Demand signals and trustworthy data

First, Northstar forecasts bottles newly wanted next week before supply limits. This target differs from revenue, shipments, capacity, and a sales quota. State the product, location, bucket, and cutoff. If customers requested fifty but only forty shipped, investigate the ten-unit difference: some customers may wait, some cancel, and some leave without a recorded request. Sales can be censored by stockouts. A stockout flag tells us observed sales may understate demand; it does not reveal the missing quantity. Preserve observed shipments and any documented lost-demand estimate separately. A confident-looking replacement number is still an assumption when nobody measured the unmet requests. An order is firm evidence, but adding it to a forecast can double count. If next week's forecast of fifty one already includes a ten-unit customer order, fresh demand remains fifty one. Match orders to forecast scope using SKU, customer where relevant, and due period. Only genuinely incremental information justifies increasing the forecast. Six old valid units owed plus fifty one new units imply fifty seven obligations. This is the workload before stock, not fifty seven newly wanted units. If three old units are cancelled, obligations become fifty four while fresh demand stays fifty one. Distinct columns make the effect of cancellations and carryover clear. With eighteen usable stock units and ten arriving before demand, the fifty seven obligations leave twenty nine units to obtain. This does not establish that a supplier can deliver twenty nine. It identifies the requirement for the supply team, which must assess timing, quality, and constraints. Keep demand and feasible shipments as separate views. Define returns and replacements too. A return can reduce revenue without creating negative fresh demand. If management needs net sales and unconstrained demand, publish both with a bridge. The purpose is a shared definition that another planner can reproduce, not a single number that conceals incompatible business questions. Use one row per SKU, location, and week. Raw orders may be finer, so retain event identifiers while aggregating. Two exports of the same event are duplicates; two different orders with the same date and quantity may both be valid. Remove records based on verified identity, not just similar appearance. Keep the raw file and a transformation log with original value, replacement, reason, and evidence. Moving an event between weeks changes weekly totals but not the grand total. Removing a duplicate changes both. Reconcile these effects so a colleague can trace the model back to source records. A confirmed zero means the period was observed and no target demand occurred. A blank means the measure is absent. Closure can produce zero sales without revealing normal open-store demand. Stockout sales of eight establish eight sold, not total demand of eight. Do not replace every ambiguity with zero. Keep observed values, quality flags, and modeled values in separate fields. Neighboring clean weeks of twelve and fourteen suggest thirteen as a retrospective interpolation scenario, not a measured fact. That interpolation would also be unavailable to a forecast issued before the later week. Preserve alternative estimates and their timing. Check unique keys, continuous weeks, known product codes, units per pack, and impossible signs. Twelve bottles and twelve cases are not interchangeable. Returns need their own event type. Reconcile totals by product and period before and after transformations; a correct overall total can conceal quantities assigned to the wrong week. Decide which checks block publication and which warn. A duplicate key may block release, while one documented estimate may allow a qualified forecast. Give unresolved items owners and deadlines. Recurrent defects should lead to changes in capture or export processes, not a permanent dependence on private spreadsheet fixes. Think of a plan as a set of claims you can check. Ask what each row means, who asked for the goods, and when those goods are due. Then ask which part you saw and which part you had to guess. In the lab, keep the raw rows on one sheet and each change on the next. Try a late receipt and a lost row. Watch how the gap moves. A sound plan lets a new team member trace that move back to its cause. End with one clear owner for each open fact, and a date to check it.

1. Name the target

   Original flat diagram with separate labeled input and output boxes, using Northstar bottle icons.

   • Name the target
   • Fresh demand 51; obligations 57; net requirement 29.
   • Check units and timing
2. Keep a demand bridge

   Original calculation card with aligned quantities and an arrow showing one change.

   • Keep a demand bridge
   • Fresh demand 51; obligations 57; net requirement 29.
   • Check units and timing
3. Connect to the decision

   Original comparison chart with clearly labeled cases and a small assumption note.

   • Connect to the decision
   • Fresh demand 51; obligations 57; net requirement 29.
   • Keep assumptions visible
4. Choose a row key

   Original flat diagram with separate labeled input and output boxes, using Northstar bottle icons.

   • Choose a row key
   • Repair duplicates and missing observations while preserving real zeros and uncertain stockouts.
   • Check units and timing
5. Separate zero and missing

   Original calculation card with aligned quantities and an arrow showing one change.

   • Separate zero and missing
   • Repair duplicates and missing observations while preserving real zeros and uncertain stockouts.
   • Check units and timing
6. Make checks operational

   Original comparison chart with clearly labeled cases and a small assumption note.

   • Make checks operational
   • Repair duplicates and missing observations while preserving real zeros and uncertain stockouts.
   • Keep assumptions visible