Digitization: Key to Boost Productivity in Manufacturing Lines

Every step of the automotive supply chain is undergoing a transformation, from the materials needed to produce vehicles to the perception of mobility as a whole. Chief among these trends is digitization, as the introduction of new tools such as artificial intelligence (AI), machine learning (ML) and internet of things (IoT) is reshaping the industry.

“In recent years, digital technologies have been drastically enhanced and undergone the transition from expert application to people’s everyday lives. Just as the introduction of the steam engine and the spread of electricity changed society in the past, so is digitalization having a profound impact on society and the economy today,” writes Thomas Moller Thomsen, President, FIA Region.

Mexico has historically been known as a manufacturing powerhouse in the automotive sector. If the country wants to remain a powerhouse, the entire automotive industry has to commit to digitalization. Both automakers and customers are demanding more optimization across the supply chain, improved competitiveness and overall safer operations.

One of the most praised aspects of Mexico’s automotive industry is its quality, as it has proven to be a key element in determining the competitive level of the sector, says Francisco Solano, Head of Technology and Portfolio NoLA, Logicalis.

While opinions on the future of mobility are varied, most experts agree that the use of interconnected, electric-powered vehicles will allow for the development of a more sustainable and safer industry. The implementation of digital tools across the industry will also benefit the end consumers, allowing them to access real-time information about their vehicle and enhance its performance, explains Lorena Isla, Director of Latin America – Mobility, Frost & Sullivan. “AI and ML create an immense opportunity to improve operations across the entire production chain,” she adds.

The relationship between technology and the industry is indispensable, as these actors are the main driving forces behind each other, adds Solano.

Autonomous driving, shared-mobility models, customized insurance contracts, remote diagnostics and predictive maintenance services would not be possible without the industry’s consistent efforts to adopt AI, ML and IoT. But to adopt these technologies correctly, the sector must first start by digitizing its manufacturing and assembly lines, while also implementing these tools to achieve vanity measures, differentiate themselves from the market and add value across entire processes, says Federico Crespo, CEO, Valiot.

“Digitalization in the manufacturing fields helps businesses to have a real-time context on the current state of the plant, which allows them to make better and faster decisions in the heat of the moment,” says Crespo. This technology can also give companies better forecasts by analyzing more variables, simultaneously increasing flexibility in the production lines.

The agility required by the industry, especially after the COVID-19 pandemic has led the sector to blur the lines between engineering, logistics, manufacturing and production. Technology tools, such as AI, IofT and ML, play a critical role in this area by exponentially improving the flexibility of production lines, says Ana Nuñez, Digital Supply Chain Account Director, SAP. However, companies must look for realistic solutions that go hand in hand with the concept of lean manufacturing, which will allow them to “make affordable and practical implementation projects that really seek to improve a company’s manufacturing operations,” adds Nuñez.

AI will play a defining role in the quest for fully automated plants, as data is becoming the most valuable asset in the industry, says Roger Guerrero, Head of Factory Automation and Motion Control, Siemens Mexico, Central America & Caribbean. Data analysis can help companies to identify time, energy and production waste throughout the entire production chain, ultimately improving the company’s productivity, quality and profitability. However, these improvements will not be achieved unless data logging on the shop floor becomes a critical component for manufacturing companies, adds Guerrero.

The automotive sector faces several other challenges in adopting AI, Ml and IofT, such as significant implementation costs. Digitalization often requires a considerable investment in employee training and infrastructure, including hardware and software. When implementing new and “unknown” technology tools, manufacturing companies often face a cultural impact, as there will surely be frictions that might initially make the process slower and more difficult for both the company and the employees.

There are also security concerns involved, including data privacy. Thus, strong cybersecurity practices are required to protect this information. Robust data security measures, including encryption and authentication methods, can ensure that sensitive data is protected from unauthorized access.

A key challenge in Mexico will be the long age of its vehicle fleet, which could difficult the integration of new technologies with the legacy systems used by older models. Regulatory compliance can also be a challenge, particularly when it comes to data privacy and security.