Spend less time wrangling spreadsheets and more time getting the forecast right.
Get the Demand Planner briefAI now handles the repetitive parts of demand planning like cleaning sales history, flagging outliers, and drafting the narrative for your monthly demand review. It can compare statistical forecasts against actuals and suggest where to adjust, then explain the gap in plain words. This frees you to focus on judgment calls like promotions, new product launches, and supply constraints.
Paste these into Claude or ChatGPT and replace the bracketed parts with your own details.
I forecast [units] for [product] in [period] but actual demand was [units]. Here is the recent history and any known events: [paste data and notes]. List the three most likely reasons for the gap and tell me whether to adjust the next forecast.Write a short demand review summary for [product family] covering [period]. Use this data: forecast accuracy [number], top movers [list], and known drivers [promotions, launches, supply issues]. Keep it to five bullet points for a leadership meeting.Here is [number] months of weekly sales for [SKU]: [paste data]. Identify any spikes or drops that look like one-time events rather than real trends, and suggest a cleaned series I can use as a forecast baseline.I am launching [product] in [market] on [date]. It is similar to [comparable product]. Based on that comparable, propose a 12-week ramp curve in units and list the assumptions I should confirm with sales and marketing.Using this list of SKUs with their average demand and demand variability [paste data], group them into stable, seasonal, and erratic. For each group recommend a forecasting approach and a review frequency.Your full AI playbook for your role — updated every week. Tap any card for a step-by-step walkthrough and examples.
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