Ways How Technology Streamlines Demand Forecasting

It is actually hard to predict the future of your business. You will never know for sure what is waiting around the corner, but still, some questions about the future should be answered. How much inventory will you need? Will it change with time? Where will your business be in a year?

The answers are crucial no matter what industry you work in, that is why business owners need to consider demand forecasting sooner or later. The forecasts’ results should be as accurate as possible – and this is the toughest part.

Technologies have come a long way tin their ability to generate fast and accurate demand forecasts, but how much do we know about it? Here is your guide on modern tech solutions for successful forecasting.

Demand planning vs. Demand forecasting: Is there a difference?

Planning or forecasting – which term is the right one? There is a difference you should know.

Demand forecasting is an analytical assessment process of potential demand for the company’s products in the future. It is based on algorithms and uses historical data (the past demand), market trends, and uncontrollable factors (competition, seasons or weather). 

For some specialists, demand planning and forecasting are synonymous, but actually, forecasting is a starting point of planning.

Demand planning includes forecasts of both products or services and items used in the supply chain. Simply put, planners take care of inventory optimization, demand forecasting, and compare the forecasts with sales.

Read also: Streamlining Your Warehouse Management with Digitalization



Just like demand planners, we are focusing our attention on demand and supply because these are the two pillars any business is standing on.

The harm of poor demand planning can be huge. Imagine your favorite store constantly having an “out of stock” problem. Will you still be loyal to this brand?

Or here is another example: Consumers will not probably notice that you deal with an overstock issue, but financial losses damage all facets of business processes.

The importance and benefits of demand forecasting and planning

How many companies opt for advanced demand forecasting nowadays? According to the AIMMS research, the heyday of demand forecasting has yet to come.

Only 9% of respondents are using such advanced technology as machine learning on a daily basis, and 40% are actively gathering information about it to improve their positions on the market. Other methods that disturb the minds of forecast planners include statistical modeling, demand sensing, “best fit” functionality, and more:


Without question, technology is a thing to consider now, not tomorrow. While other market players are actively using modern solutions to make reliable forecasts, you can fall behind choosing solely traditional demand forecast methods.

But competitors are not the only reason to investigate the latest tech trends. These are a few more  demand forecasting benefits:

  • Supply chain optimization: you know how much and what types of inventory should be in stock at the given day. It helps to plan spendings beforehand and avoid overpayments and holding costs. 
  • Waste reduction: this is a huge global problem that influences not only the company’s budget but also local environmental situations. Demand forecasting reduces overstocking and makes a business more eco-friendly and responsible.
  • Growing customer satisfaction: thanks to more accurate forecasts, companies can reduce stockouts and make their customers happier. Every empty-handed visitor is an opportunity for the closest market competitors. And if you have a delivery option, your clients will now get their offers without any delays.
  • Optimized sales and marketing efforts: let’s say there is a product that will have a lower demand level in the future or, on the contrary, is expected to bloom and become popular. These are signals for sales and marketing departments to do their best in cross- and upselling.
  • Reduced spending for expedited shipping: with a good forecast, you can assume how many inventory items your working process will require. So, there is no need to order something urgently from your suppliers, which subsequently reduces extra spending on shipping.
  • Smarter labor management: realizing when demand peaks can happen allows hiring temporary staff and optimizing the company’s workflow. The same goes for the shift planning: companies know what and how many workers will be needed at a certain time.
  • Improved logistics and distribution: knowing the level of demand beforehand allows planners to map out the right number of vehicles and optimize warehouse activities, which includes receiving, identifying, holding, picking products and inventory items.


What are the various methods of demand forecasting using technology?

Nobody has ever promised that technologies would save a business from all market storms and surprises. Nevertheless, with modern software we can improve forecasting accuracy – let’s take a look.

Machine Learning

Lots of data sources and a growing demand – coping with these two issues is quite a challenge for any business, and only the smartest tools can manage it.

Machine learning demand forecasting uses traditional statistical data as well as other additional sources: past sales reports (historical data), marketing polls, weather forecasts, as well as shares and retweets from social media.

Read also: Extending the Impact of Data Across an Organization


No signal or pattern will pass unnoticed because of the mathematical algorithms underlying machine learning. Also, ML-based software is able to retrain forecasting models and regularly adapt them to the changing conditions. These two capabilities allow for higher demand forecasting accuracy.

The most widely-used application of machine learning coupled with the statistical method is predictive analytics. With its help, experts can both predict demand and find out what impacts sales and the customers’ behavior.

In practice

Luxottica, one of the world’s largest eyewear companies, uses ML to predict demand for the new eyewear models added to the collection regularly. As this is a big company, the number of styles reaches 2000 items.

Thanks to digital innovations, the retailer decreased the number of errors by 10%, which is a lot for a global brand.


And here is another example: At the beginning of 2019, it was announced that Carrefour Group was about to start using SAS Viya, a solution to build up their AI-powered demand forecasts. The retailer has more than 12,200 stores all over the globe, and the main aim is to reduce waste and out-of-stock issues. 

Read also: Driving Digital Transformation through AI


The testing period should take 18 months, and after that Carrefour will create an algorithm developed individually for the retailer’s needs. 


The Internet of Things is a powerful helping hand for demand forecasting, but has yet to be  widely used. You could be one of those pioneers who obtain additional benefits by making accurate predictions via IoT.

Inventory management has changed a lot since IoT got in the game. Every asset is being tracked, and managers get real-time information about it. This tracking capacity was hard to imagine several decades ago, but look where we are now. 

Read also: How to Optimize Supply Chain through IoT, Analytics and Automation


Besides, IoT tools help a lot with forecast planning by analyzing demand and noticing any shifts in it. Historical trends, weather, seasons – all these factors matter, and tech tools are able to gather this data thoroughly. In other words, this is another instrument to achieve all the goals of demand planning: reducing the risks of waste or out-of-stock issues.

In practice

Costa Coffee, a UK coffee shop chain, is one of those companies that realized the benefits of IoT for demand planning. Apart from shops, the retailer has thousands of Costa Express machines, where people can quickly get a cup of coffee.

These machines work as advanced IoT devices by sending real-time data about sales and stock to the managers. The company gets this information every four seconds along with sales-stock comparison. The final step is a generated prediction about the likely demand.


5 Things to consider before adopting technology for product demand forecasting

So, it is great, it brings new opportunities, it helps achieve your marketing goals – what is next? You should know where to start in order to make things right. These are the points to consider:

  1. Business goals. No trending technology is worth your attention if it does not match your vital needs. 

These are some key signals that indicate machine learning is perfect for your business:

  • The new product’s launching
  • A product with a short life cycle
  • Struggling with seasonal demand forecasting
  • Analyzing a big number of changing factors
  • Upcoming promotional events
  1. Your budget. What amount money are you ready to pay to achieve your goals? At this point, calculating the ROI would help you make the final decision.
  2. Tech experts in your team. Are there any experts at your company that can maintain the ML software and interpret the results of forecasting? Consider this question before starting the forecasting journey. 

The machine makes calculations and predictions with no help from outside, but there should be someone to figure out what features would benefit a business the most and why the machine generates this or that forecast.

  1. Hiring developers to create a unique solution for your business. Maybe you are struggling to find a perfect tool among the already existing tools. Or you do not need very many features in one solution. These situations usually bring business owners to the doorsteps of software development companies.
  2. Long-term advantages. If your company is in crisis mode and you need quick results, or on the contrary, you are building a long-term business strategy, consider consulting a team of experts. Individual specification and professional developers are the best fit for any scenario when it comes to revolutionary technologies.

It takes minutes to predict the future

Artificial intelligence, machine learning, and the Internet of Things can do wonders when it comes to demand forecasting and planning. No matter if you want to predict the amount of inventory or the demand in a particular season, big data is your superpower.

Many business owners choose modern technologies to optimize their supply chain, increase customer satisfaction, reduce waste of products and inventory, and even improve sales and marketing activities. The only question that remains is, what software would meet your individual demands? And we know how to answer that.

Complex machine learning solutions is a big passion of the Eastern Peak developers. Tell us more about your business, and we will transform your approach to supply and demand planning.

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