Safety performance measurement in collectivized oil companies in China: Contribution of leading indicators to lagging indicators

With the collectivization of the Chinese oil industry, oil companies have been expanding in size. However, the intensified differences in the safety performance of subsidiaries have severely hindered the collaborative management of the headquarters. Understanding the safety status of each member is urgent for parent companies and their subsidiaries to identify gaps and make improvements. A unified set of safety performance indicators and a practical measurement tool are essential for the Chinese oil industry. Hence, this study identified a set of safety performance indicators encompassing both leading and lagging indicators using data envelopment analysis (DEA) and entropy weight method (EWM) to reveal the critical factors affecting the safety performance of the oil industry. A total of 300 front-line workers from eight subsidiaries of an oil company participated in the survey. The identified indicators were preliminarily weighted using EWM. Then, DEA was employed to measure the safety performance of the eight subsidiaries, demonstrating that management commitment was the most crucial factor in distinguishing safety performance; safety culture was more differentiated than risk management. Safety performance was not entirely positively correlated with safety investments, but the reasonable allocation of safety resources played a vital role. In addition, the weaknesses in each subsidiary's safety management were identified, and the quantitative effects of each leading indicator on safety performance were obtained.     

基于云模型和TODIM的应急预案评估方法研究*

为提高应急预案评估的科学性，提出基于云模型和TODIM法的应急预案评估新方法,利用云模型表征语言评估信息，通过得分函数处理云值，提出基于云值的主客观权重计算方法，以弥补单一赋权法求解评估指标权重的不足；基于云模型和TODIM法对候选应急预案进行优劣排序，并验证本文方法的可行性和有效性。研究结果表明:基于云模型和TODIM法的应急预案评估新方法计算出的数值结果合理、准确，研究结果可为相关部门提高应急管理能力提供借鉴。