In a recent supply chain and merchandising transformation project, the Columbus Consulting team focused on reducing manual decision-making by shifting core planning and allocation activities to AI-driven demand and inventory models. The work centered on moving from human-built demand calculations to system-generated forecasts and recommendations, allowing planners and distribution teams to operate by exception rather than constant intervention. Human judgment is applied only when risk thresholds, compliance rules, or financial exposure require it.

To enable this shift, we helped the client migrate their legacy merchandising environment to a cloud-based platform with the explicit goal of simplifying the application stack and consolidating core merchandising data. By centralizing sales and inventory information, demand forecasting is now embedded directly into planning and allocation processes, replacing manual calculations with statistical and machine learning models.

While the platform is still stabilizing and adoption is ongoing, early indicators point to improved forecast stability, faster allocation cycles, and reduced planner effort. Planners are spending less time reconciling data across systems and more time reviewing system-generated recommendations. Formal measurement of forecast accuracy, inventory alignment, and labor efficiency will occur once usage reaches steady state.

The transformation also established a closed-loop AI feedback cycle. Forecasts generate planning recommendations, while actual sales, exceptions, and returns flow back into the core platform to continuously refine the models over time. This feedback loop supports greater automation while preserving human oversight for material exceptions.

Equally important is the reduction in operational complexity. By consolidating overlapping applications and standardizing data models in the cloud, the client has moved toward a single source of truth for inventory and stock ledger data, improving visibility and enabling a more scalable operating model without proportional labor growth. In addition, business continuity and disaster recovery capabilities has also been strengthened as part of the transition.