Pizzaro - Golrang Industrial Group
Demand modeling, transaction segmentation, KPI frameworks, and product roadmap execution.
FMCG and retail operations
System overview
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Continue to project detailsInventory workflows feed scikit-learn demand models and improved inventory planning, with a 30% workflow efficiency gain. Separately, SQL and Python analysis of 500,000+ transactions surfaces behavioral segments. KPI frameworks, PRDs and a prioritized backlog align three cross-functional teams and accelerate roadmap delivery by two sprint cycles.
Context
Pizzaro needed better operational intelligence across inventory workflows, customer behavior, and team execution.
Problem space
Rule-based heuristics and fragmented prioritization were creating overstock risk, weaker targeting, and slower delivery cycles.
Solution architecture
Built scikit-learn demand models to improve inventory workflows and reduce overstock incidents.
Analyzed 500K+ transaction records with SQL and Python to surface behavioral customer segments.
Aligned 3 cross-functional teams with KPI frameworks, PRDs, and a prioritized backlog.
- Inventory efficiency
- 30%
Improved operational workflow performance.
- Customer engagement
- 15%
Increased through behavioral segmentation.
- Sprints faster
- 2
Accelerated roadmap delivery.
Technology stack
- Python
- SQL
- scikit-learn
- PRDs
- KPI frameworks
- Product Strategy