January 2019

Executive viewpoint

Solid information helps address controllable costs
Jennifer Schwartz / CTG, Inc.

In a commodity industry such as oil and gas production, remaining cost-competitive is vital. Owner/Operators are aware of variable—but controllable—cost areas, so those who can leverage information to gain visibility into these spaces are better-positioned to execute changes. Our oil and gas clients who leverage information for cost control focus on three areas:

  • Reliable operations—Focus on running plans reliably, the way they were designed to run
  • Optimization—Aligning production with external variables to stay cost-competitive
  • Continuous improvement—Running an efficient, safe and compliant facility, that enables “flex” for the unknown
  • When controlling costs, information is the tie that binds. Business leaders are tasked with creating alignment between their control plane, engineers, and business functions to enable competitive advantage. Each group’s activities and decisions require data. The IT groups structuring data to support these teams need to understand how the data align with the overall enterprise business model, to provide meaningful business answers. 

Reliability. Production reliability of assets, and the equipment therein, is a part of lean operation and, often times, tactical and strategic plans. Determining reliability requires completing calculations that require data. With calculations that support everything from operations, maintenance and process design to equipment procurement, the data quality used becomes critical.

Using data that enable sound calculations can ensure that you know what’s needed to maximize your facility’s output within design constraints. The goal is to anticipate, or predict, how to best run your equipment and asset in current configurations. When you can forecast interruptions, personnel are positioned to proactively mitigate impacts.

Reliability requires effective field data collection. Data that relate to lifecycle costs should be identified, and variables that impact those costs should be isolated and trended. When the data required to support the equipment’s objective is understood, and the capture method established, managing that information to enable asset descriptions, performance or cost analytics will help optimize time-to-value.

As data collection pushes to the edge of your enterprise, the ability to trend and analyze improves. However, the need to manage and control the data also increases. Without an effective information management program for field data in place, your reliability calculations could be at risk.

Optimization. Generating the optimal amount of product from the resources available, both human and material, requires data and measurement. Instruments, coupled with operational variables, are obvious data sources for generating efficiency calculations.

However, even with proper instrumentation and variable measurements, control of information generated is vital to support ongoing optimization. Advanced technical solutions, such as IoT or machine learning, detect and enable action on issues more quickly, driving up profitability and improving the asset’s competitive stance.

Via integration with control system components, visibility and analysis of variables across the production function can be improved. Using devices to automate certain tasks can deliver trends that help proactively improve uptime and quality of work flow, and may even lead to future design modifications. 

Continuously improving. As data collection extends to an enterprise’s “edge,” the ability to collect data increases in a way that improves your business, provided it is properly managed and consumed. The design principles used in Industry 4.0 can deliver practical guidance, as you pursue changes that lead to increased cost control. For instance:

  • Interoperability can improve your ability to collect data from new, unknown spaces, helping to drive field data collection.
  • Creation of “connected” technical environments that provide transparent information, using such methods as “digital twins,” can shorten time to action.
  • Data aggregation and visualization help keep human intervention where it needs to be, and out of unsafe areas.
  • Driving decision-making at basic, earliest points of impact—often before passing along to humans—enables human intervention as a point of escalation.

Even without automated data collection, correlative analysis, such as reviewing logbooks against real-time data, can raise awareness of potential problems or hazards. This reduces cost by addressing small issues before they become major problems by exposing leading indicators.

The type of transformation an organization looks for will drive its approach to managing information. Multi-dimensional data processing requirements, patterns, predictions, trends, or enterprise class reporting will impact your approach. You use the same data for different types of answers when questions are structured properly. Data scientists may provide the structure, but you still need business acumen to build out understanding.

Optimizing reliability and improving your operation means minimizing downtime in “controllable” areas. Process or design changes can focus on areas that allow for improved quality or efficiency. Maximizing quality and efficiency will lead to improved cost-competitiveness and a better bottom line. wo-box_blue.gif

About the Authors
Jennifer Schwartz
CTG, Inc.
Jennifer Schwartz specializes in enterprise information programs, consulting, strategic planning, and mobile solution architecture. She has 18 years of experience with improving operational efficiency, reducing costs, and formulating strategic plans for clients. As the Enterprise Information Management solution lead for CTG, Ms. Schwartz focuses on business process management and automation, providing best practice guidance, and executing special projects that help transform data into action.
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