Articles, Industrial IT, Smart Manufacturing

Key Topics to Consider in Your Engineering and Manufacturing Excellence Journeys

This article was written by Alex Oliveira, Reliability, Engineering and Maintenance at Eli Lilly and Company.

With the advance of modern technologies, IoT, Industry 4.0 and all revolution technology brings to manufacturing floor, it is fundamental do not lose track of old, but very effective, engineering practices, in order to ensure manufacturing excellence through continuous improvement and a robust reliability program. The topics below are a summary of what was presented at Pharma TechOps San Diego conference in March, 2019.

Engineering Tools bringing value (FMECA)

Engineering tools are a must in the context of manufacturing excellence. In order to be effective and efficient while assessing equipment, planning actions, constructing a robust control strategy, the usage of right tools is critical path to such accomplishments. Among a broad range of options, FMECA (Failure Modes, Effects and Criticality Analysis) is a very good choice not just during design and qualification steps, but to empower maintenance strategy and boost other elements of manufacturing control strategy. Its Criticality Analysis gives a quantitative bonus to the qualitative approach of old FMEAs, bringing additional value to assets, equipment and systems mapped through this powerful tool.

Proper RCFA (Root Cause Failure Analysis) utilization

RCFA, more than a regulatory step of our GMP controls, is the cheapest way to avoid asset’s failure recurrences and consequently reduce maintenance cost. A good RCFA tool, irrespectively of using market software options (e.g. RealityCharting, Isograph, Reliasoft, etc) or more simple and traditional alternatives (e.g. Ishikawa, FTA, 5 Why’s, etc), need to be part of your training plan for team building and adequately investigate issues in manufacturing floor. Cross-functional teams, along with a good and experienced facilitator, can accurately identify contributing and root causes, as well as trigger action plans to mitigate or eliminate such causes. Failures not addressed result in high costs with reactive maintenance, downtime impact, and potential regulatory risk to your processes. A good RCFA strategy favors asset’s reliability, as well as GMP control strategies.

Deployment of Robust Reliability Analysis (RAMS)

A good method favors efficient processes. Although this phrase is a common place, on regard of Maintenance & Reliability, among many options, a good choice is RAMS (Reliability, Availability, Maintainability and Safety). Some people consider RAMS just for Design Phase and/or to specific areas such as space industry or high speed trains manufacturing, but this is a paradigm. RAMS methodology can be deployed on maintenance, with very low implementation cost, using Analytics & Business Intelligence tools tracking CMMS databases. With simple equations processing thousands of maintenance data, as well as crossing such information with Historian data coming from automations systems, allows great results through robust engineering analysis. Additionally, good savings with assets management (anticipating downtime of evolving failure modes in equipment) are granted with such robust methodology, as well as better business decisions over assets.

Risk Assessments enabling safer environment (HAZOP)

Not addressed hazards can ruin your business, once lives cannot be replaced. On this context, a good way to avoid incidents and promote a safe environment is proactively assessing risks and hazards by means of adequate RA (Risk Assessment) tools usage. The idea is not reinvent the wheel, but make use of consolidated risk assessment tools (e.g. HAZOP, ALARP, QRA, LOPA, etc) to properly identify and prioritize action plans to address issues. Hazop is a good option in face of its synergy with FMEA, facilitating groups’ formation and saving money with combined training (FMEA, FMECA and HAZOP). The point is that RA is one of the best solutions to proactively avoid safety incidents.

Eng Standards application with periodic assessments

Any Engineering Organization has to be built over a solid basis. A good Engineering Standards Governance Process not just create such basis, but also contribute with GMP compliance and Engineering costs reduction. Using corporate engineering standards, along with good international norm packages (like from IEC, DIN, BS), is fundamental to assure regulatory requirements are gap assessed since design phase. During Design Review (or Design Qualification) we have the best opportunity to audit new equipment according to our own requirements and our country’s requirements. It’s cheaper to adequate an equipment while it’s not installed in our facility (and during Factory Acceptance Test phase assess it), what will provide additionally less maintenance costs, more operational safety and regulatory compliance. Latter, periodic Engineering Standards gap assessments can close the loop, assuring our equipment and instalations maintain its compliance state.

Analytics allowing DDM (SAP Business Objects for IBM MAXIMO)

DDM (Data Driven Maintenance) it’s a powerful method for maintenance costs reduction, as well as manufacturing processes’ improvement. Decisions based on data, and not just on insights, are obviously more reliable. DDM can be facilitated making usage of company’s own Analytics conventional tools. Our ERP (Enterprise Resource Planning) packages can come with (or be upgraded to have) analytics and business intelligence tools. Such tools empower the usage of maintenance history of our CMMS systems, generating profit with process optimizations with very little investment.

Most of analytics tools are very user friendly, with a very fast learning curve, besides the fact of being normally compatible with distinct systems. Therefore, such tools are good options even for companies with distinct industrial systems (i.e. SAP as ERP and IBM Maximo as CMMS).

The usage of historian systems (e.g. for OEE purposes) do not replace analysis obtained with CMMS systems, once they bring distinct deliverables; The best way is benefit from both sources of information in order to get the best of each.

Patterns identifications in CMMS work orders can be very opportune for AI (Artificial Intelligence) algorithms, in the same way they currently are being applied for Operational Optimization. The way Work Orders are registered, key words used, services duration, and many other parameters can be powerful elements to boost maintenance savings, when they are processed by AI algorithms.

Conclusions

Maybe there’s a long way yet to go until most of automation level of artificial intelligence, cognitive maintenance and machine learning to be reliable enough and cost effective, for all industrial processes. But with the fast paced speed that new technology breakthroughs are emerging every day, the so dreamed desire of full Industry 4.0 benefits can turn into reality sooner than we think. Traditional engineering methods and tools, empowered by modern automation resources as analytics and business intelligence tools make a good partnership to speed up our reliability, maintenance and operational excellence journeys inside new scenario of digital era.

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