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Delta DMD·Distribution and logistics·Southeast Europe·Blueprint and delivery

Operational AI for a regional distribution business that needed measurable results, not a pilot.

Use cases shortlisted
12
Use cases delivered
3
Operating model
Owned internally
Delta DMD illustration
Photo of Novica Sarengaca, Delta DMD
“In the FMCG industry there is a constant challenge: making sure that every customer visit results in a complete and relevant order. That is why we worked with the Datablooz team to build an AI system that understands customer behavior and suggests relevant products. The results are measurable and visible in our day-to-day work, and the AI recommendations have become an indispensable part of every customer visit.”
Novica Sarengaca · Project Manager / SAP Business Analyst, Delta DMD
At a glance

What we shipped

Delta DMD operates at the scale where small efficiency gains translate into meaningful margin. The mandate was to find AI use cases that could reach production quickly and be operated by the internal team.

Challenge

Distribution is an operations business. AI has to survive the daily reality of the warehouse, the fleet, and the order book. The previous attempts had been decks and demos that never reached a production environment.

Approach

Blueprint → AI Pilot → Production launch → Scale and operate.

We followed the Datablooz Delivery Model. See our process.

  1. Blueprint

    Mapped the operational pain points to AI opportunities. Filtered the list by ROI, feasibility, and organizational readiness.

  2. Proof of concept

    Prototyped the top three use cases in parallel. Each prototype was evaluated against a concrete business metric.

  3. MVP in production

    Shipped the validated use cases into production with the Delta DMD team involved in every deployment step.

  4. Scale and operate

    Handed off to the internal team with a documented runbook, monitoring in place, and a quarterly review cadence.

Outcomes

Business, technical, and governance outcomes.

  • Three AI use cases running in daily operations.
  • Internal team owns the day-to-day operation of each system.
  • Documented runbook covering incidents, retraining, and escalation.
  • Shared quarterly review to assess business impact and next moves.
Architecture and stack
  • Python
  • Scikit-learn
  • Airflow
  • BigQuery
  • Streamlit for operator tooling
Governance

Each deployed system has an owner inside Delta DMD, a documented failure mode, and a quarterly governance review with Datablooz.

Working on something similar?

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