The Role of Technology in Modern Demand Forecasting

If you're in manufacturing, you know that accurate demand forecasting is critical. It's the difference between smooth operations, happy customers, and a healthy bottom line – versus scrambling to meet unexpected demand, dealing with excess inventory and having liquidity issues, or losing out on potential sales and not meeting your Sales / EBITDA targets. But with constantly shifting customer preferences, disruptive market trends, and global events throwing curveballs, it's also one of the toughest nuts to crack. While often reliable in stable environments (especially in settings with lots of high-frequency transactions and no data sparsity), traditional stats-based forecasting methods aren't built for the complexity and volatility of today's market. They rely on historical data and often miss those subtle signals, indicating a major shift is on the horizon. Traditional stats-based approaches are also not that effective for businesses with high data sparsity (e.g., larger tickets, choppier transaction volume) That's where AI/ML-enabled forecasting comes in. Unlike foundational stats forecasting, it can include various structured and unstructured data, such as social media sentiment, competitor activity, and various economic indicators. One of the most significant advancements in recent years is the rise of powerful open-source AI/ML packages for forecasting. These tools, once the domain of large enterprises with extensive resources or turnkey solution providers (with hefty price tags), are now readily accessible to companies of all sizes, offering a significant opportunity to level the playing field and drive smarter decision-making. The power of AI and ML in demand forecasting is more than just theoretical. Companies across various industries are already reaping the benefits: • Marshalls: This UK manufacturer used AI to optimize inventory management during the pandemic. It made thousands of model-driven decisions daily and managed orders worth hundreds of thousands of pounds. • P&G: Their PredictIQ platform, powered by AI and ML, significantly reduced forecast errors, improving inventory management and cost savings. • Other Industries: Retailers, e-commerce companies, and even the energy sector are using AI to predict everything from consumer behavior to energy demand, with impressive results. If you're in manufacturing or distribution and haven't explored upgrading your demand forecasting (and S&OP) capabilities, I highly encourage you to invest. These capabilities are table stakes nowadays, and forecasting on random spreadsheets and basic methods (year-over-year performance, moving average, etc.) is not cutting it anymore.

Forecasting is a common application of data science, and it's crucial for businesses to manage their inventory, especially those with perishable items effectively. In a recent tech blog, the data science team from Afresh shared an innovative approach to accurately predict demand, incorporating non-traditional factors such as in-store promotions. Promotions are common in grocery stores, helping customers discover and purchase discounted items. However, these promotions can significantly alter customer behavior, making traditional forecasting methods less reliable. Traditional models struggle to incorporate these factors, often leading to higher prediction errors. To address this challenge, Afresh’s data science team developed a deep learning forecasting model that integrates various features, including promotional activities tied to specific products. The model's performance was evaluated using a normalized quantile loss metric, showing an 80% reduction in loss during promotion periods. This example highlights the superior performance of this solution and showcases the power of deep learning in solving a critical issue for the grocery industry. #machinelearning #datascience #forecasting #inventory #prediction – – –  Check out the "Snacks Weekly on Data Science" podcast and subscribe, where I explain in more detail the concepts discussed in this and future posts:    -- Spotify: https://lnkd.in/gKgaMvbh   -- Apple Podcast: https://lnkd.in/gj6aPBBY    -- Youtube: https://lnkd.in/gcwPeBmR https://lnkd.in/gWRgTJ2Q 

Yesterday’s sales can’t see tomorrow’s storm, But AI can 😎 Most manufacturers still build demand forecasts based on one thing: 𝐡𝐢𝐬𝐭𝐨𝐫𝐢𝐜𝐚𝐥 𝐬𝐚𝐥𝐞𝐬. Which is fine… until the market shifts. Or weather changes. Or a social post goes viral. (Which is basically always.) That’s why AI is changing the forecasting game. Not by making predictions perfect—just a lot less wrong. And a little less wrong can mean a lot more profitable. According to the Institute of Business Forecasting, the average tech company saves $𝟗𝟕𝟎𝐊 per year by reducing under-forecasting by just 1%, and another $𝟏.𝟓𝐌 by trimming over-forecasting. For consumer product companies, those same 1% improvements are worth $𝟑.𝟓𝐌 (under-forecasting) and $𝟏.𝟒𝟑𝐌 (over-forecasting). (Source: https://lnkd.in/e_NJNevk) And were are only talking 1 improvement%!!! Let that sink in... All that money just from getting a little better at predicting what customers will actually buy. And yes, AI can help you get there: • By ingesting external signals (weather, social, events, IoT, etc.) • By recognizing nonlinear patterns that Excel never will • And by constantly learning—unlike your spreadsheet But it’s not just about tech. It’s about process: • Use Forecast Value-Added (FVA) to track which steps help (or hurt) • Get sales, marketing, and ops aligned in S&OP—not working in silos • Focus on data quality—AI is only as smart as your ERP is clean • Plan continuously—forecasting is not a set-it-and-forget-it task Bottom line: If you’re still relying on history to predict the future, you’re underestimating the cost of being wrong. Your competitors aren’t. ******************************************* • Visit www.jeffwinterinsights.com for access to all my content and to stay current on Industry 4.0 and other cool tech trends • Ring the 🔔 for notifications!