What are Predictive Analytics?
In short, predictive analytics are data sets that can be analyzed and to help predict future events and give operations the ability to react and make changes to the current state. In this case, the data is specialized for warehouse distribution and the operations around functions that pertain to fulfillment. WAMAS can leverage AI to make predictions on several different factors. Order history, current warehouse volume SKU slotting suggestions and fulfillment performance, and then best practices across multiple distribution centers or best in class factors within a segment.
Leveraging AI and Predictive Analytics in Warehouse Management
There are a lot of data points when it comes to fulfillment distribution. Not only are there factors inside the four walls, but also across multiple facilities. Even outside factors give valuable insight—this is why more supply chain and distribution teams are relying on a predictive analytics tool.
It’s not just the cool to have feature within a warehouse management software (WMS) tool. Predictive analytics software is being implemented in distribution and fulfillment centers worldwide, much of it coming from data already created during the existing fulfillment process. Historical data sets are now being analyzed in real-time to determine the similarities of current day order profiles to previous days processing in an effort to provide best practice insights. Further, generated data is giving insights on inventory demand, inventory optimization, operation processes, and customer demographics. Everything management needs to know is now available with a few clicks.
Inventory demand is driven by consumer behavior along with peak seasons and historical data.
However, one should not simply assume that this is easily based on the past. With the onset of the Novel Coronavirus, historical demand curves were rendered inadequate due to non-normal demand for certain inventory. Products that were expected to be consumed were either no longer needed, or substituted for a similar product. Customer buying habits changed in a matter of days, which required substantial adjustments to the existing AI algorithms to support customer trends. Granted, a pandemic is not the norm, when it comes to predicting data, but it is a factor and has shown the logistics sectors the need for adapting to a fast-paced change. Warehouse management tools can see inventory spikes across a large footprint, which makes it easier to prepare.
Optimizing physical locations helps operations process the work in a more efficient manner. By leveraging the historical data trends as well as the future forecasts, AI can suggest items to be re-located within the warehouse or be co-located with items typically ordered together. This would create natural efficiencies for direct labor. This increases the speed at which these items can be picked and thus, gives greater throughput throughout the day. Physical location optimization not only gains more throughput but also saves on labor costs.
Operation processes benefit from predictive analytics too. These can include finding bottlenecks in current procedures, defining low performing distribution centers, or even understanding how a piece of equipment is getting ready to require maintenance. These tidbits of data are combined to give a bigger picture of an overall synopsis for operations. It’s this type of data that provides reporting necessary for warehouse operations to review and enables decision-makers to make changes if needed.
Consumer insights give details regarding trends in inventory, behavior shifts such as same-day delivery, and even flavor preferences vs. locations. No matter what the data point, a good WMS should be able to give valuable insight into how to manage warehouse fulfillment. AI and predictive analytics coupled with an engine that can look into the historical processing of work can allow a supply chain to become holistically more efficient. By reducing the overall strain on resources, both human and mechanical, a supply chain can become more nimble and react to changing customer needs faster.
Predictive Analytics Isn’t Just for Retail Fulfillment
While the majority of the retail e-commerce boom has driven predictive analytics for fulfillment, other industries are starting to take notice. Manufacturers of CPG or consumer products are now starting to reap advanced analytics benefits. As more and more consumers engage with technology and accept touch-points data is playing a role in helping streamline operations and move towards a faster, leaner, and more accurate distribution fulfillment. All of this leads to more satisfied end-users and better customer service.