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

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 from having excessive amount of goods in stock or out-of-stock situations:

  • Increased customer satisfaction: Stockouts reduce customer satisfaction while being available with your product anytime boosts customer satisfaction. Thus it improves brand perception and increasing 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 for these products to go out of fashion or become 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 up 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.

Source: Mckinsey

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. 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.

What are the common pitfalls?

Any business may fail as a result of preventable pitfalls and cannot reduce the cost of inventory as much as they aim. Business 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 real-time in their data sources.
  • Since demand forecasting is a field of predictive analytics, common pitfalls of analytics also applies for 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%.

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 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:

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