Demand Sensing Solution: Enabling Data-Driven Demand Planning for CPGs

Even though the COVID pandemic’s turbulences have eased out, many, the aftershocks are still being felt by many, especially the business leaders across all industries. The one significant realization that came out during the trying times was how inefficient the traditional planning and demand forecasting methods were in handling such unprecedented complexities. Many CG companies were stunned by the panic buying trend following the sudden announcement of the complete lockdown, and many shelves remained unstocked for days. That left buyers with no essential items. Disruptions were felt across the length and breadth of the global supply chain network, compelling companies to hunt down more efficient, technology-backed demand-sensing solutions to stay future-ready. 

An AI-powered TradeEdge Demand Sensing Solution leverages the capabilities of Machine Learning to help supply chain teams keep up with the shorter product life cycles, frequent changes in customer buying behaviors, and other complexities arising from a volatile business environment. 

Evaluating the role of demand sensing on the supply chain 

Traditional demand forecasting methods are increasingly falling short of filling the gap between planning and actual goods shipments. Hence, a cascading effect impacting the supply chain, both upstream and downstream, is expected. Industries, especially consumer packaged goods, handling perishable items or products with little shelf life, need real-time visibility into what is happening down in the supply chain to avoid excess stockpiling of expired items in their warehouses. They need an intelligent solution to optimize their existing inventories fully and, at the same time, stay market-ready.  

Hence, real-time visibility is one of the critical functional areas of cognitive demand sensing solutions, building supply chain resiliency at scale through: – 

Optimizing inventories: As mentioned earlier, data-driven, accurate demand forecasting using an AI demand-sensing solution can easily project optimum inventory levels. This will guide businesses towards safety stock and minimal inventory obsolescence, eventually transcending into increased revenue, profitability, and working capital. 

Improving product mix: Without intelligent solutions mapping customer behavior and sentiment towards product variants, improving product mix would be impossible. When demand sensing is used to map and predict customer preferences at a granular level, businesses can effectively improve product mix offerings and enhance customer satisfaction. 

Informed decision-making: Customers expect on-time delivery of items in the correct quantity and quality. To keep up, companies require accurate empirical data in real-time to make crucial decisions at the right time to improve customer satisfaction, better pricing and increase brand loyalty. Traditional demand forecasting methods considering historical data fall short of predicting short-term market demand. And, since product demand is rapidly changing, a comprehensive and intelligent demand-sensing tool has become the need of the hour. 

Factoring in consumer sentiments: Manually tracking large volumes of data and analyzing them might not eventually reflect accurate outcomes. That’s because manual methods are subjected to mindless errors, colored in bias, and omit granular details. Therefore, projecting future demand using such flawed methods will only generate half-baked outcomes, not good enough to match supply with customer preferences and sentiments. Contrarily, cognitive demand sensing allows automated data collection and processing while ML and algorithms capture granular data and present them as they are, minus human errors and individual bias. 

What is a demand sensing solution, and why is it critical for advanced forecasting? 

A demand sensing solution leverages the latest technologies like Artificial Intelligence, Machine Learning, and real-time data to capture what’s trending in the market presently and create forecasting of demand based on the current realities in a shorter time frame. 

Backed by real-time insights on internal and external influences, demand sensing adds a new level of sophistication to identify trends and patterns in demand patterns sooner than they had already occurred. And when we refer to internal and external factors, we imply weather patterns, socioeconomic factors, competitor activity, geographic data, internal sales data, social media, point-of-sale data, and more – each impacting the demand-supply for businesses at a granular level. These factors send signals that demand sensing tools capture on time and integrate into their planning and projecting process to handle demand volatility. These tools pick up sudden changes in the wind direction sooner than the weather forecast and aptly predict what is likely to rule the market in the coming days from sources closer to the end buyers. Hence, CPG and other companies are always ready with enough supply to respond to demand changes in time. 

 Any short-term fluctuations are easily captured in demand sensing radar aforehand, providing the supply chain with enough bandwidth to adjust their longer-term forecasts and take appropriate action. 

How ML-powered demand sensing solution helps CPGs manage disruptions 

The pandemic disruptions have nullified legacy forecasting methods. Even though the past can predict the future and trends repeat themselves, the new normal presented an alternate reality altogether. Hence, relying on past patterns only will keep CPGs half-blind to sudden changes in the near future. On the other hand, Machine Learning, a unique AI capability, automatically learns and improves by itself. Its algorithms discover patterns in data and construct mathematical models to predict future data, continuously learning and adjusting outputs by themselves without human help. This gradually improves its predicting accuracy over time. A demand-sensing software solution uses ML capabilities to generate more complete estimations without considering human guesses and biased ideas. Hence, there’s no room for error when predicting demands. More importantly, ML can analyze hundreds of thousands of combinations at any given time or evaluate many different models to see and improve forecasting results. It keeps CPGs, and other companies in constant tune with their supply chain, anticipates disruptions ahead of time, and takes appropriate actions. 

A demand-sensing solution is the need of the hour for CPG companies and other businesses striving hard to stay competitive and engage customers with a responsive, on-time supply of goods aligning with current demand. Arguably, the world, as we knew earlier, has changed post-pandemic, and it is only fitting that businesses find better tech-based solutions to immune themselves from such disruptions in the coming days. AI and ML-enabled demand sensing is the best bet for all.