Stock Keeping Unit Analysis

Understanding Forecast Accuracy (MAPE) in Supply Chain In supply chain and operations, Forecast Accuracy can make or break your planning efficiency. One of the most widely used metrics to measure this is MAPE (Mean Absolute Percentage Error) — a simple yet powerful way to understand how close your forecasts are to reality. Here’s why MAPE matters and how it helps teams make smarter decisions: ✅ What is MAPE? It calculates the average percentage difference between your forecasted demand and actual demand. ✅ Why is it important? Accurate forecasts help reduce: • Stockouts • Excess inventory • Production delays • Cost overruns ✅ The Formula: |Actual – Forecast| / Actual × 100 ✅ Example: Forecast = 100 units Actual = 80 units MAPE = 25% ✅ Industry Benchmark: • ≤10% → Excellent • 10–20% → Good • 20–30% → Acceptable • >30% → Needs Improvement ✅ Where MAPE is used: • Inventory Management • S&OP • Production Planning • Logistics & Distribution • Financial Planning ✅ Limitations: • Not reliable when Actual = 0 • Distorted by promotions or demand spikes • Not suitable for new product launches ✅ How to Improve MAPE: • Clean and validate data • Reduce forecast bias • Use better forecasting models • Segment SKUs (ABC/XYZ) • Strengthen collaboration between Sales & Supply Chain teams Accurate forecasting isn’t just a number — it’s a competitive advantage. Teams that track and improve their MAPE consistently create more reliable, scalable, and cost-efficient operations. --- #️⃣ Hashtags: #SupplyChain #ForecastAccuracy #MAPE #DemandPlanning #SOP #InventoryManagement #Logistics #OperationsManagement #SupplyChainExcellence #PlanningAndScheduling #DataDrivenDecisions #BusinessAnalytics #ProcessImprovement #LeanSupplyChain #SupplyChainProfessionals

Look at the image attached to this post. Which forecast seems more appropriate: 1) the red straight line, 2) or the purple wavy line? Many demand planners might choose (2), thinking it better captures the ups and downs. But, in many cases, the straight line is just fine. Here’s why. In a previous post on Structure vs. Noise (https://lnkd.in/ekZA__aE), we talked about how a time series is made up of different components (such as level, trend, and seasonality), and how the main goal of a point forecast is to capture the structure of the data, not the noise. Noise is unpredictable, and it should be treated by capturing uncertainty around the point forecasts (e.g., prediction intervals). So the answer to the question above comes to understanding what sort of structure we have in the data. In the attached image, the only structure we have is the level (average sales). There's no obvious trend, seasonality, no apparent outliers, and we do not have promotional information or any explanatory variables. The best you can do in that situation is capture the level correctly and produce a straight line for the next 10 observations. In this case, we used our judgment to decide what’s appropriate. That works well when you’re dealing with just a few time series. Petropoulos et al. (2018, https://lnkd.in/eVXQBjh9) showed that humans are quite good at selecting models in such a task as above. But what do you do when you have thousands or even millions of time series? The standard approach today is to apply several models or methods and choose the one that performs best on a holdout sample using an error measure, like RMSE (Root Mean Squared Error, see this: https://lnkd.in/easRx9KX). In our example, the red line produced a forecast with an RMSE of 10.33, while the purple line had an RMSE of 10.62, suggesting that the red line is more accurate. However, relying only on one evaluation can be misleading because just by chance, we can get a better forecast with a model that overfits the data. To address this, we can use a technique called "rolling origin evaluation" (Tashman, 2000: https://lnkd.in/eTQp8djX). The idea is to fit the model to the training data, evaluate its performance on a test set over a specific horizon (e.g., the next 10 days), then add one observation from the test set to the training set and repeat the process. This way, we gather a distribution of RMSEs, leading to a more reliable conclusion about a model’s performance. Nikos Kourentzes has created a neat visualization of this process (second image). For more details with examples in R, you can check out this section of my book: https://lnkd.in/ePJW-6UZ. After doing a rolling origin evaluation, you might find that the straight line is indeed the best option for your data. That’s perfectly fine - sometimes, simplicity is all you need. But then the real question becomes: what will you do with the point forecasts you’ve produced? #forecasting #datascience #machinelearning #businessanalytics

Forecasts are worthless if they don’t drive action. This document shows how to turn forecast errors into insights: # 1 - Compare Forecast vs Actual Pattern, Not Just Values Look for trend breaks: promotions, seasonality shifts, competitive actions Insight: shows whether the model or the business behavior changed # 2 - Separate Volume Error from Mix Error Your total forecast may be right but SKU mix is wrong Insight: points to cannibalization, launches, or customer preference shifts # 3 - Slice the MAPE (forecast error) MAPE at total level hides the real problem; slice by SKU, region, channel, and planner Insight: find where the system is breaking, not the average # 4 - Track Bias Consistently MAPE shows how much you miss; bias shows how you think Insight: positive bias = optimism; negative bias = fear of stockouts # 5 - Connect Error Spikes to Events Overlay error trend with business events; launches, stockouts, price changes and map everything Insight: turns disconnected numbers into cause-and-effect stories # 6 - Use FVA (forecast value added) to Check If Adjustments Helped or Hurt Measure whether human overrides improved or worsened accuracy Insight: helps remove emotional adjustments from the process # 7 - Build an Error Heatmap One view showing where the biggest misses are by SKU, month, region Insight: quickly identifies where planning attention is needed # 8 - Weekly Error Deep Dive Pick the top 5 SKUs with the biggest misses; ask: “what changed?” and “who owns the correction?” Insight: makes forecasting a feedback loop, not a ritual Any others to add?

A few years back, I ran a forecast error report for a global pharma client.   The overall accuracy looked healthy. But inventory write-offs told a different story.   We weren’t losing money on the forecast—we were losing it on the wrong products. So we zoomed in with a sharper lens. We didn’t just look at errors.   We classified SKUs—A, B, C.   Then overlaid forecast bias on each class. And that’s when the picture turned clear. One A-class SKU—high revenue, high velocity—had a persistent under-forecast bias.   Every quarter.   Which meant constant stockouts and lost sales. Meanwhile, several C-class items had over-forecast bias, inflating dead inventory. Same metric (bias), but now targeted at SKU importance.   That’s where real planning intelligence begins. We acted.   Adjusted safety stocks for C SKUs.   Improved forecast models for A SKUs.   And in just one quarter, we slashed working capital by 9% and boosted service levels by 6%. Because in supply planning, accuracy without relevance is just noise.   It’s bias + ABC classification that turns noise into strategy. Supply Planning is not just about what you stock—it's about what you shouldn’t stock. Are you still measuring forecast bias in isolation?

𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 𝗙𝗼𝗿𝗲𝗰𝗮𝘀𝘁 𝗔𝗰𝗰𝘂𝗿𝗮𝗰𝘆 𝗖𝗮𝗹𝗰𝘂𝗹𝗮𝘁𝗶𝗼𝗻 "𝗜𝗳 𝘆𝗼𝘂 𝗰𝗮𝗻’𝘁 𝗺𝗲𝗮𝘀𝘂𝗿𝗲 𝗶𝘁, 𝘆𝗼𝘂 𝗰𝗮𝗻’𝘁 𝗶𝗺𝗽𝗿𝗼𝘃𝗲 𝗶𝘁!" – This holds true for demand forecasting in supply chain planning. A good forecast minimizes stockouts, avoids overstocking, and improves service levels. But how do we measure forecast accuracy? 1️. 𝗠𝗲𝗮𝗻 𝗔𝗯𝘀𝗼𝗹𝘂𝘁𝗲 𝗘𝗿𝗿𝗼𝗿 (𝗠𝗔𝗘) – Measures Overall Forecast Error in Units: MAE calculates the average difference between actual and forecasted demand, showing the true magnitude of errors. 𝗠𝗔𝗘= 𝟭/𝗻 ∑〖𝗮𝗯𝘀 (𝗔𝗰𝘁𝘂𝗮𝗹𝘀-𝗙𝗼𝗿𝗲𝗰𝗮𝘀𝘁)〗 Example: If actual demand = 100, 150, 200 and forecast = 90, 160, 180, then: MAE = (∣100−90∣+∣150−160∣+∣200−180∣)/3=13.33 units Pros: > Easy to interpret (units of demand) > Useful for comparing multiple forecasts Cons: > Doesn’t indicate if errors are consistently positive or negative > Doesn’t penalize large errors more than small ones 2️. 𝗠𝗲𝗮𝗻 𝗔𝗯𝘀𝗼𝗹𝘂𝘁𝗲 𝗣𝗲𝗿𝗰𝗲𝗻𝘁𝗮𝗴𝗲 𝗘𝗿𝗿𝗼𝗿 (𝗠𝗔𝗣𝗘) – Measures Forecast Accuracy in % : MAPE expresses forecast error as a percentage of actual demand, making it easy to compare across products or industries. 𝗠𝗔𝗣𝗘= 𝟭/𝗻 ∑ (𝗮𝗯𝘀 (𝗔𝗰𝘁𝘂𝗮𝗹𝘀-𝗙𝗼𝗿𝗲𝗰𝗮𝘀𝘁))/𝗔𝗰𝘁𝘂𝗮𝗹𝘀  Example Calculation: MAPE = (∣100−90∣ / 100 + ∣150−160∣ / 150 + ∣200−180∣ / 200)) / 3 × 100 = 8.89%  Pros: > Expressed in percentage → easy to understand > Works well when comparing multiple product forecasts Cons: > Skews results when demand is low > Can’t be used for zero-demand periods 3️. 𝗠𝗲𝗮𝗻 𝗦𝗾𝘂𝗮𝗿𝗲𝗱 𝗘𝗿𝗿𝗼𝗿 (𝗠𝗦𝗘) – Penalizes Large Forecast Errors MSE squares the error values before averaging them, making larger errors more impactful. 𝗠𝗦𝗘= 𝟭/𝗻 ∑〖(𝗔𝗰𝘁𝘂𝗮𝗹𝘀-𝗙𝗼𝗿𝗲𝗰𝗮𝘀𝘁)〗^𝟮 Example Calculation: MSE = ((100−90)^2 + (150−160)^2+ (200−180)^2) / 3 = 200  Pros: > Penalizes large errors more than small ones > Differentiates between "small" and "big" forecasting mistakes Cons: > Squaring the errors amplifies outliers > Harder to interpret since it’s not in demand units 4️. 𝗥𝗼𝗼𝘁 𝗠𝗲𝗮𝗻 𝗦𝗾𝘂𝗮𝗿𝗲𝗱 𝗘𝗿𝗿𝗼𝗿 (𝗥𝗠𝗦𝗘) – More Interpretable than MSE RMSE is simply the square root of MSE, bringing it back to the original demand units. RMSE= √(MSE) RMSE = √(200) = 14.14  Pros: > More interpretable than MSE (same unit as demand) > Useful when large errors are critical Cons: > Still penalizes large errors more than small ones > Harder to compare across different product categories The key is to continuously monitor, refine, and adapt based on insights from these metrics. Because in supply chain planning, the goal isn’t a perfect forecast—it’s a smarter, more resilient one! #SupplyChain #Demandforecasting #InventoryManagement #DemandPlanning #CostOptimization #Logistics #Procurement #InventoryControl #LeanSixSigma #Cost #OperationalExcellence #BusinessExcellence #ContinuousImprovement #ProcessExcellence #Lean #OperationsManagement

Provocative title, I know... but MAPE is lying to us. Over the past year, I’ve learned a lot about how and why MAPE (and even RMSE) distort the way we measure forecast accuracy, and how those distortions quietly influence decisions across supply chains. An RMSE of 100 can mean something very different for two products: it’s acceptable for a high-volume item but catastrophic for a low-volume one. MAPE seems scale-free, but it exaggerates errors for small or intermittent products and penalises over-forecasts far more than under-forecasts. The result? We think we’re managing forecast accuracy but in reality, we’re managing statistical illusion. In my new article, I explain: - Why MAPE and RMSE fail for cross-product comparison - How to use Forecast Value Added (FVA) and Scaled Errors (MASE, RMSSE) instead - Why bias matters as much as accuracy, and how to calculate it - Why the geometric mean is the right way to aggregate results across SKUs If you work in forecasting, analytics, or supply chain planning, this one’s for you. Curious... does your organisation track bias alongside accuracy, or just focus on error percentages?

🚀 You don’t need expensive simulation software to teach key aspects of Supply Chain Management (SCM)! 🔍 In one of my sessions on Demand Forecasting, I focused on Understanding Forecasting Errors and provided a hands-on experience using Microsoft Excel. ✅ Key Forecast Accuracy Metrics Explored: 📌 BIAS – Detecting over/underestimation in forecasting 📌 MAD (Mean Absolute Deviation) – Measuring average forecast error 📌 MAPE (Mean Absolute Percentage Error) – Evaluating percentage-based forecast accuracy 📌 Tracking Signal – Monitoring forecast deviations for adjustments 💡 Why Excel? 📌 Easily accessible – No need for costly tools 📌 Practical application – Real-world forecasting scenarios 📌 Data-driven decision-making – Helping students and professionals enhance demand planning Excel is a powerful tool for teaching real-world supply chain analytics. Hands-on learning makes the concepts more engaging, practical, and impactful! 📢 How do you incorporate Excel in your SCM teaching or professional work? Let’s discuss in the comments! 👇 #SupplyChain #DemandForecasting #ExcelForAnalytics #ForecastingAccuracy #SCM

“We’re 90% confident in Q4.” Actual close rate on those deals: 23%. 😬 Wanna know the worst part? Nobody was surprised. Not the board. Not the CFO. DEFINITELY not the VP who said it with a straight face in the Tuesday review. Forecast misses embarrass people. The weird thing is that, with some exceptions, they never really surprise them. That distinction matters a helluva lot more than you think. The inflation starts at the bottom, of course, and compounds on the way up. Kinda like a snowball rolling uphill. Rep has a deal that’s 60% real. Manager needs coverage to hold, so 60% rounds up to 70%. VP needs a board story, so 70% becomes 85%. By the time it lands on a slide, three layers of leadership have endorsed a number that's super inflated. Every person in that chain knows its bullshit, btw. Rep knows the deal is soft. Manager knows half the pipe is nonsense. VP knows there’s a 30-40% haircut baked into every Commit call. But admitting it means doing something about it. So everyone just...kinda goes along to get along. 🤷♂️ Slide advances. Quarter ends badly. Repeat. This happens because forecast accuracy isn’t tracked. Think about that for a second: You measure quota attainment. Win rate. Cycle length. Pipeline coverage. ASP. But the metric that tells the board whether your business can predict its own business? Nobody’s keeping score. Here's an idea: start keeping score! Run a trailing four-quarter accuracy rate. Take the Commit number your team called at the start of each quarter, divide by what actually closed. That’s your number. Teams running this for the first time might land somewhere between 55-65%. Which means 35-45% of every Commit call was bullshit, sitting right there in a spreadsheet with no room to blame macro or timing or “that one deal that slipped to January.” Now track it by manager. You’ll find one at 82% and another at 51%. Same CRM, same methodology, same forecast stages. That’s a coaching conversation you couldn’t have before because the problem had no measurement. Now backtest it. Pull your last four quarters of Commit calls and run the accuracy math retroactively. Say you find your trailing accuracy is 62%. That means for every $1M in Commit, $380K was bunk. On a $20M annual plan, that’s $7.6M in fake pipeline your board was using to make hiring, marketing, and investment decisions. Real dollars were allocated. Real lives were impacted  Now run it forward. If you improve accuracy from 62% to 80% over three quarters, your CFO stops buffering your projections. The 25% haircut they’ve been quietly applying to every number you present goes away. On a $20M plan, that’s the difference between getting resourced for $15M and getting resourced for $20M. Same org, same market. You just got $5M in capacity back because the people holding the budget started believing your numbers. Credibility compounds. So does bullshit. Pick one. Use this nifty diagnostic if it would be helpful.

When a Metric Shift Sparked a 12% Forecast Accuracy Boost 🚀 In supply chain planning, the metric you choose can make or break your strategy. For years, I relied on MAPE (Mean Absolute Percentage Error) to judge forecast accuracy until I realized it told only half the story. MAPE treats every SKU equally. That means a tiny miss on a low-volume item can distort your entire accuracy picture… even when you’re doing great on the products that actually drive revenue. Enter WMAPE (Weighted Mean Absolute Percentage Error). Unlike MAPE, WMAPE gives higher weight to forecast errors on high-volume or high-impact items providing a more business-relevant, bottom-line view of accuracy. Here’s how I applied it: Extracted forecast and actual data from SAP S/4HANA across diverse SKUs. Built a side-by-side dashboard in Excel comparing MAPE and WMAPE. Found that traditional metrics were hiding key issues in top SKUs. Collaborated with demand planners to adjust statistical models where it truly mattered. That switch led to tighter alignment between planning and production and a 12% sustained improvement in forecast accuracy. WMAPE transformed how we measured performance and responded to errors. It moved the conversation from “What’s our overall accuracy?” to “Where does inaccuracy actually hurt the business?” If you want your metrics to drive meaningful action, WMAPE deserves a spot in your toolkit. #WMAPE #DemandPlanning #ForecastAccuracy #SupplyChainOptimization #PlanningExcellence

Your overall MAPE might look healthy. But your highest-value SKUs could still be missing the mark. Most planning teams report one forecast accuracy number across the entire catalog. It’s clean, easy to present, and convenient for the monthly S&OP deck. But the problem with MAPE is that it gives every SKU equal influence. A 90% error on an item selling 50 units a month may have little financial impact. But a 15% error on an item selling 50,000 units a month can create major inventory problems, service issues, and unnecessary cost. That’s where Weighted MAPE, or WMAPE, helps. WMAPE weights forecast errors by volume, so your most important products have the greatest influence on the result. 🎯 MAPE tells you how accurate the average SKU is 🎯 WMAPE tells you how accurate the business is We regularly see teams discover that their headline MAPE looks acceptable while their top revenue-driving SKUs are consistently over-forecasted or under-forecasted. It’s also why one forecasting algorithm rarely works across an entire catalog. High-volume products with years of daily history behave differently from slow-moving or intermittent items. Our Demand Planning application evaluates more than 20 forecasting models and automatically selects the best fit for each SKU. Because improving the average is useful — but getting your most important products right is far more valuable. When did you last compare your MAPE and WMAPE? Chao-Ming Ying Co-founder and CTO, NewHorizon.ai PS - Click the link in my bio to see how SKU-level model selection can improve forecast accuracy where it matters most in your organization.