Forbes estimates the cost of downtime alone is over $50 billion each year for industrial manufacturers. This is more than substantial and can often be the difference between a company making or losing money. The article points toward predictive maintenance gaining prominence to significantly reduce downtime, to learn faster and predict failures long before stoppages can ever happen.
A common misconception when it comes to Digital Transformation and Industry 4.0 is that with the rapid adoption of the technology gamut associated with them, the manufacturing operation would, as a step change, transform into a fully automated endeavor, orchestrated almost entirely by autonomous AI applications, self-optimizing and self-driven.
As industrial and energy market incumbents rebound from Covid, digital transformation has become a key pathway for companies to build resilience to risks and ensure business continuity. To that end, many enterprises have sought to simplify their technology (tech) stack from the ISA-95 (Purdue) model to a leaner tech stack in which real-time process automation can thrive. This means the impetus on having data engineering, management, and analytics-- that is, a data-oriented business model as the business driving force-- has become a top priority. As part of this permanent shift, enterprises must also seek to accelerate digital transformation of their frontline workforce and related operational processes to realize higher efficiency gains, if not implemented already. By doing so, enterprises will gain operational intelligence through the frontline workforce, meaning all events associated with the workforce can be tracked, managed, and optimized for performance improvements and productivity gains.
Digitizing tasks and activities for frontline workers, while providing real-time operational visibility and driving process optimization and improvement, across all areas of business operations.
