Using the Last Several Years of QHSE Data to Improve the Next Several Years of QHSE Performance

摘要

On an enterprise-wide scale, worldwide operations from major "operators" such as ExxonMobil, Shell, and ConocoPhillips and "service" companies such as MI-SWACO, Premium Drilling, Scorpion, and Exterran as well as multiple others, are applying a common approach to collect and analyze data from a myriad of "risk reduction" activities. These risk reduction activities, ranging from reactive (incident-based) to proactive (assessment-based), provide a wealth of data and information that can be mined to determine risk and to improve processes and performance. On the surface, each of these organizations simply use a common "mechanism" to manage their own unique set of QHSE risk reduction processes and ultimately analyze the resulting data - i.e. the "outcome" data. However, at a deeper level, these companies are not only collecting data resulting from the outcomes (e.g. incident reports, spill quantities, near miss types, root causes, inspection findings, etc.) but also the "work practice behaviors" that reflect the organization's tendencies in executing such processes. With such a vast dataset from both "outcomes" and "work practice behaviors", these companies have created a unique opportunity to find the "real" leading indicators of performance - i.e. those activities, practices, factors, conditions, etc. that are both practically measurable and proven to have a mathematical relationship to loss outcomes. Along with a structured process improvement approach, such as the Six-Sigma DMAIC framework, organizations can leverage this unique opportunity to find (Define, Measure, Analyze) and execute (Improve, Control) the Leading Indicators which truly do affect performance outcomes. Many companies track and analyze Leading Indicators in isolated areas of their businesses but few are applying Leading Indicators to rival the age-old "incident rate" as the primary Key Performance Indicator (KPI) for judging an operation's QHSE performance. There are several reasons for this dominance including the practicality of a "near" standard, normalized performance metric that can deliver an "apples to apples" comparison of loss rates across the enterprise, difficulty in consolidating the "outcome" data and the "work practice behaviors" from these events on a corporate scale, and other cultural and marketplace obstacles (i.e. lack of top management buy-in, resource costs, etc.).