AI, Machine Learning, Cloud for Smart Manufacturing
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AI, Machine Learning, Cloud for Smart Manufacturing

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Antonio Gozain By Antonio Gozain | Senior Journalist and Industry Analyst - Thu, 03/24/2022 - 12:39

As digitalization increasingly permeates the automotive supply chain, cloud services are becoming crucial to the democratization of technology, substantially benefiting manufacturing operations.

“The cloud has enabled the democratization of artificial intelligence (AI) and machine learning (ML). Thanks to the power of computing, I can use these technologies even from my smartphone. The real challenge no longer lies in receiving answers but in knowing how to ask the right questions to determine the correct KPIs to feed to the algorithms that make up ML and AI. The more accurate the KPIs, the better answers,” said Miguel Villalpando, Country Manager, Vias.

The strategic importance of smart manufacturing is “undeniable,” according to Deloitte. The correct implementation of technology across manufacturing plants brings improvements in costs, throughput, quality, safety and revenue growth. The combined capabilities of industrial internet of things (IIoT), cloud computing, robotic process automation, AI and ML, among others, have greatly improved manufacturing over the last few years, according to Deloitte.

Despite the proven efficiency of these technologies, before the pandemic most companies “were very hesitant to digitize their processes,” said José Rivero, Country Manager México, Infor. After the pandemic hit, however, digital transformation became “absolutely necessary” for inventory control, traceability and the optimization of operations, he added.

Traceability plays an essential role in the automotive industry, according to Syspro. Greater traceability in parts lifecycles and supply chains, gives auto manufacturers the opportunity to mitigate the impact of product returns and recalls. It also allows companies to review existing materials, products and processes to reduce the risk of such events to reoccur. While the technology to implement traceability systems, ML and AI has existed for years, cloud computing opened the door for smaller players to take advantage of them, agreed experts.

“The data generated during manufacturing processes is relevant but, until very recently, it was not affordable for many. Storage capacities are now very affordable and enable users to record data in a well-structured manner and generate backward traceability and forward predictability. This allows degrees of detail never seen before and helps companies to know how to react to circumstances,” said Federico Crespo, CEO, Valiot.

Cloud computing has enabled players to manage massive amounts of data, said Marcelo Saparrat, CIO, Tecnoap. This technology enables the creation of sophisticated model ensembles for the generation of highly sophisticated demand prediction models, he added. In addition, “the sensorization and collection of events throughout the supply chain give [the model] a multivariable nature and make supply chain management much more flexible.” Companies have the opportunity to act more assertively and make more optimal decisions than in traditional scenarios, said Saparrat.

Demand planning is a cross-functional process that helps businesses meet customer demand for products while minimizing excess inventory and avoiding supply chain disruptions, according to Netsuite. Demand planning combines sales forecasting, supply chain management and inventory management, and uses data from internal and external sources to predict future demand.

ML and AI have enabled more assertive demand planning, said Rivero. These tools are more important than ever before, he added, due to the situation that the automotive sector is undergoing worldwide, with disruptions and shortages that have impacted production for the past two years. “It is also about administering scarcity. Under these circumstances, optimizing and managing scarcity has become crucial. Collaboration is also important [and technology] enables companies to connect with suppliers and even end customers to manage the flow in the production chain,” said Rivero.

The cloud allows companies to connect interlocutors, said Villalpando, so information is exchanged automatically between different groups and people. Tier 1s, 2s and 3s can be connected to the same model and collect information effectively, without exchanging emails and waiting for a response, he added.

Big companies are already taking advantage of the opportunity. Volkswagen, one of the most important employers in Mexico with over 15,000 employees in its two plants, has already started a digital revolution in its plants across the world. The German automaker is investing US$1 billion during the coming years in its three plants in North America to successfully implement technology such as cloud software, intelligent robots and AI, which require exhaustive training, as reported by MBN. With the unified software launch, Volkswagen will be able to optimize collaboration between its plants, upgrade the work environment for its employees and suppliers and improve the overall manufacturing process.

While bigger players continue to take advantage of AI, ML and the diverse tools both technologies offer, smaller actors can now benefit from them, thanks to the democratization of cloud computing, said Rivero. For example, Infor offers CloudSuite Automotive, a system tailored for the industry business processes of auto part manufacturers. This solution is necessary to build smart, end-to-end value chains “with the greatest possible degree of visibility, reliability and agility. A cloud-based ERP solution can help free up capital, while giving you the flexibility to meet your evolving operational needs,” according to Infor. Tools such as CloudSuite Automotive are now available for smaller players thanks to the cloud, said Rivero.

The digital revolution will continue transforming all industries, including the automotive sector, and making jobs evolve, said Crespo. “Learning human-machine language since childhood is key.”

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