The ALS Sample Journey
9 de April de 2025
APC-3 CINRG Particle Counter
22 de April de 2025The sugarcane sector operates in an abrasive environment with a high rate of premature wear, negatively impacting the efficiency of mechanisation.
In this case study, we analyse the return on investment (ROI) from investing in predictive monitoring through the integration of maintenance software—Condition Monitoring Systems (CMS).
Volume of equipment analysed
The machine fleet analysed included 2,000 pieces of equipment, focusing on the engine compartments, which
represent the highest maintenance cost.
Monitoring format
The monitoring approach for engines was “mid-life predictive,” meaning analyses were conducted halfway through the
service life of both engine oil and coolant. This monitoring aimed to generate data to support preventative inspections.
Service orders
The integration of software and pre-established rules generated more than 8,000 work orders. Action prioritisation was guided by the principle of avoiding the highest potential costs.
Financial impact
The monitoring implemented achieved a 70% adoption rate and avoided maintenance costs amounting to USD 2.06 million. If the adoption rate had reached 100%, the savings could have amounted to USD 2.95 million, had the programme achieved full adoption.
Strategic Economics
ROI/Moic:
14x the amount invested in the follow-up analysis of
fluids: USD 2.95M – USD 0.21M = USD 2.74M avoided cost
And what ROI are you planning for your company’s maintenance?
Make it a reality with ALS, ensuring predictability and safety!
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