S&OP: Why Forecast Archiving Is a Must-Have
The S&OP (Sales & Operations Planning) process is often presented as the beating heart of the supply chain. Yet in many companies, it remains poorly structured, incomplete, or under-resourced. One of the most common gaps? Loose management of demand planning, and above all, the total absence of forecast archiving. Without a forecast history, it’s impossible to measure, improve, or justify your decisions. Here’s why S&OP deserves far more rigorous attention, and why a simple forecasting tool isn’t enough. The Key Steps of the S&OP Process: An Overview S&OP is an integrated planning cycle, typically monthly, that aligns sales, operations, and finance functions around a shared plan. It’s classically broken down into five steps: 1. Data collection: sales history, inventory levels, open orders, market data. This is the raw material of the entire process. 2. Demand planning: building medium- and long-term sales forecasts by combining statistical trends with commercial input. This is the central step we’ll detail below. 3. Supply planning: comparing demand forecasts against production, procurement, and storage capacity. The goal is to identify gaps and prepare action plans. 4. The pre-S&OP meeting: a working session between supply chain, sales, and operations leaders to resolve misalignments identified in the previous step. 5. The executive S&OP meeting: validation of the integrated plan by senior management, along with the associated financial and strategic decisions. Each step depends on the quality of the one before it. And it all starts with demand forecasting. Demand Planning: The Step That Shapes Everything Else Demand planning is often underestimated in its complexity. It’s not simply about applying last year’s growth rate to sales. It requires combining two complementary, and potentially conflicting, sources of information: Statistical forecasts: calculated automatically from sales history, they provide an objective, reproducible baseline. They capture trends, seasonality, and demand cycles. Manual adjustments: added by sales or marketing teams to factor in contextual information, an upcoming promotion, a new listing, a lost customer, a one-off event. These adjustments are valuable, but they introduce a degree of subjectivity. The real question, rarely asked, is this: do these manual adjustments actually improve the final forecast? Or do they, in fact, degrade the accuracy of the baseline model? Without systematic forecast archiving, this question will remain unanswered. Why Archiving Your Forecasts Is Essential (and Often Overlooked) This is where most companies fall short. They generate forecasts every month, use them to plan, then overwrite them with the following month’s new forecasts. The result: no usable record of past forecasting performance. Yet keeping a forecast history, every version, for every SKU, with creation date and revision level, is an absolute prerequisite for: Calculating forecast performance KPIs: MAE (Mean Absolute Error), bias, and more. These indicators show whether your forecasts are improving over time and where errors are concentrated. Measuring Forecast Value Added (FVA): FVA is a powerful indicator that compares the accuracy of the final forecast (after manual adjustments) to the raw statistical forecast. If FVA is negative, it means human intervention is degrading forecast quality, valuable information for refining your processes. Comparing calculated forecasts vs. manually entered data: by keeping both versions side by side, you can identify which product families, regions, or sales reps benefit from human adjustment, and which ones are better off trusting the model. Building a culture of accountability: when adjustments are tracked and evaluated after the fact, sales teams become more disciplined in their estimates. In short: without a forecast archive, your S&OP is blind to its own performance. You’re planning without knowing whether you’re actually improving. A Simple Forecasting Tool Isn’t Enough: What You Really Need Many supply chain teams still work with spreadsheets or basic forecasting modules built into their ERP. These tools let you generate a forecast for the current month, …and that’s about it. They’re not designed to: Store and version forecasts over time (monthly snapshots, cycle-to-cycle comparison) Automatically calculate forecast KPIs across your entire SKU portfolio Distinguish the statistical contribution from the human contribution in the final forecast Flag high-error products that deserve closer attention Feed a structured S&OP report for decision-makers A demand planning tool worthy of the name should be a forecasting performance management platform, not just a number-crunching calculator. The difference between the two comes down to one thing: the ability to learn from the past to better anticipate the future. This is exactly what SKU Science delivers: a platform that combines the power of machine learning and statistical models, the flexibility of manual adjustments, and, most importantly, automatic forecast archiving with KPI and FVA calculation for every SKU, at every S&OP cycle. Conclusion: Build Your S&OP Around a Forecasting Memory S&OP isn’t just a monthly meeting. It’s a continuous improvement process that can’t function without memory. Archiving forecasts, measuring FVA, comparing statistical models against human judgment, these practices aren’t reserved for large enterprises with dedicated data science teams. They’re accessible to any organization with the right tools. If you want to turn your S&OP process into a real performance driver, start by asking yourself a simple question: do you know whether last month’s forecasts were better than the month before? If you don’t have the answer, it’s time to change your approach. Discover how SKU Science helps you structure your demand planning, archive your forecasts, and drive your S&OP with reliable KPIs. Request a free demo
