A lot of industries throughout the supply chain pipeline like manufacturing, retail, and even the services supply chain rely on demand forecasts to anticipate and plan. They use demand forecasts to plan all activities like production speed, resource allocation, raw materials purchase, and even stock levels. They usually rely on standard demand forecasting models that consider historic demand over a period during different sales cycles to determine future demand. But in certain unforeseen scenarios, be it a catastrophic weather event, a pandemic like the one we are going through, or even a new trend causing a sales spike, the previously forecasted demand might be inaccurate and causes disruptions in the supply chain due to shortage of supply. The paper discusses a method to continuously monitor the external events in real-time, stream such events, cluster them and then arrive at an offset in the demand based on what was seen in the past and the context of occurrence of the events, like the point in the sales cycle when the event is occurring, domain attributes like weather and another sociopolitical climate. This helps to increase the reliability of the demand forecasts and helps supply chain planners react to such unforeseen events effectively and improves the resilience of the supply chain.