Demand Forecasting in the Age of AI & Machine Learning

Businesses face different inventory challenges when they are dealing with supply chains. Especially in the current climate, addressing supply chain issues is paramount. Demand forecasting helps businesses reduce supply chain costs and bring significant improvements in financial planning, capacity planning, profit margins, and risk assessment decisions.

According to Gartner’s survey, demand forecasting is the most widely used machine learning application in supply chain planning. The study highlights that 45% of companies are already using the technology, and 43% of them are planning to use AI-powered demand forecasting within two years.

Machine learning algorithms improve forecasting methods in accuracy and optimize replenishment processes. With these advances, companies are minimizing the cost of cash-in-stock and out-of-stock scenarios.

What is demand forecasting?

Demand forecasting is a field of predictive analytics and, as its name refers, it is the process of estimating the forecast of customer demand by analyzing historical data. Organizations use demand forecasting methods to avoid inefficiencies caused by the misalignment of supply and demand across the business operations.

With demand forecasting methods, companies can improve their decision-making processes about cash flow, risk assessment, capacity planning, and workforce planning.

What are the benefits of demand forecasting?

Demand forecasting helps organizations optimize their supply chain, sales, and marketing operations and prevent having excessive amounts of goods in stock or out-of-stock situations:

Improving accuracy by time

Machine learning algorithms learn from existing data and make better predictions over time.

Increased customer satisfaction

Stockouts reduce customer satisfaction while being available with your product boosts customer satisfaction. Thus, it improves brand perception and increases customer loyalty.

Improved markdown/discount optimization

Cash-in-stock is a common situation for retail businesses. In this situation, certain products stay unsold longer than expected. This causes higher-than-expected inventory costs and increases the risk of these products going out of fashion or becoming obsolete, thereby losing their value.

In these scenarios, organizations sell their products with reduced margins. With accurate demand forecasting, such scenarios can be minimized.

Improved manpower planning

Demand forecasting for the full year can support HR departments to make efficient trade-offs between the part/full-time employee mix, optimizing costs and HR effectiveness.

Overall efficiency

Accurate demand forecasts help teams focus on strategic issues rather than firefighting to reduce/increase stocks and headcount to manage unexpected demand fluctuations.

AI in Demand Forecasting

According to Mckinsey Digital, AI-powered forecasting can reduce errors by 30 to 50% in supply chain networks. The improved accuracy leads to a 65% reduction in lost sales due to inventory out-of-stock situations, and warehousing costs decrease around 10 to 40%. The estimated impact of AI in the supply chain is between $1.2T and $2T in manufacturing and supply chain planning.

Traditional forecasting models such as ARIMA, AutoRegressive Integrated Moving Average, and exponential smoothing methods, where only historical data is considered, are getting outdated because of the increased amount of data generated from businesses and external sources. With the implementation of machine learning into businesses’ supply chain management, companies can improve the accuracy of forecast results and optimize their replenishment plans.

Machine learning carries demand forecasting to the next step; it enables enhanced forecasts based on real-time data using internal and external data sources such as demographics, weather, online reviews, and social media. With the help of external data and modern machine learning algorithms, supply chain networks can outperform networks managed more manually by data analysts and adapt to external changes.

For new products that lack historical data, machine learning forecasting tools can identify clusters of prior products with similar characteristics and lifecycle curves and use those datasets as a substitute to make predictions.

What are the common pitfalls of demand forecasting?

Any business may fail as a result of preventable pitfalls and cannot reduce the cost of inventory as much as they aim. Businesses can avoid these unfortunate results by taking measures to look for these all-too-common pitfalls:

• When the marketing data is excluded, the error margin of demand predictions may increase since marketing data and marketing effectiveness have a big impact on future sales.
• New products don’t have sufficient historical data. That’s why organizations’ accuracy expectations should not be high for newly released products.
• If the organization’s supply chain network is hard to take different actions due to its inflexibility, the business struggles to obtain the value of demand forecasting. The purpose of demand forecasting is to make changes to reduce the cost of supply chain management by optimizing processes.
• Forecasts can only be as good as the input data. Companies need to ensure that important changes are reflected in real-time in their data sources.
• Since demand forecasting is a field of predictive analytics, common pitfalls of analytics also apply to demand forecasting as well.

What are modern AI-powered demand forecasting tools?

• amoCRM
• Capsule
• COLIBRI
• ClosePlan
• Effectmanager
• FutureMargin
• Pipedrive
• Smart Demand Planner

FutureMargin is an AI-powered demand planning software. One of its customers is Martinus, a major East European omnichannel e-commerce bookstore. The partnership of FutureMargin and Martinus resulted in;

• 84% increase in the ratio of products expedited on the day of placed order
• Improvements in average order fulfillment time by 14%.

For more on-demand forecasting

• To see the full list of tools, feel free to visit our up-to-date list of demand planning software vendors on our website.
• These off-the-shelf software can deal with common demand forecasting cases. However, if you have a specific demand forecasting challenge unique to your industry, it may make sense to build a custom solution for it:

• If you wonder how analytics has evolved through AI technology, our Exploring Analytics & AI article uncovers the insights for businesses.
• Demand forecasting is also an application in retail analytics, feel free to check our article about it.

If you want to implement analytics but are not sure about handling it with internal resources, we recommend our guide about analytics consulting.

Don’t hesitate to contact us if you have more questions: