Demand Sensing: How to Get Started with it?

    Every supply chain manager wants to determine exactly which product would surge in demand in the future, at what period, and what would be the quantity. But, one can never anticipate 100% accurate demand forecasting. Digital technologies, such as Machine Learning and Artificial Intelligence, bring supply chain managers closer to accurate demand forecasting through demand forecasting software

    While demand forecasting works properly for long-term and mid-term demand planning, it is usually less effective for short-term planning, and this gap is filled in by demand sensing. Read on to know more about demand sensing, what it is, how it is different from demand forecasting, its benefits, and advantages, and how supply chain planners get started. 

    What Is Demand Sensing?

    Demand sensing is a blend of technology and methodology to forecast demand for the near future depending on short-term data. It is the process of instantly picking up on short-term patterns so businesses can estimate what consumers need, when, and where. Demand sensing allows you to modify the existing demand plan with the latest information on sales data, thereby boosting profit.

    Difference Between Demand Forecasting & Demand Sensing

    Demand forecasting uses data from about a year, whereas demand sensing utilizes data acquired in a few days or hours to make accurate short-term forecasts. Demand sensing evaluates short-term trends for estimating what might happen in a volatile market. 

    Demand Sensing Aspects

    • Short-term forecasting provides greater data granularity for scrutinizing the daily demand data to the end customer and instantly detecting changes in demand behavior. 
    • Extending supply chain transparency by observing the trends in point-of-sale (POS) promotion, social media, weather, NPI, internet search, economic data, and  IoT to modify the forecast and inventory stocking and placement. 

    Both processes minimize demand unpredictability and allow crucial alterations to the forecast without waiting for the following forecast cycle. 

    Benefits of Demand Sensing

    Demand sensing can be of great advantage to supply chain operations in the following way:- 

    1. More Supply Chain Flexibility

    With short-term data gathered from demand sensing, supply chains can overcome market volatility or meet unanticipated disruptions like the covid-19 pandemic effectively. Demand forecasting software provides the required technology for making supply chains more powerful and flexible. 

    1. Better Predictability

    Demand sensing utilizes a wide variety of signals involving real-world instances such as retail sales, market changes, promotions, and order patterns for comprehending trends quickly. 

    1. Improved Inventory Management

    Demand sensing can offer daily demand data for optimizing inventory levels, making the supply chain flexible and minimizing stock levels. It can reduce the handling cost and the expense of excess production. 

    What are the Best Practices for Demand Sensing?

    For successfully integrating demand sensing into your business, supply chain managers can utilize the following things mentioned here. 

    1. Begin with Sell-In Data

    A simple method for integrating demand sensing in the supply chain is to utilize granular historical data. It can be done by examining sell-in demand data briefly and then modifying the forecast accordingly. Shipping history should also be utilized to consider sell-in data derived from any ERP software or supply chain planning system. It can be utilized for correct B2B demand forecasting. 

    1. Include all Feasible Data Sources

    For getting accurate results in demand sensing, it is essential to take into account all critical data, such as downstream sell-out data, which involves:

    • Channel data
    • Customer order data
    • Consolidated point-of-sale data, including real-time data on quantity, region, date, and time.

    This data can assist in quickly identifying trends, alerting and warning the supply chain of near future interference, and closing the bridge between what is happening in the supply chain and the plan.  

    1. Include all External Aspects

    One of the significant aspects that assist in accurate and advanced demand sensing is that it includes multiple information points not taken into account in conventional forecasting approaches. While applying demand sensing, a supply chain planner might consider the following aspect:

    • Track data of competitors such as stock-outs or promotional discounts. This data can assist you in regulating your offering to gain a competitive edge. 
    • Companies that depend on seasonal patterns should also consider weather information. The data can aid in sensing the short-term effect of weather changes on consumer behavior and demand and assist in adjusting production, distribution plans, and raw material procurement. 
    • Take into account macroeconomic factors encompassing the entire stock market, sales data, employment data, and the country’s GDP. This affects the end consumer’s demand. 

    The primary aim of integrating external aspects is to expand the forecast horizon estimated through demand sensing. The more factors are considered, the more the range of the forecasted events. 

    When you consider all the three aspects together – the sell-out and sell-in data and the appropriate demand casuals, you can obtain a precise picture of demand. It also offers the basis for highly automated demand sensing that liberates demand planners from using their business knowledge to improve their customer service and forecasting.  

    About Us

    Fountain9 offers effective demand sensing tools for DTC, retail, and e-Commerce. Their software, Kronoscope, uses AI for estimating demand accurately. It helps to prevent out-of-stock situations. The software also recommends methods of reducing the impact of stockout situations. 

    Chronoscope considers nine unique factors such as holidays, promotional marketing, seasonality, historical sales trends, pricing changes, and markdown events to predict future demand and optimize prices. 

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