Planner Coding: Capturing Unforeseen Events in Forecasting

In the world of demand forecasting and predictive analytics, historical data often fails to capture real-world disruptions. Events such as stockouts, uneven order placements, and unexpected surges in demand can skew forecasts, leading to inaccurate future predictions. This is where Planner Coding comes into play. It allows businesses to make historical adjustments that reflect past anomalies, ensuring a more accurate forecast.

The Planner Coding tool, as seen in the image above, provides an Adjustment Panel where users can modify historical data based on specific event types. These adjustments help refine forecasts by accounting for real-world disruptions. The tool includes four key adjustment types: Uplift, Year-over-Year (YoY), Offset, and Stockout.

Let’s explore what each of these means and how they impact forecasting.

1. Uplift

Uplift adjustments are used when an external factor causes a temporary or sustained increase in demand. This could be due to a marketing campaign, a promotional event, or a seasonal trend that isn't naturally captured in the forecast model. By adding an uplift adjustment, planners ensure that future forecasts recognize the impact of such events and account for them accordingly.

Example: If a holiday sale caused an unexpected spike in sales, adding an uplift adjustment ensures that future forecasts do not treat the surge as an anomaly but as a recurring trend during that period.

2. Year-over-Year (YoY) Adjustment

The YoY adjustment allows planners to compare and adjust data based on past trends from the same period in previous years. This is particularly useful in industries with strong seasonal patterns.

Example: If last year’s sales during the back-to-school season saw an unexpected dip due to supply chain issues, a YoY adjustment can be applied to normalize the data, ensuring this anomaly doesn’t negatively impact this year’s forecast.

3. Offset

Offset adjustments help correct discrepancies that occur due to time shifts in orders or sales data. This is crucial in cases where demand is shifted forward or backward due to special events, production delays, or supply chain disruptions.

Example: If a bulk order that usually arrives in December was delivered in January due to logistical delays, applying an offset adjustment ensures that demand shifts are reflected correctly in future predictions.

4. Stockout

Stockouts occur when a product is unavailable for sale, leading to artificially low recorded demand. If not adjusted, forecasting models may incorrectly assume a natural drop in demand instead of recognizing that the product was simply unavailable. The Stockout adjustment corrects this by estimating what demand would have been if inventory had been available.

Example: A best-selling item was out of stock for two weeks, causing sales to drop to zero. By applying a stockout adjustment, the forecast model understands that demand was still present but unmet due to inventory issues.

Why Planner Coding Matters

Without these adjustments, forecasting models can misinterpret historical data, leading to poor decision-making in inventory planning, supply chain management, and financial forecasting. Planner Coding ensures that demand predictions are more aligned with real-world conditions, reducing forecasting errors and improving business agility.

By leveraging tools like the TrueGradient Planner Coding interface shown above, businesses can refine their forecasts to reflect actual market behaviour, leading to better operational efficiency and customer satisfaction.

Final Thoughts

The ability to make precise historical adjustments using Uplift, YoY, Offset, and Stockout ensures that forecasting models remain accurate and reliable. In an unpredictable business landscape, Planner Coding is an essential tool for ensuring that past disruptions do not cloud future decisions.

Embrace the power of Planner Coding and take control of your demand forecasting today!

Click for demo today or email us at info@truegradient.ai !