In today’s volatile supply chain environment, an ocean of variables affects demand. As a result, professionals often find themselves diving into deep and murky waters to seek the right information for accurate predictions. AI and machine learning (ML) technology act as sonar, sweeping every dark corner and collecting information you might otherwise not consider, granting you greater visibility.
AI demand forecasting can help you more accurately stock for demand, which lowers overall risk for your company. Discover more about using AI for demand forecasting, including its benefits, involved techniques, and use cases.
What is AI Demand Forecasting?
AI based demand forecasting is a technique that uses artificial intelligence to process and point out patterns in vast amounts of data. Combining historical sales data with third-party sources tracking social media trends, weather patterns, upcoming events, and more, AI demand forecasting works better than manual methods to predict previously unpredictable quick shifts in demand.
How AI Demand Forecasting Works
Using AI in demand forecasting takes four key steps — collecting, modeling, analyzing, and integrating. Dive deeper into each part of the process to see how every piece enhances accuracy in demand planning.
Data Collection and Integration
AI technology takes in and processes much more information than people can, much faster. An AI-driven forecasting platform collects data from multiple sources in one place, then detects and displays patterns in a clear, cohesive, and digestible manner. When your tool integrates seamlessly with the sources you already use for data collection — as well as any additional sources you might want to add on — you can centralize multiple types of information to automatically feed your demand-predicting machine.
Predictive Modeling Techniques
Once all data is aggregated in the right place, AI algorithms comb through every piece of information to highlight relevant patterns. One of the most useful aspects of using AI for forecasting is its ability to handle highly complex and nonlinear datasets, which can prove next to impossible — or at least incredibly time-consuming — using manual methods. By handling your predictive models for you, AI turns a sea of possibilities into a clear picture of market dynamics. The picture grows even clearer over time as ML enables the tool to automatically improve with the addition of more and more data.
Real-Time Data Analysis
Traditional forecasting methods using only historical sales data have been more of a set-it-and-forget-it experience, making supply chain planners miss out on the most current updates to demand factors. On the other hand, predictive models powered by AI and ML are fed by a constant stream of new information coming in through integrations, so forecasts update in nearly real time. Instead of clinging on to a first guess based on outdated data, you’ll have the latest insights available at your fingertips.
Integration With IoT
Connecting even more of your system data together, your AI demand forecasting software can incorporate information from the Internet of Things (IoT) for full insight into the other end of the process — demand fulfillment. AI can parse through the extremely high volume of product information in your IoT network to see demand in action, further informing the predictive model and helping you with timely and accurate inventory replenishment.
Benefits of AI Demand Forecasting
With a better view into a complex and ever-shifting market environment, you can build an optimized demand plan. Find out how using AI to build an accurate demand forecast for real-time market conditions can also help your company reduce risk and waste.
Improves Inventory Management Accuracy
More accurate demand predictions lead to better-informed inventory management. Use AI to avoid missing important demand signals from external sources that might otherwise slip through the cracks, which indicate when to stock up or hold less inventory. Right-sizing your inventory puts money back into your pockets and space back into your warehouses and distribution centers, ultimately improving profits.
Reduces Supply Chain Costs and Waste
For the same reasons that more accurate demand forecasting leads to better inventory management, it also leads to better production planning. When you know actual demand for your products, you can better match production numbers to fulfill orders without making and holding excess. As a result, your company will save on production and transportation costs, and avoid a situation where you create more of a product than demand dictates, which is a waste of materials.
Helps Navigate Market Complexities
Reach beyond internal data to better grasp the complexities of today’s uncertain and volatile markets. Your AI tool can automatically intake data in different formats from several sources to help you chart a path to clarity. Plus, as you add more information to the system, it will quickly learn more about your sector and its complexities to continuously improve prediction accuracy.
Best Practices for Implementing AI Demand Forecasting Systems
When you’re ready to turn your AI-powered demand predicting plan into action, set and stick to a clear path to ensure efficiency with your tools and processes. Start by:
- Defining objectives: Set goals to unite every relevant department — such as IT, accounting, and executives. Tie your goals for a new predictive modeling system back to overall business objectives to prove its worth to every stakeholder. Then, consider the KPIs that will best measure system performance, using percentages measuring forecast accuracy improvement, inventory reduction, and time from implementation to initial results.
- Selecting the right models: Decide which insights will give you the clearest, most actionable view into demand. Consider the time to plan horizons most relevant for your business, then feed each model with the types of data most relevant to each time period. With these preparations in place, you’ll be able to see both real-time and long-term demand predictions at a glance for more informed decision making.
- Continuous monitoring and adjustment: Set regular review intervals for your new system. As it uses ML to improve itself, you can add new data sources and refine models manually to further contribute to its accuracy.
Use Cases: Industries Benefiting From AI Demand Forecasting
While any business with inventory and a supply chain can benefit from better demand forecasting, a few key industries can achieve massive improvements quickly using AI-powered software.
Retail Sector
Retail companies can use AI forecasting tools to maintain inventory levels better-matched with demand. Beyond demand, AI algorithms can help retailers make sense of more targeted segmentation based on consumer purchases, preferences, and demographics. Also, an AI forecasting tool can better predict the impact of ads and promotions on demand than traditional models, helping retailers right-size their stock during peak periods.
Manufacturing Industry
Better predictions of future demand drive more informed production decision making for manufacturers. AI doesn’t just statically predict demand for existing products; it can also point out patterns in consumer preferences to help manufacturers make data-driven decisions about new products and features. Similarly, manufacturing companies can implement competitive pricing strategies by running data about market conditions and competitor pricing through AI algorithms.
Supply Chain
Supply chain planners make smarter and faster decisions by using the streamlined processes afforded by AI-driven forecasting platforms. For example, when planning transportation routes based on location-specific demand, you also have to consider optimizing warehouse space, supply chain scenario planning, and collaborating with suppliers to acquire raw materials and finished goods. Logistics companies can focus their efforts more efficiently with accurate demand plans.
Unlock the Power of AI-Based Demand Forecasting
AI demand forecasting tools transform stagnant, error-prone formulas into complex and adaptable predictors of future demand. Instead of more data causing more headaches in manual configurations, every new piece of information enhances AI algorithms through machine learning. With use cases across industries like retail, manufacturing, and logistics, AI technology improves every aspect of demand and inventory planning, leading to savings and reduced risk.
At GAINS, our AI-driven demand planning and forecasting platform integrates all of your data collecting sources and tools in one centralized place. With more accurate algorithms and greater intelligence, our software helps you build a more resilient supply chain that moves your business — and your inventory — forward faster.
AI for Demand Forecasting FAQs
Need quick, straightforward answers to direct your tech stack decisions? Find out more about AI tools and how to use them to create more accurate demand forecasts below.
What are the AI tools for demand planning?
AI systems compile complex data from multiple sources, then search for and highlight patterns that inform demand planning across multiple time to plan horizons. With more accurate forecasts of future demand, companies can optimize their inventory planning, anticipate sudden shifts, and optimize their entire supply chain.
How can AI be used for demand forecasting?
When incorporated into demand forecasting, AI can parse complex and non-linear datasets to recognize more nuanced, accurate, and adaptable insights. Supply chain managers, retailers, and manufacturers can use this information to better plan production targets, inventory management strategies, and transportation routes based on anticipated demand.