Demand forecasting with AI and ML algorithms

In the wake of the pandemic and the events that have shaped the past few years, companies in the discrete sector – among others – are experiencing moments of exceptional acceleration.

Customers’ behaviors and expectations are constantly and rapidly evolving, and as an increasing number of companies adopt optimized supply chain practices and cloud-connected networks, the competition is getting fiercer. Demand forecasting is therefore important for the supply chain since it helps characterize key operational processes such as demand-driven material resource planning (DDMRP), inbound logistics, production, financial planning, and risk assessment.

Demand Forecasting in Details

Demand forecasting combines both qualitative and quantitative forecasts, both of which are based on the ability to collect data (both quantitative and qualitative from endogenous and exogenous data sources) from different data sources along the supply chain.
Quantitative data are usually internally sourced and can come from sales numbers, peak buying periods, and internal market analysis based on exogenous data. Modern technologies employ advanced analytics through the use of artificial intelligence (AI) and machine learning (ML) to analyze and process in-depth, complex data sets.
Using these new technologies, supply chain managers are thus able to make decisions based on accurate predictions, increasing the levels of supply chain resilience.

How Machine Learning (ML) and Artificial Intelligence (AI) help improve demand forecasting

As of now, improving demand forecasting and reducing forecast error has become a strategic imperative for companies regardless of the industry.

With increasing levels of product complexity and market volatility, traditional methods are struggling to keep up with SKU volume growth. Therefore, by applying Machine Learning algorithms, companies are now able to process very large data sets effectively and quickly.

Machine learning employs complex mathematical algorithms to automatically recognize patterns, capturing demand signals and identifying complex relationships in large datasets. These can include both endogenous and exogenous sources of information. In addition, the algorithm can learn and self-correct quickly, moving rapidly toward an optimal result. Machine learning effectively addresses the weaknesses of traditional statistical prediction models and significantly improves prediction accuracy.

The algorithms implemented by sedApta for demand forecasting

Today, sedApta provides its customers with capabilities to more accurately forecast demand and generate a more robust inventory replenishment plan. This makes it possible to optimize inventories and increase the ability to react to unexpected events brought about by the dynamism of the market and the state of the logistics infrastructure as a whole.


Use of exogenous variables to increase the accuracy of demand forecasting

Among the variables used may be macroeconomic variables (e.g., GDP), energy price trends, sector-specific variables, and variables related to particular situations (e.g., movement flows during pandemic to cleanse time series of the ‘covid effect’). Depending on the type of exogenous variable, each appropriately weighted, one can forecast demand in the medium to long term (e.g., GDP exogenous variable) or refine it in the short to very short term (e.g., weather forecast exogenous variable).


Demand Forecasting Optimization

Traditional algorithms, selected through best-fit logics, are applied in combination with multivariate ML algorithms. The latter, by exploiting the endogenous variables in the time series and any available exogenous variables, make it possible to identify conditions that are inherently complex to predict in which, in the past, traditional algorithms and/or user-made corrections within a collaborative environment have proven inaccurate. ML algorithms signal the probability that such conditions will reoccur and propose a corrective factor for the prediction to the user.

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