Summary

A global manufacturer implemented a predictive maintenance planning web app that reduced downtime, improved accuracy, and streamlined operations across 94 country-level organizations.

Challenge

The client, a Swedish industrial manufacturer of sustainable productivity solutions, faced inefficiencies in defining and managing maintenance requirements for millions of machine configurations. Manual processes led to administrative burden, inaccuracies, excessive downtime, and wasted resources.

The objective was to create a scalable solution that enabled timely, optimized maintenance planning. The system needed to integrate with core platforms, generate multiple schedules per second, and reduce dependency on manual administration.

Solution

Marlabs developed a robust web application through a phased approach, combining predictive capabilities, advanced UI, and secure integrations.

Proof of concept

In the first phase, Marlabs created a prototype using MS Access and VBA to validate core functionalities. This proof of concept demonstrated the system’s potential value and helped align stakeholders. Architecture design, business analysis, and data management were key components of this phase.

Prototyping

The second phase involved developing .NET application screens and a conceptual SQL model. The team enabled basic plan generation and refined the user interface. Business analysis and quality engineering ensured the prototype met operational needs and could scale effectively.

Advanced development

In the final phase, Marlabs enhanced the system with administration tools, approval workflows, and integrations. A React-based UI improved usability, while logic for maintenance plan creation enabled tailored schedules for each machine serial number. Quality testing ensured reliability and performance.

Results

The predictive maintenance solution delivered measurable improvements:

• Faster plan generation: Machine experts directly created schedules, reducing delays and errors.
• Accurate spare part management: The system eliminated challenges in identifying correct spare parts.
• Reduced repeat visits: Maintenance accuracy minimized unnecessary service calls.
• Lower quality costs: Improved accuracy and fewer errors led to measurable cost savings.
• Decreased scrap rates: The initiative significantly reduced return and scrap parts.
• Improved global efficiency: The solution supported 94 organizations, enhancing consistency and collaboration.

Impact

The predictive maintenance initiative delivered lasting benefits:

• Faster plan generation by machine experts
• Accurate spare part identification
• Reduced repeat maintenance visits
• Lower quality-related costs
• Decreased scrap and return rates
• Improved efficiency across global operations