Demand Forecast

In today’s fast-moving markets, anticipating demand with precision is critical to making confident, informed decisions. ConverSight’s Demand Forecast empowers business users to plan proactively by transforming historical data into actionable demand insights—without needing technical expertise.

Built with flexibility and control at its core, this feature guides users through a structured forecasting journey. From configuring scenario preferences and selecting the right datasets to reviewing and adjusting projections, every step is designed for usability and impact. Users can create new forecasting scenarios, apply classification logic, fine-tune values and publish forecasts organization-wide—all within an intuitive interface. Whether you’re refining forecasts manually or using AI Demand Forecast models, the platform gives you complete visibility and control.

This documentation walks you through each section of the application, providing step-by-step instructions to help you confidently create, manage and publish forecasts that drive results.

Accessing Demand Forecast

The Demand Forecast application can be accessed by navigating to the Launchpad tab and selecting the Demand Forecast app.

Launchpad Tab

Demand Forecast App

Forecast Tabs Overview

The Demand Forecast page includes three tabs that provide users with access to different forecast statuses:

• All Forecasts – Displays all forecasts, including created, in-progress, and published ones.

• In Progress – Displays only the forecasts that are currently in progress.

• Published – Displays only the forecasts that have been published.

Demand Forecast Overview

Filters

To streamline the view, users can filter forecasts based on Forecast Types and Datasets by selecting the Filter button available on the Demand Forecast page.

Filters

Under Forecast Type, the following options are available:

• All Forecast Types – Displays all forecasts in the system, regardless of how they were created.

• AI Model Forecast – Displays only those forecasts that were generated using AI models.

• Manual Forecast – Displays only the forecasts that were manually created by users.

Filters

Sort

Users can organize the list of forecasts using the Sort feature, which allows sorting based on specific preferences. Forecasts can be sorted in ascending or descending order, as well as by newest first or oldest first, depending on user requirements.

Sort

Sort

Activities

The Activities section provides a summary of key timestamps, including when the forecast was created, last analyzed and last edited.

Activities

Clicking the three-dot menu opens a dropdown with two options: Edit and Delete.

Activity

Selecting Edit allows users to modify the existing forecast.

Edit

Selecting Delete prompts a confirmation popup. Click Delete in the popup to permanently remove the forecast.

Delete

Delete

Create New Scenario

To create a new forecast, users can click the + Create New Scenario button on the Demand Forecast page. Before generating the forecast, the application analyzes the input data. During scenario creation, it gathers user preferences and configures the necessary data setup to enable accurate forecasting.

Create New Scenario

The forecast creation process consists of four key stages:

• Preferences

• Data

• Analyze Data

• Forecast Data

NOTE
The Help feature available on each page provides users with a guided overview of the screen. It explains the purpose of the page, the expected outcomes, and the actions users can take.

Demand Forecast Help

Demand Forecast

Preferences

The Preferences stage defines the foundational settings that guide how a forecast is generated and interpreted. It ensures the forecast aligns with business goals by setting horizons and units, standardizes methodology through classifications and statistical parameters, and improves reliability by addressing data quality issues such as outliers or missing values. By configuring these options up front, users create a structured base that makes the subsequent stages—Data Setup, Analyze Data, and Forecast Data—more accurate, consistent, and relevant to decision-making.

The Preferences stage includes four steps to configure the foundational settings for a new forecast:

• General Settings

• Classifications

• Statistics

• Data Quality

Preferences

General Settings

In this step, users are required to provide essential details required to setup a new configuration.

Field	Description
Forecast Name	Users must provide a unique name for the forecast scenario, which will serve as the reference table name for the forecast output. The Forecast Name field supports a maximum of 20 characters.
Description	Provide a clear and meaningful description for your configuration to help identify its purpose and facilitate future reference.
Forecast Horizon	Specify the number of months to forecast.
Period Unit	Select the forecast granularity such as Monthly, Daily, or Yearly.
Forecast Period	Users can select the desired forecast period while creating a new forecast configuration. This option is available only at the time of creation and will be disabled when editing an existing configuration.
Model Type	Users can choose the type of forecasting model required. The available options are Single Model and Multi Model. Multi Model is available only for premium users.
Choose Model	Choose the forecasting model to apply. At present, SARIMAX (Seasonal Autoregressive Integrated Moving Average with exogenous factors) is the default and the only available option.

General Settings

Classifications

In the Classifications step, users can segment and categorize items based on specific thresholds and demand patterns to refine their forecasting strategies. The app automatically categorizes items into three classes—A, B, or C—based on how much each item contributes to a specific metric (such as revenue, sales volume, or demand), when all items are considered together.

The available classification methods include:

• ABC Classification

• XYZ Classification

ABC Classification

ABC classification groups items based on how much they contribute to a selected metric, such as sales or revenue.

• A-class items are the top contributors and together make up approximately the first 80 percent of the total value.

• B-class items contribute the next 20 percent, bringing the total to around 100 percent.

• C-class items include any remaining items beyond the A and B groups.

NOTE
The HelpThese classifications are based on the ranking of items by their contribution, not on the total number of items. In most cases, items fall into A and B categories, and C-class may not always include additional items.

XYZ Classification

XYZ classification helps group items based on how much their demand changes over time. This is measured using a statistical value called the coefficient of variation.

• X-class items have low demand variation, meaning their demand is stable and predictable.

• Y-class items show moderate changes in demand.

• Z-class items have high demand variation, making them more unpredictable.

The app uses predefined threshold values to automatically assign items to these categories. This helps improve the accuracy of forecasts by identifying which items are stable and which are more variable. Users can define thresholds for each classification as needed. Additional options such as thresholds for runner, repeater, and stranger categories, as well as classifications based on recency, sparsity, and others, are currently unavailable and will be introduced in future updates.

Classifications

Statistics

The Statistics step allows users to fine-tune their forecasting models by adjusting key parameters:

Confidence Level: The confidence interval indicates the level of certainty in the forecast results. By default, the confidence level is set to 95%, meaning the actual demand is expected to fall within the forecasted range 95% of the time. This helps assess the reliability of the forecast.

Seasonality Detection: Seasonality detection identifies recurring patterns in the data, such as monthly spikes or seasonal dips. A Medium sensitivity level is applied by default to maintain a balanced approach—capturing meaningful trends without reacting to short-term noise.

Trend Analysis: Trend analysis reveals the long-term direction of demand, such as whether it is increasing, decreasing, or stable. This is done using linear regression applied over a default time window of 12 months. The analysis window can be adjusted to define how many months should be included in the trend evaluation.

Statistics

Data Quality

The Data Quality step enables users to manually configure how data issues should be addressed. Users can choose to enable or disable the following rules based on their specific forecasting requirements:

• Outlier Imputation – Allows users to handle abnormal or extreme values in the dataset that could impact forecast accuracy.

• Missing Value Strategy - Enables users to define the missing entries in the dataset.

These options are enabled by default. The default strategy applied is Rolling Statistical Mean, which uses a moving average technique to fill or adjust data values, ensuring the dataset is prepared for consistent and accurate forecasting.

Data Quality

After completing the above steps, users must click the Save Configuration button. This action will navigate them to the Data Setup page.

Save Configurations

Data

The Data stage requires users to select the dataset and specific columns that will be used to generate the forecast. To meet the minimum data validation criteria, users must select one dimension column, one metric column and one date column.

To begin:

Step 1: Click the Explore button.

Explore

Step 2: From the dataset dropdown, choose the desired dataset.

Dataset Selection

Step 3: Select the relevant Subject Area.

Subject Area

Step 4: Drag and drop the appropriate dimension, metric and date columns into the designated sections.

The Available column Sections:

Dimension columns for forecast generation: Used to generate forecasts at the lowest level (e.g., forecast demand for each SKU).

Dimension columns for forecast roll-up (optional): Groups forecasts into higher-level summaries (e.g., SKU → Category → Region).

Metric to use in forecast: The numeric value the model will use to generate demand predictions.

Metric to use in ABC classification (optional): Used for classifying items into A (high value), B (medium value), or C (low value) categories, which helps in prioritization.

Metric to use in roll-up: Defines which metric should be summed when aggregating forecasts from lower to higher levels.

Date Column: Ensures the forecast is tied to a time dimension (day, week, month).

Filter Data (optional): Optional filters to limit the dataset (e.g., Region = North America, Product Type = Electronics).

Step 5: Once the selections are complete, click Done.

Choosing columns

NOTE
If the selected columns meet the validation rules, a checkmark will appear next to each criterion, confirming the requirement has been satisfied.

Rules Validation

The Selected Columns section will display the currently chosen dimension, metric and date columns. If changes are needed, click Reset to clear the selections, then click Explore again to reselect the desired columns.

Columns Reset

After completing the Data Setup, users have two options:

• Click Save Data Setup to save progress and return later to continue the forecasting process.

Save Data Setup

• Click Start Analyzing to proceed to the next step. Choosing this option will open a windows asking whether to Save and Start Analysis or Cancel. Click on the Save and Start Analysis button to go to the Analyze Data stage.

Start Analysis

Start Analysis

Analyze Data

In the Analyze Data step, users can thoroughly review and prepare their data before running the forecast. This stage provides the flexibility to classify data, select specific SKUs for forecasting and either input forecast values manually or reuse values from previous forecasts.

To streamline navigation, users can search for specific dimension columns using the Search bar.

Search Dimension

If there are updates to the dataset, clicking the Refresh Data button will reload the most recent data.

Refresh Data

The Analyze Data page also displays key summary metrics, including:

Total SKUs: The total number of SKUs available for forecasting

Segments: Groups of data based on dimension columns (e.g., forecasts per SKU).

• Segment A: Represents high-priority or high-value items, typically the top 10–20% of SKUs.

• Segment B: Indicates medium-priority or moderate-value items, generally the next 20–30% after Segment A.

• Segment C: Covers low-priority or low-value items, usually the bottom 50–70% of SKUs.

Forecastability Score (1–5) – Indicates how predictable the dataset is.

• 1 = Low predictability (erratic data).

• 5 = High predictability (stable patterns).

Execution Category: Defines how forecasts will be executed:

• Model Run: Displays the forecasting model applied to the selected SKUs.

• Manual: Indicates the number of SKUs with manually entered forecast values.

• Previous Forecast: Represents the number of SKUs where forecast inputs were taken from previously generated forecasts.

Detailed Data View

Users are presented with a comprehensive table view of their dataset, where each row is analyzed and classified using various statistical and forecasting techniques.

The table includes the following columns for in-depth analysis:

Column Name	Description
Dimension Name	This is the name of the item or segment being forecasted.
Segment	Helps group items into priority segments (like A, B, or C) so customers can focus efforts on what matters most.
Model Suggestion	AI recommends the best-fit models based on data patterns like trend, seasonality, and variability. Each comes with a short definition and ideal use case to guide selection.
Selected Model	The model chosen to generate the final forecast. Athena AI auto-selects the best option, but users can override if needed.
Suggested Action	The Suggested Action column tells you what action to take such as including in the model run, excluding, or using the previous forecast based on the latest data behaviour. The suggested action is determined dynamically each time you run the analysis, according to predefined thresholds.
Applied Action	Shows what action was actually used for this item in the forecast run: manual adjustment, model inclusion, etc. On the first run, the applied action will match the suggested action. If you rerun the analysis, the applied action preserves the previously saved value (ensuring consistency), while the suggested action may change if your data has changed.

Forecasting Model Definitions

This list defines each of the available forecasting models, outlining their core function and ideal use case.

Model Name	High-Level Definition & Best Use Case
Naive	The simplest baseline. The forecast for the next period is simply the value of the last observed period. Best for: Erratic, random-walk data where complex models would fail.
Seasonal Naive	A powerful seasonal baseline. The forecast for next July is the actual value from last July. Best for: Data with very strong, stable, and predictable seasonality.
Simple Moving Average	The forecast is the average of the last ‘n’ data points. Best for: Smoothing out noise in data that has no clear trend or seasonality.
Simple Exponential Smoothing	A weighted average of past observations, where more weight is given to recent data. Best for: A more intelligent way to smooth noisy, non-patterned data.
Seasonal Exponential Smoothing	A versatile and robust model (also known as Holt-Winters) that captures the three core components of a time series: level, trend, and seasonality. Best for: A wide range of general forecasting tasks.
AutoARIMA	A sophisticated statistical model that automatically discovers complex patterns like trends, seasonality, and autocorrelation. Best for: A powerful, general-purpose model for a wide variety of time series.
Croston Optimized	A specialist model designed explicitly for intermittent demand. It separately forecasts the size of a sale and the time between sales. Best for: Any SKU with sparse, intermittent demand (low ADI score).
Simple AR model (Linear Regression)	Uses past values as predictors in a linear model to forecast the next value. Best for: Capturing simple linear trends and basic autocorrelation quickly.
Prophet	A forecasting model from Facebook designed to be robust and easy to use. Best for: Business time series with strong, human-scale seasonalities and holiday effects.
NBEATS (AUTO)	A deep learning model that can learn extremely complex, non-linear patterns without manual feature engineering. Best for: A powerful, state-of-the-art generalist.
AutoARIMA + Seasonal Naive (Ensemble)	A hybrid approach that averages the forecasts from a complex and a simple model. Best for: Improving the accuracy and stability of forecasts for strongly seasonal SKUs.

Available Models

Model Selection Rules

This table provides the definitive logic for selecting the top three forecasting models for an SKU. The rules should be processed in order of priority; the first rule an SKU matches is the one that should be used.

Priority	Pattern Name & Condition	Top 3 Recommended Models	Reasoning
1	Intermittent ADI Score <= 2 OR CV Score <= 1	1. Croston Optimized 2. Naive 3. NBEATS (AUTO)	Croston is built for this. Naive is a safe fallback. NBEATS works if enough data is available.
2	Strong Trend + Strong Seasonality Trend Score >= 4 AND Seasonality Score >= 4	1. AutoARIMA + Seasonal Naive (Ensemble) 2. Prophet 3. NBEATS (AUTO)	Ensemble is robust. Prophet and NBEATS excel with complex patterns.
3	Strongly Seasonal (Only) Seasonality Score >= 4 AND Trend Score <= 2	1. Seasonal Naive 2. Seasonal Exponential Smoothing 3. Prophet	Seasonal Naive is unbeatable for strong, stable seasonality. Seasonal Exponential Smoothing and Prophet are strong alternatives.
4	Strongly Trended (Only) Trend Score >= 4 AND Seasonality Score <= 2	1. AutoARIMA 2. Seasonal Exponential Smoothing 3. Simple AR (Linear Regression)	AutoARIMA captures complex trends. Seasonal Exponential Smoothing handles trends. Simple AR is fast for linear trends.
5	Smooth & Predictable (Low Signal) CV = 5, ADI = 5, Low Trend/Season	1. AutoARIMA 2. Simple Exponential Smoothing 3. NBEATS (AUTO)	For stable data: AutoARIMA finds subtle patterns. SES is reliable. NBEATS learns stable profiles.
6	Stable but Noisy (Moderate Signal) CV IN (3, 4), ADI = 5, Low Trend/Season	1. Simple Exponential Smoothing 2. Simple Moving Average 3. Seasonal Exponential Smoothing	SES and SMA smooth moderate noise. Seasonal Exponential Smoothing offers robust smoothing.
7	Erratic (Continuous but Highly Variable) CV <= 2, ADI = 5, Low Trend/Season	1. Naive 2. Simple Moving Average 3. Simple Exponential Smoothing	For noisy data: Naive is safest. SMA and SES help smooth randomness.
8	Unforecastable / Low Signal (Any SKU not meeting criteria above)	1. Naive 2. (Do Not Forecast) 3. Simple Moving Average	No clear pattern: Naive provides a stable baseline. Often best to not forecast and rely on policy. SMA is a low-risk alternative.

SKU Forecastability Scoring Framework: Key Performance Indicators (KPIs)

1. Coefficient of Variation (CV)

CV = σ / μ

Measures relative variability. Lower CV indicates more stable demand.

• CV < 0.3: Very stable (Score: 5)

• CV 0.3-0.5: Stable (Score: 4)

• CV 0.5-0.7: Moderate (Score: 3)

• CV 0.7-1.0: Variable (Score: 2)

• CV > 1.0: Highly variable (Score: 1)

2. Intermittency (ADI)

ADI = Avg interval between demands

Average Demand Interval measures sparsity of demand.

• ADI = 1.0: Continuous (Score: 5)

• ADI 1.0-1.3: Near continuous (Score: 4)

• ADI 1.3-1.7: Regular (Score: 3)

• ADI 1.7-2.5: Intermittent (Score: 2)

• ADI > 2.5: Highly intermittent (Score: 1)

3. Trend Strength

R² of linear regression

Measures how well a linear trend fits the data.

• R² > 0.7: Strong trend (Score: 5)

• R² 0.5-0.7: Moderate trend (Score: 4)

• R² 0.3-0.5: Weak trend (Score: 3)

• R² 0.1-0.3: Very weak (Score: 2)

• R² < 0.1: No trend (Score: 1)

4. Seasonality Strength

Seasonal Index Range

Difference between max and min seasonal indices.

• Range < 0.3: Weak (Score: 5)

• Range 0.3-0.5: Mild (Score: 4)

• Range 0.5-0.8: Moderate (Score: 3)

• Range 0.8-1.2: Strong (Score: 2)

• Range > 1.2: Very strong (Score: 1)

5. Recency

Months since last sale

How recently the SKU had sales activity.

• 0-1 months: Very recent (Score: 5)

• 2-3 months: Recent (Score: 4)

• 4-6 months: Moderate (Score: 3)

• 7-12 months: Old (Score: 2)

• Greater than 12 months: Very old (Score: 1)

6. Volume/Frequency

Avg monthly units sold

Average sales volume indicates forecast importance.

• Greater than 1000 units: High volume (Score: 5)

• 500-1000: Good volume (Score: 4)

• 100-500: Moderate (Score: 3)

• 20-100: Low volume (Score: 2)

• < 20: Very low (Score: 1)

Detailed Element Overview

When a user clicks on an element, a detailed view is displayed. This view contains multiple sections:

• Overview

• Statistics (Stats)

• Outliers

• Analysis

Overview

The Overview section provides key forecastability measures, visualizations, and reasoning from Athena AI.

Metrics

Athena Forecast Score

• Coefficient of Variance: A percentage-based measure of variability, helping spot volatile vs. stable items.

• ADI (Intermittency): Measures the sparsity of demand. It answers the question: “How often does this item sell?” A low ADI means the item sells frequently and consistently.

• Trend Strength: Measures the direction of demand over time. It answers the question: “Is there a consistent, long-term growth or decline in sales?”

• Seasonality Strength: Measures the cyclicality of demand. It answers the question: “Does demand have predictable peaks and troughs that repeat over a set period?”

Overview

Forecast Visualization

• Forecast Line Chart: A historical line plot that displays the progression of data points over time, showing how values have changed across a historical period. It helps in visualizing trends, patterns, and shifts in the data, making it easier to compare past behaviors and understand long-term movements.

Athena AI Reasoning

• Provides an explanation of why a particular model is best suited by analyzing the statistical properties of the historical data.

• Considers factors such as variability, seasonality, trend, and patterns in the dataset to justify the model selection.

• This reasoning provides transparency into the decision-making process, helping users understand how data characteristics influence model suitability.

Overview

Statistics (Stats)

The Stats section provides descriptive statistical measures of the forecast.

• Mean: Gives the average expected demand over the forecast period.

• Standard Deviation: Measures how spread out the forecast values are, which affects planning confidence.

• Lower Bound: Sets the minimum acceptable range for forecast data; values below this may be anomalies.

• Upper Bound: Sets the maximum acceptable range for forecast data; values above this may be anomalies.

• First Quartile (Q1): Shows the lower end of typical demand range, useful for identifying underperforming periods.

• Third Quartile (Q3): Shows the higher end of typical demand range, useful for identifying peak performance.

Statistics

Outliers

The Outliers section highlights deviations from expected demand patterns.

Metrics

• Q1 (25th Percentile): Represents the point below which 25% of the forecast values fall; highlights the lower range of demand.

• Q3 (75th Percentile): Represents the point below which 75% of the forecast values fall; highlights the higher range of demand.

• Interquartile Range (IQR): The difference between Q3 and Q1; shows the middle spread of data and helps identify variability.

• Lower Bound: The minimum threshold for expected forecast values; values below this may indicate anomalies or outliers.

• Upper Bound: The maximum threshold for expected forecast values; values above this may indicate anomalies or outliers.

Athena AI Reasoning

• Explains outlier detection and its significance based on dataset properties.

Visualization

• Outlier Line Plot: Highlights data points in a time series that deviate significantly from the expected pattern. The underlying line shows the original data, while outliers are distinctly marked to make anomalies easy to spot. This visualization helps in quickly identifying unusual spikes, drops, or irregular behavior in the series.

Outlier

Outlier

Analysis

The Analysis section provides advanced diagnostic graphs to understand demand behavior.

• Autocorrelation Function (ACF) Plot: Shows the correlation between a time series and its past values across different lags. Useful for identifying repeating patterns, seasonality, and how strongly current values depend on previous observations.

• Partial Autocorrelation Function (PACF) Plot: Displays the direct correlation between a time series and its lagged values after accounting for shorter lags. Highlights the independent influence of each lag.

• Seasonal Decomposition of Time Series (SDL) Plot: Separates the series into trend, seasonality, and residual components.

  • Trend: Captures long-term movement.

  • Seasonality: Reflects repeating short-term cycles.

  • Residuals: Shows random variation not explained by the other components.

• Observed Graph: Displays raw historical demand.

• Trend Graph: Shows long-term upward or downward movements.

• Seasonal Graph: Captures repeating demand cycles.

• Residual Graph: Highlights random noise or unexplained variability.

Analysis

Forecast Inclusion / Exclusion Rules

The Demand Forecast app automatically evaluates each item or segment against a set of predefined conditions to decide whether it should be included in, excluded from, or carried over in the forecast. These rules are applied dynamically during each analysis run, based on the latest data and configured thresholds.

Inclusion Criteria

An item or segment is included in the forecast when it does not meet any exclusion conditions. In such cases, the action field is marked as Included.

Exclusion Criteria

An item or segment is excluded from the forecast when it meets any of the following conditions:

Outlier Instances greater than or equal to seven Represents the number of periods in which the product’s demand deviates significantly from the expected range. A threshold of seven is applied to filter out products with excessive and unpredictable demand variations.

Zero Instances greater than or equal to six Represents the number of periods in which the product recorded zero sales or demand. A threshold of six indicates that the product is considered inactive if it has recorded zero demand for at least five periods in the observed timeframe.

Negative Instances greater than or equal to six Represents the number of periods with negative sales values, often caused by returns or adjustments. A threshold of six is applied to prevent erroneous forecasting for products with excessive returns.

Frequency less than six Represents how often a product was purchased or ordered within the observed timeframe. A value below six indicates irregular demand, making the product less reliable for forecasting.

Recency greater than or equal to five Represents the number of periods since the product was last sold. A value of five or more indicates low recent demand, suggesting the product is outdated for forecasting purposes.

Bulk Actions

The Bulk Actions feature enables users to apply forecast-related actions to multiple dimension values at once, streamlining the data preparation process. This feature becomes visible only when a dimension name or multiple dimension names are selected.

Users can choose from the following bulk options for the selected data:

• Include in Model Run – Marks the selected items to be included in the forecasting model.

• Input Manual Numbers – Allows users to manually enter forecast values for the selected items.

• Use Previous Forecast – Uses values from a prior forecast for the selected items.

• Selected Model- Displays the model you have chosen to generate the forecast

Bulk Actions

NOTE
The Use Previous Forecast option is disabled for scenarios that are being created for the first time, as no historical forecast data is available.

Additionally, users can apply a growth adjustment to the selected items by clicking Apply Growth Factor, entering the desired percentage and then clicking Apply to confirm the changes.

Growth Factor

If any changes need to be reverted, the Undo Actions button allows users to undo all applied bulk actions instantly.

Undo Actions

NOTE
To run the forecast, the Applied Action must be set to Included in Model Run for at least two SKUs.

After making the necessary changes during the analysis process, users can click the Save Analysis button to save their progress. This ensures that all modifications made to the data and applied actions are retained.

Save Analysis

Once the analysis is saved, click on the Run Forecast button to initiate the forecasting process based on the current data setup and applied actions.

Run Forecast

Forecast Data

The Forecast Data page presents forecasted values in a detailed table format, enabling users to review projections at a granular level. Users can utilize the Search bar to locate specific dimension columns quickly and efficiently.

Forecast Page

Adjust Forecast

To make changes to forecasted values, users can click the Adjust Forecast button. This functionality allows for manual modifications tailored to specific business requirements.

Adjust Forecast

The forecast table includes the following

Column Name	Description
Dimension Name	This is the name of the item or segment being forecasted.
Date	This represents the period associated with each forecast entry.
Prior Approved	Displays the forecast value that was approved or published earlier for the specified item and period.
Suggested	Represents the values currently recommended by the forecasting model for the specified dimension and date.
Change	Displays the difference between the model’s suggested value and the previously approved forecast.
Actual Value	The true historical values observed for that period and dimension.
Forecast	The final forecast value for this entry. This column is editable, allowing users to review or override model suggestions before publishing.

Upon clicking Adjust Forecast, users can select any cell in the table to enter updated values. When a cell is selected, a Growth Factor section also appears, allowing users to apply growth factors directly to the forecasted data.

Forecast Adjustment

Once the adjustments are complete, click Save Changes to apply and retain the modifications.

Save Changes

NOTE
All manual inputs and adjustments made to the forecast will persist and carry over when using the Use Previous Forecast option. This streamlines the forecasting process by eliminating the need to recreate forecasts from scratch.

Columns

Users can customize the table view using three options: Forecast Values, Forecast Volume, and System Columns. To apply these filters, click the Columns button and choose the desired options from the dropdown.

• Forecast Values: Displays the forecasted unit value of an item or segment when enabled.

Forecast Values

• Forecast Volume: Displays the forecasted volume of an item or segment when enabled.

Forecast Volume

• System Columns: Displays system details such as forecast creation date, creator, last updated date, last updated by, and the forecast period when enabled.

Forecast Volume

Export

Click the Export button to export the forecasted data in CSV format. This allows for offline review or further analysis.

Export

Import

Users can upload a CSV file using the Import CSV button. This is useful for two scenarios:

• Uploading a custom CSV file to run the forecast

• Making changes to the downloaded CSV file and re-uploading the updated version

Import

After adjusting values manually or importing the updated CSV, users must click Save Changes to apply the updates and then click the Go Back button if no further changes need to be done.

Demand Forecast

NOTE
The download and import CSV functionality are available exclusively for manual forecasted values. You cannot overwrite model-generated (automated) predictions directly using this feature—only those values marked for manual forecasting can be updated in this way.

Publish

The Publish button enables users to make the created forecast available across the organization. Once published, all users within the organization can view and edit the forecast as needed.

Publish Forecast

Upon clicking the Publish button, a confirmation popup will appear. To proceed, click Publish Forecast to finalize and share the forecast.

Demand Forecast

Forecast Published