M. Genzel, McKinsey & Company, Pittsburgh, Pennsylvania; A. SHANKAR, McKinsey & Company, Houston, Texas; B. MCDONNELL, McKinsey & Company, Philadelphia, Pennsylvania; and D. GONZALEZ, McKinsey & Company, Austin, Texas
Refinery reliability issues have impacted product prices recently, creating both challenges and opportunities for refiners. This article explores the processes and digital tools that can advance reliability systems and unlock value, starting with the fundamentals.
Unplanned refinery outages create opportunities for reliable operators to capture margin. Major external events often lead to sudden spikes in product prices. For instance, winter storm Uri led to refinery outages across the U.S. from December 2021–January 2022 (FIG. 1). However, external events often mask underlying reliability issues, as seen in the summer of 2023. During this period, product prices suddenly increased, despite a lack of major inclement weather events. Several North American oil refineries experienced loss of containment and unexpected unit malfunction, pointing to potential reliability-related production losses.
Markets responded swiftly to these unplanned outages, with peak margins increasing by approximately 75% compared to the seasonal low for the same year and product cracks price increases of between $6/bbl and $12/bbl.
The authors’ company’s work with refiners suggests reliability-related lost profit opportunities can range from $20 MM/yr–$50 MM/yr for mid-size refineries. Conservative estimates indicate that for a 200,000-bpd refinery on the U.S. Gulf Coast, experiencing better reliability processes and performance than industry peers during these outages could generate between $6 MM and $12 MM in market uplift over a single month. Commercially savvy refiners, better positioned to take advantage of margin capture opportunities, may likely see even higher gains.
Additionally, the knock-on effects of improved reliability can reduce costs on equipment replacements and the need for maintenance contractors.
Establishing rock-solid reliability fundamentals is not easy, but is critical for success. Building and maintaining a best-in-class reliability program is difficult, and many refiners have not done enough to strengthen basic practices. Effective reliability processes must be embedded in equipment design and maintenance practices and require consistent execution. To succeed, reliability programs must be supported by a culture of ownership across functions and organizational levels (FIG. 2).
The four fundamentals of reliability. In the industry, we have observed four foundational reliability levers that determine the “what, when and how” of reliable operators.
Digital tools can accelerate improvements in reliability processes. Augmenting a rock-solid reliability program with digital solutions can help refiners improve the efficiency and sustainability of fundamental reliability processes, potentially creating a competitive advantage.
Developing robust asset strategies efficiently via FMEA. FMEAs can be extremely time-intensive processes, requiring reliability engineers to list failure modes while simultaneously processing thousands of unstructured maintenance records (FIG. 3). Instead, refiners can look to use automated FMEA to develop robust equipment strategies.
An automated FMEA tool reduces manual hours significantly by reading asset descriptions, scraping original equipment manufacturer (OEM) manuals, and reviewing work order history to draft a hierarchy of equipment systems, subcomponents, failure modes and maintenance actions.1 Reliability engineers can then review and modify outputs at each step.
Improving maintenance efficiency via a generative AI assistant. Given recent trends in workforce turnover, the swift building of capabilities and the implementation of safeguards are both critical to ensuring a maintenance organization's efficiency and quality of execution.2
A generative artificial intelligence (AI) maintenance “assistant” allows technicians to ask targeted questions about specific equipment and conditions, reducing troubleshooting time, as shown in FIG. 4.
A generative AI assistant can also synthesize data from maintenance logs, checklists and manuals, enabling technicians to be more efficient and freeing up supervisor time (FIG. 5). Similar tools can help operators run their plants reliably, for example, facilitating issue identification during rounds, creating high-quality maintenance notifications, rapidly troubleshooting issues and accessing procedures during operational upsets.
Machine-learning and AI tools enable organizations to achieve consistent maintenance execution and increased productivity. Refiners can utilize these tools to streamline work processes, update procedures and refresh training manuals, helping the next generation of refiners reduce downtime, improve productivity and deliver real business results.
Takeaways. The journey to becoming a best-in-class operator begins with mastering the basics. Digital solutions, built on a solid reliability program, are not a replacement for the foundations of reliability in refining, but can help operators rapidly mature their systems and gain a competitive edge. HP
ACKNOWLEDGEMENTS
The authors want to thank Will Armstrong, Anand Divakaruni, Hunter Legerton, Patrick Neise, Eric Porter, Vinny Prakash, Alex Sierra and Ian Wells for their contributions to this article.
LITERATURE CITED
Matt Genzel is a Partner in McKinsey & Company’s Pittsburgh, Pennsylvania office. He serves clients in the energy and materials sectors and is a core member of McKinsey's chemicals, metals and mining, and oil and gas practices. Genzel advises clients on a variety of issues related to strategy and operations, including corporate strategy, digitally enabled transformation, manufacturing improvement, cost reduction, capital project optimization and organizational change. An engineer by training, he has extensive experience analyzing large-scale, capital-intensive business operations. He regularly manages client performance transformations from diagnostic through design and execution, all the while building client capabilities to ensure long-term success. Prior to joining McKinsey, Genzel spent 7 yr with a construction general contractor in the New York metro area. As a project manager and senior engineer, he focused on rehabilitating transportation infrastructure, including bridges, subways and railroads. He is a licensed Professional Engineer.
Anantharaman Shankar is a Senior Expert in McKinsey & Company’s oil and gas practice based out of Houston, Texas. He serves clients on refining strategy, asset valuation and operations improvement topics, and is also responsible for crude oil and refined products pricing models and refinery optimization tools for the practice. Shankar is a chemical engineer by training and obtained an MBA from Rice University.
Bill McDonnell is an Associate Partner in McKinsey & Company’s Philadelphia, Pennsylvania office, with a specific focus on driving operations-led transformations in the company’s global energy and materials and other industrial practices. McDonnell joined McKinsey in 2017, and previously worked as a reliability engineer at Shell and a project engineer at ExxonMobil. He holds a degree in mechanical engineering from Pennsylvania State University.
Diego Giraldez is an Engagement Manager in McKinsey & Company’s Austin, Texas office. He serves clients across the energy, chemicals and advanced manufacturing sectors, where he focuses on a variety of topics related to operational excellence, maintenance and reliability, and cross-functional transformations. Prior to joining McKinsey, Giraldez spent 6 yr with an integrated oil company with roles at both refining and chemicals manufacturing sites. He is a chemical engineer by training and holds a degree from MIT.