Streamlining Supply Chain Operations with Better Forecasts

Forecasting solutions touting the use of AI/ML models are hard to avoid these days. But there is a hidden risk that most companies tend to ignore. The latest models are great but I worry these are being applied in a way that will only amplify “the bullwhip effect”. What do I mean? Bull whip effect is the distortion of the demand signal as it travels from the consumption end of the supply chain to the production end, while traversing multiple physical and informational nodes along the way. As a result, the demand signal at the production end could be orders of magnitude variable than actual consumption. This is nothing new and we have known about this effect for two decades plus. As we apply algorithms to forecasts, unless we account for the bullwhip effect, we are bound to amplify distortion despite best intentions. Now, this is not about outlier elimination which I believe algorithms do a pretty good job of eliminating. I am talking about misinterpreting noise as signal, over-interpreting variability and causing inventory gyrations that ultimately hurt customers. A classic example is using order/shipment data at Distribution Centers for forecasting or worse yet, factory shipments as a proxy for demand. Most S&OP plans only focus on order and shipment data without systematically factoring in channel inventory and demand. So what is the fix? In my opinion, if you are a consumer company (CPG, Hi-Tech, Retail, Pharma/Healthcare, and even manufacturing), build the capability to forecast a demand signal that is as close to the final consumption point. For example, a CPG brand could forecast retail/e-commerce sell-through demand, normalize it for channel inventory and then propagate that signal up into the supply chain. And the best part - those same AI/ML models will work much better for you. To be honest, B2B and industrial companies also benefit from a similar approach by getting closer to end customer demand. Better yet, this unlocks better demand intelligence which fuels better S&OP judgements, new product forecasting quality, lifecycle management, capacity planning and more. If you are looking for a 10x transformation, this is one of them. It’s bizarre to me when I see companies side-stepping this fundamental step and then complain about forecast accuracy, or data cleanliness or something else hurting their supply chain service levels and costs. Leaders who are pursuing unlocking growth from their supply chains while reducing cost-to-serve need to lead from the front in championing this capability. 

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!

There’s a lot of uncertainty about what the global supply chain will look like in the coming months. I’ve felt like we’ve been playing supply chain on “hard mode” for the past 5 years, banging our heads trying to “get it under control.” Ironic, I know, given our namesake, Ministry of Supply. • 2019 - Regulatory: US-China relations and Sec. 301 changes • 2020 - Demand: COVID volatility • 2021 - Supply: Lengthening lead times • 2022 - Demand: Post-pandemic consumer boom • 2023 - Supply: Post-pandemic inventory bullwhip peak • 2024 - Demand: Inflation-induced softening • 2025 - Regulatory: Uncertainty in global production ecosystem We played MIT’s “The Beer Game” back in 2017 at a Ministry of Supply retreat. It’s a classic simulation that teaches about asymmetric information in production and distribution across 4-5 stages. Orders stream in steadily until, suddenly, an order spikes — without fail, people overcompensate. The key to managing this is resisting the impulse to overreact. Two years ago, and we found ourselves with ballooned inventory at 2x our target levels. Our inventory turns had dropped from 3x to 1x per year. The Problem was Twofold: •Rational: Safety stock is hypersensitive to demand volatility and lead times, especially when they length unpredictably. • Emotional: In theory, a rational actor would order proportionately… but we don’t. As my colleague Ian would say, “It’s like riding a wave; you can never see the bullwhip when you’re in it.” The desire to “gain control” over demand volatility and lead time uncertainty leads us to “plan further out.” Thanks to Sean Willems and Steve Graves, who introduced us to a radically different strategy: Don’t fight volatility. Design for it. The Solution: 1. Multi-Echelon Forecast - Split product forecasts. We use “fabric platforms” where shared fabrics are used across SKUs, pooling demand risk and shortening lead time forecasts. 2. Innovate to Standardize Materials - A double-dye cationic process now lets us create our solid and heathered Kinetic suits from a single fabric, pooling demand. 3. Shorten Reorder Cycles - Shifting from 2-4 buys a year to 12 increases PO frequency and shortens lead times, improving accuracy over forecasts. Connected forecasts like Crest, Flagship, and Singuli help place POs quickly. 4. Strategic Inventory Placement - Use safety stocks of raw materials and intermediate parts based on lead times. Undyed fabric is cheaper than a finished blazer and pools demand across products. 5. Communicate Inventory & Sales with Suppliers - Sharing forecasts and downstream sales data lets suppliers help create the materials strategy. Moving from emails to bi-weekly calls has made all the difference. Hope this helps with robustness in an uncertain climate. Thanks to partners Lever Style, Motives, SINGTEX Group , Teijin Limited, Toray Industries, Inc. for being part of this journey.