Supply chain management is getting more complicated as the data to be analyzed for the optimization of supply chains across the globe is becoming more sophisticated. Now, more than methods such as time series regression analysis are needed to plan business and supply chain operations. Demand trends today are changing at hyperspeed, and planners need accurate demand variability analysis to prepare their operations better. This is where demand sensing can be helpful for planners.
Demand Sensing is the process of picking up on short-term demand trends for any product so that planners can predict short-term customer buying behavior. The method of demand sensing is generally performed for predicting short-term demand using the analysis of patterns in POS (Point-Of-Sale), social media, Weather, IoT, Internet trends, and economic data to make near-term predictions. Demand sensing helps leverage the high level of granularity in data, analyze daily demand patterns from the end customers, and detect changes in demand in real time.
The process consists of the following steps:
This methodology has numerous benefits for a business, and some of these are discussed as follows:
The primary goal of this method is to help planners understand short-term demand trends and then make decisions since what happened some days ago instead of based on data available from the last year. This way, planners can overcome the latency issues they face with time-series statistical methods. In other words, demand sensing focuses on removing any supply chain lag by reducing the time between events and reaction to those events.
This method uses detailed short-term data for making near-future forecasts. Here, the near future can refer to hours or days, depending upon the agility of the supply chain. This forecast, built using AI/ML principles derived from daily POS data, will be more accurate than the daily forecast created from a manually entered day-weight calendar.
Demand sensing is a handy tool for businesses looking to create forecasts for the short term. When this technique is applied to long-term demand goals, the accuracy is limited only. It can be applied to a 4 to 12 weeks horizon only. Other demand planning methods will be used if demand trends beyond 12 weeks are required.
Another limitation of demand sensing techniques is that they are designed for something other than B2B manufacturers. They are more apt for fast-moving consumer goods (FMCG). In these techniques, the planning is done based on the data obtained from the point of sale (PoS), which is available only in B2C industries but not for B2B industries.
This technique for predicting demand trends is too much dependent on data accuracy. Even the slightest inaccuracy in data may lead to inaccuracy in prediction leading to disruption of the entire supply and business operations.
Predicting the demand for the near future can be a great advantage for businesses. It can help them quickly optimize their inventory, ensure the availability of manufacturing capacity, align logistics, and plan finances to meet customer demands and avoid stock-outs.
Demand forecasting with traditional time-series analysis is not accurate for short-term prediction. For this, companies require robust, intelligent, and agile advanced analytics solutions that can leverage the capabilities of artificial intelligence and machine learning for big data analytics and provide real-time insights for businesses. 3SC offers intelligent supply chain planning solutions such as Integrated Business Planning, Risk Management & Digital Twin, Enterprise Data Management, and CarbonX, which can analyze big chunks of data and provide insights and recommendations for optimized business operation and seamless supply chain management. With the help of our intelligent business planning solutions, companies can ensure seamless, efficient, and sustainable supply chain management and business operation planning.
Demand sensing is used for short-term prediction of demand trends with a time horizon of 4 to 12 weeks. Demand forecasting is used for long-term forecasts of demand trends with a time horizon beyond 12 weeks.
Some of the best practices during sensing demand include starting with sell-in data, using all possible data sources, and considering all external forces to obtain the most accurate prediction of demand trends.
Some prominent examples of demand sensing can be seen in the Automobile Industry, Fast-Moving Consumer Goods Industry, and Healthcare & Pharmaceuticals industry.
It is used when the market conditions are volatile, and demand trends change rapidly. Short-term demand prediction can help form a resilient and efficient supply chain in such a condition.