Planning without execution: a mixed-methods maintenance maturity assessment of a sub-Saharan gas processing organisation
This mixed-methods study of Ghana's Atuabo Gas Processing Plant reveals that despite improving preventive maintenance compliance, the organization remains in a "Developing-to-Competent" maturity stage due to predictable annual execution failures driven by fragmented governance and a lack of reliability-specific workforce training, highlighting the critical gap between planning structures and operational reality in resource-constrained sub-Saharan contexts.
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In the heart of Ghana's energy infrastructure lies the Atuabo Gas Processing Plant, a facility where raw natural gas is transformed into the fuels that power homes and industries. Keeping such a massive machine running is not merely a matter of fixing broken parts; it is a strategic discipline known as asset management. At its core, this field asks a simple but profound question: does an organization have a plan to care for its equipment, and more importantly, does it actually carry out that plan? Experts measure this through "maintenance maturity," a concept that describes how well a company moves from simply reacting to breakdowns toward a state of perfect, predictable reliability. In the world's most advanced industries, this maturity is often taken for granted, but in resource-constrained regions, the gap between having a plan and executing it can be the difference between steady energy supply and costly interruptions. Understanding where a facility stands on this path is crucial, yet most studies on the subject rely on data from wealthy nations, leaving a blind spot regarding how these challenges play out in places like West Africa.
A team of researchers set out to fill this gap by examining the Atuabo plant, operated by the Ghana National Gas Company. They wanted to see if the plant's maintenance practices were merely a paper exercise or a deeply embedded culture of reliability. To get a true picture, they did not rely on a single source of information. Instead, they combined two distinct streams of evidence: three years of hard, numerical records showing exactly which maintenance tasks were completed on time, and a detailed survey of 84 workers who actually perform and manage that work. This approach allowed them to compare what the computer system said happened against what the people on the ground felt was happening. The goal was to find the specific bottlenecks that stop a well-intentioned plan from becoming a successful reality.
The numbers told a story of steady, if imperfect, effort. Over a thirty-six-month period, the plant completed roughly two-thirds of its scheduled maintenance tasks on time. While this rate improved slightly from one year to the next, the improvement was not statistically significant enough to declare a major transformation. However, a deeper look at the data revealed a striking pattern: every single year, the plant's performance collapsed in June. In that specific month, the rate of on-time completion dropped to less than half of what it was in other months. This was not a random fluctuation; it happened with such consistency that the researchers could predict it with high confidence. A similar, though less severe, dip occurred in December. The data showed that the plant was capable of world-class performance, as evidenced by three months in 2025 where completion rates exceeded ninety percent, but this capability was not yet consistent enough to be considered a permanent feature of the operation.
The survey of the workforce provided the "why" behind these numbers. The workers confirmed that the planning structures were actually quite sound; they knew what needed to be done and when. The problem was not a lack of a plan, but a failure in execution. The staff reported that while they had good schedules, they frequently ran into barriers that prevented them from finishing the work. These barriers included delays in getting spare parts and a lack of access to equipment because it was still being used for production. The researchers found that the workforce was generally competent in general administration and project management, but lacked specific, specialized training in reliability engineering. Many workers held certifications in general management, but very few had credentials specifically focused on the science of keeping machinery reliable. This created a situation where the team was excellent at organizing the paperwork but less equipped to solve the complex technical problems that arose when the plan met reality.
By weaving these two stories together, the researchers placed the plant in a specific category of maturity: it is moving from a "developing" stage toward a "competent" one. It has moved past the point of simply reacting to emergencies and has established a system for scheduled work, but it has not yet reached the level of a world-class organization where data is used to predict and prevent problems before they happen. The study ruled out the idea that the plant's struggles were due to a lack of planning or a total absence of skilled workers. Instead, the evidence pointed to a very specific, addressable issue: the organization needs to invest in reliability-specific training for its staff and adjust its schedules to account for predictable seasonal bottlenecks, particularly the recurring June slump. The findings suggest that the solution is not to build more complex planning systems, which the plant already has, but to strengthen the execution of the existing ones by ensuring the workforce has the right tools and the schedule is resilient enough to handle known constraints. This approach offers a clear path forward for similar facilities in the region, showing that even in resource-constrained environments, the key to improvement lies in diagnosing the precise gap between intention and action.
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