant application partnership has progressed the furthest.

As an aside, engaging in dynamic reoptimization in a way that does not totally disrupt the initial plan is not easy. If hundreds of loads are changed because of a replan, that could entail calling hundreds of carriers to cancel planned moves along with hundreds of new retenders. That should not be undertaken lightly. Not all TMS solutions are capable of “threaded” dynamic optimization that replans in a way that leaves most of the initial plan untouched. JDA is.

Finally, SNEW data can improve the agility of supply chains. On the demo grounds, JDA was showing a demo of how their supply planning data enhanced with TransVoyant feeds could improve the ability to respond to supply chain disruptions. In the demo, a ship is coming from China and heading for the port of Long Beach with critical raw materials. But part way across the Pacific Ocean, TransVoyant alerts the supply planning application that a strike is probable at the destination port. The machine learning algorithms further predict a delay of unloading at that port of 10 days. At this point, the supply planner can then view which customers will be impacted by the delay. The planner than runs scenarios and determines how to hit desired service levels at the lowest cost.

Here, analogous to TMS, not all supply planning applications can do this. In JDA’s supply planning application, the plan is “pegged” to customer orders which allows a planner to drill down to see the impact of a disruption and then create scenarios which can be saved and viewed side by side.

These new application capabilities are exciting. But this is new ground. A few cautions are in order. Machine learning may be overhyped. It is one thing for a security agency to collect data for years in the hope of finding correlations to threat events. But for industry, data must have a much more immediate ROI.

Secondly, when digital edge data is used to predict a disruption, planners need to understand the margin of error associated with that prediction. Some predictions will be far more certain than others. And in planning how to best deal with that disruption, some events will be of a duration that might create the need for the planner to create many scenarios. Some sort of decision support may be needed in those instances to help guide a decision.

In conclusion, I talked to one JDA executive that said something that resonated with me. When he joined the leading supply chain application company twenty some years ago, supply chain optimization was hot. He said that he knew that there was a lot of hype around the emerging applications. But he felt if only a small portion of what was being conceived could be actualized, he could feel good about his career choice. He feels the same way now. I do too.


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