Let us face it: demand trends are getting harder to predict. Customer expectations and external business factors become more complex every year, so traditional prediction methods cannot cope with the growing amount of versatile data sources. But what tool can? The answer lies in technology. 

To be fair, no technology provides 100% accurate predictions. Nevertheless, machine learning (ML) demand forecasting algorithms significantly change the current state of things. Our task for today is to find out more about the role of technology in business predictions. 

This article will answer your questions about ML forecasting, its benefits, pre-implementation tips, and market use cases.  

How Does ML-Based Forecasting Differ from Traditional Forecasting Models? 

To make it clearer, first it’s necessary to formulate what demand forecasting is. 

Demand forecasting is the process of predicting future demand for a product or service using corporate data. There is a list of traditional methods that have been used for many years. There is also an innovative approach based on machine learning. Let us figure out the difference between them. 

Traditional vs. ML-based demand forecasting

Traditional forecasting: long-term predictions for products on stable markets 

Even though we call statistical methods “traditional”, they too have recently become somewhat innovative. Now all forecasting calculations are conducted automatically with the help of specialized software. 

The foundation. All traditional methods are based on data from previous years. In other words, statistics needs historical data to provide valid insights. 

Read also: Not only machine learning: how you can implement computer vision in 2023

Reasons to use it. Traditional methods have a long history, hence, they are more popular and easily incorporated into the existing tech infrastructure. For example, one can calculate trends in Excel or an ERP system with no additional technical skills required.  

Also, more advanced traditional methods can apply several forecasting instruments at once to achieve higher forecast accuracy. From whatever side you look, statistics seem a more understandable route for a business trying out forecasting for the first time. 

When to use it. Statistical methods are good for: 

  • Middle- and long-term perspectives,
  • Products with more or less stable market performance, 
  • Calculating market rates for a brand in general rather than individual brand products. 

What is below the surface? You should remember that traditional models work best only in stable market conditions when historical data looks similar throughout the years. But we all know it is not always true. Global crises provoked by wars and pandemics are highly damaging for almost any business. Traditional forecasting does not have appropriate tools to help you with that. 

Demand forecasting with machine learning: higher accuracy at the cost of higher complexity 

The world is not standing still. Computational powers are rising, and so are customer demands. Both technical advancements and market fluctuations have led to the emergence of ML-based prediction models. 

The foundation. Machine learning algorithms have gone further, using myriads of data sets from all sorts of environments. Along with the already mentioned historical data, innovative tools consider customer reviews, social media markers (such as shares, likes, and the number of followers), macroeconomic factors, news, and so on. 

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Reasons to use it. ML models consider more factors than any traditional method. They work as indicators capturing all types of signals from various data sources. Plus, algorithms are continuously learning and adapting to new conditions on the go. Forecasting looks like a multi-layered process, which guarantees more reliable results. 

When to use it. ML forecasting is the best fit for: 

  • Short-term demand planning, 
  • New products with little or no historical data, 
  • Unstable market conditions. 

What is below the surface? We will not surprise you by saying that advanced technologies require specific skills and knowledge. Indeed, ML produces forecasting automatically, on its own. But to make machine learning models start working, you need the help of experienced data scientists. They identify the best data sources to help algorithms deliver precise predictions and analyze the results. 

As you can see, the business world is not giving up traditional methods, but ML capabilities make companies more adaptable to uncertainty. 

Is It Worth Trying? Top Business Benefits of Machine Learning for Demand Forecasting 

Using ML for demand forecasting in retail

No one wants to deal with highly volatile business data. But in most cases, entrepreneurs do not have a choice: new challenges require new ways of dealing with things. So why do businesses opt for machine learning? 

  • More accurate strategic planning in all business departments. Quality forecasting in retail goes beyond supply chain limits. Thus, being aware of upcoming market changes is the ace in the hole for sales managers and marketers too. ML forecasting solutions decrease the percentage of assumptions in business strategies and allow companies to face the future well-prepared. 
  • Sufficient amount of inventory available in each category. Modern forecasting software provides information on the required number of goods so the store’s inventory does not gather dust on the shelves. 

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  • Higher customer satisfaction. Thanks to detailed inventory planning, customers will always find their favorite items in stock and, hence, will be more satisfied with the store’s service. 
  • Increased sales. All the previous points soon lead to increased sales and conversions. With ML in your toolset, your business will always be in the thick of things, ready for what is coming next. 
  • Decreased goods waste. The cost of food waste has already reached 1 trillion USD. Meanwhile, people in the US throw away around 2,150 pieces of clothing each second. These figures look overwhelming, but they are not a verdict. Not only do intelligent algorithms help you save your money, but they also take good care of our planet by predicting demand for each market more accurately.  
  • Optimized personnel workload. Improved supply chain predictions allow managers to identify the busiest days and hours, evenly distribute the workers’ workload, and provide optimal shift support. 
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Information is a more powerful instrument than many would think. Now, let us check out how ML-based forecasting can look in practice. 

Real-Life Use Cases of ML Demand Forecasting in Retail 

They refuse to take gigantic business risks in unstable market environments. Instead, they have started implementing machine learning, and it works. See for yourself. 

Danone 

The Danone brand started using AI/ML capabilities in 2022 to optimize supply chain operations across the global chain. The company’s goal is to integrate and power up the efforts of operational, commercial, and financial branches. Innovations enable real-time planning and, through that, simplify decision-making. 

“Rising consumer demand paired with supply chain disruption are creating a need to digitalize the company’s end-to-end supply chain planning platform so that all stakeholders can collaborate in real time”, Farzana Allegacone, vice president of technology and data for design to delivery at Danone. 

Nestlé 

Being one of the Global Fortune 500 companies with thousands of brands and employees, Nestlé had to consider innovation to remain resistant to emerging (and often risky) market trends. Its transformation journey started in 2017. 

Before that time, company brands and representatives had been working in isolation from one another, which gradually led to data disunity. When Nestlé started considering machine intelligence in their operations, they found out that tangible results can only be achieved with one powerful data analytics solution. 

“When you have 40 brands doing their own tests-and-learns with different pilots and different proofs of concept suggested by different agency partners, there’s no common learning agenda. It gets a little bit unwieldy,” Orchid Bertelsen, the head of digital innovation and transformation at Nestlé USA. 

The corporation eventually developed the FAIR data framework in order to make data management transparent across the whole organization. “FAIR” stands for Findable, Accessible, Interoperable, and Reusable. 

As of 2020, it was stated that 80% of all Nestlé’s forecasts were conducted by machines, and only the remaining 20% required human involvement. The improvements allowed the company to reduce inventory stock by around 20% and still meet customer demands. 

Nike 

Nike has stopped its contract with Amazon in order to obtain full control over customer data and improve demand forecasting results. 

“There’s a lot of talk about how much we need data, but actually we need the right data, and we use some serious analytics behind it to turn it into value creation”, Hannah Jones,  President of Nike Valiant Labs. 

Using data science and ML algorithms, Nike aimed to optimize inventory and provide an outstanding customer experience. With these goals in mind, Nike announced Consumer Direct Offense in 2017, supported by a so-called “Triple Double strategy”:  

  1. 2x innovation, 
  2. 2x speed,  
  3. 2x direct connections with customers. 

As of 2019, Nike’s revenue grew by 13%, reaching 10.33 billion USD (higher than expected). Also, digital sales increased by 38% and even reached a 70% rise on Black Friday. 

IKEA 

Like many other businesses, IKEA used statistical methods for business predictions based on sales and demand patterns from previous years. But it changed in 2021 when the company announced an advanced tool for efficient demand forecasting called Demand Sensing. 

Demand Sensing can use the data from up to 200 sources for each SKU (Stock Keeping Unit). It also analyzes various impactful factors like weather forecasts, seasonal changes, customer behavior patterns during holidays, etc.  

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Another Demand Sensing advantage is its focus on local demand patterns going up to a bigger region, market, or country. It helps IKEA’s managers detect subtle demand changes, which often pass unnoticed with traditional forecasting. 

H&M  

In 2019, H&M reported an increase in sales of its full-price goods. Some would think that the brand’s designers have a unique sense of fashion trends and customer preferences, but it is not the case.  

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The fashion group has thoroughly investigated the AI field and the advantages it may bring. One of the company’s goals in this regard was to optimize logistics and demand forecasting to cut the stock and, subsequently, the number of goods sold at a discount.  

On the one hand, AI helped H&M position itself as a sustainable company. On the other, it allowed the brand to earn more money with fewer goods available in stock. 

What to Consider Before Implementing ML Forecasting Models 

Machine learning does seem to be a miraculous invention. But miracles will not happen without human involvement. There are three fundamental points to work on if you decide to walk the AI route. 

1. A well-defined problem to work on

Machine learning is a trend, but it does not mean you need to use it no matter what. In other words, apply algorithms only if you have a specific problem to solve that significantly impacts your business results and success. 

Before consulting data scientists, ask yourself what exactly you want to know about the future demand and what period of time you have in mind. Maybe, you are facing regular overstocking or customer complaints about the goods being out of stock. Make sure to put your concerns on paper first and only then contact ML specialists. 

2. Data amount and quality

The more business data you have, the better results your ML forecasting model will provide. But that is not all. Machine learning algorithms for demand forecasting need structured data pieces to guarantee meaningful insights. 

Structured data is well-organized information represented in the form of a database or spreadsheet. But, as you might guess, the most valuable data pieces are often unstructured, for example, user reviews, videos and images, call center recordings, etc.  

Here you will find a guide to unstructured data management to help you add some order to the existing flow and prepare for ML renovation.

3. Desired business performance to measure the final result

Now, what will change in your organization after applying ML forecasting models? You should consider what business metrics will demonstrate the success of provided innovations.  

Possible options: 

  • Sales dynamics, 
  • The amount of wasted inventory in stocks, 
  • Bounce rates, 
  • Conversions, 
  • ROI dynamics, etc. 

Keep in mind that the metrics you choose should correspond with initial goals. They signal engineers and data scientists if algorithms work properly and actually benefit your company.  

Conclusion 

What seemed unrealistic just yesterday, today becomes an efficient instrument for strategic and tactical planning. Machine learning is here to stay and nourish your business with insightful predictions. 

We have learned so far that: 

  • Companies use both statistical and ML-based forecasting to solve business issues and get prepared for challenges ahead; 
  • Machine learning for demand forecasting is the best fit for short-term planning in unsteady market conditions; 
  • With the help of intelligent forecasting, businesses can optimize stock inventory and supply chain operations, as well as significantly improve relations with customers; 
  • A lot of global brands (H&M, Adidas, and IKEA among them) have been using AI and ML for years and now yield the fruits of their endeavors; 
  • Today, ML solutions are accessible to a wide range of businesses, big and small; 
  • The success of an ML project depends not only on the engineer’s skills. Businesses should also prepare by defining their goals, cleaning up corporate data, and outlining expected outcomes. 

Our team of AI engineers and data scientists specializes in various business tasks, including demand forecasting and data-based decision-making. Describe your problem, and we will be there with the fitting solution. 

About the author
contentwriter
Polina Sukhostavets Content Writer

Polina is a curious writer who strongly believes in the power of quality content. She loves telling stories about trending innovations and making them understandable for the reader. Her favorite subjects include AI, AR, VR, IoT, design, and management.

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