David Immerman is a consulting analyst at S&P Global Market Intelligence. He is responsible for executing on a range of custom research initiatives and development of thought leadership across technology sectors including industrial IoT, digital transformation, edge computing, AI/machine learning and fintech, among others, and verticals such as manufacturing and automotive. Before joining S&P Global Market Intelligence in 2022, David ran competitive intelligence at Interos, a supply chain risk management software startup. Prior to that, David served as a senior research analyst for four years at PTC, a global industrial software provider. At PTC, David conducted research initiatives including competitive intelligence and developed thought leadership content across IIoT, augmented reality, digital transformation, digital twins and technology impact on discrete manufacturers. Prior to PTC, David was a research analyst on 451 Research’s IoT team, mostly covering smart transportation, including automotive technologies such as fleet telematics, connected cars, ADAS and automated driving. David holds a bachelor’s degree in business administration from Marist College.
Manufacturers are rolling out machine-learning deployments at unprecedented scale. What makes a manufacturing ML use case ripe for adoption depends on a mix of criteria spanning challenges and opportunities. Here we measure the most impactful ML use cases for manufacturers in correlation to adoption.
Manufacturers are on a fast track to increasingly autonomous production and are placing sizable bets via acquisitions of both physical and digital systems to differentiate from incumbents and emerging competitors and become a one-stop shop for this autonomous future.
OEE is a universally known measurement of the efficiency of a manufacturing process across availability, performance and quality. The metric's lack of improvement over time, across companies of different sizes and production types, may indicate that action is needed to achieve real improvements.
Manufacturing operates on old and new information, yet much of this information or data takes an unstructured, siloed and/or inaccessible format. Generative AI can unlock these black boxes of data to create value for manufacturing companies. In this report, we look at the top use cases for the industry.
The term "digital twin" is expanding into real-time interactions as the cornerstone of industrial metaverse use cases. They represent a major step in digital transformation, with multiple processes and IIoT streams forming into contextual representations of state, which can be used in many applications.
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