in Blog

August 21, 2019

Enhance Demand Planning with Predictions Based on Machine Learning


Artur Haponik

CEO & Co-Founder

Reading time:

7 minutes

Did you know that millions of decisions are made every hour? Those decisions are influencing billions of manufacturing, supply chain, and logistics processes. These numbers are absolutely impressive, but the problem is that a lot of these business decisions are not accurate or totally wrong. That’s the harsh truth of the world of business that can’t cope with the highly competitive market. And what is even worse, even already great experienced companies which managed to survive over the decades are not immune to failure. The constantly growing competitiveness is dangerous both to new and old businesses. That is why businesses started looking for solutions like demand planning based on machine learning to help them to improve the decision-making process.

However, there are a lot of things and techniques which can significantly simplify your life and help your company to stay afloat. Demand planning is one of them, and it can be especially efficient if you do it with the help of machine learning consulting. Keep reading to discover what demand prediction is, what benefits it can bring you, and how to make it more efficient.

Why сompanies need demand prediction, planning and machine learning

As usual, let’s start with a bit of theory. So, demand planning means forecasting the demand for your service or products. It works on the following 4 steps:

  • Step 1: creating a statistical forecast using past sales data. This is required to understand the previous demand patterns and fluctuations.
  • Step 2: get in touch with your customers, manufacturers, and distributors. In this way, you will discover the balance of demand and supply.
  • Step 3: deal with the forecasts. And remember that forecast creation is not a one-time thing to do. Forecasts have to be relevant, so you will have to update them on a regular basis.
  • Step 4: talk to your staff. Review the forecasts with your team to make sure that the company is able to meet the demand.

Demand planning – benefits

These four steps can bring numerous benefits to your business, and here are some of them:

You won’t face an unexpected income loss because of high demand and low supply. Being always ready for such fluctuations, you will have enough products in stock. This will allow you not only to improve the sales process but also to increase the customers’ loyalty.

You will be able to create a more efficient schedule for production, warehousing, and shipping. This is especially important for those periods when a lot of orders are expected.

You will control your costs in a more efficient way. Predicting the demand, you will know how much material you will need to produce the required number of items. And, therefore, it will be possible to order the material in advance. If you do this ahead, the prices can be more favorable. Besides, you won’t order more material than you need and waste your budget.

You will also improve labor-management — demand planning will allow you to optimize shifts. Apart from this, you will understand if you have enough members on your team to fulfill the needs of the customers.

Demand prediction and planning for decision supporting

We mentioned several advantages of demand prediction and planning, but the most important and obvious of them deserves a particular paragraph. That’s decision supporting.

So, with the help of demand planning, you will be able to improve the decision-making process. Your decisions won’t be spontaneous — they will be data-driven instead and, therefore, more efficient.

Here is an example for you. Imagine that you own an e-commerce company selling diverse types of clothing. Thanks to your forecasts, you understand that in May, June, and July the demand for summer clothes is expected to grow significantly. In turn, in December, January, and February the demand for these clothes will be extremely low. According to this data, you can increase the prices for summer clothing for the period from May to July, and decrease them for the wintertime. Such a decision seems to be pretty logical, but in your case, it will be also supported by a forecast.

Other decisions can be also backed by demand planning: adding new products to your catalog, terminating certain offers due to their expected irrelevance, and so on.

Demand prediction (planning) using Machine Learning

Obviously, demand prediction and decision support can become much easier with the help of machine learning and data science consulting. First of all, machine learning ensures higher accuracy and veracity of the forecasts. Humans can still make some mistakes while analyzing the data (especially if there are loads of data to be checked!), but machine learning doesn’t have such a downside. Secondly, the forecasts created with machine learning will be absolutely unbiased.

Thirdly, a demand planning system based on machine learning allows the automation of the updates. You and your team won’t have to develop new forecasts on a weekly basis on your own — the model will do everything on its own according to the most relevant data. Fourthly, machine learning guarantees much faster data processing speed, which can be crucial in certain cases.

To simplify your demand prediction process, you can develop a decision support system or DSS, if shortly. This is a special program used to gather and analyze the data, produce comprehensive reports, etc. In the beginning, this system can be operated by humans, but then you can embed AI techniques to make it intelligent and independent. In this way, you will get your forecasts and some extra time for dealing with other important tasks.

What to remember when planning and predicting the demand

When working on a demand planning system based on AI, you should keep in mind the following things:

Data matters

Machine learning and artificial intelligence are useless without data — you won’t be able to develop forecasts in case you have no information about your past sales. Therefore, there is no sense to predict the demand in case you have started your business yesterday or last week. Collect statistical data for a couple of months (at least), and then use it to create the forecasts. And if your company has already been present at the market for some time, analyze the data you have and make sure it is accurate enough for sales forecasting.

Moreover, since demand planning should be done on a regular basis, start collecting the data systematically. After it becomes your habit, the process of demand planning will get even smoother.

Go for multiple machine learning models

A single machine learning model may not cover all your needs. That’s normal, especially if your data is too complicated and you want your forecast to meet numerous requirements. To solve this problem, don’t try to develop something universal. In this case, it is better to apply several machine learning models and use all of them simultaneously. Such a strategy will give you a much better result.

Test your solution

To increase your forecast accuracy, test the developed solution at every moment in the collected data set. This recommendation applies both to known and unknown data. If you don’t do this, the accuracy of the predictions will be doubtful. Moreover, a prototype developed without proper testing may fail at any time.

Use deep learning models for demand planning and machine learning

Deep learning is a part of machine learning, but with more complex architecture and implementation details. Many deep learning models are not sequential and require a lot of data for training. Besides, the results they deliver can be hard to explain. But, deep learning models could give much more accurate results. In the demand planning, even if you will improve the percentage fraction of prediction accuracy it could result in millions of dollars of savings.

Now you are ready to demand forecasting and bringing your company to success. However, if you still have any questions, feel free to ask us. We are always glad to help you with demand planning, AI, machine learning and many other things. Just get in touch with us, and we will quickly get back to you.


Machine Learning