Amazon Supply Chain and Logistics

Improving demand planning for products with varying lifecycles with AWS Supply Chain

Product strategies are shaped by a combination of consumer demand, technology, and competition. With each variable change, the industry witnesses the introduction of new products, often accompanied by new features harnessing the latest technologies for enhanced user experience. This also initiates the phased retirement of the prior versions of the products. A prime example is the annual release cycle of smartphones, marked by the introduction of new models and the phased retirement of their predecessors.

A new product that lacks demand history presents a significant operational challenge for organizations and for their demand planners. New product introductions require precise forecasts to support new product demand, and to smoothly decrease the forecasts for the older, or legacy product. The absence of demand history requires demand planners to rely on their experience to draw parallels between new and legacy products. Such manual forecast adjustments are sub-optimal and inefficient and complicated by the challenges of fine-tuning forecast accuracy. Organizations seek automated solutions for faster and more efficient demand planning.

In this blog post, we will delve into the solution provided by AWS Supply Chain Demand Planning to tackle the challenge associated with new product introductions lacking historical sales data. We will explore both the product lineage and product lifecycle features and guide you through the essential steps to set-up your data and configurations.

Manage Lifecycle Phases

Product forecasts must align exclusively with the active lifecycle of the product to ensure forecast accuracy. Any oversight in this approach can cause critical inventory challenges, such as the accumulation of excess stock. AWS Supply Chain Demand Planning allows you to define the product lifecycle, which ensures that forecasts are only created for the product’s active lifecycle. You can establish forecast parameters for product introduction and retirement to minimize the risk of shortages for new products and overstocking for discontinued products.

To define the product lifecycle boundary for products with a phased launch profile, you can ingest Launch On (product_available_day column), Retire By (discontinue_day) dates in the product master data file. An example of sample data setup for the product master with highlighted fields is shown in the screenshot below.

Product master data setup sample

For more details on data set-up, refer to the Product Lifecycle user guide. To learn about the steps and prerequisites to transform and upload data from various legacy systems, please visit our earlier blog.

Additional configuration offering more flexibility

Next, building upon the configurations highlighted in our previous blog, you can choose to control Launch On, and Retire By values, tailoring them to suit your specific business requirements. This becomes crucial when seeking additional flexibility beyond standard launch or retirement date, especially for strategic inventory management purpose. The setup screen is shown in the following screenshot, where the forecast start and stop dates can be setup according to your product lifecycle.

Forecast configuration setup screen

Connect the dots for precise forecast

Effective demand planning requires planners to include sales history of prior models or alternate products to create an accurate forecast. With Product Lineage, you can now establish links between products and their predecessor versions or alternate products. This link incorporates rules defining the extent of history to be used for forecasting, creating a surrogate history for the product.

For products which have little to no history, you can take advantage of product_alternate entity and ingest data using the following steps.

  1. You can define the source product in alternative_product_id column whose forecasting pattern needs to be copied over to destination products specified in product_id column.
  2. The alternate_product_qty indicates the weight assigned to the historical sales of the alternate product for consideration, while the effective period indicates the duration of the history of alternate product to be considered. A sample data set-up for the product_alternate entity is shown in the following screenshot. The highlighted fields indicate the alternate product and the extent of its history to replicate for the target product.
    Additional data setup screenshot
  3. For more details on data set-up, refer to the user guide on Product Lineage.

Demand plan in action

After you have ingested data and set the forecast start and end configurations, the application generates your demand plan. On the screen, you will notice the product lifecycle phase – NPI or EOL for contextual review. If your forecast incorporates lineage history, a text indicator provides you with additional context and transparency.

Deman plan output


The Product Lineage and product lifecycle features automate crucial process, improve forecast accuracy, and reduce the need for manual adjustments. This refined approach also enhances operational efficiency, facilitating proactive supply chain management for new products.

AWS Supply Chain is available without any up-front licensing fees or long-term commitments. It provides a scalable solution that scales with your needs and Demand Planning is available to all AWS Supply Chain customers. Visit AWS Supply Chain to learn more and get started. You can also visit the AWS Workshop Studio for a self-paced technical overview of creating an instance, ingesting data, navigating the user interface, creating insights, and generating demand plans.

Vikram Balasubramanian

Vikram Balasubramanian

Vikram Balasubramanian is a Senior Solutions Architect for Supply Chain. In his role, Vikram works closely with supply chain executives to understand their goals and problem areas and align them with best practices in terms of solution. He has over 17 years of experience working with several Fortune 500 companies across different Industry verticals in the supply chain space. Vikram holds an MS in Industrial Engineering from Purdue University. Vikram is based out of North Dallas area.

Harini Kidambi

Harini Kidambi

Harini Kidambi is a Product Manager for AWS Supply Chain Demand Planning. She has over five years of experience in supply chain and analytics with both BlueYonder and Amazon Web Services (AWS). She works with AWS Supply Chain customers to help understand their business needs, align technical solutions and user experience, and deliver the greatest business value.