Why do companies need demand forecasting tools?

machine learning

For companies, demand forecasting provides an estimate of the number of goods that their customers will purchase in the future. Critical aspects such as turnover, profit margins, cash flow, risk assessment and mitigation plans, capacity planning, all depend on demand forecasting.

Demand forecasting activities are fundamental because they support companies in planning production in the best possible way, in guaranteeing the availability of products according to market needs, in defining the budget to be allocated in new investments and in monitoring the gap between actual and forecast sales to optimise production. Starting from this type of analysis, all aspects related to the product are then defined, such as storage, pricing and shipping.

The techniques used to make these estimates are divided into qualitative, which are based on market research or expert panel forecasts, and quantitative, which are based on mathematical-statistical models to be applied to the historical series of demand and can be made for a single product or the entire production line and with different time horizons.

Even today, in most companies, these analyses are still carried out using tools such as spreadsheets and traditional statistical methods, but the high volatility, a characteristic now intrinsic in many markets, leads to the need to evolve towards means capable of updating the data acquired by the entire supply chain at any time so as not to make decisions on inaccurate or delayed forecasts.

In forecasting, it is also fundamental to integrate the data acquired internally with external information such as mark-economic data, market data, consumption trend surveys as well as monitoring the impact of individual variables such as events capable of modifying the demand and perception of the value of the product.

Predictive sales analysis

One of the most widely used branches of artificial intelligence in business is machine learning or machine learning. Using Machine Learning for forecasting makes it possible to estimate the demand for a product, to understand which will be the drivers of sales and to read in advance the changes in purchasing and consumption behaviour of a certain part of the customer base about the variation of certain conditions.

A machine learning based forecasting solution can take into account, through the integration and constant updating of a single database, past sales information, data from ERP, CRM, consumer survey results and customer reviews on the product.

By examining this integrated dataset together with the sales data trend, the Machine Learning system can identify the relevant variables, i.e. those whose variation determines a change in the number of products required by the market. By identifying these factors and the impact they have on sales, it is possible to obtain increasingly accurate forecasts. Moreover, due to the very nature of machine learning algorithms, any comparison between forecasts and actual sales results becomes useful information to refine future forecasts.

Compared to traditional forecasting methods, those based on Machine Learning constantly monitor forecasts, allowing analysts to make any changes to the model to make it even more efficient.

In addition to forecasting demand, machine learning is often used by companies to describe demand in real-time, monitoring variations and providing indicators to guide other choices, such as pricing or production planning. 

Who needs a demand forecasting software?

Not all markets and companies equally need to implement a sophisticated forecasting solution based on Machine Learning, but in the current context, the high volatility of demand and the need to be able to respond quickly to its variations has led more and more companies to adopt technologies of this type.

In highly competitive scenarios, in which the consumer has several options and access to a large amount of information about the different offers on the market, being able to forecast sales trends allows companies to have the necessary information to orient their strategic choices and gain an advantage over their competitors.

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