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Case study (anonymized): Computer manufacturer

Context and goal

Client

Mid-sized computer manufacturer/assembler that assembles configurations from imported components and sells to B2B and B2C channels.

Goal

a single system for managing supply, configuration, pricing and sales, with built-in AI forecasts for demand, availability and margin.

Challenges (before)

Low transparency

On profitability of B2B customers by segment.

Demand forecasts

Low-accuracy demand forecasts and overstocking of slow-moving items.

High fluctuations in availability

Frequent stockouts of key items.

Manual pricing

Slow comparisons to the competition

Solution: software platform covering the entire cycle:

Dynamic pricing

Final prices = delivery + labor + other costs + competitive prices (scrape/feeds).

B2B analytics

AI estimates delivery frequency, volume, margin, profit per customer and recommends terms/refinement of routes..

Supply and planning

AI model predicts stockouts and recommends orders/MOQ.

Custom configurator

Automatically selects compatible components, takes into account availability and deadlines.

Integrations: ERP, suppliers, e-commerce, competitor price catalogs.


 

Implementation

1

Duration 

12 weeks (data → models → pilot → production)

2

Scope

5 main suppliers, 2 channels (B2B/B2C), 3 price lists

3

KPI

Defined and tracked in real time (see Results).

Results (6 months after implementation)

 -47%
Stockouts
-47%, which reduced lost sales by ~–390K BGN annually (calculated proportionally)

 88%
Prediction accuracy
62% → 88% (+26 pp)

 +3.2%
Gross profit margin
+3.2 pp through dynamic pricing

-93%
Price formation time
~2 h → 8 min (–93%)

+22%
Competitive win-rate
+22% after automatic comparison of market prices

+28%
Inventory turnover
+28%, working capital release: -18%.

B2B logistics
Same volume with -14% fewer courses; profit/customer +11% through reconfiguration of terms.
Order-to-ship
-35% thanks to configurator and real-time availability
Revenue YoY
Extrapolation from 6 months: +19% with a stable marketing budget.

What we learned / next steps

AI predictions + dynamic pricing work best with weekly retraining and fresh competitive prices.




A/B pricing experiments

Adding automated tests for price optimization


MOQ negotiations

Automated MOQ negotiations with suppliers


Clustering B2B customers

For more precise discounts

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