The Three Zeroes for Managing Production Operations

The Three Zeroes for Managing Production Operations
The Three Zeroes for Managing Production Operations

Asset Performance Management 4.0 (APM 4.0) must assure all assets perform their intended duty as efficiently as possible for its whole lifecycle with minimal risks. Above and beyond asset availability and productivity assurance, APM 4.0 can also greatly influence safety and environmental compliance. Throughout the industry all executives at industrial manufacturing companies take safety and environmental protection very seriously. A company protects its license to operate by performing well in the two key performance indicators (KPIs) that assess safety and environmental achievements. Doing badly can severely restrict company’s capability to do business and produce its intended goods. After safety and the environment, productivity is the executive’s next important KPI. We call these critically important items the three zeros (0, 0, 0) and every executive wants all three:

  • Zero casualties – do no harm to people

  • Zero environmental incidents – do no harm to the planet

  • Zero breakdowns – do no harm to the operational profitability

That trifecta of zeroes in that order is the emotive and visceral representation of the main job of a C-level executive and drives down to all his reports. This blog will show how APM 4.0 products contribute to the effort by targeting 0, 0, 0 in performance initiatives that can make it happen.
Condition-based monitoring (CBM) tools in APM attempt to prevent equipment failure by reading sensor signals to assess health and predict troublesome conditions. However, recently developed novel CBM applications provide earlier, more accurate warnings than their aging counterparts in much more user-friendly ways that take less skills and time to build and run working solutions. So, what’s changed? The older tools use sequences of rules and/or equipment models made by experts from engineering and statistical principles. They take a long time both to develop and implement, don’t work so well, deliver results needing intense scrutiny that are often false, and need more expertise to keep them up-to-date as conditions change.

The new state-of-the-art applications now use pattern recognition technology underscored by AI/machine learning and need only data to learn extremely accurate behavioral patterns of normal, abnormal, and explicit failures. They learn from the past and from the discoveries are able to predict the future. The AI/machine learning technology is what you’ve seen automating driving, doing facial recognition, and credit card fraud detection. It is the most pervasive analytical technology used in solutions world-over and has made older techniques redundant. The best APM 4.0 solutions embedding AI technology offer superior forward-looking capabilities.
The advanced novel applications look across archives of data for minuscule changes in many, many variables and across time, to “see” and do what humans cannot; recognize multi-dimensional patterns. Such patterns become signatures of future events so the applications then persistently examine incoming data every few minutes to provide early warnings of imminent conditions, forever, and always continue to learn and adjust automatically. This capability means novel applications can do two things people and less capable products can’t:

  1. Detect patterns of equipment deterioration and failure earlier in weeks and months,

  2. See the signatures of very specific root causes and failure modes in data patterns, and

  3. Recognize the process operating conditions that cause equipment damage and may cause catastrophe if not corrected.

Additional time to understand and act is the essential APM 4.0 element that enhances safety and environmental performance? When a piece of equipment suddenly fails without warning it can cause a catastrophe. You’ve seen the news-worthy disastrous events. The operators are often not ready and sudden unexpected process changes can cause off-spec product that can further damage machines, or result in product waste going to flare stacks into spill-over ponds, or rivers and the ocean. Additionally, personnel safety may be compromised during the breakdown event, with emerging unknowns, when everything is rushed with potentially ill-considered decisions.
Michael BrooksThe extremely early warnings of impending failures afforded by new technologies can give much needed time to take the equipment out of service in an orderly, safe and environmentally sound manner. Equally, when such applications can inform the users very early about the exact root cause of the impending failure maintenance and operations can plan precisely and prepare early for a known circumstance. In doing so they minimize the production interruptions, can consider cost/risk alternatives for how best to deal with the issue. It could be dangerous and need action immediately. Potentially, a slight change in operating conditions may extend the time-to-failure with less consequences on product delivery schedules. Or with 40 or 50 days’ notice it may be possible to build intermediate inventory and reduce the impact on overall production. But the extra time allows all of the foregoing possibilities to occur in a calculated, orderly manner reducing unwanted incidents and side effects.
For example, when the patterns show that damage-causing conditions are process related, the APM 4.0 application can deliver precise prescriptive advice to adjust the process appropriately to avoid further degradation and thus forestall breakdowns and consequent maintenance services. The prescriptive advice might declare failure cannot be avoided (the bearings are “square”) but still provides additional time to plan and prepare for service and repair. In all cases of degradation and impending failure the APM 4.0 technology approach offers time and advice to assure ongoing plans are orderly, safe, and environmentally protective – assuring the C-level executive keeps a 0, 0, 0 record.

About The Author

Mike is Global Director APM Solutions at AspenTech. Previously, he was COO of Mtell, which pioneered machine learning for managing the health of industrial equipment. Mike has also served as a venture executive with Chevron Technology Ventures and held senior roles at five startups. He began his career as an engineer at Esso and Chevron.

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