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Transforming Maintenance Operations with Predictive Asset Management

Transforming Maintenance Operations with Predictive Asset Management

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Customer Overview

William Grant & Sons is a globally recognized Scotch whisky and spirits producer behind iconic brands such as Glenfiddich, The Balvenie, Hendrick’s Gin, and Monkey Shoulder.

Operating large-scale distilleries and bottling facilities worldwide, the company places strong focus on operational reliability, production continuity, and product quality.

Challenges

Before implementing IFS solutions, maintenance operations were largely reactive, with engineering teams addressing issues only after equipment failures occurred.

Operational data was fragmented across:

  • Spreadsheets
  • Paper records
  • Disconnected maintenance workflows

This limited the company’s ability to:

  • Monitor asset performance
  • Analyze downtime causes
  • Optimize maintenance planning
  • Improve long-term equipment reliability

As manufacturing operations expanded globally, the company needed a more proactive and data-driven maintenance strategy.

Solution

William Grant & Sons implemented IFS Enterprise Asset Management (EAM) alongside AI-driven predictive maintenance capabilities to digitalize maintenance operations and improve asset visibility.

The solution enabled the company to:

  • Centralize maintenance data
  • Standardize maintenance processes across facilities
  • Track equipment performance in real time
  • Support preventive and predictive maintenance strategies
  • Improve downtime analysis and operational reporting

With access to real-time maintenance KPIs and asset data, engineering teams gained better visibility into recurring issues and equipment performance trends.

Most importantly, the company successfully transitioned from reactive maintenance toward predictive maintenance powered by AI and operational analytics.

The project helped reduce downtime, improve production efficiency, and establish a more data-driven maintenance culture across the organization.

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