AI-Powered Demand-Sensing: Transforming Supply Chain Planning and Forecasting
Find out how demand-sensing technology could herald a positive shift in supply chain forecasting in this report from Kearney and AWS.
The transformative potential of AI-driven demand-sensing technology is reshaping supply chain forecasting and planning processes. This innovative approach leverages a wealth of internal supply chain and external market data to enhance the accuracy of predictions, even within the context of ongoing market volatility.
Companies have built resilience into their supply chains post-pandemic, shifting their focus from reactive adaptation to proactive strategic planning. However, the current market environment, marked by factors like omnichannel distribution, shifting consumer trends, and unexpected global events and geopolitical tensions, presents complexities for accurate forecasting.
How demand-sensing differs from conventional forecasting:
- It recognizes the need for a richer set of sourcing, production operations, shipment, order, inventory, and sales data encompassing the complexities of today’s supply chains
- Data is captured, structured, integrated, and shared in near-real time.
- External data is increasingly crucial both because of data availability and validation.
- It uses artificial intelligence (AI) and machine learning (ML), guided by human intervention, to fill in visibility gaps.
- Sensing can build precise, short-term forecasts of customer demand on a daily or even hourly basis.
Demand sensing in supply chain management
Demand sensing powered by artificial intelligence and machine learning offers real-time insights into customer behaviors and potential outcomes, effectively addressing the complexities of today's supply chains. Demand sensing is distinct from conventional forecasting in key ways: it recognizes the need for a richer set of sourcing, production operations, shipment, order, inventory, and sales data encompassing the complexities of today’s supply chains and the range of variables that can drive potential disruption. This data is captured, structured, integrated, and shared in near-real time, providing for the first time a current, transparent, dynamic view of the supply chain.
Through the integration of external data from suppliers and vendors, demand-sensing technology not only elevates the accuracy of forecasting but also encourages collaboration across the entire supply chain ecosystem. Demand sensing in a supply chain facilitates a shift from traditionally adversarial relationships among supply chain partners to more collaborative endeavors, grounded in shared objectives.
Even the highest-quality internal data is, by itself, no longer sufficient for extrapolating the future."
External data is increasingly crucial, and not only because 80 percent or more of today’s supply chain data is now generated externally, by suppliers, vendors, end users, and third parties.
It is also important for validation, since recent historical internal data is often corrupted for forecasting purposes by the impacts of COVID.When it comes to demand sensing in supply chain, history is no longer a useful indicator of the future.
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