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Increase Profit, Reduce Risk by Knowing All the Answers

Increase Profit, Reduce Risk by Knowing All the Answers

Quantifying the Value of Accurate Reliability Modeling

When it comes to plant design and reliability, making the best decisions should never be done on “gut feel” or over-simplified analysis. You need quantifiable answers you can trust to make the most profitable decisions.

To achieve this level of accuracy, it’s essential to have a reliability, availability and maintainability (RAM) analysis tool that can handle the real-world challenges of today’s process industries. Small over-simplifications in a simulation can add up to big margins of error! Designing a facility, the correct way the first time can save millions — or even billions — in design corrections, debottlenecking or over-spending on capital.
 

To illustrate this point, I’d like to look at a number of real-world challenges that have been analyzed by Aspen Fidelis Reliability™, a robust RAM and risk analysis tool.

 

 

Fidelis in the Field

For each of these cases, simplistic RAM tools or Microsoft Excel required the user to ignore or oversimplify the challenges they were facing. The difference in the level of accuracy between the “simple” method and the Fidelis method was calculated. The results are represented in terms of percentage of accuracy so as to extrapolate the real impact on revenue across multiple industrial verticals. 

 

Applying this to an example vertical (a typical 200 kbbl refinery at $20/barrel), an increase of 1 percent in accuracy would represent $14.6 million annually. 

 

Challenges Examined:

  • Flow optimization/complex routing of flows

  • Tank/buffer optimization and tank leveling

  • Conditional or variable impact of failures

  • Impacts from logistics and supply chain

  • Equipment aging or incomplete renewal after maintenance

  • Non-time-based failures

  • Spare parts optimization

For the baseline model, all seven of these real-world challenges were integrated. For each subsequent case, one challenge was systematically removed, and the simulation was rerun. In this way, Fidelis was able to quantify the delta between robust stochastic modeling and the simplified alternatives. 

 

In addition, a case was run with all seven real-world challenges removed. This case represents the full impact of ignoring the complexities that compose each typical processing and manufacturing facility around the world. 

As you can clearly see, you can’t afford to oversimplify or ignore the real-world complexities that make the process industries run! The proof is in the ROI.  Using a conservative refinery (200 kbbls/day) as an example, annual margins in error ranged from $-30M to $+41M. If the absolute value of error was summed, the delta between Fidelis and a simplistic RAM tool would have amounted to 9.3 percent ($136M). 

 

 

Accuracy Drives Profitability

Can any manager be expected to make the big decisions with almost a 10 percent margin of error in their “quantitative analysis?” The obvious answer is no!

 

Aspen Fidelis Reliability uses a system approach to reliability. It allows you to quantify the true value of any improvement project, maintenance change, operations improvement or supply chain constraint. In addition, Fidelis will give you an accurate, comprehensive bad-actor list, quantified by lost revenue and production — not just by maintenance. You can more effectively perform lifecycle analyses on assets, including asset utilization, overall equipment effectiveness and other parameters that define operating conditions, reliability and costs of assets.

 

With the right tools, decision-makers can maximize the economics of business decisions by going beyond the equipment level and accurately predicting future asset performance of the whole system.

 

Check out this video to see the differences for yourselves, or download my full-length white paper on this topic.

 

 

 

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