{"id":2922,"date":"2025-07-06T06:24:05","date_gmt":"2025-07-06T06:24:05","guid":{"rendered":"https:\/\/www.aihello.com\/resources\/?p=2922---be2a14f3-5dbe-4b48-85e7-9be871f12b2d"},"modified":"2025-09-04T10:32:28","modified_gmt":"2025-09-04T10:32:28","slug":"ml-in-demand-forecasting-the-hero-of-supply-chain","status":"publish","type":"post","link":"https:\/\/www.aihello.com\/resources\/blog\/ml-in-demand-forecasting-the-hero-of-supply-chain\/","title":{"rendered":"Machine Learning in Demand Forecasting: The Hero of Supply Chain"},"content":{"rendered":"

\"demand<\/p>

Many business executives complain that the top pain point to them is the sudden negative variations in the demand of the product. Changes that no one saw it coming! The world\u2019s largest IT research firm, Gartner came up with a term for the same problem that many organizations suffer from : Demand Volatility.\u00a0<\/span><\/p>

There are numerous factors that affect the demand of a particular product or service, be it weather fluctuations, trends, availability of alternatives, period of recession and looking at the power of Social media- even posts by Social influencers or celebrities impact the buyers.\u00a0\u00a0<\/span><\/p>

For instance, let’s look at both sides of the coin here.\u00a0<\/span><\/p>

Daniel Wellington, a Swedish watch startup, is one company that uses a global network<\/a> of influencers to market their watches. One of the influencers on Instagram posted an eye-catching and well-composed image with a caption that not only talked about the watch in photo but also how her followers could get one with a discount. Due to its use of social influencers, Daniel Wellington now has a verified Instagram account with 4.6 million followers through which it has earned a large market and can attract potential buyers.\u00a0<\/span><\/p>

Now on the other hand, <\/span>the teenage climate activist Greta Thunberg\u2019s denial to fly for environmental reasons prompted the \u201cflight shame\u201d movement that caused a five-percent <\/span>decrease<\/span><\/a> in air passenger numbers in Sweden.<\/span><\/p>

\"magic<\/p>

If only people had a Magic Crystal Ball similar to that shown in movies, they could just have easily predicted such unexpected outcomes. So what can be done about this, as it\u2019s a challenge faced world-wide. Simple Solution- In this era of Industry 4.0, we need to use the technology advancements made in Artificial Intelligence and Machine learning<\/a> to achieve the business goals.\u00a0<\/span><\/p>

MACHINE LEARNING VS TRADITIONAL APPROACH IN\u00a0 DEMAND FORECASTING<\/span><\/h3>

\u201cWhile any organization can throw technology at planning, it makes more sense to pause a minute and think about what type of digital technology is appropriate.\u201d<\/span><\/i><\/p>

Tim Payne<\/span><\/i><\/p>

VP Analyst, Gartner Research & Advisory<\/span><\/i><\/p><\/blockquote>

For those of you who may need a kick-start, Demand Forecasting is the estimation of the plausible future of your product\/service in terms of either profit or loss. When Machine learning<\/a> was leveraged in the forecast applications, it provided better accuracy and showcased effective upsides in the end-to-end supply chain management. Moreover, the use of conventional methods such as spreadsheets and a lack of collaboration among sales, marketing, and demand planning teams has only added to the complexity.<\/span><\/p>

Old-fashioned Statistical forecasting method, needs stability. They rely on historical data and assume that history will repeat itself. But the only thing constant in this world is change, hence statistical methods often fail to foresee illogical alterations in customer preferences or predict when market saturation will occur.<\/span><\/p>

\"ML\"<\/p>

When ML is applied to demand forecasting, it not only uses historical data but also other internal and external components. This increases the efficiency of the demand forecast. Additionally, as more and more firms are now connecting in a better way, there is huge Data visibility. Thus, the manufacturers are taking the side of cutting edge technologies like ML, cognitive computing and getting real time data via Internet of Things (IoT) sensors.\u00a0<\/span><\/p>

So you might be wondering what are these \u201c<\/span>internal<\/span><\/i>\u201d and \u201c<\/span>external<\/span><\/i>\u201d agents I am talking about above ? Look at the below table for the list, as they play a vital role in the whole process of demand forecasting with ML and ultimately determine if you are winning or losing.\u00a0<\/span><\/p>
INTERNAL FACTORS<\/b><\/td>EXTERNAL FACTORS<\/b><\/td><\/tr>
Product Price changes<\/span><\/td>Political : government policies & regulations, public holidays, taxes<\/span><\/td><\/tr>
Promotions<\/span><\/td>Economical : currency fluctuations, labor wages, unemployment rate<\/span><\/td><\/tr>
Discounts<\/span><\/td>Social: media coverage, customer\u2019s sentiments on social media<\/a>, competitors promotions<\/span><\/td><\/tr>
Seasonality<\/span><\/td>Technology : Product Life Cycle, upcoming trends\/innovations<\/span><\/td><\/tr>
Lost sales due to Stock outs<\/span><\/td>Environmental : Weather, Natural and Man-made disasters<\/span><\/td><\/tr>
Network Expansion\/Reduction<\/span><\/td>Legal influences\u00a0<\/span><\/td><\/tr>
And many more<\/span><\/td>Pandemics like Coronavirus<\/span><\/td><\/tr><\/tbody><\/table>

\"MachineLearning\"<\/p>

Still confused, how exactly does it work? Keep on reading. Machine learning applies complex mathematical algorithms to automatically detect patterns, apprehend demand signals and recognize the relationships between the variables in large datasets. Apart from analyzing enormous volumes of information, the smart system constantly updates the model, retrains it. As a consequence of this, it adapts to changing conditions addressing the volatility issue and performing well in complex scenarios.\u00a0<\/span><\/p>

Need some proof? Companies that added machine learning to their existing systems report an increase of<\/span> 5 to 15 percent<\/span><\/a> in forecast reliability (up to 85 and even 95 percent). In addition to this, your team gets rid of time-consuming manual adjustments and recalibrations. <\/span>Food and beverage major PepsiCo, cosmetic giant L\u2019Or\u00e9al, and manufacturing conglomerate 3M were among leading organizations in<\/span> Gartner\u2019s Supply Chain Top 25 rankings<\/span><\/a> that use ML in their daily forecasting and planning.\u00a0<\/span><\/p>

COMMON DEMAND FORECASTING SOLUTIONS FUELED BY MACHINE LEARNING<\/span>\u00a0<\/span><\/i><\/h3>

Machine Learning in Demand Forecasting has been implemented in distinct ways, according to the data availability, business goals, task complexity, and so on across various industries. But these two are the most common methods found, that have proven to be very helpful :<\/span><\/p>

1. PREDICTIVE SALES ANALYTICS<\/span> :<\/span><\/p>

An enterprise application of machine learning joined with statistical methods is predictive analytics.<\/span> Several patterns can be detected with the use of historical and transactional data, which further can tell us of any risks or opportunities in future. One of the best features of predictive sales analytics is that of risk management. It can unveil the relationships between various factors and examine risk for a particular set of numerous scenarios, by this it assigns a score or weight to the risk assessment.\u00a0<\/span><\/p>

\"predictive-sales-analytics\"<\/p>

But do note that for the accuracy of forecasting and also for predictive analytics to be valuable, the data should also be maintained properly, it should be equally accurate as well.\u00a0<\/span><\/p>

Predictive analytics does the following set of operations, so we can uncover our future :<\/span><\/p>