This website does not support Internet Explorer. Please, switch to Edge, Chrome, or Firefox browser to view this page.

Learn about IE support
o9 video

Watch next

July 17, 2023

How to Create a Demand Plan

In this video, we discuss the two approaches to creating a demand planning forecast: manual and statistical. We look at the advantages and disadvantages of each approach, and we discuss how they can be combined to create the most accurate forecast.

Demand planning is the process of forecasting future demand for products or services. This forecast is then used to make decisions about inventory levels, production schedules, and marketing campaigns.

There are two main approaches to creating a demand planning forecast: manual and statistical.
- Manual forecasting involves entering the forecast into a spreadsheet or system table by hand. This can be a time-consuming and error-prone process, but it gives planners the flexibility to incorporate their own judgment into the forecast.
- Statistical forecasting uses a mathematical method to generate a forecast. This can be a more accurate approach, but it requires historical data and can be difficult to implement.

In practice, most demand planning organizations use a combination of manual and statistical forecasting. This allows them to take advantage of the strengths of both approaches.

For example, a planner might start with a statistical forecast and then adjust it manually to account for known factors that could impact demand.

The best approach to demand planning will vary depending on the specific business and the type of product or service being forecast. However, by understanding the two main approaches, businesses can make informed decisions about how to create the most accurate forecast.

How do you create a Demand Plan? There are two approaches to creating a demand planning forecast: manual and statistical. Manual means entering the forecast into a spreadsheet or system table by hand. In other words, the planners create the prediction.

Statistical means using a mathematical method to generate a forecast. A system, engine or algorithm builds a prediction from data that you provide. These two forecasting methods can coexist. Let's look at manual forecasting first.

In its most basic form, a manual forecast is created by hand. So the first time it is created, can take a long time. But thereafter, each forecast cycle can begin with the previously approved forecast, and therefore the new forecast will only need refining. Manual forecasting can actually be achieved in different ways.

One option is to use somebody else's forecast, such as from sales, marketing, finance or collected from the customer as a starting point. These forecasts can then be consolidated and manually amended by the planners to become the demand plan. Another manual forecasting approach is to use a lack of sales history. This means taking actuals data from the past and placing those values into the same time bucket.

But in the future, demand planners can take a copy of this lagged historical data and make adjustments to it, such as percentage lift or decrease. To turn it into the demand plan. The second method to creating a demand forecast is to automate it with computing science. Statistical forecasts are mathematical solutions that typically use historical data to generate a demand forecast.

These automatic solutions can range from basic, such as a moving average that will generate a simple forecast like this one, to typical enterprise solutions that create best fit forecasts. These best fit forecasts will often use artificial intelligence to choose the most appropriate forecast from a selection of time series models, thus creating more dynamic and complex predictions. And finally, there are the next generation solutions with artificial intelligence, machine learning and Open-Source algorithms. These solutions use lagging and leading indicators from internal and external sources to create incredibly detailed and accurate forecasts.

In summary, then demand plans can be created manually by hand or can be built using automated algorithms that range from simple to complex. These approaches can be combined to suit the type of data, horizons and resources that are available and to meet the needs of the business.

O9 whitepaper industrial telecom mockup

Building critical supply and demand capabilities for Telecom network operators

Platforms with advanced supply and demand capabilities enable better, faster decisions.

I agree to the privacy policy.

Resources

View our related articles, white papers, use cases & videos

video

Who Does Demand Planning and How to Organize a Demand Planning Team

4 min
Auto draft thumbnail
video

Machine Learning for Demand Forecasting

2 min
Auto draft thumbnail
resource

Planning Imperatives for Efficient Growth

O9 whitepaper efficient growth mockup 2
resource

Building advanced supply and demand capabilities for Telecom network operators

O9 whitepaper industrial telecom mockup
video

o9 Demand Planning - Analysis Cockpit

0
Auto draft thumbnail
video

If Forecasting Is Never 100% Accurate, What’s the Point?

3 min
If forecasting is never 100% accurate, what’s the point? thumbnail
video

o9 Digital Brain — Driver based Machine Learning

3 min
Auto draft thumbnail
resource

Retail best practices for the holiday season

White paper mockup resource detail page copy
video

Who Uses the Demand Plan and How?

5 min
Auto draft thumbnail
video

How To Define Your Demand Planning Horizon

4 min
How to define your demand planning horizon thumbnail
video

o9 Demand Planning - Forecast Layers and Assumptions

3 min
Auto draft thumbnail
resource

AI/ML Forecasting - LATAM whitepaper

O9 whitepaper aiml mockup (1)
© – o9 Solutions, Inc.
Privacy Policy Cookie policy Support o9 Guide
tree nation
tree nation