Unlocking efficiency: How automation sharpens forecasting in the energy industry

By Thiago Pinheiro Faury

Editor’s Note: The SCM capstone Forecasting Drilling Bits Demand: A New Horizon for Oil & Gas Supply Chains was authored by Thiago Pinheiro Faury and supervised by Dr. Ilya Jackson ([email protected]). For more information on the research, please contact the thesis supervisor.

In the fast-paced world of energy-exploration technology, companies are constantly on the lookout for innovative strategies to help them stay ahead of market dynamics. For one global energy company, the key to unlocking efficiency was transforming how it predicts the demand for drilling bits—essential tools in oil and gas exploration.

Transforming forecasting: From manual to automated excellence

Demand for drilling bits is complex to forecast due to their variety in design and the need for application-specific manufacturing methods, ranging from build-to-order to engineered-to-order. Each bit type requires detailed planning to align with market demand—a challenging task when relying on manual methods across 30 geographic units. Manual forecasts typically cover up to three months, but this period is inadequate for manufacturing certain types of drilling bits and planning long-term capacity and production schedules.

Traditionally, the company relied on manual and descriptive forecasting methods. This approach often led to inaccurate forecasts, excess inventory, increased costs, and sometimes a dip in market share due to unmet customer demands. However, a shift toward automation and predictive analytics has revolutionized their forecasting, resulting in more accurate predictions and optimized operations.

By adopting automated causal and time-series models, the company significantly enhanced its forecasting accuracy. With the new models, the global mean absolute percentage error (MAPE) rates have dropped by 65%. This leap in accuracy isn’t just a number—it’s a game-changer for managing inventory and optimizing resource allocation.

The path to precision: Streamlining data processes

One might wonder how this significant improvement was achieved. The company streamlined its data processes by consolidating information into a unified Python dataset, including both leading and lagging indicators such as rig count and market share to predict revenue and activity. This setup allowed them to generate accurate predictive models, separating data into training and testing sets for rigorous performance assessment.

Enhanced data visualization through Excel pivot tables with conditional formatting helped highlight performance trends across models and geographic units. Findings revealed that traditional time-series models, such as Croston, Theta, and FourTheta generally outperformed more complex causal models, particularly in geographic units with specific business models like high tier and high volume, tied to consistent activity patterns.

While automated forecasts offered considerable global advantages, especially for activity, their effectiveness varied locally (i.e., at a geographic unit level), underscoring the need for tailored approaches in different market contexts.

Future-ready: Strategic outcomes and prospects

This technological advancement is part of the company’s broader strategy to enhance its Sales and Operations Planning (S&OP) and improve forecast accuracy. This solution aims to deliver precise demand predictions, seamlessly integrating automated forecasting into the company’s Integrated Business Planning (IBP) software. This approach is designed to optimize inventory management, reduce operational costs, and quickly adapt to market changes, ensuring the company remains competitive and prepared for future challenges.

As this global player continues to refine its forecasting models, it is setting the stage for a future where data-driven decisions foster sustainable growth and improved customer satisfaction. This journey into the world of automation demonstrates that in the quest for operational excellence, sometimes the best way to predict the future is to redefine it.

Every year, approximately 80 students in the MIT Center for Transportation & Logistics’s (MIT CTL) Master of Supply Chain Management (SCM) program complete approximately 45 one-year capstone projects.

These students are early-career business professionals from multiple countries, with two to 10 years of experience in the industry. Most of the research projects are chosen, sponsored by, and carried out in collaboration with multinational corporations. Joint teams that include MIT SCM students and MIT CTL faculty work on real-world problems. In this series, they summarize a selection of the latest SCM research.

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