Predictive Planning and Analysis: A Practical Guide

Modern businesses operate in an environment where decisions must be faster, smarter, and backed by data. Predictive planning helps organizations anticipate the future using historical data and algorithms—leading to more confident, proactive decision-making.

This guide explains predictive planning, the key analytical methods behind it, and how platforms like Jedox AIssisted™ Planning bring these capabilities into day-to-day business processes.

What Is Predictive Planning?

Predictive planning combines past data, statistical models, and machine learning algorithms to forecast future outcomes. Once the prediction is generated, business plans—budgets, staffing, inventory, production—are built around these expected values.

Where Predictive Planning Is Used

Across industries, predictive planning improves accuracy and agility:

• Finance: Rolling forecasts, revenue prediction, expense modelling

• Supply Chain: Demand forecasting, inventory optimization

• HR: Workforce planning, hiring needs

• Operations: Capacity, resource, and production planning

• Sales: Pipeline forecasting, quota planning

Example:A company predicting next quarter’s sales using historical trends and seasonality can pre-adjust its inventory, marketing spend, and staffing. As new weekly or daily data comes in, the forecast updates automatically.

Core Methods Used in Predictive Planning

Predictive models typically fall into three categories:

Time Series Forecasting

Time series forecasting predicts future values based on data collected at regular intervals (daily, weekly, monthly).

It looks for patterns in:

• Trend – upward or downward movement over time

• Seasonality – recurring patterns (weekend dips, festive spikes)

• Cycles – long-term business or economic cycles

• Noise – random fluctuations

Common use cases:

• Monthly revenue forecasting

• Energy consumption prediction

• Project duration estimates

Regression Models

Regression answers questions like:📌 “How much?”, “How many?”, “What will the numerical value be?”

Examples:

• Future sales volumes

• Budget needs

• Manpower forecasting

• Price prediction

• Demand modelling

Regression identifies relationships between one or more drivers (inputs) and a target variable (output).

Classification Models

Classification answers:📌 “Which category?”, “Will it happen?”, “What class does this belong to?”

Examples:

• Customer churn risk (High / Medium / Low)

• Fraud detection

• Inventory stock-out risk

• Lead scoring and prioritization

• Project success vs. failure

These models help businesses sort, categorize, and prioritize outcomes.

AIssisted™ Planning in Jedox

Jedox’s AIssisted Planning integrates predictive algorithms directly into planning workflows. It automates forecasting and provides wizards for:

• Time series predictions

• Driver-based forecasting

• Classification

• Data preparation

• Driver analysis

Jedox can automatically select the best algorithm based on the input data—making predictive planning accessible to non-technical users.

Time Series Forecasting in Jedox

Prerequisites

• Minimum 3 years of historical data

• A model using a standard time dimension (Day/Week/Month)

• A target version (e.g., Forecast)

• A measure defined for storing forecast values

Process Overview (Wizard-Driven)

1. Source Setup

   • Select time range

   • Filter by dimensions such as version and measure

2. Target Setup

   • Choose forecast version

   • Select target measure

   • Define the number of months to predict

   • Recommended: Prediction type = “Best”

3. Review Selections

   
   

   The wizard displays the chosen data and configurations.

4. Execution & Results

   • View forecast results with accuracy indicators

   • Automatically generated upper and lower variations

   • These trends can be used as a base for further planning

Driver-Based Prediction in Jedox

Driver-based modelling predicts outcomes based on selected causal factors (drivers).

Steps:

1. Select history and driver measure

2. Apply dimension filters

3. Choose target version and measure

4. Select number of forecast months

5. Recommended: Use “Best” prediction type

Remaining steps mirror the time series workflow.

Classification in Jedox

Classification helps categorize outcomes such as:

• Risk level

• Churn likelihood

• Inventory shortage probability

• Lead conversion probability

Setup Steps:

• Select the time range

• Choose the driver measure

• Include influential attributes/dimensions

• Set the target version and measure

• Choose forecast period and prediction type

Data Preparation in Jedox

Real-world datasets often have missing or incomplete values. Jedox’s Data Preparation module helps fill gaps before forecasting.

Techniques:

• Interpolation

  • Fills missing values within historical periods

• Extrapolation

  • Predicts values for missing future periods

Example:If sales data is missing from Nov–Dec 2025, extrapolation can estimate those values based on prior trends.

Proper data preparation ensures higher accuracy across all prediction types.

Conclusion

Predictive planning transforms business decision-making from reactive to proactive. Using techniques like time series forecasting, regression, and classification—combined with tools such as Jedox AIssisted™ Planning—organizations can: