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In today’s fast-paced business environment, supply chain networks are constantly influenced by both micro and macroeconomic conditions, as well as shifting customer demand patterns. Companies must enhance their efficiency and agility while proactively refining supply chain functions to boost performance and meet customer expectations. Effective supply chain management encompasses various functions that coordinate resources and business processes, with demand planning being a crucial component. This process ensures timely stock replenishment, improved capacity management, and optimal sales revenue.

In a previous article, we discussed how AWS Supply Chain enhances supply chain visibility, thereby increasing resilience. In this post, we will explore AWS Supply Chain Demand Planning, a specialized module designed for accurate demand forecasts, adaptable to market conditions, and continuously learning from evolving demand patterns and user inputs to enhance accuracy.

Understanding the Demand Planning Process

Accurate demand forecasting is vital for organizational success. Inaccurate forecasts can result in inventory discrepancies—leading to overstock or stockouts—which can increase costs, cause missed sales opportunities, and negatively impact customer satisfaction. For instance, overestimating demand can lead to excess inventory, tying up cash flow and increasing storage expenses. Conversely, underestimating demand can lead to stockouts, affecting customer experience and satisfaction, potentially causing customer loss.

Demand planning is essential for efficiently allocating resources to meet customer needs without overextending capacities. It involves systematic evaluation, forecasting, collaboration, and regular reviews, which shape supply chain strategy and operational performance. The typical demand planning process can be condensed into four main categories:

1. Data Integration: This foundational step involves gathering historical sales data, current orders, inventory levels, and other relevant metrics. Sources can include ERP systems, CRM tools, and external market intelligence reports. Integrating diverse data sources provides a comprehensive view of demand factors.
2. Forecasting: Once data is integrated, statistical models and algorithms are applied to predict future demand. The forecasting process blends empirical data with industry insights, where machine learning (ML) significantly enhances accuracy.
3. Collaboration: Demand planning requires communication across departments such as sales, marketing, finance, and operations. By integrating insights from various teams, organizations can align statistical forecasts with market intelligence, promotional strategies, and business initiatives, thereby enhancing accuracy and acceptance of the forecast.
4. Continuous Review and Adjustment: This iterative process involves comparing actual sales data against forecasts to identify variances. Analyzing these discrepancies helps refine forecasting models and adjust future predictions. Regular reviews ensure the demand plan remains relevant and reflects current market conditions and internal strategies.

Each category is interdependent: accurate data underpins forecasts; collaboration adds depth; and ongoing reviews prevent forecasts from becoming obsolete. This interconnectedness keeps the process dynamic, accurate, and aligned with market realities and business aspirations.

Benefits of AWS Supply Chain Demand Planning

AWS Supply Chain offers several advantages over traditional demand planning methods:

• Automation: Demand Planning automates numerous manual tasks, such as data entry and calculations, allowing for quicker forecast generation and minimizing errors.
• Leveraging ML: ML analyzes historical sales and real-time data (like open orders) to produce forecasts and refine models, improving accuracy. This reduces the risk of stockouts or excess inventory. AWS Supply Chain also employs ML for lead-time variability detection, enhancing supply planning accuracy.
• Efficient Collaboration: In-application collaboration tools facilitate consensus among team members, improving coordination, expediting decision-making, and reducing errors.

The next section will outline how AWS Supply Chain supports the demand planning journey, focusing on the initial three critical phases: data integration, forecasting, and collaboration. A step-by-step guide will be provided for newcomers, detailing how to effectively set up AWS Supply Chain and utilize its advanced capabilities to transform demand planning. Future articles will cover the fourth phase, continuous review and adjustment, highlighting its integration with AWS Supply Chain based on best practices.

AWS Supply Chain Demand Planning Prerequisites

To get started, you need an AWS account. If you do not have one, follow the account creation process outlined in the guide on how to create and activate a new AWS account. Additionally, you’ll need an AWS Supply Chain account. If you’re not yet a customer, visit AWS Supply Chain to learn more and begin your journey.

Setting Up Demand Planning

The first step involves ingesting data into the AWS Supply Chain data lake. This data lake utilizes ML models to understand, extract, and transform disparate data into a unified model. As an admin, populate the AWS Supply Chain Data Lake with necessary data entities for Demand Planning to generate accurate forecasts. The full list of required fields can be found in the AWS Supply Chain User Guide. For improved accuracy, ensure optional entities are included, as detailed in the guide.

Once the data is ingested, check user permissions. As an admin, you can add users to Demand Planning and manage their permissions accordingly. Invitations for new users are sent after permissions are established. At the bottom of the screen, you can select one of four role-based permission levels (Admin, Data Analyst, Inventory Manager, or Planner).

After selecting user roles, navigate to AWS Supply Chain on the left pane and click “Get Started” to launch the Demand Planning module. The subsequent step involves specifying the forecast generation timeframe, known as the planning horizon. You will enter the forecast interval and duration for which you want the application to create a forecast plan. For example, if you seek a monthly forecast for the next six months, select “Monthly” for the time interval and input “6” for the period.

The next step allows you to configure forecast granularity based on your requirements. Here, select the level of forecasting by choosing the hierarchy attributes for site, channel, and customer.

After completing these steps, you will be well on your way to enhancing your demand planning process. For more insights on creating a safe workplace, check out this informative blog post here. Additionally, for updated information on workers’ rights, refer to this authoritative source here. For career opportunities in Learning and Development, visit this excellent resource here.