In anticipation of his upcoming Predictive Analytics World for Manufacturing conference presentation, Analytics in Manufacturing Supply Chains – Predicting Behavior In Chemical Industry Supply Chains, we interviewed Gary Neights, Senior Director at Elemica. View the Q-and-A below for a glimpse of what’s in store at the PAW Manufacturing conference.
Q: What are the challenges in translating the lessons of predictive analytics from other verticals into manufacturing?
A: Two things come to mind. First is the number and variety of signals that need to be processed. Data from customers, distributors, suppliers, contract manufacturers, carriers, and third party warehouses may need to be analyzed to get the full picture. Second is that predictive data may need to be acted upon quickly and decisions can commit resources such as manufacturing capacity, raw materials, or logistics capacity. Decision support systems are critical.
Q: In your work with predictive analytics, what behavior do your models predict?
A: One example is under supply or over supply conditions. Over supplying finished goods may lead to price discounting while under supplying material to a downstream manufacturing process may shutdown operations. Another example is predicting which perishable materials in a complex supply chain network are nearing expiration so they can be expedited to an appropriate manufacturing facility.
Q: How does predictive analytics deliver value at your organization? What is one specific way in which it actively drives decisions?
A: Product-by-product and plant-by-plant predictions can lead to information overload and indecision. Supply chain decisions have financial impact and need to be taken in near real-time. For example, if rail cars to a manufacturing site are predicted to be late. Do I dispatch trucks as a rush shipments… or dip into safety stock? If trucks, how many? Over the long-term data may be analyzed systematically and accounted for during periodic planning cycles or contract renegotiations.
Q: Can you describe a successful result, such as the predictive lift (or accuracy) of your model or the ROI of an analytics initiative?
A: In one example the predictive system drove inventory replenishment accuracy from less than 55% accuracy to greater than 80%. This allowed a 20% reduction in safety stock levels. The number of leased railcars was reduced by 40%. This drove a working capital savings of greater than $400K / year for one product.
Q: What surprising discovery have you unearthed in your data?
A: The bullwhip effect can occur at a micro level. In one case a supply chain planner was tightly controlling resupply – on the phone every day to make sure they never ran out of stock. A change in today’s demand drove new truck shipment. A graphical review made obviously that the manual planning process was systematically driving large inventory swings. It did not correctly account for lead times as well as shipping, manufacturing, and receiving calendars. This was corrected by a correctly tuned predictive system.
Q: Sneak preview: Please tell us a take-away that you will provide during your talk at Predictive Analytics World for Manufacturing.
A: A common theme I hear is that the farther you are from the consumer the harder it is to get accurate demand data. We will share one approach that supports manufacturers systematically aggregating demand to improve predictive accuracy.
Don’t miss Gary’s conference presentation, Analytics in Manufacturing Supply Chains – Predicting Behavior In Chemical Industry Supply Chains, at PAW Manufacturing, on Wednesday, June 22, 2016 from 2:15 to 3:00 pm. Click here to register for attendance.