Chapter #2
Making external market intelligence work in the demand plan
How manufacturers can select, combine and use external data to make demand planning more responsive.
In my previous blog, I wrote about why sales history is not always the same as market demand, especially for manufacturers selling through dealers and distributors.
Looking beyond sales history, companies may have access to registrations, industry volumes, construction permits, investment indicators and economic outlooks. These sources are often discussed in management meetings or presented in PowerPoint, but they do not always influence the demand plan.
As a result, the business may see that the market is weakening or accelerating while the operational forecast continues to rely mainly on sales history, local assumptions and manual adjustments.
The challenge is not accessing external data, but turning the right market signals into a repeatable input to demand planning.
This starts by defining what is missing from the current demand view: an early indication of market change, validation of market-share movements, an explanation for changing dealer orders, or a more credible outlook for the months ahead.
The relevant data depends on that need. For example:
- Market volumes and expected market share show whether the overall market is expanding or contracting.
- Permits and tender activity can provide earlier signs of activity in construction and infrastructure.
- Dealer inventory helps distinguish a market movement from a change in channel stocking.
The goal is to select a small number of signals that improve a specific planning decision instead of adding every available dataset to the process.
Testing relevance with business knowledge and data
Commercial teams may know that permit activity in a particular region affects demand for a product group several months later. Data analysis can test whether that relationship has been visible over time, with a certain lag, and for which markets or products.
Correlations can help identify useful patterns and show whether an indicator adds value beyond sales history. They can also reveal when a familiar signal is not as useful as expected, but they are not proof alone.
Signals can move together because of seasonality or wider economic conditions, and historical relationships can change. A relevant signal must be timely, reliable, and understandable enough to support a practical decision.
Bring the signals into the planning workflow
External data should be visible alongside the forecast, where planning decisions are made. A planner reviewing a country and product group should see the statistical baseline, recent sales and dealer input together with the market indicators that provide context.
For example, the forecast may assume stable demand while registrations are declining, dealer inventory is high and permit activity is weakening. At the same time, the local sales team may expect a major project later in the year.
The right response is not an automatic forecast reduction. It is an informed discussion about whether the project supports the plan, whether inventory is appropriate and whether the wider market trend affects the expected outcome.
Move towards exception-based planning
Across many countries, products and dealers, planners cannot manually assess every relationship between internal and external data.
AI and automated data analysis can help analyse historical relationships and time lags, identify where signals have improved the forecast, and highlight where the current plan diverges from market indicators.
Planners can then focus on exceptions such as market volumes declining while the statistical forecast remains stable, or registrations improving while dealer orders continue to fall. Rather than reviewing every signal, their attention shifts to the areas that warrant further interpretation and discussion.
Technology can identify that signals disagree, but planners and commercial teams explain why: a major project, a local competitor change, a supply constraint or a customer decision not yet visible in the data.
Ultimately, embedding market intelligence turns insight into action and enables better, more confident demand decisions.
Want to see this in practice?
Join us on October 21, when I’ll speak with Chantal Esterhuyse from Kobelco Europe about how they combine smart algorithms, dealer input and market intelligence in their demand planning process.
News
News
