The topic of Predictive Quality Analytics, or predictive quality analysis is one of the most recurring, especially on corporate boards, although many times it does not emerge as an explicit need. When people talk about identifying techniques to reduce waste, defective products, and, in general, analyzing issues related to the quality of goods and production processes, they are in fact talking about Predictive Quality Analytics, that is, the ability to use data to identify possible problems a priori.

Your Content Goes HereInits academic definition, Predictive Quality Analytics identifies the process of extracting useful information and analysis from data, using statistical algorithms and machine learning to identify operational patterns and predict trends and outcomes.

This is a practice normally adopted in the Data Driven approach and is used to predict production stops, bottlenecks and inefficiencies in general, with the ultimate goal of limiting losses in end-product quality due to errors and non-idealities introduced by production processes. This is an emerging trend in industrial applications of artificial intelligence, which has already proven to offer significant improvements within manufacturing processes in the manufacturing sector.

For example, in a 12-line application case, it was possible to optimize costs by a total of $250,000 per year by simply eliminating the overloading of production lines.

The topic of Predictive Quality Management has many points of contact with the Industrial Internet of Things, and it offers the best results in companies where the digital transition process is already in an advanced state. However, it is applicable at every level.

Initiating a process aimed at Predictive Quality in manufacturing can also be an entry point for the digital transition. Data management with predictive analytics and artificial intelligence tools can become a driver to enable the digital transition, for example by highlighting missing or incomplete data sets, of which more systematic and structured collection is needed even better predictive models and further optimize both production costs and final output quality.

The Benefits and Opportunities of Predictive Quality Analytics
Again, the main benefits a manufacturing company can benefit from adopting predictive quality analytics techniques are primarily economic.

First of all, increasing product quality means getting fewer defective or reject parts, reducing the costs of defect resolution and management of residue, waste, or rework.

A second effect achieved is greater satisfaction for the end customer, in the case of consumer products, in parallel with reduced costs for handling warranties, recall campaigns, and replacements, for example in the case of durable goods or machinery production.

Among the advantages related more closely to the production chain, an analytical management of product quality also leads to the possibility of isolating defects more quickly, identifying the causes that generate them early and acting on them. It also makes it possible to reduce scrap, even in real time, by quickly adapting processing to environmental and machinery conditions, resulting in higher revenue margins on production.