Economic Justification for Predictive Maintenance in CNC Machining: An Industrial Case Study on Cost and Downtime Reduction

Theophilus Ikiedideike ¹ , Stanislaus Chinemerem Philip ²

^1,2Department of Mechanical Engineering, Rivers State University, Port Harcourt, Nigeria.

Abstract

For manufacturers, unplanned downtime on critical assets like Computer Numerical Control (CNC) machines is a significant and persistent financial burden. Traditional reactive and preventive maintenance strategies are often financially inefficient, either by incurring excessive downtime costs or by wasting resources on unnecessary part replacements. This study presents an industrial case study from a Nigerian fabrication and machining facility, analyzing 12 months of operational data, including failure rates, repair times, and detailed cost breakdowns, to build a comparative financial model. A predictive maintenance (PdM) model, developed using MATLAB and real- time sensor data, is proposed, and its economic impact is projected against the established reactive baseline. The analysis of the facility’s CNC fleet (N=5) shows the incumbent reactive strategy costs ₦27.22 million annually, resulting in 450 hours of downtime. The proposed PdM model reduces total annual maintenance costs by 43.7% to ₦15.34 million and cuts equipment downtime by a remarkable 73% to 120 hours. The primary savings (68%) originate from the mitigation of downtime-related production losses. The model demonstrates a full payback period for the required diagnostic equipment investment in approximately 7 months. This paper provides a clear financial framework for managers, demonstrating that a strategic shift to predictive maintenance offers a rapid, quantifiable return on investment and significantly enhances operational efficiency. 

Keywords: Predictive Maintenance (PdM); Cost-benefit analysis; CNC machining; Downtime reduction

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