Striking the Balance: Technology and Human Insight in Industrial Reliability

In the age of automation, artificial intelligence, and data analytics, the temptation to rely solely on technology for industrial problem-solving is strong. However, the truth stands stark: technology alone isn’t the ultimate fix for solving industry challenges. While reliability technologies can play a crucial role in finding efficiencies, organizations facing staffing challenges are also exploring technology as a substitute for human presence in critical roles. Yet, a holistic approach that leverages both technology and human intelligence is needed to fully recognize results.

The success of technology is contingent upon the availability of precise, dependable data. Any deficiencies in data quality, whether due to incompleteness or inaccuracies, impede the ability to make sound decisions based on the analysis conducted. In tandem, collaborating with experts plays a pivotal role in deciphering outcomes and adapting decisions to suit unique contextual circumstances. However, human resources are limited. Here, a data-driven approach becomes crucial.

A data-driven reliability approach harnesses precise, contextual data to fuel informed decision-making in industrial settings. By meticulously collecting, organizing, and interpreting data, this framework enables confident, strategic choices. It ensures the right data powers intelligent insights, allowing for scalable, efficient, and impactful decision-making processes across facilities. A data-driven approach to reliability can help facilities incorporate the best of technology and human expertise into a single approach, ensuring all departments work toward a shared objective.

Illustrative Case Study: Brewing Industry

Consider a prominent brewery, among the world's largest, churning out over 500,000 gallons of product daily. Despite boasting cutting-edge technology, this facility grapples with a staggering $50 million annual loss attributed to lost production caused by unplanned downtime. To curtail this substantial financial setback, the brewery aims to cap unplanned downtime at a mere 3%, translating to a potential revenue increase of around $24 million.

Achieving this target requires cohesive teamwork across each department within the brewery—despite the fact that they each have their own focused concerns. For example, the primary focus of the brewing side of the facility is to mitigate any risks associated with health, safety, and the environment (HSE) that may result from equipment failure. Simultaneously, departments like bottling and packaging place paramount importance on mitigating production losses due to equipment downtime.

Despite their unique departmental concerns, employing a data-driven reliability strategy prioritizes the organization's pivotal actions. This approach fosters a unified pursuit of common objectives while allowing departments to meet their individual Key Performance Indicators (KPIs).

A data-driven approach to reliability empowers facilities to quantify the impact of specific failures and prioritize tasks based on their criticality. By embracing this method, the facility can associate potential failures with specific failure modes, allowing for a more targeted focus on core concerns. This, in turn, facilitates the implementation of the most impactful mitigation activities while optimizing resource allocation for enhanced efficiency.

Conclusion

The allure of technology in industrial problem-solving often overshadows the fact that it's not a standalone solution. While reliability technologies hold immense potential, their efficacy hinges on a synergy between technology and human intelligence. This collaboration ensures technology serves as a practical tool guided by human understanding, maximizing its potential for sustained success in shaping a more effective industrial landscape.