How AI and a data-driven culture can boost smart manufacturing

Digital metamorphosis in industry depends on the implementation and combination of various technologies. The use of AI and data analysis allows you to optimize processes, increase production efficiency and quality, as well as make more informed and automated decisions. These technologies are crucial for reducing operating costs, minimizing downtime, and increasing productivity.

In this article, you will understand how a data-driven culture and the use of AI enhance smart manufacturing, covering solutions such as predictive maintenance and prescriptive analytics, optimization of production parameters, and supply-chain management. 

But the great challenge lies in putting these solutions to work effectively at the service of efficiency, quality and increased production. 

How can the industry make the most of this technological revolution?

Building a data-driven culture in industrial activity

The recent enthusiasm for generative AI has led to a renewed focus on creating data-driven culture within organizations. However, technical challenges and cultural changes in management represent significant obstacles to the adoption and maximization of the potential of these technologies.

In industrial organizations, having a mature data structure is essential to achieve the so-called smart manufacturing (automating informed decision-making) and boost the benefits that digitalization provides them.

Establish data quality criteria

The evaluation of data quality includes four key dimensions: Accuracy, Completeness, Consistency, and Timeliness.
 

In addition, there are automated and specialized tools to check data quality and help detect problems immediately.

Create data governance policies

Data governance defines clear protocols for the collection, storage, and use of data in industrial operations. Manufacturers need to use data architecture strategies, the right technologies, so that a growing number of decision-makers trust the much-desired single source of truth.

The heterogeneity of the system makes the creation of interoperable data complex, because the systems generate the data within specific parameters:

• Companies designate data administrators to oversee data management practices;
• Administrators ensure compliance with governance policies and maintain data quality across the organization.

How can AI and data help the industrial operation? 

Automation

Strategic integration of data and AI drives industrial automation, improving operations, production, efficiency, and quality. Technologies such as predictive and prescriptive maintenance, automated real-time decisions, contextualization and data enrichment, optimize processes. 
When the data generated by the industrial operation is properly integrated and the appropriate AI algorithms are applied, it is possible to optimize production parameters and maintenance schedules, reduce downtime and identify the causes of problems, allowing the implementation of corrective measures efficiently.

Supply-chain

Generative AI revolutionizes supply-chain management by forecasting demand, adjusting inventory levels, and optimizing production. By simulating various production scenarios, based on historical data and market trends, these technologies improve operational efficiency, avoid stockouts and reduce costs, making the supply chain more resilient and agile. 

In supply chain management, AI acts as an intelligent assistant, tracking inventory and managing the logistics of supply-chain operations by the minute. This integration of AI not only increases operational efficiency, but also strengthens the resilience of the supply chain in manufacturing.

Industrial Operator

Technologies also improve the working environment and the well-being of operators:

• Augmented and virtual reality - in remote maintenance and training, accelerating learning;
• Digital Twins - in the optimization of performance, in the health of operators and in the reduction of absenteeism;
• AI – in the prevention of occupational risks (in physical and mental health), simulating scenarios, combating presenteeism, absences due to illness, lack of manpower and making industrial work more pleasant.

In addition to increasing productivity, these innovations promote talent retention, strengthening commitment and reducing turnover.

Sustainability

Intelligent automation optimizes resources, reduces waste and adjusts production processes according to demand.

The use of Big Data, IoT and Machine Learning (ML) allows you to monitor and manage energy consumption, increasing efficiency, reducing costs and ensuring high standards in industrial production.

Through continuous learning and adaptation, data and AI in industry transform assembly lines into adaptable and guided environments, depending on the data received, and ultimately increase productivity and reduce costs.

Measuring ROI and business impact

Manufacturers measure success with Key Performance Indicators (KPIs) such as Overall Equipment Effectiveness (OEE), efficiency, cost and quality reduction, placing increasing importance on supply chain resilience, sustainability metrics and waste reduction.

AI applications are becoming essential to industrial competitiveness, helping to mitigate challenges such as supply-chain volatility, rising costs and shortages of skilled labor.

How to thrive in the era of Smart Manufacturing?

In short, for manufacturers to thrive in the era of smart manufacturing, a data-driven culture backed by robust infrastructure, governance and AI applications is critical.

Izertis helps companies in the industrial sector to take advantage of data effectively, and thus achieve greater efficiency, resilience and competitiveness!