Founded in 1975, this leading player in the sugar industry has distinguished itself as the pioneer and premier sugar manufacturing company in its region. With a rich history spanning several decades, the company has not only established a strong presence but also set industry benchmarks in terms of production capacity and technological innovation.

The company proudly reports an annual turnover of €250 Million, emphasising its financial stability and market influence. Operating six advanced sugar factories strategically located across key regions, these facilities boast a collective daily crushing capacity of 40,300 tonnes of sugar cane. Additionally, the company generates 140 MW of power from its integrated operations and owns five distilleries with a combined capacity of 297 KLPD, further cementing its status as a diversified leader in the sugar and energy sectors.

Over the years, the company has grown and adapted to meet global sugar market demands. It's known for its excellence, sustainability, and innovation, setting industry standards for efficiency and environmental responsibility.

Problem

The company encountered significant challenges in effectively managing maintenance operations for its expansive sugar production machinery. Operating on a large scale across multiple facilities, the complexity of overseeing these operations presented several hurdles. One of the primary concerns was the need to anticipate equipment issues in advance and mitigate unplanned downtime. This was crucial not only for ensuring continuous production but also for optimising productivity and minimising operational costs.

The traditional reactive approach to maintenance proved inadequate in the face of the company's scale and operational demands. Frequent equipment breakdowns and unplanned maintenance activities disrupted production schedules, leading to inefficiencies and increased expenses. To maintain competitiveness in the dynamic sugar industry, there was a pressing need for a proactive solution that could predict potential failures, streamline maintenance processes, and ultimately enhance overall operational efficiency.

Solution

To address these challenges effectively, Crux Digits proposed and implemented an advanced AI-driven predictive maintenance system tailored specifically to the company's operational requirements. This innovative solution harnesses the power of sophisticated machine learning algorithms to analyse comprehensive datasets encompassing both historical maintenance records and real-time equipment performance metrics.

The AI-driven predictive maintenance system operates by continuously monitoring key indicators and patterns within the machinery's operational data. By identifying subtle anomalies and deviations from normal performance metrics, the system can forecast potential equipment failures before they occur. This proactive approach enables maintenance teams to intervene preemptively, scheduling repairs and maintenance activities during planned downtime periods rather than reacting to unexpected breakdowns.

Impact

• Achieved a remarkable reduction in unplanned downtime to less than 2%, ensuring continuous and uninterrupted sugar production.
• Implemented AI-triggered alerts that proactively notify maintenance teams of potential equipment issues, allowing timely intervention and minimising production disruptions.
• Streamlined maintenance operations, leading to a significant 35% decrease in on-site maintenance activities.
• Enhanced overall operational efficiency by optimising resource utilisation and improving equipment reliability.
• Improved sustainability by reducing energy consumption associated with unplanned downtime and optimising production schedules.

These technological improvements have further enhanced the company's overall operating efficiency and optimised production cycles.