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How Digitalizing Frontline Data Boosts Oil Field Service Profitability

Like most industries, oil field service companies have invested in digitalizing the back-office and leveraging the data to drive better visibility and decision making. In the field and on the frontline, digitalization is just starting. Paper is the most common system for ticketing, equipment maintenance, field inspections and safety checks which leaves that data trapped in paper silos. Companies are collecting their frontline data; they just can’t effectively use it. To add to these challenges, a recent Microsoft survey of the Energy industry showed that nearly a third of frontline workers feel that they do not have the digital tools they need to do their jobs effectively.

Using Digital Data to Improve Field Operations

With the attrition of experienced workers and labor shortages, digitalizing frontline operations offers a real advantage. In addition to guiding workers and offering on the spot training, digitalizing the frontline can also deliver data to improve operational efficiency. Usage data from digital workflows can provide visibility into the execution of frontline activities. OFS companies can track missed inspections, maintenance and safety checks to ensure compliance. Data from operations can also be used to compare performance across teams and to measure turn-around times for asset maintenance, creating opportunities for training and improvement.

Boosting Cost Recovery with Digital Data

A common challenge for OFS companies is recovering the costs of excessive wear on assets that occur on the job. Digitalizing asset inspection and repair operations can provide the basis for cost recovery programs using the data to more accurately assess cost of usage and also deliver evidence to customers for the recovery of wear and damage charges. This data becomes particularly effective when utilizing modern digital frontline capabilities for images and video records.

Reducing The Costs of Inventory Management

Eliminating frontline paper silos and connecting workers delivers a stream of data that can greatly improve inventory management and reduce costs. Digitalizing the frontline provides a more accurate and timely view into the actual usage trends for equipment and replacement parts, enabling asset managers to more effectively purchase and allocate parts inventory needed to match operational demands. In addition, connecting frontline maintenance teams with inventory levels and location can also reduce the costs of unnecessary parts purchases.

Improving Asset Utilization

Maximizing the utilization of field equipment employed on jobs is critical to the success of OFS companies. Data from digitalizing the frontline can significantly boost ROI by providing an understanding of asset condition and location, driving process efficiency and delivering real-time data for better asset allocation. Analyzing the efficiency of asset turn-around, coupled with performance and inventory data, helps get equipment back into the field more quickly by identifying common process delays and ensuring application-specific training. Having real-time visibility of asset condition and location can enable better allocation of assets among offices and faster response times to customer requests.

Data-driven Pricing Optimization

Understanding the true costs of asset deployment and maintenance provides critical insights for pricing models in OFS. Using data from frontline maintenance and repair activities can deliver visibility into the overall cost of operations and local/regional differences in the costs of repairs. Full visibility into costs enables the development of both more accurate pricing models that account for variations in location and usage, and also more innovative pricing programs or service contracts that provide OFS companies competitive advantages while ensuring profitable operations.

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The business impacts of digital transformation are far reaching. The elimination of paper, connection of workers to workers, workers and machines, standardization of processes and collection and visibility to new data can all drive significant impacts to the top and bottom lines.

Well known analysts and consulting firms, such as McKinsey, cite observed improvements in their research including:

10-20%
cost of quality improvements
10-30%
throughput increases
15-30%
labor productivity increases
30-50% reduction in machine downtime
Gain insight into the impact that ROO.AI can make on your business with our business impact assessment tool