Every supply chain decision begins with a forecast.

The forecast determines how much inventory we purchase, where we position

it, how we schedule production, and whether we can meet customer

demand while managing costs.

Inaccurate forecasts have significant consequences.

Over-forecasting leads to excess inventory, higher working capital

needs, and increased risk of obsolescence.

Under-forecasting results in stockouts, missed revenue, and poor customer

service. The challenge is that demand signals are rarely

straightforward. Historical demand is often affected by promotions,

one-time events, customer buying patterns, and market

disruptions. Sometimes the data does not yet exist because it

depends on future events. Improving forecast accuracy is

not just about using a better forecasting algorithm.

It is about building a reliable demand signal by combining historical

demand, business intelligence, and a structured planning

process. Over the next few minutes, we will demonstrate how Board

supports planners in identifying forecast risk, improving the quality

of the demand signal, and creating a more trusted forecast to drive better

supply chain decisions. Board addresses forecast accuracy through three

capabilities. First, we identify forecast risk by showing where

forecast performance is failing.

Second, we improve the demand signal by automatically detecting and correcting

anomalies that can distort forecasting results.

Third, we enrich the forecast with future business events such as

promotions, campaigns, and customer activities before they become

sources of forecast error. Let's take a quick look.

On the Board Demand Planning homepage, the dashboard gives a complete view of the

planning cycle, including workflow status, open actions,

forecast performance, and how demand is performing across both high and

low demand items. Board immediately guides planners to the areas that need

attention. Forecast accuracy metrics, workflow progress,

and demand alerts are all visible in one place without searching

through thousands of products and locations.

Rather than spending time looking for problems, planners can focus directly on the

areas that present the greatest business risk.

Let's start with forecast performance.

Board presents the key forecast accuracy indicators that planners use every

day. At the top, we see MAPE and bias, as well as forecast versus

budget, and actual versus budget.

In this example, we have a forecast bias of 19%,

which tells us we are consistently forecasting higher demand than we are

actually seeing. Even a 1% bias can be problematic because,

over time, it can drive unnecessary inventory buildup.

The regional summaries help us understand where performance differs across the

business, while the bias analysis shows which product families are

contributing most to forecast error.

Gravel is one of the product families contributing to over-forecasting.

At this point, we know where forecast performance is deteriorating.

The next step is to understand why.

Forecast accuracy starts with a clean demand signal.

Board automatically analyzes all historical demand, identifying

anomalies and outliers that do not align with expected trends or

seasonality. These are shown as blue bars here.

We can see several demand spikes within the gravel product family.

Filtering further on gravel, which we saw was contributing to around 77%

of the forecast accuracy issues, we can determine whether a spike represents

true market demand or a one-time event that should not influence future

forecasts. In this case, we remove or adjust the demand spike and

update the baseline demand. For illustrative purposes, we will use a larger

number. We save this, and the actual data updates immediately.

The change is reflected straight away in the planning process, ensuring that future

forecasts are based on realistic demand patterns rather than being distorted

by historical events. Instead of spending hours manually searching for

exceptions, Board automatically identifies the issues, guides

planners to the areas requiring attention, and allows them to make

corrections quickly. Forecast accuracy is not only about correcting the

past; it is equally important to capture future business intelligence.

Here, we see how this is achieved through new product introductions, end-of-life

activities, and events planning. We will focus on events planning.

Historical demand alone cannot predict upcoming promotions, marketing

campaigns, seasonal events, or other customer activities.

Board provides a dedicated events planning process.

Planners can review upcoming events and apply predefined event profiles.

For example, if marketing is planning a promotion for key gravel products,

we assign the appropriate event profile here.

Taking a scenario such as the World Cup, Board automatically calculates the

expected demand uplift, visible here, 5% for

profile two and 5% for profile three.

So if we are selling 100 items per week, we would expect to sell

105, 105, 110, and so

on. This approach ensures promotional demand is incorporated into the

forecast proactively rather than appearing later as an unexpected

forecast error. By combining cleansed historical demand with future

business events, Board creates a much more reliable demand signal.

With a cleaner demand signal and future events incorporated, Board

automatically refreshes the forecast.

Let's now look at the consensus forecast.

In the consensus forecast process, all forecast inputs are brought together on a

single screen. We can compare historical sales, statistical

forecasts, consensus forecasts, budgets, and forecast

confidence intervals all in one place.

The forecast can be adjusted directly for week 42, for example.

Looking at the screen, we can see a freeze period represented by the blue area.

No changes can be made here as the S&OP process requires that

data remain stable for execution.

For week 42, however, we can show how straightforward it is to make an

adjustment. We will increase the value so it is clearly visible

on-screen and then save it. The system immediately asks for a reason

code because every change is tracked in Board.

For this example, we will record this as the promotional activity we discussed

earlier. That entry will appear here, and this is where the expected

demand uplift would be displayed.

Every adjustment is tracked and governed.

Board clearly distinguishes between editable and frozen periods,

giving complete visibility into who changed the forecast and why.

This creates a trusted forecast that combines advanced analytics with real-world

business knowledge. What we have seen follows the same process used by leading

demand planning organizations. Board identifies forecast risk

through forecast accuracy analysis.

It improves the quality of the demand signal through demand cleansing.

It incorporates future business intelligence through event planning, and it brings

everything together within a governed consensus forecasting process.

The result is a structured and repeatable planning process that improves both

forecast quality and planner productivity.

Forecast accuracy is not the end goal.

Better business decisions are. By creating a cleaner demand signal and

aligning stakeholders around a trusted forecast, organizations can

reduce inventory risk, improve service levels, increase planner

productivity, and respond faster to changing market conditions.

Board helps organizations move beyond spreadsheet-driven forecasting and

establish a single trusted demand forecast that drives inventory

planning, supply planning, and the broader S&OP

process. The result is higher forecast accuracy, faster planning

cycles, and greater confidence in every supply chain

decision.