Techniques for Forecasting in Supply Chain Management

Forecasting off pipeline stages is like using self-tanner before a beach trip. It gives you false confidence, washes off fast, and fools absolutely no one who gets too close. “30 opps in stage 3 × 40% = $1.2M forecasted.” Bueno. Now subtract the 9 deals that haven’t moved in 30+ days. Then subtract the 5 with no economic buyer involved. And the 8 that don’t have next steps or a MAP. Still $1.2M? lol nah...didn't think so. Stage-based forecasting is pretty broken, mainly because pipeline stages are opinions. Velocity and conversion, on the other hand, are facts. Buyers don’t care what CRM column they’re sitting in. They care about friction, fit, and fear. And your forecast should reflect all three. Here’s how to fix it: 1. Pair conversion rate with conversion velocity. - Let’s say Stage 3 deals have a 30% win rate.  - But they take 52 days to close on average. - If it’s day 50 of the quarter, and that deal just hit Stage 3? It’s not real revenue. It’s next quarter’s homework. One RevOps team I know added “days to close by stage” into their forecast model. They realized 63% of late-stage pipeline wouldn’t close in time based on historical cycle length. The result? They re-weighted forecastable revenue by stage age × velocity. Forecast accuracy jumped 21% in two quarters. 2. Use behavioral signals, not just stage tags Stop assuming every Stage 4 opp has a 60% chance of closing. Start tagging based on buyer actions - not rep motion. What to track: - Was an economic buyer involved in the last call? - Did the buyer ask about implementation timeline? - Has procurement been looped in? - Are multiple stakeholders engaged and documented? Deals with 3+ of these signals close 2 - 3x more often. AND they close faster. Build a behavioral scoring model and overlay it on top of your CRM stages. 3. Build pipeline coverage by real math Forget the “3x coverage” rule of thumb. If your conversion rate from Stage 2 to Close is 18%, and your quarterly target is $1M, you don’t need $3M in pipeline. You need $5.56M in qualified opps. Idea: A CRO we work with built a stage by stage conversion model with time-based decay curves. They found that 22% of their pipeline had aged out of viable range, and 19% of Stage 1 deals had <5% chance of conversion. So they cut their pipeline headline by 41% - and finally forecasted accurately for the first time in six quarters. tl;dr = Forecasting isn’t about hope. It’s judgment × math × motion. If you’re still forecasting based on pipeline stage alone, you don’t have a sales process. You have a spreadsheet-shaped fantasy. And fantasy doesn’t hit number.

 No one-size-fits-all in demand forecasting. This document shows 21 forecasting techniques for planners: 1️⃣ Naive Forecast ↳ “Tomorrow = Today”; best for highly stable, low-variability SKUs 2️⃣ Moving Average ↳ Calculates average demand over a fixed window; smooths noise but lags behind trends or seasonality 3️⃣ Weighted Moving Average ↳ Gives more weight to recent periods; useful when recent trends are more relevant than older data 4️⃣ Simple Exponential Smoothing ↳ Forecasts using a smoothing constant (alpha) to weight recent demand more heavily; best for flat, non-seasonal data 5️⃣ Holt’s Linear Trend Method ↳ Builds trend into exponential smoothing; suitable for items with consistent upward or downward movement 6️⃣ Holt-Winters (Triple Exponential Smoothing) ↳ Adds seasonality on top of level and trend; ideal for seasonal SKUs 7️⃣ Linear Regression ↳ Finds a straight-line relationship between a dependent variable (e.g., sales) and one independent factor (e.g., price) 8️⃣ Multiple Linear Regression ↳ Accounts for several demand drivers at once (promotions, discounts); good for mature categories with complex dynamics 9️⃣ ARIMA (AutoRegressive Integrated Moving Average) ↳ Great for time-series data with trends and autocorrelation 1️⃣0️⃣ SARIMA (Seasonal ARIMA) ↳ Adds a seasonal component to ARIMA; helpful when monthly or quarterly patterns repeat reliably 1️⃣1️⃣ Transfer Function Models ↳ Combine ARIMA with external input variables (e.g., advertising spend or GDP); useful for planning with known economic factors 1️⃣2️⃣ XGBoost / LightGBM ↳ Powerful tree-based algorithms; handles outliers, nonlinear relationships, and multiple variables 1️⃣3️⃣ Random Forest ↳ Builds multiple decision trees and averages the outputs; reduces overfitting and works well with many predictors 1️⃣4️⃣ Neural Networks ↳ Mimics the human brain; excellent at capturing nonlinear, complex relationships 1️⃣5️⃣ Prophet (by Meta/Facebook) ↳ Designed for business users; automatically detects trends, holidays, and seasonality 1️⃣6️⃣ LSTM (Long Short-Term Memory Networks) ↳ A type of deep learning specifically for sequences; excellent at modeling long-term dependencies in time series 1️⃣7️⃣ Support Vector Regression ↳ Effective for high-dimensional, noisy datasets; less popular than others, but still powerful in niche applications 1️⃣8️⃣ Expert Judgment ↳ Relies on domain knowledge when data is unreliable or missing (e.g., for new products or crisis situations) 1️⃣9️⃣ Delphi Method ↳ Structured technique using rounds of anonymous expert feedback until consensus is reached; great for strategic forecasts 2️⃣0️⃣ Sales Force Composite ↳ Structured technique using rounds of anonymous expert feedback until consensus is reached; great for strategic forecasts 2️⃣1️⃣ Consensus Forecasting ↳ Final demand plan formed through cross-functional alignment (demand, supply, finance) in the S&OP process Any others to add?

Forecasting is hard. Finding analysts who do it well is even harder. Too often, I see forecasting either: 1. Overcomplicated: Applying complex ML models just to predict a moving average (?!), or 2. Oversimplified: Running regressions without understanding what the coefficients even mean. I personally use 4 forecasting methods to model a range of outcomes, from conservative to aggressive: 1. ARIMA - Smooths time series data, w/o seasonality adjustment. 2. SARIMAX -  Like ARIMA, but accounts for seasonality. Likely to be the safest and conservative forecast. 3. Prophet -  Captures non-linear trends and seasonality. Often the most accurate. My favorite model for growth forecasts. 4. Manual Projection – aka Olga's secret, overly complicated manual projection. I plot every available metric’s historical D/D, W/W, M/M, and Y/Y % change and analyze their: (a) correlations and relationships (b) seasonal thresholds. It takes ages to complete, but it delivers the most precise forecast. If done right. If I can account for everything the teams are doing. Which is rarely the case. 😬 When reporting, I typically present only Prophet alongside my Projection, keeping ARIMA and its variations for myself as checks. There are many time series models out there: MA, AR, ARMA, ARIMA, SARIMA, Exponential Smoothing, VAR, and more. Forecasts are fun.