Our client’s current inventory management database was not functioning optimally, which was resulting in missing inventory, slow loading times and an overall lack of flexibility.
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Their current systems were outdated, not centralized and was not adaptable for their online growth.

To help their 20-25 employees work more efficiently, we modernized our client’s inventory system, using machine learning to create the most effective inventory pick routes and recommendations for inventory placement, as well as automating notifications for outdated inventory. We also updated their overall software stack.Our first step was to see, hear and observe the client’s current use cases, to determine their pain points and what they would like to achieve. We also took measures to get a picture of how their customers interacted with the business and how employees were using the existing software.

It was important that we then built an understanding of the client’s current technologies and the adaptions that could be made to them, in addition to identifying which existing systems would need replacements and upgrades.

Once we had completed the research stage, we created wireframes of the web interface that employees would use and began testing, which allowed us to continue to improve and tweak the software. When the initial versions of the interface and inventory tracking database were finalized, our team then continued to work with the client to continually optimize the new software.

All their locations could operate as fulfillment facilities for online orders. Their database was able to better track their inventory and the crucial data that goes along with corresponding products.