Let the demand marketing plans for and the demand production prepares for be the same figure
A marketing plan produces a demand assumption: this campaign will bring this much in sales, this launch will generate this many units, this season this product group will grow by this rate. The sales team’s targets, production’s plan,
purchasing’s orders, and finance’s cash flow also each rest on a demand assumption. Integrated demand forecasting is, instead of making each of these assumptions separately, producing a single, jointly owned demand figure by combining historical sales data, seasonality,
the marketing calendar, campaign and launch plans, price changes, channel- and segment-based trends, and the sales team’s field knowledge.
This figure is the same starting point for marketing’s plan, production’s schedule, purchasing’s requirements list, and finance’s budget.
When demand forecasting is not integrated, every function works with its own figure. Marketing plans a major campaign but production does not know about it; the campaign succeeds and the product is not in stock.
Or production plans according to last year’s curve while marketing has decided to remove that product from the portfolio; the warehouse fills with a product that will not sell. The sales team works with optimistic figures, finance with cautious ones, and
monthly meetings are spent debating which figure is right. Because the forecast is made once and left, nobody looks at it after the first deviation; decisions revert to intuition.
The result is either missed sales or excess stock; both are the demand marketing created going to waste.
In Minerva, integrated demand forecasting is a process in which the supply chain planning module works on the same data as marketing, sales, and product management. Historical sales movements by product, product group, channel, region, segment, and business partner,
together with seasonality and trends, form a statistical baseline forecast; the campaigns and launches on the marketing calendar, planned price changes, and
the assumptions in the scenario plan are added on top of this baseline as effects. The sales team and dealers contribute their own regional and customer knowledge to the forecast through the B2B portal and planning screens; figures from different sources
are turned into a single consensus forecast and approved. The approved forecast is used directly by production planning, material requirements planning, purchasing, and budgeting; actual sales are continuously
compared with the forecast, the reasons for deviation are seen by campaign, channel, and product, and the forecast is renewed every period with this learning. Machine learning and simulation capabilities are used to improve forecast accuracy and to see in advance
the demand effect of alternative scenarios.
Do not forecast demand separately in every department’s own spreadsheet; build a single integrated forecast that marketing, sales, production, and finance all look at as the same figure.