In today’s fast-paced business environment, efficient demand planning is essential to ensuring smooth operations and maximizing profitability. With the introduction of demand driver-based forecasting in AWS Supply Chain Demand Planning, businesses can now take their demand planning to the next level by incorporating external factors that drive customer demand into their forecasting models. This guide will provide an in-depth look at how demand driver-based forecasting works in AWS Supply Chain Demand Planning, along with best practices for optimizing your demand planning process.
What is Demand Driver-Based Forecasting?¶
Demand driver-based forecasting is a method of forecasting customer demand by considering external factors that influence demand, such as price changes, promotional events, and other market dynamics. By integrating these demand drivers into the forecasting process, businesses can make more accurate predictions of future demand and make informed decisions about inventory levels, production schedules, and pricing strategies.
In AWS Supply Chain Demand Planning, demand planners have the ability to input historical and/or future demand drivers directly into the AWS Supply Chain Data Lake. This data can be managed at any level of granularity, allowing users to configure reasonable defaults for partial data in the demand drivers. The machine learning algorithms in AWS Supply Chain Demand Planning then refine the demand forecast by correlating historical trends with these demand drivers, resulting in more accurate and actionable forecasts.
Benefits of Demand Driver-Based Forecasting¶
Demand driver-based forecasting offers several key benefits for businesses looking to improve their demand planning processes:
- Increased Accuracy: By incorporating external demand drivers into the forecasting model, businesses can make more accurate predictions of future demand and reduce the risk of overstocking or stockouts.
- Better Decision-Making: With more accurate demand forecasts, businesses can make more informed decisions about inventory management, production scheduling, and pricing strategies.
- Improved Customer Service: By accurately predicting demand and ensuring the right products are available when customers need them, businesses can improve customer satisfaction and loyalty.
- Cost Savings: By optimizing inventory levels and production schedules based on demand driver-based forecasts, businesses can reduce inventory holding costs and minimize waste.
How Demand Driver-Based Forecasting Works in AWS Supply Chain Demand Planning¶
Step 1: Input Demand Drivers¶
Demand planners can input historical and/or future demand drivers into the AWS Supply Chain Data Lake, such as price changes, promotional events, and other market dynamics. These demand drivers can be managed at any level of granularity, allowing users to configure defaults for partial data.
Step 2: Machine Learning Algorithms Refine the Forecast¶
The machine learning algorithms in AWS Supply Chain Demand Planning refine the demand forecast by correlating historical trends with the input demand drivers. By analyzing these correlations, the algorithms can generate more accurate and actionable forecasts.
Step 3: Review and Adjust Forecasts¶
Once the demand forecasts are generated, demand planners can review and adjust them based on their domain knowledge and expertise. By incorporating additional insights and adjusting for any anomalies, businesses can further improve the accuracy of their demand forecasts.
Best Practices for Demand Driver-Based Forecasting in AWS Supply Chain Demand Planning¶
To make the most of demand driver-based forecasting in AWS Supply Chain Demand Planning, consider the following best practices:
- Collect and Input Relevant Demand Drivers: Ensure that you are collecting and inputting relevant demand drivers that have a significant impact on customer demand, such as price changes, promotional events, and seasonal trends.
- Monitor and Evaluate Forecast Accuracy: Regularly monitor and evaluate the accuracy of your demand forecasts to identify areas for improvement and adjust your forecasting process accordingly.
- Collaborate Across Teams: Foster collaboration between demand planners, data analysts, and other stakeholders to leverage their unique perspectives and expertise in refining demand forecasts.
- Automate Routine Tasks: Use automation tools within AWS Supply Chain Demand Planning to streamline routine tasks and free up time for demand planners to focus on strategic decision-making.
- Stay Up-to-Date on Market Trends: Stay informed about market trends and changes in customer behavior that could impact demand, and adjust your demand drivers accordingly.
By following these best practices and leveraging the capabilities of AWS Supply Chain Demand Planning with demand driver-based forecasting, businesses can improve the accuracy of their demand forecasts, optimize their inventory management processes, and make better-informed decisions that drive profitability and growth.
Conclusion¶
Demand driver-based forecasting in AWS Supply Chain Demand Planning offers businesses a powerful tool for improving their demand planning processes and making more accurate predictions of future demand. By incorporating external demand drivers into the forecasting model, businesses can optimize their inventory management, production scheduling, and pricing strategies to maximize profitability and customer satisfaction. By following the best practices outlined in this guide and leveraging the capabilities of AWS Supply Chain Demand Planning, businesses can gain a competitive edge in today’s dynamic market environment.
In conclusion, AWS Supply Chain Demand Planning with Demand Driver-Based Forecasting revolutionizes the way businesses approach demand planning by incorporating external factors that drive customer demand into their forecasting models. By following best practices and leveraging the capabilities of AWS Supply Chain Demand Planning, businesses can make more accurate and actionable demand forecasts, optimize their inventory management processes, and make better-informed decisions that drive profitability and growth.