Firefighting to Forecast Control: Building an Import-Adjusted Rolling Demand Planning & Governance Framework
Engagement Lead: Alvis Lazarus, Hesol Consulting (further represented as Hesol)
Sector: Import-Dominant Food & Beverage Distribution, Australia
Scope: Functional Requirements Architecture, System Capability Mapping, and Supply Chain Benchmarking
Executive Summary
Our client, Stellar Distribution, is an inventory-owning national distributor of premium branded, trending, and long-tail Food & Beverage (F&B) products across Australia. Operating a highly complex supply chain involving multi-temperature storage (dry, chilled, and frozen) and direct international imports from the USA, Mexico, and New Zealand, the business faces structural headwinds that traditional wholesale models fail to account for.
With prolonged sea-freight lead times, container-driven Minimum Order Quantities (MOQs), and retail-grade On-Time In-Full (OTIF) requirements from major accounts, any breakdown in planning results in catastrophic cost-to-serve penalties or empty shelves.
Hesol Consulting was engaged to perform a comprehensive “As-Is Dipstick Study”. The goal: break dependency on manual spreadsheet-driven workflows, benchmark performance against direct structural peers, and architect a digital-first governance framework ready for execution in an upcoming Odoo ERP transition.
Confidentiality Note: To protect corporate privacy, all specific metrics, turnaround times, dollar values, and exact capacities have been securely masked at their respective places throughout this document.
The Opportunity: Evolving the Import Planning Model
The engagement identified significant opportunities to elevate Stellar’s existing architecture into a highly resilient system. Rather than relying on manual, planner-dependent spreadsheet updates for customer forecast consolidation, there was an excellent opportunity to establish a centralized digital ingestion tool. Transitioning from traditional, experience-based static safety stock targets to system-driven parameters offered a clear path to minimize costly emergency stock transfers and premium logistics interventions.
Furthermore, the project opened the door to provide with tailored, realistic performance metrics specific to an import-heavy enterprise. Standard wholesale benchmarks often advocate for rapid domestic inventory turnover, creating a strategic opportunity to educate stakeholders that balancing inventory velocity against extended sea freight horizons protects the business from sudden “Import Cliffs” and stock-outs.
Stellar possessed a strong foundation, and this framework presented the ideal opportunity to introduce a precise system blueprint connecting collaborative customer inputs, statistical baselines, and automated supply controls.
The Solution
1. Structural Peer Group Benchmarking
Creating a customized benchmark set by filtering hybrid wholesalers and cold-chain importers. We established a board-level “Gold Standard Scorecard” declaring that for Stellar’s business model, controlled availability beats aggressive velocity.
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- Blended Network Average Target: [MASKED] inventory turns (adjusted for sea-freight variability).
- Import-Adjusted Target OTIF: [MASKED] range (recognizing that chasing maximum possible fulfillment demands hyper-inflated working capital).
- Days Inventory on Hand (DOH): Normalized to a safe buffer of [MASKED] days.
2. The Integrated Rolling Demand Forecasting Framework
We engineered a multi-stage Functional Requirements Document (FRD) that standardizes the operational logic of the business across the following pillars:
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- Customer Portfolio Classification: Automatically categorizing clients based on historical forecast submission patterns into “Forecast Providers” vs. “Non-Providers” (dynamically auto-grouped into a virtual portfolio named “OTHERS”) to isolate true demand signals.
- Hybrid Statistical Ingestion: A strict statistical calculation engine utilizing trailing historical data to establish a neutral baseline (Base Average + Trend Growth Factor + Seasonality Indices) to counteract volatile customer biases.
- Automated Exception Flagging: Building collaborative validation loops where system variances exceeding defined thresholds trigger an immediate Corrective Action Report (CAR) and force accountability onto the sales metric owners.
- Dynamic Decoupling Buffers: Replacing static rules with automated safety stock calculations tied to demand fluctuations, freight volatility, and supplier lead-time variability.
3. Digital Architecture Mapping & Gap-Fit Evaluation
To support the transition from CIN7 to Odoo ERP, Hesol designed and evaluated four distinct future-state planning paths:
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- Option 1 (Primary Target): Unified Netstock Demand Planning + Netstock IBP + Odoo ERP.
- Option 2 (Transition Step): Netstock Demand Planning + Inventory Planner + Odoo ERP.
- Option 3 (Tactical): Automated Spreadsheets + Inventory Planner + Odoo ERP.
- Option 4 (Lean Alternate): Netstock Demand Planning + Custom Min-Max Engine + Odoo ERP integrated via Robotic Process Automation (RPA).
We stress-tested these options against an Out-of-the-Box (OOTB) capability grid, concluding that while a significant portion of Stellar’s required demand logic could be executed natively inside Netstock, crucial gaps around customer forecast governance and historical data re-calculations required structured validation workshops before deployment.
Delivered Results
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- A Defensible Roadmap: Transformed an irregular planning culture into a sequenced roadmap spanning MIS implementation, dynamic replenishment set-up, and automated inventory health analysis.
- Unified KPI Definitions: Established concrete measurement guidelines for top operational indicators (MAPE, Bias, OTD, and Inventory Turnover) pulling data directly from the system architecture.
- Mitigation of System Migration Risk: Delivered robust functional requirements prior to the core ERP build, ensuring that internal business logic controls the system parameters, rather than vendor configurations dictating business practice.
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