مدل ارزیابی برای مدیریت ریسک تجهیزات : موارد صنعت پتروشیمی

This study presents an assessment model that examines quantity and quality factors for equipment risk management in the petrochemical industry. The proposed model has five dimensions—financial performance, logistical support, service level, learning and innovation, and risk control. This evaluation model uses 13 strategy subjects and 78 performance-measurement indicators. Performance assessment indicators are initially established and revised based on expert opinions collected via a questionnaire. Further, the analytical network process (ANP) is utilized to calculate the weights of indicators in each layer and to construct assessment models with applicable and valuable references. To determine model practicability, this study assesses four subsidiaries of the case company. Each subsidiary has a capitalization exceeding TWD 50 billion. In addition to evaluating company performance in terms of each dimension and indicator, the proposed model provides a valuable reference for decision-making in equipment risk management.

As a producer of petroleum and natural gas, the petrochemical industry produces over 95% of the global output of organic chemicals and derivatives such as plastics, detergents and chemical fertilizers. Statistics from Taiwan’s Board of Trade show that the output value of the domestic petroleum industry exceeds TWD 3 trillion, i.e., more than 30% of overall value for manufacturing output. Petroleum continues to profoundly impact Taiwan’s economic development. Although the petrochemical industry worldwide has grown rapidly over the last five decades, major accidents still occur frequently, resulting in considerable losses. For example, a leakage of toxic gas at a Union Carbide facility caused over 2000 deaths in Bhopal, India, and an explosion in the LPG plant in Mexico caused over 500 deaths. According to a 1998 investigation by the American Petroleum Institute, of 100 major incidents in the last 30 years, 44% were caused by machinery failure and 12% by unknown causes. Thus, proper equipment risk control is essential to preventing major incidents. Preventive and provisional maintenance have been transformed into risk- and asset-oriented reliability maintenance in the evolution of equipment risk management. Notably, current industrial practices have certain defects, which provide material for research into subjects such as inadequate cognition levels, lack of professional knowledge, absence of evaluation tools, and disarticulation of horizontal department integration. While focusing on equipment risk management, the anatomization of difference, and potential defects in the petroleum industry, this work integrates a three-stage procedure, two-stage questionnaire and advice from industry and academic experts to construct relevant strategic subjects and performance evaluation indicators. The proposed evaluation model comprises five major dimensions—financial performance, logistical support, service level, learning and innovation, and risk control. For performance evaluation, academic and practical implementations by businesses have generally been based on group opinions, expert opinions, management decision models, and mathematical programming (Forker, 1997, Chen et al., 2004, Yang et al., 2009, Promentilla et al., 2008 and Sucky, 2007). Based on the relative importance weights calculated using the analytical network process (ANP) (Saaty, 2005), this study develops a novel system for assessing equipment risk. To verify the effectiveness of the assessment system, actual operations at four subsidiary companies are evaluated. Evaluation results indicate that firms can reduce equipment failure rates, decrease maintenance expenses, detect failure risk, improve diagnostic techniques, improve maintenance quality and reinforce the synergy of enterprise maintenance resources.

This study gathered substantial amounts of data regarding equipment risk management and practical methods in the petrochemical industry, integrated comments from experts in relevant fields, developed assessment constructs and performance measurement indicators, and then calculated the weights of measurement indicators via ANP. Finally, an equipment risk-management assessment model was constructed. Based on the results of this study, we conclude the following: 1. The proposed model contains five dimensions, i.e., financial performance, logistical support, service level, learning and innovation and risk control, as well as 13 strategy subjects and 78 indicators. 2. The major dimension of concern is risk control (23.1%). Other dimensions sorted by decreasing weights are logistical support (22.1%), service level (20.4%) financial performance (20.2%), and learning and innovation (14.0%). The first three dimensions account for over 60% of overall performance, demonstrating that achieving excellent performance for these dimensions is vital in the complex, dangerous and high-risk petroleum industry. A relatively lower proportion of overall performance for the learning and innovation dimension indicates a concern for contingencies in labor deployment and flexibility. 3. For strategic subjects, based on evidential analysis, the first five major items are contracted manufacturer management (13.6%), risk planning and assessment (9.5%), inventory management (9.3%), improvements to key function (9.3%), and cost control (8.4%). These strategic subjects are equally distributed throughout the five dimensions with a 50.1% influence on overall equipment risk-management performance. Executives should consider these items when attempting to maximize performance. 4. For performance measurement indicators, we conclude that the first five indicators affecting equipment risk-assessment performance in the petroleum industry are accident occurrence (2.76%), differences in planned annual maintenance expenses (2.57%), hazardous environmental event (2.54%), support for employees acquiring licenses (2.47%), and violation of operational safety regulations (2.32%). According to the summary of practical advice for equipment risk management in the petroleum industry, effectively integrating operating data, equipment diagnostic signals, control equipment operation and irregular process sequences is critical for successful petroleum equipment risk management.