AI in Demand Forecasting: Overview, Use Cases, and Benefits

By applying AI to a variety of internal and external data, companies can more accurately forecast demand for their goods and services, helping them adjust their budgets, manufacturing schedules, inventory levels, shipping priorities, and other key functions while cutting costs. In this article, we explore how AI-based demand forecasting works, where it’s being applied, and how organizations just getting started can benefit from it.

What Is AI in Demand Forecasting?

AI-based demand forecasting entails using machine learning and predictive analytics to more accurately estimate future demand for products or services. The wide range of data that’s analyzed includes data on historical sales, sales pipelines, consumer behavior, demographics, competitor activity, seasonal and market trends, weather events, holiday schedules, and economic conditions—even real-time data on website traffic and social media engagement.

The incorporation of AI into demand forecasting helps companies better align product and inventory levels with actual demand, as AI-based data analytics can uncover patterns and relationships that legacy demand forecasting systems can’t. It also helps reduce manual, inefficient demand forecasting processes. Research from McKinsey & Company shows that AI-powered forecasting for supply chain management can reduce errors by 20% to 50% and product unavailability by up to 65%.

Key Takeaways

• Demand planning encompasses every part of the business, from the production floor to the sales office. Therefore, each team must be accountable for the data it provides to help ensure accurate forecasts.
• AI’s strength lies in its ability to crunch massive amounts of data rapidly, so that human demand forecasting specialists can focus on interpreting and communicating the results.
• With the introduction of external data in areas such as weather events, competitor activities, and consumer trends, demand forecasters can now provide more precise views of the business landscape.

AI in Demand Forecasting Explained

Every business wants insight into what and how much consumers and businesses will buy from it in the future, so that it can better manage its spending, investments, inventory levels, product plans, staffing, and marketing promotions. Obtaining this visibility—accurate and up to date—can change the course of a business.

AI-powered demand forecasting applications can process enormous amounts of data much more quickly and precisely than conventional tools. As such, they can transform the previously manual, slow process of forecasting demand into a highly automated and real-time activity, providing business leaders with the insights they need to meet, even anticipate, ever-changing customer needs.

Effective demand planning requires coordination among several different business departments, including sales, marketing, finance, supply chain, and production. One major goal is to maintain the right inventory balance—sufficient to meet customer demand but not so much that the business takes on unnecessary inventory carrying costs or get stuck with goods it needs to heavily discount.

PwC has identified the ability to anticipate customer demand as one of the three areas in retail with the biggest AI potential, as retailers begin to use deep learning (a type of AI that uses neural networks to teach computers to make decisions and continually learn) to predict customer orders.

How Does AI for Demand Forecasting Work?

Organizations can start by combining historical sales data stored in their ERP systems with data on the results of marketing campaigns and promotions, as well as with external data on consumer trends, economic conditions, weather patterns, competitor activity, and other factors. They can then feed that massive data set into a forecasting model to make demand predictions.

For example, a pharmaceutical company in the Middle East is using AI-powered hyperparameter tuning (hyperparameters are the variables that control machine learning model training) to support better predictions of future drug demand and optimal inventory and production levels. By experimenting with a wide range of parameter configurations, the company can forecast demand based on different scenarios.

AI-Powered Demand Forecasting vs. Traditional Forecasting

In traditional forecasting, human analysts use mainly historical sales data and current order data to predict future production needs, eliminating any outlier anomalies. While useful for companies in a stable market with steady sales, such traditional demand forecasting is limited by the number of data sources a human can absorb and tends to fall short in fast-growing and volatile markets.

AI-powered demand forecasting uses machine learning and generative AI to quickly analyze large amounts of data from the numerous internal and external sources described above. This creates a more comprehensive forecast that can be easily updated based on new or shifting data inputs. AI-based forecasting is also better at generating long-term forecasts.

Benefits of AI in Demand Forecasting

Implementing AI in demand forecasting presents numerous benefits, with more to be gained as the technology advances. What follows are some of the biggest benefits.

• Improves planning. AI can improve planning because organizations can apply it to numerous large data sets, both internal and external, to identify complex patterns and create a more comprehensive, accurate forecast.
• Provides real-time insights. Because AI can continuously analyze huge data sets quickly and autonomously, it can produce insights in near real time so that users can adjust their plans in accordance with the latest market and other conditions.
• Improves scalability. Instead of needing to increase staff levels as the organization’s demand forecasting needs and data sources grow, it can apply AI to handle the increased scale and complexity.
• Optimizes inventory. To the extent that AI-based demand forecasts are accurate and up-to-date, they can help companies maintain enough inventory to meet that forecast demand without having to carry too much of it.
• Increases revenue. The cumulative benefits of AI-powered demand forecasting, including optimized inventory levels, increased customer satisfaction, and reduced errors, can lead to increased revenue, all other factors being equal.

Use Cases of AI in Demand Forecasting

AI-based demand forecasting can be used to improve a variety of processes in different industries, including:

Supply Chain Planning

To the extent that AI-based demand forecasting can accurately model the impact of economic and political shifts, natural disasters, market trends, changing consumer behavior, and lots of other factors, it can help supply chain planners procure just the right quantity of supplies needed to meet demand. The forecasts can also give planners the information they need to recommend investments in starting up new production lines or shuttering ones that are less valuable. They can even be used to recommend appropriate staffing levels for each production line.

• Transportation forecasting. Accurate, AI-driven demand forecasts are integral to an organization’s ability to plan its truck, rail, air, ship, and other transportation capacity requirements for its products. For example, transportation managers can use demand forecasts to pre-book logistics needs, including trucks and shipping container space, which can improve transit times and save the company money.
• Reverse supply chain forecasting. Just as organizations can use AI to forecast demand for their products, they can apply it to historical and current data to forecast product return volumes and the associated costs.
• Shipping logistics. With AI-enabled demand forecasting, companies not only can more accurately predict sales, they can also better project shipping requirements and manage delivery times for those products, whether they’re going to local outlets, directly to consumers, or shipping cross-border where companies need to take into account the impact of export regulations. Demand forecasting can also give supply chain managers the data they need to import the right quantities of raw materials to ensure that assembly line managers have what they need when they need it.
• Warehouse optimization. AI-powered demand forecasting helps manufacturers and retailers analyze demand signals from marketing, production line, and point-of-sale systems, as well as external data sources, so they can understand how much warehouse capacity they’ll need and adjust their operations accordingly.

Manufacturing

Manufacturers are using demand forecasting AI tools to adjust production capacity and optimize warehouse space based on customer demand. These tools use data on past sales, current promotions, consumer trends—even external data on competitor behavior and the impact of recurring events.

• Product demand forecasting. AI-driven product demand forecasting uses machine learning to predict demand for existing products and anticipate demand for new products. Sophisticated demand forecasting tools not only provide insights into future sales, but they can also estimate return volumes.
• Seasonal analysis. Manufacturers use AI to analyze data on factors that influence seasonal trends, including subtle patterns related to weather, changing buyer preferences, holiday spending, and dips during off-peak time periods. They then use those analyses to predict fluctuations in consumer demand.
• Waste reduction. By using AI to anticipate and react to changes in demand, manufacturers can avoid excess production and the associated waste of production resources and time.

Finance

Finance leaders need to have confidence in their cash forecasts and organization’s liquidity levels. With the right demand planning tools, they can use data to develop more accurate budgets, better manage cash flow, and establish tighter relationships with other stakeholders within their organization.

• Market forecasting. By applying AI-based predictive analytics to historical and trend data, finance organizations can identify correlations and causalities to inform forecasts of future market conditions. Such AI-based forecasts recalibrate based on new data, improving their accuracy. Organizations are using GenAI built into new demand forecasting tools to create commentaries that explain their forecasts and highlight key factors.
• Risk assessment. Finance teams can use AI-driven predictive demand planning tools to identify issues that increase business and audit risks, such as the impact of sudden demand drops or supply shortages, or potential problems that can trigger audits by affecting the accuracy and reliability of financial statements.

Healthcare

Healthcare professionals are using AI demand forecasting tools’ pattern recognition and analytics capabilities to forecast operational needs, improve resource allocation, and even forecast patient outcomes.

• Hospital resource allocation. AI-based predictive analytics, applied to data on patients, community health, and seasonal health trends, can help healthcare organizations forecast demand for medical supplies, staff members, and real estate space. Supply chain managers can use this data to order supplies, including perishable or hard-to-obtain items that shouldn’t be overstocked, while team leaders can use the data to plan work schedules, balancing urgent care and elective appointments.
• Demand for medications. Anticipating the demand for medications at a healthcare facility’s pharmacy requires an understanding of the importance of a particular medication, the historical levels of consumption, the likelihood that physicians will prescribe it, and how external forces (such as the time of year, vaccination levels, and prevalence of illness in the community) will impact demand.
• Tailoring treatment plans. Healthcare organizations can use AI-based analytics to predict patient recovery times and potential complications, and then use those insights to develop personalized recovery plans that adjust based on patient progress.

Automotive

Automobile manufacturers and the largest dealers use predictive analytics to analyze consumer patterns and trends and competitive behavior. Some auto manufacturers are combining AI forecasting tools and social media sentiment measures to gain insight into customer preferences, product quality issues, and optimal pricing. The factory of the future will be able to use multiple data sources to autonomously recognize demand, configure a production plan, assemble the necessary assets for manufacturing, and learn and adapt with the aid of real-time feedback.

• Dealership inventory management. AI demand forecasting tools can help auto dealers maintain optimal inventory levels using data on past sales, market trends, and customer sentiment. Such analyses can help dealers reduce their carrying costs and develop pricing strategies that change based on market conditions.
• Predictive maintenance. AI-based smart manufacturing tools can identify when equipment on assembly lines is about to fail and generate alerts so that manufacturers can fix the equipment before its downtime disrupts production.
• Lean manufacturing. Automated and connected demand forecasting can help automakers optimize demand-to-delivery execution and respond effectively to market changes and customer demand. This foresight into future demand can support lean manufacturing efforts, as manufacturers, which source components globally, can better ensure timely delivery of crucial parts.

Retail

Retailers are able to collect huge amounts of internal data from point-of-sale systems, websites, and customer loyalty programs, along with external data on factors that influence customer demand, such as weather patterns, holiday and seasonal trends, and consumer spending patterns. They can analyze that data with the help of AI to better predict demand.

• Trend analysis. AI-based analyses of trend data is important for accurate demand planning. This data can include nutritional and fashion trends, consumption patterns, and forecast weather conditions.
• Real-time inventory management. Planners can apply AI-based analytics to sales and return data pulled from central merchandising systems to adjust inventory levels in real time, optimize merchandising opportunities, and avoid carrying too much inventory, especially products with a short shelf life.
• Persona development. Using customer data gathered from point-of-sale systems, loyalty programs, and customer surveys, retail demand forecasters can create customer personas to better understand the behavior and desires of a certain type of valued customer. Analyzing this data with the help of AI, demand planners can optimize inventory levels at store locations to appeal to these customers.
• Price optimization. Setting prices at levels that will maximize profits is crucial for all retailers in this low-margin industry. But setting prices without a detailed analysis of the latest sales data and the impact of price changes on future sales can impede profitability and alienate customers.

Hospitality

Hoteliers can use AI to analyze their properties’ historical data, along with market trends, competitor activity, and the impact of fluctuating seasonal demand, to more accurately predict periods of high and low demand. With this data, hotels can optimize pricing, staffing levels, and marketing strategies to maximize profits.

• Booking and scheduling. This refers to the process of managing reservations for rooms, restaurant tables, and other bookable items such as spa treatments and sports facility use. Hotels can use AI to analyze historical data on these bookings, along with data on market trends, consumer tastes, and competitor activity, to forecast demand for these services.
• Seasonal forecasting. Hoteliers can track profitability and performance indicators over time to identify seasonal patterns. Using AI-based demand forecasting software, hoteliers can maximize profits during high-demand seasons and minimize the impact of low-demand seasons by reducing headcount, simplifying offerings, and even closing a section of rooms to reduce heating or cooling costs.
• Concierge and customer service. When hoteliers analyze up-to-date data on customer actions and preferences, including their own customer data, as well as surveys, competitive analyses, market stats, and other data, they can more accurately predict guest needs and personalize offerings that meet those needs. Beyond demand forecasting, AI tools can even assign guests to the room that best meets their preferences and requirements and offer relevant recommendations for restaurant reservations, hotel amenities, and local attractions.

How to Implement Demand Forecast AI in 11 Steps

Demand forecasting is a complex process that spans multiple teams within an organization. Key steps to implement demand forecasting include the following.

1. Define the objectives. Before revamping your demand forecasting process, identify organizational priorities, develop a strategy that addresses the most pressing concerns, and create a roadmap.
2. Collect data from key sources. Demand planning takes place in multiple parts of the business, including sales, marketing, operations, supply chain, and finance. The members of this cross-functional team need to agree on what internal and external data to collect and analyze to develop accurate forecasts.
3. Pre-process data. Once you identify the appropriate data, gather it in a staging location so that analysts can review it, adjust it if necessary, and clean out irrelevant data. Determine which types of historical data will be used and the optimal date range.
4. Select a model. Organizations can use different models in their AI-based demand forecasting. One popular demand forecasting tool offers 15 industry-standard and proprietary statistical models. The selection of a model or forecasting method depends on the needed inputs, such as sales, market opportunities, external events, demand patterns, and typical product lifecycles. Those models can be modified in response to sudden internal or external changes. Forecasting models include linear and non-linear regression. Linear models presume a straight-line relation between variables, while non-linear models are more complex. Variations of exponential smoothing models give more weight to the most recent data in predicting future demand.
5. Train the model. Training an AI demand forecasting model involves selecting and gathering data, preparing it, choosing the appropriate algorithms, and evaluating forecast accuracy.
6. Integrate with existing IT infrastructure. Integrated demand planning systems can ingest data from a variety of sources, including sales, marketing, manufacturing, supply chain, and transportation management systems. Cloud-based systems that are built to work together make it easier for demand planners who need quick access to data from every part of the company. When demand planning systems are siloed, crucial data can go missing and forecasts may be inaccurate and out of date.
7. Validate, evaluate, and adapt model performance. AI models learn from historical data and the feedback they obtain from users on an ongoing basis. Models use this knowledge to inform the next best options—for example, scaling back production of a good or diverting it to other goods based on a forecast of declining demand for that product.
8. Generate insights and predictions. Demand planners are using AI- and machine learning-powered forecasting software to process huge amounts of data and identify patterns that a human might never notice. Based on those patterns, demand planners can make predictions, for example, on the impact of raw material price hikes, changes in customer sentiment, or shifts in seasonal demand that business leaders can use to refine strategies.
9. Deliver real-time updates. Demand forecasters can use AI tools to instantly uncover changes that could impact demand, such as operational disruptions or external events. Those shifts in demand patterns allow forecasters to create updated, real-time forecasts for organization leaders.
10. Serve actionable recommendations. AI algorithms constantly learn as they’re fed more data, helping them generate more accurate demand forecasts that company leaders can use to modify their production lines, supply chains, marketing campaigns, and other key functions.
11. Iterate and refine models continually. Once organizations select a demand forecasting model, the relevant teams need to review, re-analyze, and refine that model. For example, demand forecasters may remove outliers that could distort the overall forecast or add a new data input that better meets the needs of key stakeholders.

Considerations for Delivering Accurate Forecasts

Setting up a reliable demand forecasting system using AI models can be difficult. Read on to understand the most important considerations for delivering accurate forecasts.

• Data quality. The quality and range of data used to train an AI model directly impacts the accuracy of the forecasts it produces. Insufficient data, poor quality data, and imbalanced data (data that focuses too much on one aspect of the overall solution) can produce AI “hallucinations” that reduce the value of the demand forecast.
• Historical data accuracy. Likewise, the accuracy of the data used to train the selected AI model helps determine the precision of the demand forecast. Planners need to find a balance between using enough data to help ensure accuracy and overloading the model with data that may not be relevant.
• Model complexity. Organizations can modify prebuilt machine learning models for some elements of demand forecasting, but to gain the most benefits companies need to train models on their own data. Preparing a high-quality data set involves collecting, validating, transforming, and cleaning historical data—a compute-intensive process. Running the systems requires less computational power, but it’s still an ongoing process. The cloud is a crucial element, as it can scale as a company’s needs grow. Cloud-based solutions are making AI-based demand forecasting affordable for many companies.
• Real-time data integrity. Missing or inconsistent data collected and analyzed in real time can lead to inaccurate forecasts. The more precise and relevant the data, the better the demand forecasts.
• Human error. As with any new system, successfully implementing AI-powered demand forecasting software and integrating it into supply chain management requires training, and that training will typically require scheduling some downtime to focus on best practices and overcome resistance to change. It’s best to work with a software vendor or integrator to take advantage of high-quality training programs that can cover the material in the shortest amount of time. Software programs that offer follow-up training embedded into the application can help.
• Model refinement. Demand forecasters refine their machine learning model predictions through continuous learning, as the models gain access to new operational data and are able to generate new, more accurate insights into the future.

Tips for Building an Effective Forecast System

In some organizations, demand forecasting has been spread across multiple departments, including sales, marketing, finance, and supply chain management. Wherever it sits, this team needs to be tightly integrated with sales and marketing. Here are some of the questions that need to be answered before making substantive changes to an existing forecast system.

• Understand your users. Make sure members of the demand planning team have clear responsibilities. For example, a demand planner aligned with the supply chain group needs to make sure that the business acquires the raw materials needed to make the right products at the right time. Another planner might work closely with marketing to gain insight into promotional activities that could influence future sales.
• Establish performance benchmarks. Forecasting systems need benchmarks that allow company leaders to assess how well the demand forecasting process is performing. Benchmarks include forecast accuracy, which will be affected by forecast bias (which occurs when there are consistent gaps between forecasts and actual outcomes) as well as error rates.
• Start small, but design to scale. It’s not possible to overhaul every element of demand forecasting in a single modernization initiative. Start small, set priorities, then come up with a long-term strategy that addresses the biggest challenges in the early stages. Each project should feed into the next one, acting as a foundation for a demand forecasting system that can scale to meet the needs of a growing organization.
• Identify and remove model biases. Imbalanced data used to train an AI model is one of the leading causes of inaccuracy, as most machine learning algorithms assume that the underlying data is distributed according to its relative importance in the overall data schema.
• Adhere to relevant standards. For industries with strict regulations governing product quality and construction, such as food producers and car manufacturers, feeding information about regulatory requirements makes it possible for demand planners to create forecasts that can keep their business in compliance with relevant standards.
  
  For example, forecasters may balance demand surge data from sales for perishable items with data from inventory control that details the amount of product that can be kept on hand without risking spoilage and the resulting wasted inventory. This coordination allows forecasters to make specific predictions that sales, supply chain, and logistics managers can use to plan the right levels of production and inventory.
• Assess and address security risks. With analyses of massive amounts of data come data security risks. For the most part, the data analyzed in cloud-based demand forecasting tools are more secure than data analyzed in on-premises systems.
• Iterate and adapt. Internal and external changes, whether it’s a shift in strategy, a major climate event, or a labor strike, require demand planners to adapt and iterate their forecasts to ensure that they’re using the right data.
• Invest in user education. The ideal demand planners have experience with statistical analysis and forecasting models. They’re able to interpret complex data sets and communicate their findings in language the rest of the organization, especially senior executives, can understand. The best kind of training for users will focus on analytic and communications skills, as well as qualitative and quantitative research skills.
• Communicate proactively to stakeholders. By generating collaborative forecasts that pull in data from all relevant parts of the business and provide the most accurate, up-to-date data sets, the demand forecaster can provide proactive guidance to stakeholders. For example, many demand planning systems allow forecasters to set up dashboards tailored to the specific needs and priorities of different departments, allowing departmental managers to view crucial metrics in one place and quickly assess overall demand and how the business can best respond.
• Build trust and address concerns. Forecast planners may be dealing with business leaders who are concerned about how AI can benefit their company. Planners can allay these concerns by showing concrete examples of how AI demand forecasting can outperform basic software, providing context for how it may boost the bottom line.

Build Stronger Forecasts with Oracle Supply Chain Planning

AI capabilities built into Oracle Supply Chain Planning can help demand planning teams build stronger forecasts and implement more accurate production, fulfillment, and inventory strategies.

AI in Demand Forecasting FAQs

How is AI used in demand forecasting?
Organizations use AI tools to analyze both internal and external data sets to identify patterns most humans can’t see in order to create more precise demand forecasts.

How does AI forecasting differ from traditional forecasting?
Traditional forecasting is conducted manually and relies mainly on historical data, which provides only a partial view of potential demand. AI-based forecasting analyzes much more diverse sources of data, including real-time data on market trends, consumer behavior, economic indicators, and competitor activity.

How do companies apply machine learning to demand forecasting?
Companies can also use machine learning models to run “what if?” analyses to see how different variables might impact future demand.

Is AI forecasting beneficial?
Yes. A benefit of AI forecasting is the ability to analyze large, complex data sets from disparate sources, allowing for more comprehensive and accurate forecasts. Another benefit is its capacity to learn from new data and adjust forecasts accordingly.

How can AI help sales forecasting?
AI can help accurately predict future sales by using historical sales data, industry data, and the current sales pipeline to quickly identify trends, patterns, and outcomes that might not be easily perceptible to a human analyst.