Demand sensing is laser focused on the short term, when it’s more important than at any other stage of forecasting to be as precise and accurate as possible. As a key complementary component of demand planning, demand sensing finds relationships in large data sets, incorporating predictive analytics to create a granular forecast. Companies rely on the results of demand sensing to enrich inventory deployment and allocation decisions to make the most effective use of inventory while satisfying demanding customers.
But both optimizing inventory and meeting customer demands is getting more and more difficult, thanks to a host of trends: the growth of omnichannel commerce, rapidly shifting consumer trends, and greater volatility and uncertainty across the globe, to name just a few.
Incorporating data from multiple internal and external sources helps tame that complexity and improve decision-making. Companies need ways to leverage larger and richer sets of sourcing, production operations, shipment, order, inventory, and sales data that they can capture, structure, integrate, and apply in near-real time.
That’s where AI comes in. AI’s ability to quickly identify patterns and discover unseen connections between demand drivers and volume is ideal to fulfill that need to rapidly identify key demand trends, enabling planners to quickly adjust planning strategies to accommodate shifts. AI eliminates noise from the signal in all that data, so it can identify non-linear, causal relationships that would otherwise go undiscovered without specific queries seeking them.
And that’s not all AI can do. The ability for forms of AI such as machine learning to learn from data in supply chain planning software drives effective demand planning, empowering strategies that modify and boost predictors for even greater strength and accuracy. Supply chain planners can also use AI to execute lookback and post-hoc analysis to identify the leading indicators of change (Which scenario was best? What data/insight did we know before we didn’t use?)
AI Demand Sensing at Work
Real-world use cases of AI demonstrate its power to transform demand sending. For example, few products are as impacted by volatility in demand as cubed ice. A change in weather, a power outage that threatens freezers full of food, a holiday that calls for lots of ice — unless it’s rained out — all mean forecasted demand can dramatically shift in ways that would be difficult to predict even two weeks ahead.
One of the world’s largest manufacturers and distributors of ice leverages AI to help. IoT sensors installed in its iceboxes monitor inventory levels in real-time. The company’s supply chain planning system analyzes that together with POS data, driver handheld data, and more every 30 minutes applying machine learning to automate demand sensing and adjust order recommendations based on that fresh visibility into what’s really happening in the market.
Machine learning helps the manufacturer compare and identify the best course of action, such as re-routing trucks, or forecast potential inventory shortages. It also powers systems to automatically re-plan replenishment-related decisions every day to optimize inventory levels across 80,000+ locations.
The Payoff of AI-Enabled Demand Sensing
AI-enabled demand sensing makes demand planning more nimble and responsive, benefiting companies in a number of ways.
Its ability to process and interpret multiple large data streams reduces data waste while enriching insights and decisions. The insights gathered through those short-term decisions go on to improve lifecycle planning and long-term forecasting. Other analyses get better, too, such as gaining insights into price elasticity by separating out the impact of pricing and promotions from regular sales.
Better demand sensing reduces out-of-stocks. Increasing the ability to predict customer purchase behavior or events in turn has a big impact on efficient use of inventory.
All of those benefits combine to create a clear competitive advantage. The ability to use leading indicators to sense changes much earlier means companies leveraging AI are better prepared to meet customer demands, and that wins revenue and loyalty.
AI-enabled demand sensing is within reach – all you need is to choose an expertly crafted supply chain planning software solution like The Atlas Planning Platform to unlock its benefits.
While AI has been a part of Atlas for a long time, innovations are always at work, and many more AI-enabled features continue to be released, bringing the power of this technology to help solve complex challenges and elevate end-to-end supply chain planning strategies. Let’s have a chat to help you discover what you can achieve with Atlas!
