The field of supply chain management has been evolving significantly, thanks to the advancements in technology. Today, businesses are better equipped to accurately forecast demand, streamline inventory management, and improve operational efficiency, all thanks to the power of data, artificial intelligence, and machine learning. A key player that has been enabling such transformation is Amazon Web Services, known for its innovative, robust, and scalable cloud solutions.
One of the exciting services offered by AWS is AWS Supply Chain, a highly effective platform designed to simplify, optimize, and enhance supply chain operations in businesses of all sizes. It’s a true game-changer, allowing organizations around the world to leveraging AI, machine learning, and accurate data analytics to bolster their supply chain process. AWS Supply Chain is designed to be flexible and integrated, working seamlessly with other AWS services to provide a holistic solution for advanced supply chain management.
Keeping up with the tradition of continual service improvement, AWS has released a new feature for the Supply Chain service – the Override Retention Capability. This comprehensive guide covers this new feature and explores how it can enhance demand forecasting and planning in supply chain operations.
Override Retention Capability – An Overview¶
Starting from today, manual forecast overrides that were previously done by a demand planner can be automatically saved and reapplied from one planning cycle to another. Demand planners often make changes to the system-generated baseline forecasts to account for known demand variations. These overrides, also referred to as forecast adjustments, account for fluctuations due to seasonality, promotions, or other variables that affect the demand forecast.
It is important to note that previously, these forecast overrides needed to be manually entered during each new planning cycle. However, with the introduction of the override retention feature, these overrides will now be remembered and then applied to the recalibrated system-generated baseline forecast in each new cycle. As a result, demand planners now have the ability to view and manage these forecast overrides across different planning cycles, all in one unified and seamless view – ultimately leading to a more efficient and effective planning process.
How It Enhances Demand Forecasting¶
The new override retention capability injects an exceptional level of efficiency in the demand planning process. By automatically saving and reapplying manual forecast adjustments, demand planners no longer need to invest time and effort in manually feeding these overrides in every planning cycle.
Another commendable advantage is the ability to manage and view forecast overrides in a unified interface. This not only makes the process more straightforward but also helps in identifying patterns and insights that could potentially enhance future forecasts, thus driving overall supply chain efficacy.
AWS Supply Chain and Machine Learning¶
Behind the scenes of AWS’s highly accurate forecasting model lies the power of Machine Learning (ML) algorithms – a significant part of AWS Supply Chain’s capabilities. AWS uses sophisticated ML models that learn and adapt from historical data points, giving it an edge with its forecasts accuracy.
It’s worth noting these ML algorithms are consistently refined to consider all possible variables that can impact demand. The new override retention capability simplifies this process by offering these ML models a head start in every planning cycle, as they can use prior overrides as additional data points.
Conclusion¶
The introduction of the Override Retention Capability by AWS Supply Chain is a commendable stride towards enhanced demand planning and forecasting. By automatically saving and reapplying forecast adjustments, AWS has undoubtedly simplified the role of demand planners and improved overall supply chain efficiency.
As AWS continues to refine and improve their services, we can look forward to further advancements that can help businesses tap into the power of data and artificial intelligence (AI), to optimize their supply chain processes and operations.