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Meet Maria — a demand planner for a mid-sized consumer-goods company. She owns the forecast: the number the whole supply chain plans against. Here’s how a typical Tuesday goes.

Morning: what happened overnight

Maria starts in her Inbox — the feed of tasks and notifications in the left sidebar. Two items are waiting under Today: “New forecast run is ready” and “Review forecast validation results.” Good. The scheduled statistical run finished on time, so she has fresh numbers to work with. Before she dives in, she glances at the Cockpit, her health dashboard. She isn’t looking at the three KPI cards this morning — she’s scanning the Exceptions queue for two types that are hers: Sales deviation (actual sales drifting away from the forecast) and Demand exception (demand behaving oddly). A couple of Sales deviation rows catch her eye — a beverage SKU (Stock Keeping Unit, one sellable item) sold far above plan last week. She notes it and moves on. More on the Cockpit in Exceptions.

Reviewing the baseline run

Next she opens the Baseline forecast — the statistical starting point Spherecast projects from sales history. The run comes with a “Products to review” list: the items whose recent sales deviated enough that the model flagged them. That beverage SKU from the Cockpit is right at the top, alongside a snack item that dropped off a cliff. Maria works the list. For the products that genuinely spiked or dropped — not just noise — she re-runs the baseline so the model incorporates the new reality instead of anchoring to a stale pattern. For a couple of others, she decides the blip was a one-off promotion last year and leaves the baseline as-is. This is judgment work, and it’s why a human sits in the loop.

Midday: into the S&OP grid

With the baseline cleaned up, Maria opens the S&OP (Sales & Operations Planning) grid — her main planning canvas. First she narrows it down: she filters to her market and her category so she’s only looking at products she owns. (Markets and Channels are terms your company may have renamed.) See Filter and display. Now she reads the metric ladder across each row:
  • Target — the top-down goal from leadership.
  • Baseline — the statistical forecast she just reviewed.
  • Baseline Adj. — the Baseline plus any included S&OP adjustment lines. This one is derived — Maria never types it. It moves only when she changes the adjustment lines feeding it.
  • Consensus — the final number everyone agrees to plan on.
She eyeballs the Forecast Gap — the distance between Consensus and Target. Where the gap is wide, leadership’s goal and the bottom-up plan disagree, and that’s where she spends her attention.

A request from Marketing

Mid-morning, Marketing pings her: they’re running a promotion on a whole product family next quarter and expect a real lift. Maria doesn’t overwrite any cells by hand. Instead she models the uplift as an adjustment line — a deliberate, documented change on top of the baseline — and she attaches a reason (“Q3 promo, Marketing request”) so anyone reviewing later understands why the number moved. Because the promotion has a seasonal shape rather than a flat bump, she reaches for a demand profile to spread the lift across the weeks the way the campaign will actually land. Before she commits, she wants to see what this does downstream. She spins up a quick scenario — a safe what-if copy of the plan — bumps the demand there, and previews the supply impact without touching anything live. It confirms the promo is orderable in time. Reassured, she comes back to the real plan.

Afternoon: consensus and push-down

Her teammates have been busy too. In the adjustments sidebar, Maria reviews the changes they’ve proposed — each with its own required reason — and works through them one by one, Accepting the sound ones and Rejecting the two that double-count a promotion she’s already modeled. This back-and-forth is the collaborative heart of the process; see Building consensus. With adjustments settled, she sets Consensus across her products — the number the business will actually plan on. Then she pushes it down, handing the agreed forecast to supply planning and kicking off a fresh supply re-run so the buyers see the new demand. Last thing before she logs off: she takes a snapshot, freezing today’s plan so that months from now she can measure how close her forecast came to reality. Accuracy tracking is how she gets better every cycle. That’s Maria’s day — a mix of statistics, judgment, and negotiation, all pointed at one honest number.
Tip: New to the vocabulary? Start with Key concepts, then see the Demand planning overview.