Why is AI based Demand Planning more Efficient?

One of the most significant challenges businesses face in the contemporary world is meeting consumers’ volatile demands. Although massive datasets are available for assessing consumer demand, the purchasing patterns continue to be complicated, and thus it is difficult to forecast demand. Many factors influence demand, from seasonality and weather fluctuations to promotional marketing, which influences customers’ buying trends. Moreover, these circumstances influencing consumer demand are uncertain and cannot be predicted.

Businesses find it difficult to predict demand accurately and maintain inventory accuracy across the entire supply chain. This is where demand sensing assists businesses in minimizing supply chain expenses and brings about essential improvements in production planning, financial planning, risk assessment decisions, and profit margins.

By analyzing the data available in excel sheets, traditional forecasting methods can no longer yield accurate results, and it has become challenging to forecast demand accurately. The solution? Machine learning and artificial intelligence algorithms enhance demand sensing accuracy and streamline replenishment planning. With these technologies at disposal, businesses are reducing the cost tied up in excess inventory and the stockout situations.

Demand sensing is the science of making predictions about short-term trends so that businesses can predict accurately what consumers would want, in what quantities, in which location and when on a real time basis. Rather than working with the same forecast for a month, you can modify the forecast with the latest sales information and make adjustments for enhancing profits. Demand sensing utilizes data gathered days or hours ago for making accurate predictions for a short-term.


Traditional Forecasts – Apt for Stable Markets

Traditional methods have been widely used for demand sensing and continue to do so. In modern times, the calculations are conducted by sophisticated software. This method analyzes historical sales data of at least two years, depending on statistical techniques, and integrate with Excel without considering additional expertise/resources. The  system can account for market trends and seasonality, provided that the market is stable, assuming that things that occurred two years ago would repeat themselves, which is false. Thus, this method fails to predict fluctuations in demand and modifications in customer preferences.

AI&ML Based Sensing

Advancements in technology and fluctuations in demand made it essential to integrate AI and ML in demand sensing. Companies can now even estimate short-term demands with ML techniques. Predictive analytics amalgamates the existing and historical data to produce meaningful insights into customer behavior under certain situations. ML and AI use external and internal information such as marketing promotions, historical data, social media signals, weather forecasts, macroeconomic indicators, competitor activity, and so on for generating data-driven forecasts.

ML utilizes mathematical algorithms to identify trends, determine complex relationships in massive datasets, and capture demand signals. Demand sensing software can examine large datasets and retrain models by adapting to the modifying circumstances while considering volatility. These abilities allow machine-learning-based forecasts to be more precise and accurate. The situations in which machine learning and artificial intelligence work the best are as follows:

  • Short to mid-term planning
  • Quick fluctuating environment
  • Volatile demand trends
  • New product introductions

However, to ensure that you can obtain accurate results, you need big data of a high quality to feed the system. The software should have high processing power, and the features to be incorporated into the software are decided by tech experts, which significantly impacts the outcome and why a particular result is generated.


Organizations can reap several benefits from utilizing machine learning and artificial intelligence algorithms in demand sensing. Mentioned below are some of the benefits that demand sensing software can bring to retail companies:

  • Accurate forecasts at every level – All the important demand planning decisions depend on demand sensing. Thus, it is necessary for making the predictions as precise as possible and constantly updating the forecasts. AI brings in benefits as it saves time by selecting the accurate demand sensing methods for the relevant product levels and also automatically integrating those. It chooses a unique forecasting model for every individual product without calculating things manually.
  • Taking into account internal and external data – With sophisticated demand sensing software, you can take into consideration not only the internal data but also the external data that affects demand. Data such as seasonality, mobility data, price indices, economic data and other information can be analyzed and included automatically.
  • Farewell to manual and time consuming processes – Manual processes are tedious and time consuming. Further, the repetitive nature of the task makes it annoying and you need to prepare numbers and figures weeks ahead before you can have the optimal starting point for a proper demand planning process. With AI all the data can be placed in the right structure and examined within minutes. Thus, a lot of time is saved which can be utilized in other tasks.
  • Informed decision making – With AI demand sensing, the right business decisions can be made. It helps to determine fluctuations in the initial stage and take the accurate measures at the right time. A good demand sensing software even suggests the best course of action while considering the internal constraints and predefined factors. The scope of ineffective interventions for meeting demand is therefore lessened and inefficiencies across the supply chain can be encountered.
  • Quick response to fluctuations – The demand for certain products might fluctuate tremendously. Examining the fluctuating demand results in a quick response to fluctuations and helps to plan actively. The capability to detect relationships between demand and external factors offers a great competitive advantage and enhances both customer satisfaction and profitability.
  • Improved relationship with supply chain partners – AI demand sensing helps to maintain optimal stock levels and the suppliers are able to strategize more efficiently. It avoids short-term requests and results in less stress for the supply chain parties. Creating a cross-supply chain demand planning process which keeps all the parties in loop leads to decreased stress levels along the supply chain.


About the Company

Fountain9 offers assistance to retail businesses to estimate demand correctly through their demand sensing software, Kronoscope, which is integrated with ML and AI algorithms to generate valuable and accurate forecasts. The software considers all the data and considers 9 critical internal and external factors that influence demand to predict demand accurately. This helps businesses maintain a proper inventory stocked to the optimal levels and satisfy demand at the right time.

there is a constant decrease in number of word counts per GP, lets make sure the word count is 1000+

@gagan@eseosolutions.com can we assign this as a keyword and link it to our blog?

_Assigned to Gagan Pratap_

this is exact repetition from a pervious GP done for this month, change this please