Wednesday, January 13, 2021

Demand Sensing - A Game-Changer for Forecast Accuracy

In a hypercompetitive marketplace, organizations are straining every nerve to keep up with evolving customer expectations, the rapid pace of innovation, and cut-throat competition. All these prevailing market challenges drive home the importance of accurate demand forecasting for organizations to drive business growth. 

Accurate demand forecasting methods hold the key for global supply chains to be agile, resilient, and future-proof. Traditional demand forecasting methods largely bank on historical sales data and a few seasonality variables and have obvious limitations in predicting demand. These long-standing demand forecasting methods leave out volumes of structured and unstructured data that may have an impact on demand as well as fall short of factoring in constantly changing consumer preferences and external market events. Traditional demand forecasting methods are not always 100% accurate, time-consuming, resource-intensive, and can be a costly exercise. Traditional demand forecasting methods are not considered adequate enough to predict demand, especially near-term demand. Such traditional demand forecasting methods result in frequent stock-outs, overstock, blocked working capital, and more importantly, an unhappy customer experience.     

The inherent shortfalls associated with traditional forecasting methods have brought on the need for a demand forecasting solution that can better grasp ever-evolving market changes and help predict demand with a substantial degree of accuracy. And this need for a high accuracy-focused demand forecasting solution can be addressed by the next-generation automated Demand Sensing solutions that are poised to transform the demand forecasting space in years to come.

Automated Demand Sensing solutions are considered the next big thing for predicting demand. Unlike traditional forecasting methods, Demand Sensing leverages a wide range of different inputs that affect data and demand plans in the short term and breaks that demand plan into daily buckets so that forecast managers can take immediate decisions.

Demand sensing is considered a more effective approach than traditional forecasting methods because the former counts on more up-to-the-minute information for forecast accuracy. It generates demand signals by monitoring customer sentiments and their conversations that enable organizations to meet consumer demand while driving the lowest cost possible across their supply chains. This much-hyped demand forecasting solution harnesses the power of emerging technologies such as Artificial Intelligence and Machine Learning and analyzes real-time consumer demand information from point of sale systems, warehouses, and shipment locations as well as factors in weather, disruptions, etc to predict demand. Demand Sensing applies complex mathematical algorithms to automatically recognize demand patterns and spot complicated relationships in large data sets. 

Demand Sensing helps organizations to substantially enhance demand forecast accuracy resulting in higher levels of customer service, provides a more responsive framework for supply chains to fulfill demand near-term with precise execution, reduces overall inventory costs, enables organizations to gain a much leaner and efficient supply chain, and augment profitability. 

The true value of Demand Sensing can be realized in a well-integrated and synchronized supply chain. This demand forecast method will serve no purpose if supply chains are unable to quickly adapt and respond to anticipated fluctuations. 


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