# Demand Pattern and Recommendation
Demand Pattern and Recommendation examines the transformed historical demand input at each configured forecast granularity level (for example, product, location, or channel) to uncover underlying patterns and characteristics in your demand data. Its primary purpose is to identify key demand pattern distribution, such as smooth, intermittent, erratic, and lumpy. It also provides statistical insights about length of history and trailing 12-month demand.
The analysis automatically triggers after successful data validation during the forecast generation process, and runs in parallel with forecast creation. However, it does not block or delay the forecasting process. The Demand Pattern analysis is triggered as part of the same workflow as data validation when you initiate forecast creation. However, any data validation failure prevents both the analysis from being generated and the forecast from being created.
By providing this analytical overview, the system helps users understand the patterns in the dataset to improve forecast accuracy.
# Demand Patterns Components
Demand Patterns analysis happens on three dimensions:
+ Demand Patterns (based on how demand changes over time and in quantity)
+ Annual Demand (total quantity demanded over a 12-month period)
+ History Length (the time period for which historical demand data is available)
The analysis categorizes your demand patterns into four distinct types: smooth, intermittent, erratic, and lumpy. Each is determined by analyzing the frequency and variability of demand. If there are eligible in-scope products with no historical data, it is grouped under the **Zero Forecast Demand** section. For more information, see [Demand pattern](https://docs.aws.amazon.com/aws-supply-chain/latest/userguide/overview_dp.html#demand-pattern).
The distribution of demand patterns across your products provides valuable insights into expected forecast reliability. Products with smooth demand patterns (showing consistent order volumes and frequencies) typically yield the most reliable forecasts, because their behavior is more predictable. In contrast, erratic or lumpy patterns, characterized by irregular spikes and varying order frequencies, generally result in lower forecast reliability due to their unpredictable nature. By understanding this distribution, demand planners can set appropriate expectations and take proactive measures.
The system also analyzes your trailing 12-month demand (subject to trimming configuration), also known as Annual Demand, immediately preceding your forecast start date. For example, assume the forecast start date is January 15, 2024 (Monday) and the planning bucket is weekly. The system considers the trailing 12 month analysis period to be from January 16, 2023 to January 14, 2024. The trailing 12-month demand analysis helps demand planners distinguish between active and inactive products, while identifying products transitioning between these states - patterns that directly impact forecast reliability. By focusing on recent history rather than older data patterns, you can make more informed decisions about which products need special attention or alternative forecasting approaches, particularly for cases like seasonal items, discontinued products, or items in phase-out. For more information, see [Forecast Algorithms](https://docs.aws.amazon.com/aws-supply-chain/latest/userguide/forecast-algorithims.html).
The history length in years is calculated for each forecast granularity (for example, product-location combination) based on the earliest and latest dates available in your preprocessed historical demand data, after adjusting the dates to the default start of the period. This analysis helps determine if products have accumulated enough historical data to generate reliable forecasts, with a minimum of two years typically needed to capture seasonal patterns and long-term trends.
![\[Raw demand history\]](http://docs.aws.amazon.com/aws-supply-chain/latest/userguide/images/raw-demand-history.png)
# Demand Patterns Recommendations
The system provides targeted recommendations based on identified demand patterns to help improve forecast accuracy. For products displaying erratic demand, characterized by irregular spikes in order volume, the system suggests incorporating potential external influences, such as promotions or price changes. In such cases, you can significantly improve forecast accuracy by collaborating with your data administrator to upload relevant demand driver data to the [https://docs.aws.amazon.com/aws-supply-chain/latest/userguide/demand_drivers.html](https://docs.aws.amazon.com/aws-supply-chain/latest/userguide/demand_drivers.html) table in the data lake. This additional context helps the forecasting models better understand and predict demand fluctuations.
For products with insufficient history (less than 2 years) or no history at all, the system recommends leveraging alternate product mapping. This approach allows you to utilize the demand patterns of similar, established products to enhance forecast reliability. Work with your data administrator to upload these product relationships to the [https://docs.aws.amazon.com/aws-supply-chain/latest/userguide/product_lineage.html](https://docs.aws.amazon.com/aws-supply-chain/latest/userguide/product_lineage.html) table in the data lake. This is particularly important because accurate seasonality and long-term trend detection requires at least 2 full years of historical data. By mapping to alternate products with sufficient history, you can establish a more reliable forecast baseline for newer or limited-history products.
# Demand Pattern and Recommendation Report Access
## First time forecast creation
When creating a forecast for the first time, under the **Demand Planning** module in AWS Supply Chain, choose **Create a Plan**. The system guides you through three steps: Data Ingestion, Plan Configuration, and finally, Forecast Generation. After completing data ingestion and plan configuration, choose **Generate Forecast** to initiate data validation. Upon successful validation, the system performs demand pattern analysis, and you see a hyperlink to access this analysis while your forecast generates.
## Subsequent forecast creation
For subsequent forecasts, choose **Generate Forecast**. You see a banner displaying three steps: data validation, demand pattern analysis & recommendation, and forecast creation. After data validation is successful and the demand pattern analysis is complete, access the report by choosing its hyperlink in the banner.
## Report content
The Demand Pattern and Recommendations report provides a summary view of exploratory data analysis at your configured forecast level for a given plan. At the top of the screen, you see five key pattern cards that show how your products are distributed: Smooth patterns, Intermittent patterns, Erratic patterns, Lumpy patterns, and Products with Zero Historical Demand.
Below this summary, you can find a detailed table breaking down patterns by the highest configured level in product hierarchy in the Demand Plan Settings. For example, if your product hierarchy configuration follows pattern product id, product group id, then you will see the summary at the product group id. For each category, you can see the following:
+ \$1 Forecasts, indicating the unique time series are eligible for forecast and its percentage of total
+ The annual demand volume and its percentage of total
+ A visual breakdown of demand pattern within that category
+ A visual breakdown of the length of history available within that category
To help you navigate this information, you can do the following:
+ Use the search box to find specific product categories
+ Download a detailed report. The report contains detailed analysis for each individual forecast at your configured granularity level
+ Sort any product category, \$1 Forecasts, and Annual Demand to focus on specific metrics. For product categories containing alphanumeric formats or blank values, using the search function may be more effective.
## Ongoing access
After each successful forecast creation, you can revisit this analysis on the **Demand Pattern** tab in the forecast review pages. In this view, the analysis responds to any filters you apply in the forecast review. The downloaded report contains analysis specific to your filtered selection.