Customer is a renowned apparel retail manufacturer working with more than 40 manufacturing facilities around the territory of Alabama. Customer has been proactive to analyze opportunities for its operation and want to tap into potential from their new business ventures.


When running large numbers of supply chain units, it is inevitable to possess huge resources at hand. At the same time, supply chain owners are always looking for ways for cost savings. This was the case with our customer too. They were intended to apply ways that could help them reduce costs in the operations of the supply chain management.  In order to build a cost saving plan, they needed to have a predictive analytics tool to help them in their endeavor.


We at SynergyLabs connected with customer to build a model integrated with AI powered predictive analytics. Our model is designed to leverage AI capabilities and offer better visibility of the work process at the supply chain as well as warehouse. Based upon its potential, the data system is able to design the best route options for any supply chain of raw materials. At first, the system harnessed data from its existing ERP systems spanning over the warehouse and classified them into various categories. Once done, all data was transferred to our AI powered platform.  Data it collected from all the existing system involved mainly transportation rates and regulations and product shipping routes.


Our customized model took 4-5 hours to harness data involving transportation data and their constraints and also data collected from the existing tool used by customer. After analyzing the data, we came to a solution for cost savings for customers, which is likely more than $1.2 million per year. And we could help customers achieve this target by leveraging predictive analytics that helps alert product flow in the warehouse.

Using predictive analytics, we re-routed their guided shipment journey from the manufacturing plant. Generally, customer followed the shipment route from their distribution center. This change to route helped customers save costs on inventory management as well as material handling.

Not only does it perk their cost savings criteria, our predictive analytics platform also improved other services in their warehouse services.

As is with inventory management, staff rightly gauged the availability of the stocks so operation did not get interrupted. While they kept a good pace with the inventory control, it also enabled them to segment inventory by product size and category.

Many times, unplanned product pricing leads to confusion among the buyers. It can have a negative impact on the user experience and directly affects the credibility of the operations. Our predictive analytics model helps build a unitary pricing model to be accepted by users across different locations and help maximize the revenue.

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