decrease in inventory costs
upgrade in WMS performance
Partnering with HQSoftware, the customer received a comprehensive warehouse management system with real-time data visibility and features for advanced customer demand forecasting. This allows for effective management of the company’s inventory and satisfies changing customer demand.
Want not to spend resources on surplus and still meet customer demand? We’re ready to help!
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Business Development Manager
The customer is a US-based company specializing in producing high-tech electronic components for various industries, including automotive, aerospace, and consumer electronics.
The upgraded WMS is a unified platform for inventory management and order fulfillment that helps improve visibility into stock levels and speed up order shipping. Workers across all locations now have access to up-to-date information, increasing overall productivity and improving decision-making.
As well, our software development team has introduced predictive analytics capabilities. These include customer demand forecasting based on historical sales data. The system provides replenishment suggestions, allowing the customer to resupply on time without excess inventory.
Using Machine Learning algorithms for data analysis, the system first generates a general forecast of the required quantity of each item for a certain period. This forecast is then filtered by specified parameters, such as the minimum required amount of goods in stock, the delivery period for receiving goods from suppliers, minimum delivery quantity, etc.
After filtration, the system provides replenishment recommendations based on current warehouse and supplier capabilities. These recommendations can be automatically applied, generating purchase orders based on a predetermined schedule. Alternatively, managers can manually generate orders using the specific recommendations provided.
During the development process, the HQSoftware team faced several challenges.
Exploring a legacy solution. As our client had a legacy system that needed modernization and transition to new technologies, our team had to understand how the system worked.
The challenge came from the fact that the team was very short on technical documentation, so we had to figure out the solution ourselves. Despite this, we were able to successfully upgrade the WMS, improving its performance by up to 40%.
Data processing. Our team needed to choose suitable Machine Learning models that would provide accurate predictive analytics outcomes and could be flexible enough to analyze new data, such as ongoing sales.
It was also important to ensure seamless data processing with a constant stream of incoming data. Our goal was to create a solution that could not only handle the current volume of data seamlessly but also sustain future growth in the database. Therefore, we designed a scalable architecture that can maintain high performance as the business expands.
By successfully overcoming these challenges, we were able to provide our customer with a high-quality, scalable solution with bug-free performance.
- 1 project manager
- 1 tech lead
- 2 back-end developers
- 1 front-end developer
- 1 data scientist
- 1 business analyst (part-time)
- 1 QA engineer
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