Case study (anonymized): Used electronics testing company
Context and goal
Client
A company specializing in testing used electronics – smartphones, laptops, televisions, home appliances, and more. The devices come from various brands, featuring different operating systems, interfaces, and access methods.
Goal
Challenges (before)
Complexity in testing multiple brands and operating systems
The devices require different access methods, making the process difficult to standardize.
Unused database of test results
A vast amount of data has been accumulated, which is not being analyzed automatically.
Difficulties in identifying recurring defects across device series.
There is a lack of a mechanism for identifying weak components by model/series.
Limited visibility on customer purchases
There is no analysis of the dynamics of purchases, prices, and profitability.
Issues with parts and inventory planning
Excess inventory or depletion of key components.
Solution:
Forecasts for required parts and inventory levels
AI forecasts the volumes of required components and minimizes the risk of shortages.
Optimization of parts procurement
The module identifies favorable price ranges and suitable moments for purchasing.
Unified device testing platform
The software automates access and testing for multiple devices and brands.
AI analysis of test results
The system identifies recurring defective components by models and series.
Implementation
Duration
14 weeks (planning → integrations → automation → AI module)
Scope
Over 180 device models, various brands and operating systems; integration with testing stations, warehouse, and ERP systems.
Defined and monitored in real-time (see "Achieved Results")
Achieved results (3 months after implementation)
+3.1 pp
Higher accuracy in detecting recurring defects
The AI analysis significantly improved the recognition of defective elements in models.
-85%
Quick device testing
The testing time has been reduced from 14 minutes to 2 minutes.
-22%
Reduced blocked capital in stock
The optimization of inventory led to more efficient stock management.
Lower average purchase price for components
AI suggests optimal timing and ranges for purchases.
+4.1 pp
Better margin for B2B clients
The analysis of purchasing dynamics allowed for the accurate determination of margins.
-42%
Fewer part depletions
The forecasting models minimized the risk of shortages of key components.
87%
More accurate forecasts for parts requests
The accuracy has significantly increased – from 38% to 87%.
+18%
YOY revenue growth
The combination of faster testing, more accurate forecasts, and more efficient parts management resulted in an approximately 18% increase in annual revenue.
What we learned / next steps
AI analysis brings direct value in processes with large volumes of data.
The identification of defective models becomes more accurate.
Optimized inventories reduce costs and the risk of delays.
Better inventory control and fewer blocked funds.
Expansion of modules and automatic self-learning
The plans include adding new device categories and a self-learning model.
Your trusted partner: Hopix
If you are looking for smarter processes, more accurate forecasts, and better profitability — Hopix is here to help. Contact us, and we will be happy to discuss how we can assist you.