Plato empowers modern manufacturing organizations to unlock 20% productivity improvement in just 60 days, with zero CapEx investments or the need for process and people changes for deployment. Founded in 2019 by Silicon Valley veterans, Plato has developed the first ever tagless Spatial Intelligence Platform (SIP) to track spatial activity patterns of operators and machines. In manufacturing environments, SIP helps organizations ensure that operators are at the right place at the right time. As automation is central to production environments, people play a critical role in maintaining the uptime of the production lines. Without trained professionals overseeing the equipment, production lines would grind to a halt.
Forty percent of global GDP is driven by physical operations, and companies of all sizes depend on manufacturing efficiency to boost profitability. From global-100 companies to small and medium-sized businesses (SMBs), managing and improving production efficiency is a constant challenge.
Companies have relied on people, process, and technology to drive advancements in manufacturing productivity for the past five decades. However, the world has reached an inflection point as these approaches are no longer providing the magnitude of improvements they once did.
Finding, training, and retaining people to operate complex and automated manufacturing lines is becoming increasingly challenging. Even with the best managers, training, and processes, production variations exist across sites, lines, and shifts that are difficult to decode and address.
This is because there is not enough context to diagnose issues with machine data alone. Explaining variations in production output requires a clear understanding of human-machine interactions. Overcoming this challenge is particularly critical when a company has operations across multiple plants, or is planning to set up new sites while maintaining consistent levels of productivity and quality everywhere.
Kaizen continuous improvement and Lean methods have been transformative, but they are now reaching saturation and are no longer scalable. The growing reliance on automation introduces challenges to the traditional Kaizen processes. The problems that now arise are inherently more complex, requiring Kaizen practitioners to have advanced infrastructure and skills in data analytics, in addition to knowledge of manufacturing processes. Even with Kaizen methods, continuous monitoring and objective observability are critical components for measuring effectiveness of these tools and for sustaining improvements.
Industry 4.0 investments have fallen short on their promise to improve productivity. Robotics and automation have dramatically improved manufacturing efficiency over the past 50 years. And while they continue to enhance production output , newer technologies like industrial IoT tools with sensors and RFID tags have not had the same impact. Companies invested millions, changed processes, and trained people with the expectation that Industry 4.0 would deliver comparable value to robotics and automation. Unfortunately, the return on these investments has been poor and many projects are stuck at the proof-of-concept stage.
In modern manufacturing, technology plays a crucial role in driving productivity. With the increasing reliance on automation, there is a corresponding dependence on humans to ensure that the manufacturing lines are operating at optimal capacity. In highly automated environments, even seconds and minutes of downtime can affect productivity. While machines are capable of making parts, they cannot solve problems autonomously. When equipment is down or needs maintenance, human interventions are critical to optimizing uptime.
Plato's technology provides the missing link for a comprehensive Operations Digital Twin by digitizing operator activity patterns
Even with fully automated production lines that utilize the same equipment and processes, companies experience variances in production output across different shifts or locations. While companies suspect that this variation stems from the complex interplay between every component of the manufacturing ecosystem including machines and operators, they have no way to track or measure these factory interactions across shift teams or various lines. Going to the “Gemba” has traditionally been the Kaizen way to observe and root-cause factory issues. However, this is not scalable in a modern factory since supervisors can’t be everywhere at all times, and the complex processes of modern manufacturing are are not always visually observable. That’s why Plato is bringing these continuous improvement methods into the AI era.
Plato’s advanced AI-enabled sensor technology is the first platform to digitize operator activity at scale for the entire site. This new spatial activity data stream is fused with factory data to provide unique actionable insights and to enable organizations to gain a full understanding of factory behavior and operator-machine interactions. The platform enables plant management to identify and address the root causes of production efficiency variations across shifts, lines, and plants.
Whether it is a process or training-related issue, Plato equips managers with meaningful and actionable data to drive a modern continuous improvement process. And unlike traditional time and motion studies, Plato captures data continuously 24/7. This allows managers to account for variables that can impact daily, weekly, or monthly production in a highly dynamic environment that is constantly changing due to new employees or products, changeovers, or process modifications. The Plato platform is easy to deploy with no process or people changes required, and delivers actionable insights in 8 weeks or less. Our pilot program enables customers to quickly see the benefits of the Plato platform in their environment. We can get you live in less than a week and you can experience Plato first-hand.
