Gartner survey has indicated that frequent fluctuation in demand is one of the critical problem areas for businesses and it encompasses every industry. Businesses face many inventory management challenges when they are managing supply chain and demand forecasting is an essential part of the growth strategies for companies. Demand forecasting allows companies to lower their supply chain expenses and increase the businesses’ profitability. Demand forecasting also is known as demand planning refers to predicting an estimate of demand for products at different times in a supply chain. It is the process of estimating consumer demand using historical data of consumer behavior. It allows organizations to plan ahead for the inventory so that the products are available as per market behavior. Businesses use demand planning methods to reduce the inefficiencies caused by a mismatch in demand and supply.
It also allows companies to analyze the difference between actual sales and forecasted sales so that they can make optimum use of their production. It also allows businesses to improve their decision-making abilities and man force planning. The outcome of demand forecasting is used as the starting point for optimizing resources and warehouse, predicting pricing, and optimizing shipping. To get the most out of demand forecasting businesses need to make sure that the data is accurate across the supply chain. The traditional methods of forecasting have been used for years and it involves a lot of human intervention. It requires organizations to manually add and analyze their data and it leads to delay or inaccuracy in forecasting. Also manually forecasting is difficult since the market is very volatile and variables need to be taken into account on a continual basis. Forecasting would need to be revised every time a variable is added and it would be a very time-intensive process to get actionable data as it requires much more effort.
As the industry now is even more complex and the market is even more volatile businesses require a much more streamlined and efficient method for continual forecasting. Hence artificial intelligence and machine learning are critical for businesses growth today. ML Algorithms can significantly improve forecasting accuracy and enable businesses to optimize their process and resources.
AI in demand forecasting:
Artificial intelligence and machine learning have been used in many ways across several industries. The integration of AI in a business depends on the quality and availability of data, complexity of the challenge, and the organization’s expectations from the forecasting solutions. It is crucial to know the need for the forecast, whether it is to determine the market for a new product or forecast the seasonal change in sales of an existing product or planning the price changes for maximizing profits. Need of demand planning and forecasting varies across industries and even in the same industries it differs from organization to organization.
Two of the methods that have been used by industries to improve their profitability are mentioned below:
Predictive analytics
As mentioned earlier the most used applications of artificial intelligence in demand planning is the ability to predict the future. Predictive analytics enables organizations to predict the demand for certain products and also identify the key drivers of sales and the impact of change in buyer behavior due to seasonal change or trend alerts.
Artificial intelligence and machine learning-enabled demand forecasting solutions to accumulate new and historical data for businesses from varied sources. It includes gathering data from the business system such as CRM, POS, ERP and also from marketing campaigns, social media interactions, surveys, market research, and much more. Once the data is gathered, it is then used to identify variables that are important to a business and those that can affect their sales.
The machine-learning algorithm uses this cleaned up data to create predictive analytics models to enable businesses to find opportunities and launch the product in the market to optimize sales. The reason the machine learning-enabled solutions are better than traditional methods is that these solutions can monitor the volatility of the market and make continual changes through the predictive analytics model. It allows organizations to compare the actual sales vs the prediction made and keep improving the prediction accuracy for the future. For this model to be useful companies need to integrate the existing data with on-ground reality. Businesses need to take into account all factors that can impact demand like economic factors, weather, change in trend, to name a few. All these factors can change the buyer’s buying behavior. Every single indicator needs to be monitored to ensure its appropriately integrated with the machine learning algorithms so that it can make the right predictions.
Demand Sensing:
Demand Sensing is another method that allows businesses to capture real-time fluctuations in buyer behavior and market demand. With these solutions, businesses are able to match consumer needs better. This method is an extremely critical method in industry that changes fast. For example, in the retail clothing industry, the trends in the clothing industry change very fast depending on celebrities’ and influencers’ opinions.
Demand sensing accumulates data from the POS systems and warehouses and backs it by the marketing insights that show customer interactions and content that is trending. This method is able to capture the demand trends on a short term basis and its able to also capture the variables causing fluctuations in demand.
In today’s day and age, it’s very important for businesses to understand their target buyer and their requirements better. To do so organizations need technology to analyze the market trends and company data to forecast the demand. Hence the demand planning solution is important as it helps businesses optimize their supply chain.
Are AI-enabled solutions right for your business?
Every business has different goals and requirements and hence the need for artificial intelligence and machine learning-enabled solutions differs. When the market in which a business operates is highly volatile and there are various factors that affect the demand and supply, demand forecasting solutions are ideal for such organizations. Areas, where the demand planning solutions have been known to be successful, are products with the smaller life cycles, products that are sensitive to the weather or also for new product launches amongst others. To understand the need for your business
IT Action Groups’ artificial intelligence-enabled demand forecasting solutions consider all factors mentioned above to effectively forecast the demand and ensure it’s aligned with the organization’s procurement cycle to optimize the supply chain cycle.
To know more about how IT Action Group uses machine learning and artificial intelligence in the demand forecasting solutions Get in touch with IT Action Group today.
