Performance monitoring and improvement are key elements of any safety management system. Companies record and review many different health, safety, and environmental (HSE) indicators on a monthly basis with the aim to continuously improve HSE performance. As a result, company management may initiate safety stand-downs and various campaigns based on month-to-month variations seen in key performance indicators (KPIs).Challenges involved with this include whether the changes seen in data represent an actual trend or whether they are just variations around a stable process. Other questions raised include whether the actions taken by management regarding an actual trend prove to be effective over time at countering a negative trend, and did an opportunity exist to identify the onset of a real trend earlier in time to enable a faster counteraction?To answer these challenges and questions, the authors investigated how control charts applied to one company's safety data detected statistically significant trends in a deterministic way. The paper describes how the authors incorporated control chart logic into a reporting engine to enable automated detection of trends and presents the authors' observations based on the results this method provided. The observations include how applying a control chart method reduced unnecessary or erratic safety initiatives due to otherwise incorrectly interpreted safety data.The paper also describes how the method provided a better indication of whether a safety campaign was having a statistically significant impact on safety performance over time and how, if it was not adding value, the campaign was stopped. Additionally, the paper examines how this method has a partially predictive capability as it enables identification of the earliest data point in a time series that could be the start of a new statistically significant trend and how this enables a company to take action before a negative trend can establish itself.
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