With an increased demand for healthier, less expensive, and higher quality food products, food and beverage manufacturing facilities are quickly adopting innovations in data technology, processing techniques, and robotics. As a result, the global food technology market is anticipated to reach over $342.5 Billion by 2027.
While these technological advancements can optimize production workflows, reduce downtime, and minimize waste, determining the right combination of technology and human work processes is critical to sustaining the long-term reliability of facilities. The long-term reliability of equipment is becoming even more critical in improving operational efficiency, increasing production output, and meeting compliance with regulations.
A data-driven approach to reliability can help food and beverage facilities implement the processes and tools they need to achieve the long-term reliability of their equipment. Data-driven reliability is a framework used to help facilities collect, organize and model the data they need to make strategic business decisions.
1. Technology Alone is Not the Silver Bullet.
The advancement of technology has led to the development of sophisticated solutions, such as automation, artificial intelligence, and data analytics. However, technology alone is not the silver bullet for solving industry challenges. Yes, technology plays a critical role in finding efficiencies, but a holistic approach that leverages both technology and human intelligence is needed to fully recognize results.
For technology to be successful, it relies on accurate, reliable data, and data quality issues, such as incomplete or incorrect data, can affect the capability of making the right decisions based on the analysis presented. Collaboration with experts is also critical to interpret the results and contextualize the decisions based on individual situations. However, human resources are limited, and a data-driven approach to reliability can help facilities incorporate the best of technology and human expertise into a single approach to ensure each department within a facility is working toward a shared objective.
For example, one of the largest breweries in the world produces over 500,000 gallons of product a day. Despite having some of the most advanced technology in the world, this facility loses $50 million a year in lost production due to unplanned downtime. To minimize the amount of lost production, this facility has a goal to meet an unplanned downtime target of 3%, which equates to about $24MM in increased revenue.
To achieve this goal, each department within the brewery has to work together to achieve a common objective. While each department has its concern, a data-driven approach to reliability brings focus to an organization’s most critical actions by ensuring that everyone works toward a shared goal while meeting the individual Key Performance Indicators (KPIs) each department is responsible.
Without a specific focus, it can be difficult to know where to prioritize limited resources and which actions to tackle first to achieve this goal. For example, the primary focus of the brewing side of this facility is to mitigate any risks associated with health, safety, and the environment (HSE) that may result from equipment failure. While HSE risk is important across the entire facility, the loss of production resulting from equipment downtime is a strong focus for other departments, such as bottling and packaging. Quantifying the extent of the impact of specific failures and prioritizing tasks based on criticality is a common challenge for the industry. With a data-driven approach to reliability, this facility can link probable failures to specific failure modes to better focus on its main concerns, action the most impactful mitigation activities, and allocate its resources more efficiently.
2. Data-Driven Reliability Helps Facilities Shift to a Proactive Approach to Maintenance.
The cost of reactive maintenance is three to five times more expensive than proactive maintenance. A data-driven approach to reliability enables facilities to holistically approach complex problems by leveraging a combination of human expertise and technology. When relying solely on human expertise, intelligence models such as Reliability Centered Maintenance (RCM) or spare parts optimization can skew conservatively, wasting time and spending. With data-driven reliability, facilities can adjust their plans to make more confident decisions based on live, connected data.
For example, a manufacturing facility that produces baby formula is experiencing 15% unplanned downtime and loss of containment failures compromising production output. The site lacks a long-term reliability and maintenance plan, and as a result, about 60% of the site’s maintenance work is reactive.
The facility has a goal to switch to a more proactive approach to maintenance and is adopting a set of foundational reliability and maintenance workflows and processes to meet this goal. These workflows aim to show how the site can use its data to drive reliability and will help the site determine the health of its assets and how its programs can be combined to shift to a more proactive approach to maintenance. Specifically, these workflows will establish integrated roles and responsibilities, KPI structure and use, and create and adopt proactive procedures for individual assets and the program while accounting for regulatory codes and standards and corporate maintenance program requirements.
With the incorporation of data-driven reliability and maintenance workflows, the site is expected to increase production and minimize maintenance spending, reducing unplanned downtime for wet and dry processes. As a result, the site is expected to increase its production by 15 days per year, equivalent to at least a $10MM improvement over the next three years.
3. Establishing a Data-Driven Reliability Culture Can Help Improve Overall Equipment Effectiveness (OEE)
A data-driven reliability culture significantly impacts Overall Equipment Effectiveness (OEE), a metric used to measure equipment performance, availability, and quality. Organizations with a culture of people working toward a shared goal of reliability will experience optimized work processes, ensuring faster, more objective, and higher quality reliability decisions across all levels of the operation.
A data-driven approach to reliability can help improve OEE in three ways:
By fostering a data-driven reliability culture, food and beverage facilities create an environment where equipment reliability and performance are valued and prioritized. This ensures that maintenance practices, data analysis, and continuous improvement efforts are aligned to maximize equipment effectiveness, drive operational excellence, and increase OEE.
Conclusion
A facility’s program is only as good as the data being used to run it. A data-driven approach to reliability that leverages the right combination of technology and human work processes is critical to manufacturers sustaining long-term success and meeting the increased demand for food. While technology is a great enabler, the true value lies between the dynamics of the experts and the analysis performed by the systems. A data-driven approach to reliability positions facilities to make this collaboration seamless, enabling food and beverage facilities to make more informed decisions in an ever-evolving landscape.