We know that a stitch in time saves nine, and that translates to saving millions in manufacturing plants. Take this scenario: A maintenance technician uses a new thermographic camera to check for hot spots on electrical connections. When one connection looked suspicious, he initiated an inspection work order to follow up on the findings. As the connection point was traced back through the plant, the crew discovered that vibration loosened a connection on the main power feed to the building and a complete loss of power was imminent. During the weekend the plant manager adjusted the production schedules while parts and skills were aligned to make the repair, averting the potential for losses in the millions of dollars.
Given the recession and volatile energy and commodity prices, manufacturing companies must avert disaster. It’s the table stakes to compete in the global economy.
But is that good enough?
Smarter maintenance has evolved to become more nuanced to not only avoid failure but to optimize plant assets in every way. Plants now apply a more scientific approach to maintenance, repair and overhaul (MRO) to decide the right work on the right assets by leveraging data to establish solid processes and well-defined response plans. The resulting intelligence can drive more evidence-based business decisions and contribute to the bottom line. On average, enterprises that optimize workforce performance save about 10% on maintenance staff budgets while increasing job security of the staff.
To win competitively, many plant managers are looking to enterprise asset management (EAM) as a critical business system platform on which to base their decisions.
Building a foundation for EAM
EAM is simply a process that gives insight into assets, their condition and work processes through the collection of data for better planning and control. With EAM, maintenance professionals can identify potential problems before they occur and use data to identify more efficient operations, fostering greater alignment of the maintenance organization’s goals with overall business objectives.
Although everyone in the plant wants the assets to function properly, there are some conflicting objectives afoot. For maintenance managers, the goal is to minimize asset downtime and respond to changing operating conditions before problems arise. For production managers, the goal is throughput, sometimes at the expense of the asset’s life.
In addition to aligning objectives for assets, another factor might exist: it’s hard for maintenance technicians to access important data to pursue smarter asset maintenance. This is most often the result of disconnected systems that different departments might have produced to support their individual work efforts. Data needs to be culled from across the plant and across divisions for a single view of the truth.
Asset relationships matter
Any maintenance manager can tell you which equipment on the plant floor causes the most downtime pain. But when it comes to effectual downtime — a breakdown or problem related to other equipment connected to the whole system — it can be much harder to identify. That’s why it’s critical to understand component relationships to better identify key assets. The relationship of one asset to another might be determined by a custom recipe for each customer order or it might be standard according to the model being produced. In either case, the subcomponents must be understood in the context of how they work together. These relationships also should include “soft” components — the lines of software code. Because these soft components control each asset, they’re important factors in uptime and equipment performance.
Once the asset relationships are understood, you can determine the approach to maintaining it, based on how critical to the operation something might be. Rating relative importance of the asset is a key factor to supporting advanced management strategies. Ranking order then determines the level of focus the team gives on improving productivity.
Monitoring of key assets
You don’t have to look farther than the 2010 oil spill in the Gulf to understand, in certain industries, some assets are so important they warrant specialized applications to manage and monitor complex processes. Automatic valves can be monitored at will for response to test system readiness, for instance. Because EAM solutions have been evolving for decades with increasing focus on precise vertical industries and deep capabilities that align with each industry’s specific needs, platforms to manage this type of visibility are readily available at a relatively low cost. This is especially true when it comes to interconnecting monitoring systems. Open standards have driven down the cost of implementing such things, making them available and sustainable for a larger group of enterprises.
Analyzing the data delivers insight
Tracking physical asset condition and history on a timely basis generates large volumes of data. Analytical tools help extract data set features and characteristics to turn them into useful information. For example, predictive analytics can reveal the state of the equipment and any correlation between failure type and states of readiness. These answers present opportunities to connect the disparate dots. Predictive analytics can help put in place measures to ensure asset health because it can identify potential problems, periodically or in real time, before they degrade the process or lead to catastrophic failure.