This research article investigates the convergence of predictive maintenance (PdM) and conflict management strategies as fundamental components for enhancing workforce safety, operational stability, and productivity in high-risk industries, with a particular emphasis on Nigeria’s energy sector. The Nigerian energy industry faces multifaceted challenges, including aging infrastructure, frequent equipment breakdowns, and limited financial resources for technological advancement. These challenges are compounded by workforce dynamics that are often complex, with cultural diversity and economic pressures contributing to a higher likelihood of workplace conflicts. As such, a dual approach that combines PdM and conflict management strategies is proposed to address these critical issues.Predictive maintenance, a proactive maintenance methodology, utilizes data analytics, the Internet of Things (IoT), and Machine Learning (ML) to anticipate equipment failures before they occur. This minimizes unexpected downtimes, optimizes repair schedules, and significantly enhances both asset reliability and workforce safety. PdM is particularly pertinent to Nigeria’s high-risk energy sector, where equipment failure not only disrupts productivity but also endangers workers and can lead to costly delays. Mathematical models, such as Mean Time to Repair (MTTR) and uptime calculations, provide insights into optimizing PdM schedules and minimizing repair times, thereby improving overall operational uptime. These metrics offer a quantifiable framework for implementing PdM, ensuring that resources are allocated based on real-time equipment conditions and predicted maintenance needs, resulting in substantial cost savings and a more efficient use of resources.Conflict management, another essential strategy, is crucial in a sector where diverse teams collaborate under challenging and high-stakes conditions. In the Nigerian context, effective conflict management can mitigate the impacts of interpersonal disputes, misunderstandings, and stress factors that could otherwise compromise team productivity and safety. This study reviews various conflict management frameworks, including the Thomas-Kilmann Conflict Mode Instrument (TKI), which categorizes conflict responses into competing, collaborating, compromising, avoiding, and accommodating. In high-risk sectors like energy, adopting conflict management strategies can enhance psychological safety, enabling employees to communicate openly, trust one another, and collaborate more effectively. This creates a safer and more productive work environment, where potential conflicts are handled constructively, fostering a cohesive team dynamic essential for operational stability.This article also presents mathematical models to guide the implementation of PdM and conflict management, optimizing each approach’s effectiveness within Nigeria’s unique socio-economic landscape. For PdM, cost optimization models are introduced, balancing maintenance costs against failure rates to establish the most cost-effective repair intervals. Conflict management is analyzed through game theory, where payoff matrices illustrate possible outcomes based on cooperative or competitive interactions. These models provide actionable insights into balancing cost-effectiveness, safety, and team dynamics, empowering Nigeria’s energy sector to adopt innovative strategies for a more stable and resilient workforce. The integration of predictive maintenance and conflict management represents a transformative approach to addressing Nigeria’s energy sector challenges. By adopting these data-driven maintenance schedules and fostering a culture of constructive conflict resolution, Nigerian energy firms can mitigate risks, enhance safety, and achieve operational resilience. This dual strategy not only addresses immediate operational challenges but also builds a foundation for sustainable growth, improved productivity, and workforce development.
Keywords: Maintenance, Nigerian perspective, Safety, advancing workforce skills, conflict management, high-risk sectors, stability
