The problem described here is a classic inventory planning puzzle, and the user has already done the hard work of defining the logic. The request is straightforward: build a ship forecast that only activates once a customer's weeks of supply drop below a target threshold. That target is in BM1, currently set to eight weeks, and the user wants it to be adjustable. The core insight is that inventory should drive the schedule, not the other way around. This is exactly the kind of thinking that separates a static spreadsheet from a responsive planning tool.
What makes this challenge interesting is the conditional nature of the trigger. For row three, with fourteen weeks of supply on hand, the ship forecast should remain empty until week nine of the sales forecast. For row four, with zero inventory, every week is active immediately. For row five, with 8.6 weeks, the first week is skipped, then the forecast begins in full. The user has tried SEQUENCE but found it didn't return the correct cells. That makes sense: SEQUENCE generates a linear array, but what is needed here is a conditional offset that respects a variable starting point. The real solution is a formula that uses the weeks of supply value in column C to determine where in the row to begin outputting sales data.
A more reliable approach would combine IF, INDEX, and dynamic array functions. The formula should check whether the current column's position relative to the start of the sales forecast is greater than or equal to the weeks of supply minus the target. When that condition is met, it returns the corresponding sales figure; otherwise, it returns zero. This keeps the ship forecast aligned with actual demand while respecting the inventory buffer. The user's instinct to make BM1 adjustable is smart, it turns the whole model into a what-if tool. Changing that single cell from eight to ten or twelve weeks instantly rebalances the entire schedule.
What this highlights is the value of building logic into spreadsheets that mirrors real-world decision rules. Inventory planners do not ship based on arbitrary dates; they ship based on stock levels relative to demand. By encoding that rule directly into the formula, the user eliminates manual judgment calls and reduces the risk of over-shipping or under-shipping. The result is a forecast that adapts automatically to changing inventory positions. For anyone managing multiple customers with different stock policies, this approach saves time and increases accuracy. The formula is the automation, and the inventory threshold is the driver. Let the data decide when to ship.