Despite representing a significant portion of the global economy and large expenditure towards digital transformation, the manufacturing industry's productivity has stalled. Plato explores the crucial blind spot at the root of this paradox, highlighting the need for a radical transformation through industrial AI.
Manufacturing plays a key role in economic health, accounting for ~16% of the global GDP and generating over $2 trillion output within the US alone. However, the sector is facing a productivity dilemma. The United States, which represented approximately 28% of global manufacturing output in the early 1990s, saw its share drop to 18% by 2020. Although there have been substantial investments in digital technologies and automation under Industry 4.0, manufacturing productivity is stagnating and often lags behind other industries. Data from the Department of Labor even shows productivity declining in some key areas.
The manufacturing landscape is filled with both old and new challenges. Operational hurdles, global competition, and the need for cost efficiency drive manufacturers to constantly seek out solutions. The recent focus on onshoring and reshoring, fueled by supply chain vulnerabilities and national security concerns, highlights the sector’s strategic importance.
Given manufacturing's significant contribution to GDP, its productivity slump has broader economic implications. The labor market further reflects this tension. While there are 13 million manufacturing jobs and over 500,000 vacancies in the US, companies struggle to fill these roles due to skill gaps and the industry's declining appeal among the workforce.
There is one critical blind spot at the root of this productivity challenge: understanding human operator behavior in factories.
Despite the digitization of numerous operational aspects, teams are still unable to capture the full picture of what operators are doing. Traditional methods to gain insights, such as Gemba walks or mezzanine observations, are outdated, time-consuming, and ultimately insufficient in providing total visibility. As a result, meaningful optimization remains out of reach.
This lack of comprehensive operator data also blocks the effective application of AI and automation. Current technological applications in manufacturing are therefore limited and unable to unlock the full potential of digital transformation.
Plato has developed a revolutionary platform to address this blind spot. The system employs advanced AI algorithms on data collected by Plato’s proprietary sensing technology, and it digitizes human operator activity without needing physical tags on personnel or equipment. Plato’s data fusion engine merges human activity patterns with digital data gathered from machines and other factory systems. Additional AI layers are then used to continuously condense and decode this content-rich data, transforming operations data resulting from all human and machine activities into objective metrics and actionable insights.
By digitizing and distilling these critical operator-machine interactions, Plato provides a virtual window into factory operations—empowering both management and operations teams with 24/7 objective visibility across shifts, manufacturing lines, and even sites. With direct access to the complex interactions of operators and machines, variations in productivity can now be instantly surfaced, root-caused, and mitigated. This AI-powered operations digital twin enables precise productivity tracking and process optimization, providing the crucial missing link in the data chain.
The US manufacturing industry is full of hurdles, but ripe with opportunity for revitalization. The key lies in embracing Industry 4.0 to bridge the gap between traditional operations and modern efficiency demands. By prioritizing the critical need to understand and optimize factory operator behavior, solutions like Plato can guide manufacturing to the forefront of innovation and productivity. Now is our chance to shape the future of the industry by moving forward with data-driven precision.
