Harnessing the Hidden Power of Data to Drive Operational Certainty

In 2020, the world learned how to adapt to times of instability as COVID-19 changed the availability of nearly everything. Many of us rushed to stock up on everyday necessities like food, while some fought over rolls of toilet paper and hand sanitizer in grocery store aisles. A few were fortunate enough to align timely trips to the local wholesale store so they could sit back without worry knowing that a bulk 90-pack of toilet paper sat safely in their home inventory for future use. Regardless of our situations, we each learned to prepare ourselves under new circumstances to avoid the undesirable shortages caused by limited supply.

Since this time, the world has learned to adapt to COVID. Many have returned to work and grocery stores have been largely restocked. However, shortages of labor and raw materials continue to play a critical role in our lives and personal supply chains. Cargo ships are queued up outside shipyards waiting to unload, suppliers have shortages of raw materials and workers to fulfill orders, and the electronics industry is rattled with what appears to be a multiyear shortage of electronic circuit boards. End-user operators today must diligently prepare themselves ahead of time to avoid key shortages that could put their plant operations in jeopardy.

 

DATA-DRIVEN OPERATIONAL CERTAINTY

While we have learned to adapt to uncertainty in our personal lives, we are largely stuck in the stone ages when it comes to managing the operational certainty of our industrial assets. We often don’t have the ability to control challenges that may come our way, but we do have the ability, as well as the obligation, to anticipate and plan for them. One single poorly calibrated control valve or failing pressure relief valve can have devastating downstream effects, including inefficient operations, fugitive emissions, unplanned downtime, or even a safety incident putting at risk our most critical assets, our people.

The effects of these problems often manifest themselves in visible ways such as long lead times for spare parts, costly expedite and air-freight fees, and countless overtime hours that drain employees. However, the solution to driving certainty through these challenges is an invisible one. That solution is data.

Data is quite possibly the largest untapped resource that exists in our world today. The problem is not a scarcity of raw data, but rather a vast gap between raw data and the people, processes and tools required to turn the information into actionable insights that can enable data-driven operational certainty. As with any challenge, we start with defining the challenge itself.

 

TWO TRADITIONAL APPROACHES

The unpredictability of valve degradation and failure coupled with supply chain disruption leads to unplanned downtime, extended turnarounds, costly expedite fees and ultimately a cloud of uncertainty around the entire operation. There are two typical approaches to addressing these challenges, both of which introduce waste, risk and excessive cost into the process.

The first is to react to challenges as they arise. When a valve is pulled for service, it is opened only to “unexpectedly” discover a set of spare parts is required to repair the valve before reinstallation. This reactionary approach throws supply chains into chaos as the unanticipated demand rockets from the end user, through the valve service provider, through the valve OEM and ultimately to the raw material supplier. Countless email exchanges and emergency phone calls are required to drive coordination across these four separate entities, ultimately ending in soaring expedite fees, transportation premiums and extended downtime. In an economic environment that is already struggling to keep up with regular demand due to worker shortages and supply chain disruptions, this approach only makes a big problem much bigger.

The second traditional approach is to perform inventory planning purely based on historical consumption. This approach assumes that future demand will exactly mirror historical demand. It’s like driving your car while only staring at the rearview mirror. While even betting aggressively with this approach somewhat reduces the amount of unexpected expedites by guessing at required spare parts, it will never come close to eliminating uncertainty, because we know that historical demand often does not reflect what the future holds. In addition, this approach ties up valuable cash in wasted spare parts inventory that was projected to be needed that ultimately was not required. While this approach is better than nothing, it still leaves an operation left to run with a high level of uncertainty and inefficiency.

 

A MODERN APPROACH TO AN OLD PROBLEM

It’s time to take a different approach by leveraging the power of data to intelligently forecast and predict the future and respond accordingly. The foundation for planning, and valve lifecycle management, starts with making data-driven supply chain decisions by using a valve asset management software. This is a software solution that tracks, centralizes and stores critical data points about an asset throughout its lifecycle, including repair history, service intervals, critical dimension measurements, visual inspection data and other relevant data. By tracking this data throughout the lifecycle of a valve, we can recognize patterns and trends that indicate which failure modes are likely to occur in a valve and when they are likely to occur.

For example, by tracking and trending the critical dimension of a disc in a pressure relief valve, we can determine approximately how many more months it may take for that disc to fall out of tolerance of the manufacturer’s specification and proactively respond by purchasing a spare disc to be ready for the next outage. Or, by measuring friction over time, we can see the wear against a control valve stem that will shortly lead to excessive fugitive emissions.

Once we establish a foundation for data analytics with asset management software, we can layer on advanced analytics by implementing a condition-based monitoring (CBM) solution. While asset management software allows for tracking and managing assets at periodic intervals, a CBM solution enables data to be collected from a valve while operating in real time.

The truth is that valves are constantly providing clues about their degradation while they are performing their regular duties, but unless we collect this real-time data and turn it into something actionable, these clues remain only as potential. With a CBM solution, operational data can be collected and analyzed, and potential failure modes can be identified in real time.

By synchronizing this information back to our asset management software, we can immediately drive countermeasures to mitigate the identified potential valve failures. We can then predict which valves are going to fail, why they are going to fail, and what parts or product upgrades will be required to restore operational certainty and return the valve to its original and intended level of performance. And, most important, we can do all this well in advance of pulling the valve to perform service, which enables us to order and procure critical components early and avoid costly supply chain disruptions and premium transportation costs.

 

CONNECTING DATA-DRIVEN DECISIONS TO ADDITIVE MANUFACTURING TECHNOLOGY

Clearly, CBM and other advanced digital trend and prediction methods will reduce the risk of excessive downtime or inefficient operation, but there will always be situations where the unpredictable happens and sites are forced to advert operations and take immediate measures. While the concept of “digital inventory” is new to our industry, it is one that certainly has a future as a fail-safe method to prevent major events. Digital inventory is the ability to rapidly manufacture parts through additive manufacturing (i.e., 3D printing) without needing to hold low-usage parts on the shelf in inventory. Situations where customers find themselves without a critical component can literally be addressed with a single print cycle on-site or at a local 3D printer in the region.

Today, additive manufacturing is evolving at light speed. Printing capacity is emerging all over the world, and most locations have some access to a local or regional supplier. With advancements in reduction of time to qualify parts, expansion of qualified materials, and general experience levels of printing growth logarithmically, suppliers are now seeing the potential for digital inventory to become a wave of the future. Major additive manufacturers can now take the supplier constraint handcuffs off and move away from the limitations and reliance on a third party to supply materials. As suppliers push out lead times and cargo ships are backlogged to transport goods, manufacturers can take more control of their own destiny by printing trim parts and other non-pressure-containing products (code-controlled) using in-house means. Current supply chain challenges will likely continue in the coming year, but innovations are exceeding this pace and those who invest in technology will lead the way and eliminate the constraints so they can support the end user.

Our world of process control and pressure management is quickly transforming through digital advancements: predictive algorithms of CBM software, feeding asset management tools to trend urgency of need and direct output of print-on-demand parts. An amazing transformation of supply chain risk mitigation, inventory optimization and overall process and plant optimization will surely yield a direct bottom line impact to all users who are proactive enough to adapt and make critical investments.

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About the Author

Nathan Brunell is the executive leader of marketing for the Baker Hughes
Valves business, with 25 years in the valve industry as a product and
marketing leader.