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Cultural Cause Analysis: Mapping Process Safety Incident and Performance Data to Cultural Cause Factors

Cultural Cause Analysis

After an accident has occurred it's vital that organizations understand what happened to ensure it does not recur.

The Root Cause Analysis (RCA) method was created to understand management system weaknesses in standards, policies and administrative controls leading to accidents. Currently, RCA encompasses (1) most industries, (2) issues, including safety, reliability and quality, and (3) analysis of near misses, chronic issues and accidents. 

Corrective actions and controls, developed from a thorough RCA, do not seem to correct the underlying drivers of behavior. This paper introduces the next evolution in the RCA discipline, Cultural Cause Analysis™ (CCA).

CCA dives further into understanding the organizational tendencies, behaviors, actions and individual cultural causes that created the environment that allowed the root causes to occur and exist, which inevitably led to the incident(s). This greater depth of understanding strengthens an organization’s ability to influence the underlying cultural issues to avoid repetitive negative results in the organization’s performance.

Process Safety Triangle

Are You Utilizing Existing Data to Support Cultural Growth? 

Today, organizations spend extensive resources to collect and manage enterprise data to support a wide
variety of operational and business functions. While these data are created to support other decisions, we
recognize an untapped potential exists to use these data to support CCA.

Monitoring culture and ultimately predicting safety performance are no longer impossible tasks. Data science tools can support the proactive evaluation of the organization’s culture when an accident does occur. The use of these technologies is the future of CCA, and for some companies, it is already a reality. 

Complete the form to learn more about the benefits of CCA, including how you can use your existing enterprise data to find cultural issues, patterns, etc.
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