How do you communicate and justify the value of AI-based predictive maintenance to your stakeholders?

AI and IIOT adds up some cost components to the equipment package with significant advantages. The concept can't be generalized and justified for all, but the best fit for few equipments; highly critical and bad actor. The strategy to convince the customer is different for the existing operational unit and new units. IIOTs advantages are but not limited to are; protect equipment from expensive catastrophic failure, capturing valuable data online to get performance and reliability insights and mobilize proactive action, and uptime assurance. Knowing technical features, translating them into real advantages, provide return on investment, and simplify / clarify the ownership and scope are the way to convince the stake holder.

The fundamental of maintenance procedures setup based on OEM guideline based on their design life cycle or best practices. Ai based predictive maintenance program evolve based on additional sensors installed and deep computation / analysis which unique to each plant. This would be another source of guidelines for executing maintenance program with optimal resources and downtime.

Alinhar a solu??o com os problemas e metas, demonstrando como a IA pode ajudar a alcan?ar melhores resultados. Compreender como os pontos problemáticos, desafios e oportunidades das partes interessadas é crucial para o sucesso. Considerando sempre os custos de manuten??o, seguran?a, confiabilidade e eficiência operacional.

AI is all of the buzz right now and it can be either easy to justify given the excitement around it's potential or difficult given the hype cycles we've been through. Right now it feels like every organization is on one end of the spectrum. To justify AI-based predictive maintenance, highlight the ROI through reduced downtime and maintenance costs and align it with business goals like efficiency and safety, and reference successful case studies. There are competitive edge differentiators and there can be low-risk pilot projects to demonstrate its value.

Understanding needs and goals entails actively listening to concerns, priorities, and pain points in maintenance operations. Tailor communication to address specific challenges and objectives, ensuring alignment with AI-based predictive maintenance solutions. This fosters trust, engagement, and successful implementation by demonstrating how these solutions directly meet stakeholders' requirements and contribute to their overarching goals and objectives.

The real value of AI-based predictive maintenance is often not clearly communicated because the financial benefits aren't quantified explicitly. In my experience, stakeholders appreciate simplicity and clarity. Avoid technical jargon and focus on explaining the direct benefits, like reduced downtime and cost savings, in clear financial terms. This approach helps stakeholders from all areas—technical to executive—grasp the practical impacts of predictive maintenance on their specific responsibilities and goals.

Predictive maintenance offers a range of benefits, including reduced downtime, lower maintenance costs, improved asset reliability, extended equipment lifespan, enhanced productivity, and better safety and compliance. By leveraging data-driven insights and predictive analytics, organizations can optimize maintenance practices, maximize asset performance, and gain a competitive edge in today's dynamic business environment.

A manuten??o preditiva baseada em IA é uma abordagem avan?ada para a gest?o de ativos e equipamentos, que utiliza inteligência artificial (IA) e análise de dados para prever falhas e realizar manuten??o antes que ocorram problemas. Identificar padr?es, tendências e anomalias que podem indicar possíveis falhas ou problemas de desempenho no futuro. Aqui est?o alguns dos benefícios chave da manuten??o preditiva baseada em IA: Redu??o de custos de manuten??o: Aumento da disponibilidade de ativos: Melhoria da eficiência operacional: Prolongamento da vida útil dos equipamentos: Melhoria da seguran?a no local de trabalho: Otimiza??o do uso de recursos: Suporte à tomada de decis?es informadas:

Emphasize key benefits such as: Cost Savings: Reduction in maintenance costs through early detection of issues. Increased Uptime: Prevention of unexpected equipment failures, leading to higher productivity. Prolonged Equipment Lifespan: Optimized maintenance schedules extend the life of machinery. Safety Improvements: Reduced risk of accidents through timely interventions.

?La IA no es la solución; es una herramienta potente?. — úsala sabiamente. El mantenimiento predictivo basado en IA nos ayuda a anticiparnos y optimizar costos para mantener una operación óptima. ?Cómo comunicarlo? ? A los ingenieros les muestro cómo la IA puede detectar anomalías que ellos podrían pasar por alto, evitando costosas averías. ? A los financieros les presento el ROI del mantenimiento predictivo, con datos concretos sobre ahorros en costes y aumento de la productividad. ? A la alta dirección les explico cómo la IA mejora la seguridad, la eficiencia y la sostenibilidad de la planta. Principalmente recordarles la importancia de la inteligencia humana sobre la inteligencia artificial. Simplifica tu gestión….

The ROI of AI-based predictive maintenance is usually straightforward to calculate. While we can't predict alternate futures (e.g., would the bearing have failed without intervention?), many sites have experienced similar failures before. Use historical data: if a past failure caused 8 hours of downtime at $10,000 per hour, preventing it saved $80,000. Subtract the time spent on corrections—say 1 hour—so the net savings are $70,000. Then, compare this with your yearly investment in the solution. Many customers see at least a 5x ROI, and you should aim for similar results.

Demonstrar o valor e o retorno sobre o investimento (ROI) da manuten??o preditiva baseada em IA é essencial para ganhar o apoio das partes interessadas e justificar o investimento na implementa??o dessa abordagem. Aqui est?o algumas maneiras de fazê-lo: Redu??o de custos de manuten??o: Aumento da disponibilidade de ativos: Prolongamento da vida útil dos equipamentos: Melhoria da eficiência operacional: Redu??o de custos de seguran?a: Análise de retorno sobre o investimento (ROI): Ao demonstrar o valor e o ROI da manuten??o preditiva baseada em IA de maneira convincente, você estará mais bem preparado para ganhar o apoio das partes interessadas e obter os recursos necessários para implementar essa abordagem com sucesso na organiza??o.

Justifying AI-based predictive maintenance means highlighting its ROI through tangible savings and operational improvements. For example, General Electric reduced unplanned downtime by implementing AI, slashing maintenance costs and boosting productivity. Detailing specific outcomes—like a 20% reduction in downtime—offers solid proof of benefits. Additionally, emphasize enhancements in customer satisfaction due to fewer disruptions and improvements in compliance, safety, and reliability. Use data to show how AI minimizes risks and liabilities. Presenting these facts and figures convincingly demonstrates the multifaceted value of AI in predictive maintenance, from cost savings to strategic advantages, making a strong case for investment.

It's a common misconception among maintenance managers that ROI from AI-based predictive maintenance is hard to quantify. However, calculating ROI by dividing the total cost of investment by the accrued benefits and presenting this analysis in a business case. More importantly, guiding stakeholders through the process of identifying key performance indicators for predictive maintenance can reassure them of the project's potential success.

Addressing these challenges and risks requires a comprehensive approach, including data governance, algorithm transparency, bias mitigation, cybersecurity measures, skills development, change management strategies, scalability planning, and regulatory compliance frameworks. By proactively addressing these issues, organizations can successfully implement predictive maintenance and unlock its full potential to improve asset reliability, reduce downtime, and enhance operational efficiency.

Explaining AI-based predictive maintenance includes discussing inherent risks, especially since many industrial firms are early adopters of this technology. Transparent communication about the limitations, uncertainties of predictive models, and expected error margins is fair and important.

In my experience, AI is facing many challenges due to complex equipment trains. Complex equipment trains include equipment with 2 drivers and gearboxes. In universal equipment like pump, blower and Cooling tower fan. AI can help greatly, and action can be taken based on trend values and little changes in parameters.

Enfrentar os desafios e riscos associados à implementa??o da manuten??o preditiva baseada em IA é fundamental para garantir o sucesso dessa iniciativa. Aqui est?o algumas maneiras de abordar esses desafios: Custos iniciais de implementa??o: Disponibilidade e qualidade dos dados: Complexidade técnica: Integra??o de sistemas: Seguran?a de dados: Aceita??o e resistência à mudan?a: Enfrentar esses desafios requer uma abordagem proativa, colaborativa e focada em solu??es. Ao antecipar e mitigar os riscos associados à implementa??o da manuten??o preditiva baseada em IA, as organiza??es podem maximizar as chances de sucesso e colher os benefícios dessa abordagem inovadora para a gest?o de ativos e equipamentos.

Acknowledge potential challenges, such as initial implementation costs, integration with existing systems, and data quality issues. Discuss strategies to overcome these challenges, such as phased implementation, employee training, and robust data management practices.

Al momento de implementar estas iniciativas, se deben definir métricas y reportes para mantener informado al equipo directivo de los avances del proyecto (logros obtenidos, complicaciones que pudieran surgir, mejoras en resultados, etc.) Adicionalmente es muy relevante hacer parte del proceso al equipo de técnicos de mantenimiento, ya que desde su perspectiva al conocer ampliamente los equipos, pueden ser el factor diferenciador para lograr que la adopción de estas técnicas sea exitosa. Es muy importante demostrar en esta etapa que la nueva tecnología no se usa para reemplazar sus puestos de trabajo, sino que para que todos puedan trabajar mejor, tras el objetivo de evitar tener averías y detenciones en los procesos productivos.

Involving stakeholders, particularly getting buy-in from the board of directors, is crucial for the seamless adoption of AI-based PdM. It is essential to address all their concerns, particularly regarding ROI. Regularly updating them on maintenance costs and tracking the success through specific metrics can help maintain their engagement and support throughout the implementation process.

Involve stakeholders in the planning and implementation process to ensure their buy-in and address their concerns. Provide regular updates on progress, challenges, and successes to keep stakeholders engaged and informed.

After calculating ROI, a pilot project should be executed to validate the initial assumptions. This phase involves collaboration with technology providers and should be used as a demonstration to reinforce the stakeholders’ confidence in the projected benefits. Ensuring transparency during this phase and setting clear expectations through defined objectives and milestones is key to maintaining stakeholder trust.

Continuously monitor the performance of the predictive maintenance system and report the results to stakeholders. Be open to feedback and make necessary adjustments to improve the system's effectiveness.

Según mi experiencia uno debe: Primero: Explicar cómo la implementación del mantenimiento predictivo basado en IA permite identificar y abordar problemas potenciales antes de que se conviertan en fallas mayores y como estas fallas mayores afectan los objetivos estratégicos de la compa?ía. Segundo: Explicar la capacidad de la IA en análisis de grandes cantidades de datos que sobrepasa la capacidad de un equipo numeroso de especialistas trabajando en lo mismo. Y con esta ventaja poder prever con mayor exactitud y velocidad el momento exacto en que se necesita una actividad de mantenimiento

As most AI-based predictive maintenance projects currently cater to early adopters, it's important to discuss the maturity of the technology openly with stakeholders. Highlighting the high costs and uncertainties related to achieving economic and technical objectives can prepare stakeholders for the challenges ahead, ensuring there are no surprises.