Data and AI technology for manufacturing is having a moment. As AI has expanded its lengthy reach into manufacturing, there have been several purpose-built releases lately from companies like Nvidia and Databricks that are helping companies make sense of the deluge of data collected on everything from physical operations to the supply chain. Snowflake is now part of this action with the debut of its Manufacturing Data Cloud. The company says this new offering will enable companies in the automotive, technology, energy, and industrial sectors to tap into the value of siloed industrial data by leveraging Snowflake's data platform, partner solutions, and industry-specific datasets. The Snowflake Data Cloud provides a platform for data warehousing, SQL analytics, machine learning, data engineering, and monetization of third-party data.

Even in inflationary times, commissaries, supermarkets and other places where food is made or sold are increasingly turning to Artificial Intelligence and Machine Learning technologies to help them streamline operations, improve the customer experience, reduce waste and give their bottom lines a boost. San Ramon, Calif.-based AI and ML specialist Impulse Logic delivers advanced predictive analytics to create the optimal product flow through a retailer's store to optimize labor availability, ensure product availability, reduce waste, and increase profits, said Matt Frost, the company's CEO. "By optimizing the journey from warehouse to shop floor, stores can improve the way they manage their inventory to drive sales," Frost said. "Our innovative AI and ML-based solution does this by reading in-store data every two seconds." That ensures that store associates can make decisions based on accurate insights and ultimately deliver outstanding customer service and satisfaction.

Manufacturing execution systems (MES) have undergone many transformations in the past several years--from simple point solutions to comprehensive shop floor systems that are now mission-critical to manufacturing operations. As I mentioned in part one of this series, the union between MES and smart manufacturing technology gives manufacturing enterprises access to new, advanced capabilities. From increased operating margins to decreased costs, manufacturers can leverage this smart combination and find themselves with a significant competitive edge globally. In part one and part two of this three-part series, we looked at four significant new aspects of MES: mobility and the use of artificial intelligence (AI), track-and-trace database capabilities and the use of many applications. The IIoT is becoming synonymous with smart manufacturing.

The first step towards understanding IoT is rather easy. Thankfully, in a welcome break from acronyms and complex nomenclature, the term IoT – Internet of Things – quite literally means just that. Things connected to the internet so the data they produce (or data about them) can be accessed from anywhere. Viewed through a different lens, IoT is a result of faster and cheaper Internet connectivity trying to meet our insatiable hunger for data. The underlying technology is not a complex one - college students rig up IoT proof of concepts as projects; electronics hobbyists can buy simple IoT kits online to play with.

AI and machine learning's potential to drive greater visibility, control, and insight across shop floors while monitoring machines and processes in real-time continue to attract venture capital. PwC's broader tech sector analysis shows a 30% year-over-year growth in funding rounds that reached $293.2 billion in 2021. Smart manufacturing startups are financed by seed rounds at 52%, followed by early-stage venture funding at 33%. The median last funding amount was $1.6 million, with the average being $9.93 million. According to Gartner, "The underlying concept of Industry 4.0 is to connect embedded systems and smart production facilities to generate a digital convergence between industry, business, and internal functions and processes."

Unpredictable spikes and drops in demand combined with chronic supply chain and labor shortages are accelerating the pace of digital transformation in manufacturing, starting with worker safety. Forty-eight percent of manufacturers say their progress on digital transformation initiatives has accelerated so much that it's years ahead of what was originally anticipated, according to a KPMG study. Keeping workers safe and connected is the primary goal of most digital transformation and hiring plans, with on-site distancing & workplace safety listed as the two highest priorities. The company's SENTRI360 platform proves effective in preventing workplace injuries and operational downtimes at several steel-heavy manufacturing companies, including Zekelman Industries and SeAH Besteel. From redesigning shop floors, to meeting social distancing guidelines, and doubling their investment in training and development, worker safety now dominates manufacturing -- even more so due to the pandemic.

Unpredictable spikes and drops in demand combined with chronic supply chain and labor shortages are accelerating the pace of digital transformation in manufacturing, starting with worker safety. Forty-eight percent of manufacturers say their progress on digital transformation initiatives has accelerated so much that it's years ahead of what was originally anticipated, according to a KPMG study. Keeping workers safe and connected is the primary goal of most digital transformation and hiring plans, with on-site distancing & workplace safety listed as the two highest priorities. The company's SENTRI360 platform proves effective in preventing workplace injuries and operational downtimes at several steel-heavy manufacturing companies, including Zekelman Industries and SeAH Besteel. From redesigning shop floors, to meeting social distancing guidelines, and doubling their investment in training and development, worker safety now dominates manufacturing -- even more so due to the pandemic.

This study tackles on a new problem of estimating human-error potential on a shop floor on the basis of wearable sensors. Unlike existing studies that utilize biometric sensing technology to estimate people's internal state such as fatigue and mental stress, we attempt to estimate the human-error potential in a situation where a target person does not stay calm, which is much more difficult as sensor noise significantly increases. We propose a novel formulation, in which the human-error-potential estimation problem is reduced to a classification problem, and introduce a new method that can be used for solving the classification problem even with noisy sensing data. The key ideas are to model the process of calculating biometric indices probabilistically so that the prior knowledge on the biometric indices can be integrated, and to utilize the features that represent the movement of target persons in combination with biometric features. The experimental analysis showed that our method effectively estimates the human-error potential.

But just what does that mean? Many of the articles are awfully vague in that regard, usually touting fancy new gear and technologies like industrial robots, Industrial Internet of Things (IIoT) devices, digital twins, augmented and virtual reality, artificial intelligence (AI) and 3D printing, as a sort-of grab-bag of digital magic. And hey, those kinds of things can certainly be great. Heck, I've done articles about every one of them and their individual potential benefits. But I've never threatened that if you're not adopting them, you're doomed.

NUREMBERG, Germany and SUNNYVALE, CA, USA, May 5, 2021 – Google Cloud and Siemens, an innovation and technology leader in industrial automation and software, today announced a new cooperation to optimize factory processes and improve productivity on the shop floor. Siemens intends to integrate Google Cloud's leading data cloud and artificial intelligence/machine learning (AI/ML) technologies with its factory automation solutions to help manufacturers innovate for the future. Siemens and Google Cloud to cooperate to transform manufacturing by enabling scaled deployment of artificial intelligence. Data drives today's industrial processes, but many manufacturers continue to use legacy software and multiple systems to analyze plant information, which is resource-intensive and requires frequent manual updates to ensure accuracy. In addition, while AI projects have been deployed by many companies in "islands" across the plant floor, manufacturers have struggled to implement AI at scale across their global operations.