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Forecasting Demand for a Wholesale Distributor
A regional wholesale distributor struggled with frequent stockouts on popular items and excess stock on slow movers. Planning relied on spreadsheets, gut feel, and vendor suggestions, making it hard to respond quickly to changing demand.
Goobo Labs AI worked with their operations and finance teams to build a demand forecasting system that combined historical sales, seasonality, and promotional data into practical purchase recommendations.
Overview
The client had several years of sales data but limited analytics capabilities. Data lived in separate systems, with inconsistent product naming and incomplete records. Buyers had experience but no shared, data‑driven view of what to order and when.
We started by cleaning and normalising sales data, aligning it with a single product catalogue and grouping related SKUs. Then we identified the product families where forecasting could deliver the biggest impact without overwhelming the team.
Solution
We built a forecasting pipeline that generated weekly demand predictions per key product, taking into account seasonality, promotions, and growth trends. The models were calibrated to favour stability and interpretability over raw complexity.
On top of the models, we delivered a simple web dashboard where planners could explore forecasts, adjust assumptions, and export purchase suggestions into their existing ERP. The system highlighted items at risk of stockout or overstock so action could be taken early.
Process
Data assessment
Audit data quality, map sources, and agree on how to handle gaps and outliers.
Model design
Choose forecasting approaches that balance accuracy, transparency, and ease of maintenance.
Pilot on key lines
Roll out forecasts for a subset of important products, compare against existing planning, and refine.
Scale & train
Extend to more products, train planners on the dashboard, and set up periodic model review.
Outcomes
Over time, the distributor saw fewer urgent stockouts on core items and reduced capital tied up in excess inventory. Planning meetings shifted from debating raw numbers in spreadsheets to discussing trade‑offs using a shared, data‑driven view.
