The client, a leading global alcohol manufacturing brand, was struggling with inaccurate forecasts driven by fragmented data, manual processes, and a forecasting stack that couldn’t scale with global growth. We modernized their demand and inventory forecasting foundation to improve accuracy, reduce regional over/understocking, and enable a faster planning cadence with centralized data and automated pipelines.
At a glance:
Industry: Manufacturing (CPG / Alcohol)
Core problem: Inaccurate demand & inventory forecasting + fragmented data + manual workflows
Primary impact: Improved forecast accuracy, centralized Azure data access, automated forecasting ops, and scalable expansion readiness
Core stack: Azure (data centralization), Snowflake (warehouse), Python (Pandas + scikit-learn), NetworkX (network analysis), Tableau (reporting)
The challenge: forecasting broke at a global scale
The existing forecasting approach couldn’t keep up with the complexity of serving multiple regions. Inaccurate forecasts created repeated overstocking and understocking, while data fragmentation and manual workflows slowed decisions and increased cost. The organization also faced limited scalability and missed opportunities—making it harder to support expansion and respond to shifting market trends.
What we found (high-level):
- Forecast outputs weren’t reliable enough for confident inventory decisions
- Data was fragmented across teams/systems, limiting analysis and creating version-control issues
- Manual processes created delays, inconsistent logic, and high operational effort
- The platform lacked the scalability needed for global growth objectives
“Downstream: getting numbers is easy. Trusting them enough to place inventory bets is the hard part.”
What “good” looked like (success criteria)
The client wanted an overhaul to achieve more accurate forecasting, streamlined data management, and a scalable backbone to support growth. We aligned on practical outcomes that planning, operations, and leadership could use immediately.
Success criteria:
- Improve forecast accuracy to reduce over/understocking across regions
- Centralize data access so teams operate from a single source of truth
- Automate forecasting workflows to increase speed and reduce manual effort
- Scale without re-architecture as the footprint expands
Solution overview
We modernized the forecasting stack end-to-end: data was centralized on Azure for accessibility, a Snowflake warehouse supported analytics at scale, Python pipelines handled cleansing and modeling, network analysis enabled smarter supply/flow reasoning, and Tableau delivered decision-ready reporting.
1. Centralized data foundation (Azure → Snowflake)
We consolidated forecasting inputs into a governed data foundation, improving accessibility and analysis. Centralization reduced time spent reconciling versions and enabled repeatable reporting and modeling across regions.
2. Automated forecasting pipeline (Python: Pandas + scikit-learn)
Using Python, we operationalized data cleansing and feature preparation (Pandas) and built forecasting models with standard ML libraries (scikit-learn). This shifted the workflow from “hand-built monthly effort” to a repeatable, automated forecasting process that runs faster and more consistently.
3. Network-aware reasoning (NetworkX)
To support complex multi-region planning and dependencies, we applied network analysis (NetworkX) to represent relationships across nodes (e.g., regions, lanes, or distribution dependencies). This helped frame forecasting outputs in a way that better matched how inventory and supply decisions propagate through a system.
3. Decision layer (Tableau reporting)
We delivered reporting in Tableau so planners and business stakeholders could consume results in a consistent, decision-oriented format—supporting faster actions and clearer executive visibility.
Implementation playbook
A reliable forecasting modernization program scales best when governance and foundations come before automation—then AI/ML layers on top. We structure delivery as phased outcomes (diagnostic → data foundation → AI enablement) to reduce risk and drive adoption.
- Phase 1: Diagnostic + KPI alignment — align definitions, owners, and planning cadence
- Phase 2: Data foundation — unify sources + quality checks + single source of truth
- Phase 3: AI enablement — forecasting + variance drivers + exception-first action workflows
Impact
- Enhanced forecast accuracy — improved accuracy reduced over/understocking
- Streamlined data management — centralized data via Azure improved accessibility and analysis
- Efficiency gains — automated processes enabled faster, more efficient forecasting
- Scalability — Azure scalability supports expansion without operational limits
Technology stack
- Azure — centralized data foundation
- Snowflake — data warehouse for analytics at scale
- Python (Pandas) — data cleansing & preparation
- Python (scikit-learn) — model development
- NetworkX — network analysis
- Tableau — reporting & consumption layer
Ready to modernize forecasting without disruption?
We centralize data and automate workflows to deliver scalable, decision-grade reporting without disrupting your current operations.
Frequently Asked Questions
What data is typically required to modernize forecasting?
At minimum: historical sales/shipments, inventory positions, product hierarchy, location hierarchy, and operational calendars. Centralizing these inputs is usually the first unlock before improving models
Why not just “tune the model” instead of modernizing the platform?
Because inaccurate forecasts are often a data + process problem: fragmented sources, manual steps, and inconsistent definitions. Better modeling helps, but repeatable improvements require centralized, governed inputs and automated pipelines.
What’s the biggest operational win after modernization?
Speed + trust: automated forecasting cycles, fewer manual reconciliations, and clearer visibility for stakeholders—so decisions happen earlier and with more confidence.