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ML Manufacturing – A Technology Primed for Innovation

Machine learning (ML) can help manufacturers increase their competitive edge by gaining predictive insights into production. The core technologies of machine learning can help manufacturers solve complex problems daily. Machine learning algorithms offer the potential to increase predictive accuracy in every phase of production, from supply chain operations to customized, built-to-order production. Several algorithms being developed are iterative, designed to learn and optimize outcomes continuously. These algorithms iterate every millisecond, enabling manufacturers to seek optimized outcomes in minutes versus months. This article at Consumer Goods Technology by Liz Dominguez discusses the technological advancement in ML manufacturing.

Best Use Cases for ML Manufacturing

Procter & Gamble is overhauling its production to lower costs, boost productivity, and increase customer satisfaction. The organization’s most recent initiative will enable it to access a digital platform that will use the Internet of Things and speed up growth and innovation. By using edge computing, machine learning, and artificial intelligence, P&G will be able to digitize data from more than 100 manufacturing facilities around the world and increase visibility. This will make it possible for staff members at the company to review production data and make data-driven decisions in real time, leading to significant advancements.

Implementing ML Manufacturing

Leveraging technology from Microsoft Azure, P&G is already implementing improvements in its baby care and paper product segments via pilot programs in the U.S., Egypt, India, and Japan. Technology will leverage algorithms, machine learning, and predictive analytics on the manufacturing line to improve efficiency and productivity. According to Vittorio Cretella, CIO, the company’s goal is to make manufacturing more intelligent, scalable, predictive, controlled, touchless, and sustainable.

The firms have established a Digital Enablement Office (DEO) as a part of their collaboration. The DEO will feature professionals from P&G and Microsoft that will work together to install the Azure platform and use the organization as an innovation incubator. In terms of product manufacturing and packaging, the office will develop high-priority business scenarios that P&G can use.

ML manufacturing, artificial intelligence, and predictive analysis are primed for innovation in the consumer goods sector. Recent analytics research from CGT and RIS shows that CGs are confident in their analytical abilities (28%) and resources (19%), placing themselves higher than their immediate rivals.

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