IBM Planning Analytics demand forecasting across global regions; H2 2025 sales disrupted by shortages.
How to Do Demand Forecasting with with AI: A Step by Step Guide | IBM
In this how-to guide and tutorial, you will use IBM Planning Analytics to generate demand forecasting with artificial intelligence (AI) driven insights. Demand forecasting is the systematic process within supply chain management that uses historical data, market trends and external economic indicators for demand planning. Predicting future customer demand is crucial for businesses to manage supply chain operations and inventory planning. With accurate data-driven forecasts, businesses can optimize inventory levels; stock levels are maintained to meet demand and ensure customer satisfaction (avoiding stockouts and shortages) without over-allocating capital to excess goods (minimizing overstocking). AI demand forecasting takes the process further by implementing scalable AI-enabled forecasting tools and predictive analytics to estimate future demand. These systems can automate the analysis of thousands of variables simultaneously, identifying correlations that a human analyst might miss. Robust demand predictions contribute to the wider organization’s budgeting and integrated financial planning. Informed with accurate demand forecasting, CFOs and finance teams can manage capital allocation in a cost-effective manner, ensuring cash flow is available for procurement when demand is expected to peak. The “how” of forecasting is generally split into two categories: qualitative and quantitative methodologies. Qualitative methods: Quantitative methods: A quantitative approach to forecasting is typically rooted in time series models, a type of machine learning model that analyzes chronological data to predict future values. Some time series methods include: For our tutorial, we will use the statistical forecasting software included with Planning Analytics to create a baseline quantitative model. For this walkthrough, you will need to set up an IBM account and register for a free Planning Analytics trial. You cannot forecast effectively without knowing exactly what you are trying to achieve. Ambiguity at this stage of the demand forecasting process leads to “forecast drift,” where the data becomes too broad to be actionable. First, identify the time frame that aligns with your decision-making cycle. If your raw material lead time is three months, for example, a weekly forecast might be too granular, while an annual forecast will be too imprecise. Next, choose the right model for your specific need. Different types of demand forecasting models serve distinct strategic needs. Finally, to follow an effective demand forecasting and planning process, you must distinguish between two primary demand forecasting techniques. For our guide, we will implement a one-year, short-term, micro-level with hybrid (passive and active demand) forecasting scenarios. We will use 2025 historical data to predict 2026 sales data for three different products across global regions. To access our example, on the Planning Analytics home page under “Your recommended tasks,” select “Update a demand plan using AI.” The quality of your future demand output is entirely dependent on the quality of your input. This stage is often the most time-consuming but is nonnegotiable for generating accurate predictions. First, collect your historical sales data. Extract data from your enterprise resource planning or point of sale systems to build comprehensive datasets. For our walkthrough, you can see that the “Overview” page lists all the units we sold in 2025. It also lists the revenue, gross margin, operating costs and net income derived from those unit sales. Next, if needed, data cleaning will help remove any noise from a dataset. For instance, if you had a one-time bulk order from a contract that won’t repeat, remove that data point. These outliers will otherwise skew your averages and lead to over-purchasing. In our Planning Analytics demand forecasting example, we will proceed with the data as it stands without more cleaning. Next, let’s identify some patterns in the data, including: Staying on the “Overview” page, we can see that units sold peaked in March and July 2025. Also, there was a downward trend in the number of units sold in the fourth quarter. Per the notes, this decline was due to inventory shortages from higher-than-expected product demand causing sales disruptions in the second half of 2025. Click “Next” to proceed. Next, we can create our baseline forecast. This is our starting point—the mathematical prediction of what will happen if current trends continue. Planning Analytics automatically generates the baseline statistical forecast for us. Let’s review and interpret the automated initial forecast and perform a “sanity check.” Reviewing the statistical forecast, we see peaks in March and July 2026, similar to the peaks from 2025. Click “Next” to continue. For our first adjustment, we receive important feedback from the sales team to address the unexpected increased consumer demand from 2025. They would like us to revise the forecast by adding 30,000 units to the US market for 2026. To do this revision on Planning Analytics, select the first demand forecast scenario, “DemandPlanScenario1,” from the Sandbox dropdown and “USA” from the Markets dropdown. Next, manually add ‘30000’ to the cell where the 2026 column and the “Sales
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