Over the last decade, the development of predictive models, machine learning, and artificial intelligence (AI) has revealed opportunities that were previously thought unattainable.

Author Nick Saraev

Photo: Freepik

Huge amounts of real-time data are flowing from nearly every piece of equipment, providing us with insights enabling predictive maintenance and proactive interventions. 

This digital transformation in equipment maintenance is revolutionising industries by enhancing safety, improving efficiency, and reducing downtime and operational costs.

But, as Palfinger executive Gunther Fleck explained at the Aftermarket Business Platform Power of 50 2023, the rollout of true predictive maintenance is still going to be challenging for many companies.

The Challenge of Predictive Maintenance

Implementing predictive maintenance represents a significant shift from traditional maintenance strategies, such as reactive maintenance (fixing equipment when it breaks down) or preventive maintenance (scheduled maintenance based on time or usage intervals). 

It uses real-time data and complex analytical models to predict when equipment will fail or require maintenance, allowing for timely intervention before breakdowns occur.

However, as Fleck highlights, several challenges are associated with rolling out predictive maintenance. Most notable is how our systems are currently constructed. 

For decades, centuries even, we’ve operated on a ‘do it ourselves’ model, meaning we limit access to data, inventory, and supply. Transitioning to a ‘do it with partners’ approach involves a significant strategic shift where cooperation and data-sharing across the industry become paramount. 

Instead of companies hoarding data within silos, predictive maintenance requires an interconnected ecosystem where manufacturers, suppliers, dealers, customers, and sometimes even competitors collaborate for mutual benefit.

The Key Steps to Achieve Predictive Maintenance

For predictive maintenance to become the norm, not the exception, companies have to embrace a shift from their traditional models to a partnership-focused approach. Fleck detailed a four-step plan:

1. Define the vision and strategy

Establishing a clear and compelling vision is critical. Leadership must articulate how predictive maintenance aligns with the broader business objectives and the value it brings in terms of safety, efficiency, and cost savings. 

A robust strategy must be developed that includes:

  • Goals: What do you intend to achieve with predictive maintenance (improve uptime, reduce accidents, extend equipment life, etc.)?
  • Roadmap: A step-by-step plan detailing how to transition from current practices to predictive maintenance.
  • Investment: Committing to the necessary investment in technology, training, and infrastructure.
  • Culture: Fostering a culture of innovation and collaboration, both internally and with external partners.
  • Data management: Deciding on data governance, ensuring that data is collected, stored, and shared securely and effectively.

This vision and strategy are the foundation for organisational alignment and dictate how the company will move forward. It should be communicated clearly and embraced by all stakeholders. 

Additionally, the strategy has to allow for flexibility to adapt to new technologies and market changes while maintaining the focus on the end goals.

2. Digitalize the whole customer journey

A comprehensive digitisation of the customer journey is essential for predictive maintenance, as it ensures continuous engagement and data flow from each touchpoint. 

The key elements that need to be digitised and integrated within the customer journey include:

  • Awareness: Use digital platforms to inform potential customers about the benefits. This could involve content marketing, social media, webinars, and digital advertising highlighting case studies and data-driven results.
  • Consideration: Employ digital tools that help customers evaluate the technology. This may include interactive websites or online demonstrations that allow customers to see the potential impact on their operations.
  • Purchase: Simplify the purchasing process with digital commerce platforms where customers can easily obtain predictive maintenance services and equipment. Integrate with CRM systems to personalise the experience and anticipate customer needs based on data analytics.
  • Service: Implement a digital platform where customers can monitor their equipment, schedule maintenance, and communicate with technicians. Use the Internet of Things (IoT) and AI to provide real-time service updates and preemptive maintenance alerts.
  • Usage: Equip products with sensors and telematics that continually collect data. By tracking usage patterns digitally, companies can provide actionable insights to optimise performance and prevent downtime through predictive maintenance interventions.
  • Loyalty: Create a digital feedback loop where customers can share their experiences. Use this data to refine your offering and keep customers engaged. Rewards programs, regular updates, and ongoing training can help foster a loyal customer base.

For predictive maintenance to be truly effective, it requires an extensive data network that incorporates and acts on feedback across all these stages. 

3. Rollout standards worldwide

Rolling out standards at a global scale is imperative for the success of predictive maintenance. 

Given the interconnected nature of modern supply chains and the international presence of many industrial companies, having patchwork local or regional standards would result in inconsistencies that could undermine the whole idea. 

Global standards ensure uniformity in data collection, analysis, protocols, and security measures across interconnected devices and systems. 

Moreover, equipment maintained based on universally adopted standards is more reliable, safer, and efficient regardless of location. It also means that insights gained from the data in one part of the world can be applied elsewhere, improving the overall effectiveness of the maintenance strategies deployed.

4. Redefine and continuously work on the service vision

As the needs of customers evolve and technologies advance, the vision of what service excellence looks like must also adapt.

This process begins by understanding that service is no longer just about providing replacement parts and repairing equipment; it has been elevated to ensuring uptime and supporting customers’ business outcomes.

It also means investing in the workforce, equipping them with the necessary skills and tools to operate in a digitised environment. Training and development become critical, as does the cultural shift to a more proactive, customer-centric approach across the organisation.

Final Thoughts

Embrace this era of predictive maintenance as not just a technical upgrade but as a commitment to a sustainable and innovative future. It’s about cultivating synergy between human creativity and technological breakthroughs to unlock the industry’s full potential. 

By doing so, we reinforce a legacy of excellence and chart a course towards a bright, efficient, and secure horizon.

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