Category: Manufacturing & Supply Chain
When forecasts diverge from real demand, the result is either stockouts and shortages or costly excess inventory and write-offs.
Demand forecasting drives production planning, purchasing, and inventory decisions, but pharmaceutical demand is shaped by factors that are hard to predict — seasonality, launches, tenders, channel behavior, and disruptions elsewhere. When forecasts diverge from reality, the cost lands on both sides: too little inventory means stockouts and shortages, while too much means excess stock, write-offs of dated product, and tied-up capital. AI forecasting approaches aim to improve accuracy and, just as importantly, to make the uncertainty in a forecast explicit so planners can plan for a range rather than a single number.
Traditional forecasts often lean on a few historical patterns; AI approaches can learn from a wider set of signals — history across products and regions, seasonality, promotions and tenders, and related external indicators — and model demand at the level of detail planning actually needs. Just as valuable as a point estimate is a sense of the range around it: models that express uncertainty let planners distinguish demand they can predict confidently from demand they cannot, and set inventory buffers accordingly. The aim is to give planners a better-informed starting point and a clearer view of risk, not to remove judgment. The tool forecasts and quantifies uncertainty; supply and demand planning professionals reconcile it with market knowledge, apply constraints, and own the plan. A forecast is an input to a decision, and the people making that decision stay accountable for it.
Accuracy claims are easy to make and hard to trust out of context, so evaluate a tool against your own data and your current method rather than a general benchmark, and look at where it does well and where it does not — new products with little history, intermittent or lumpy demand, and one-off events tend to be the hard cases. Ask whether the tool expresses uncertainty around its forecasts, because a single confident number invites the overstock-or-stockout swing you are trying to avoid. Consider how much data it needs and how it handles gaps, how easily planners can understand and adjust its output, and whether its forecasts arrive at the granularity and cadence your planning runs on. Then weigh integration with your planning, inventory, and ERP systems, so improved forecasts actually flow into decisions rather than living in a separate report.
The natural entry point is backtesting: running the model over historical periods and comparing its forecasts against what actually happened and against the forecasts the team produced at the time. That shows whether it would have been more accurate overall, where it would have helped most, and where it would have failed — all before it drives real purchasing or production. Focusing on a defined product family or region, and including the hard cases like new launches, keeps the comparison honest.
Pharmaceutical demand is shaped by seasonality, launches, tenders, channel behavior, and disruptions elsewhere, many of which are hard to predict. When a forecast diverges from real demand, the cost lands on both sides: too little inventory causes stockouts and shortages, while too much causes excess stock, write-offs of dated product, and tied-up capital.
AI approaches can learn from a wider set of signals than traditional methods — history across products and regions, seasonality, promotions and tenders, and related external indicators — and forecast at the level of detail planning needs. Many also express the uncertainty around a forecast, so planners can set buffers by how predictable demand is. The tool forecasts; planners reconcile it with market knowledge and own the plan.
A forecast is an input, not a decision, and no forecast is certain — so the useful question is how a tool expresses its uncertainty and where it tends to fail, such as new products or lumpy demand. AI is best treated as decision support: it forecasts and quantifies risk, while planning professionals apply constraints and market judgment and remain accountable for purchasing and production decisions.
Ask to evaluate the tool against your own data and current method rather than a general benchmark, and probe where it performs well and poorly — new products, intermittent demand, and one-off events. Ask whether it expresses forecast uncertainty, how much data it needs and how it handles gaps, whether planners can understand and adjust its output, and how it integrates with your planning, inventory, and ERP systems.
Kinaxis introduced Maestro Agents, agentic AI for supply chain orchestration, and noted adoption by a top-ten global pharmaceutical company for planning and demand orchestration.
Vendor: Kinaxis · press release (Kinaxis) · 2025-10-17