Parkar

Overview

Our client is a global leader in mobility and asset solutions, renowned for elevators, escalators, and industrial systems, faced mounting challenges with a reactive maintenance model that caused costly downtime, safety risks, and rising operational expenses. We partnered with our client to design and implement a predictive maintenance platform powered by IoT, Azure, and Databricks. The solution integrated real-time sensor telemetry, predictive AI models, governed data, and role-based intelligence layers to transform maintenance from a reactive process into a proactive, predictive discipline. Within months, our client achieved a 90% reduction in downtime, a 20% cut in maintenance costs, and the creation of new uptime-as-a-service revenue streams. Beyond immediate gains, the platform has positioned the client for long- term transformation—enabling smart building integration, AI-driven energy efficiency, and next-generation asset management. This case study outlines how digital innovation has strengthened operational resilience, reduced costs, and created a future-ready model for sustainable mobility.

About The Client

Challenge

Historically, Our client relied on a reactive maintenance model. Equipment was repaired only after it failed, which introduced significant inefficiencies and risks:

Unplanned Downtime: Failures in elevators, escalators, and industrial systems disrupted operations and impacted service-level commitments. In mobility, even short interruptions can affect thousands of end users.
Safety and Compliance Risks: Breakdowns raised the risk of safety incidents and created compliance challenges in heavily regulated environments.
Escalating Maintenance Costs: Emergency repairs, inefficient spare-part usage, and last-minute labor deployment increased costs. Penalties tied to contractual uptime guarantees further strained margins.

The leadership team recognized that this approach was unsustainable. They required a predictive, data-driven platform capable of forecasting failures before they occurred, optimizing resource allocation, and enabling continuous service improvements. Crucially, the solution needed to integrate with existing systems and assets, avoiding the disruption and expense of replacing the entire technology stack.

Solution

We partnered with our client to design and deploy a predictive maintenance platform that fused IoT, AI, and cloud-native technologies into a unified intelligence layer. The architecture was engineered for scalability, governance, and actionable insights.

Core Technology Components

1. IoT Data Backbone

Thousands of sensors embedded across elevators, escalators, and industrial equipment were connected to capture real-time telemetry, including vibration, temperature, load cycles, and error codes.
Near real-time ingestion pipelines streamed this telemetry data into the cloud, capable of handling high-frequency, high-volume inputs.
The system ensured low latency, allowing anomalies to be detected almost instantly.

2. Azure + Databricks Analytics Engine

Databricks Lakehouse served as the foundation for aggregating, cleaning, and processing raw IoT telemetry at scale.
Azure Machine Learning powered predictive AI models that detected anomalies, forecasted failures, and optimized maintenance scheduling.
Models were continuously retrained with live data, improving accuracy and adapting to new equipment patterns over time.

3. Governed Data Fabric

Microsoft Purview and Unity Catalog established governance, lineage, and compliance controls across the maintenance data ecosystem.
This ensured data could be securely shared across engineering, operations, and compliance teams, with role-based access controls to protect sensitive information.
A single governed data fabric simplified audit readiness and reduced regulatory risk.

4. Decision Intelligence Layer

Role-based dashboards delivered tailored insights for technicians, supervisors, and executives.
Parkar’s AIONIQ copilots transformed complex sensor data into plain-language tasks such as “Replace bearing on Asset X within 14 days.”
Natural language queries allowed non-technical users to ask questions like “Which assets are most likely to fail this quarter?” and receive instant, data-driven answers.

Together, these components turned maintenance into a real-time, predictive, and automated process, embedding reliability into day-to-day operations and reshaping the service model.

Key Results

The predictive maintenance platform delivered measurable outcomes across operational efficiency, cost savings, and business innovation:

90% reduction in downtime → Failures were anticipated and addressed proactively, significantly improving safety and ensuring uninterrupted service.
20% lower maintenance costs → Optimized spare-part usage, better workforce planning, and fewer emergency repairs reduced overall costs.
Improved safety and compliance → Continuous monitoring reduced the likelihood of unplanned breakdowns and supported compliance with international safety standards.
New revenue streams → The company launched uptime-as-a- service contracts, leveraging its predictive maintenance capability to guarantee performance and monetize reliability as a service.
Higher customer satisfaction → Consistently reliable operations enhanced customer trust and strengthened the brand’s competitive position.

Our platform's strength lies in its adaptability, enabling us to meet our users' evolving needs and transform their experiences.

The Future

The deployment of predictive maintenance has laid the foundation for a broader transformation of the company’s operations:

Smart Buildings: IoT-enabled assets can self-monitor, self-report issues, and integrate with broader smart infrastructure ecosystems.
Energy Efficiency: AI-driven optimization of equipment usage patterns can significantly reduce energy consumption, lowering costs and supporting sustainability goals.
Next-Generation Asset Management: By combining IoT telemetry with predictive analytics, the company is evolving into a leader in sustainable, intelligent mobility solutions.

By embedding AI and IoT into core operations, the our client has successfully transitioned from a reactive, cost-heavy maintenance model to a data-driven, service-first approach. This positions the organization not just as a manufacturer of mobility assets, but as a forward-looking provider of smart, resilient, and sustainable mobility services.

Our platform's strength lies in its adaptability, enabling us to meet our users' evolving needs and transform their experiences.

Predictive Maintenance Transformation in Mobility & Asset Manufacturing

Overview

About The Client

Challenge

Core Technology Components

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