The cost of poor quality is usually described as the expense of an organization's poor-quality processes in delivering its products or services. The notion was first applied to industrial processes, but it was quickly expanded to include all aspects of any company. Poor quality has an equal impact on E&P firms, services companies, and product manufacturers in the oil and gas industry. Any of these firms have the same quality goal: to improve the quality of its products or services while lowering costs. There is a lot of information out there on the cost of poor quality, how to measure it, and how to reduce it. However, the purpose of this article is to examine, from a practical standpoint, how E&P companies may execute a strategy to measure and reduce the cost of poor quality while still reaping short-term economic benefits from increased operating efficiencies.
Although the concept of "cost of poor quality" is widely accepted in the oil and gas sector, accurately and sustainably assessing the cost of poor quality has been viewed as a significant challenge. Organizations frequently adopt initiatives to address the cost of poor quality and implement a variety of steps to measure and reduce it. However, it is common to see similar projects fizzle out with time, due to the following factors: The processes in place to measure the cost of poor quality are highly manual, labor-intensive, and difficult to scale. They are primarily led by the organization's quality department, have a limited scope, and rely on historical data to understand what happened in the past but do not anticipate potential deviations in the future.
E&P firms may monitor and minimize their overall cost of poor quality efficiently and sustainably by implementing efficient digital technology readily available on the market. Companies can gain between 10% and 15% in economic efficiencies by implementing effective measures to reduce low-quality costs, which go straight to the bottom line and make companies more efficient and competitive. E&P firms typically invest heavily in software and infrastructure in order to get the most out of their resources and streamline their internal operations. One of the most common challenges they face is the difficulty and cost of integrating all their data sources in order to extract meaningful data for operational, technical, or financial decisions. The quality of their decisions has a direct impact on the organization's financial performance.
Datagration Can Help Reduce Cost of Poor Quality
Through the use of the PetroVisor platform, Datagration can assist in minimizing the cost of poor quality in a cost-effective and long-term manner. E&P firms may optimize the value of their resources and investments by dramatically increasing the quality and timing of their choices by seamlessly integrating all data sources into a dynamic and unified data model and automating important operational, technical, and financial procedures. Data integration and process automation minimize manual labor, improve overall operational efficiency, maximize hydrocarbon recovery, increase return on investment, and lower the expense of poor quality over time.
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Cómo Utilizar Un Modelo Unificado De Datos, Para Aumentar Producción, Potenciando Datos De Múltiples Fuentes