Case Studies in Maintenance and Reliability: A Wealth of Best Practices. V. Narayan

Jim Wardhaugh

       Location: 2.2.2 Large Complex Refinery in the Far East

      6.1 Background

      The organization had a very traditional functional structure. This structure is shown in a simplified way in Figure 6.1. Many of the senior managers were expatriates, but local people were very competent and were rapidly taking up senior positions. The refinery was making lots of money and, at the time, could sell all the products it could make. The focus was very much on throughput.

      The company’s attitude was certainly not one of complacency, but neither was there a real thrust to be maximizing profitability. The entire operation was waiting for a spur that would goad it into action. Then it came. A review by an American consultancy company, specializing in process plant benchmarking, showed that the refinery was a relatively poor performer in many important areas. In school report terms, it could do a lot better.

      6.2 Reaction

      The results of the benchmarking exercise were embarrassing. Nowhere was this more so than in the maintenance area, which was depicted as a very overstaffed and high-cost operation with low equipment reliability (although delivering respectable levels of plant availability). The first response to the benchmarking was one of denial. Many of the hard-working occupants of positions in the maintenance department saw this as an attack on their personal competence and commitment to the company’s performance. The results could not possibly be true. Their second reaction was fury. It was totally absurd that hard-working, committed, and competent people could be shown as poor performers. This just did not make sense. Their third reaction was to seek explanations and excuses. There must be input errors or errors in the analysis and comparison processes.

Figure 6.1 Simplified Organization Chart

      However, the results could not just be ignored. Interestingly, action was called for by personnel at all levels, from the top to the bottom of the organization. All had different motivations, but none could live with this slur; all demanded action.

      What I didn’t realize at the time was that the responses demonstrated by the workforce were following the classic Bereavement Curve (See Figure 6.2). This curve originated as a result of research by bereavement counselors and is usually attributed to Elizabeth Kubler-Ross. Change managers soon realized that this curve also fitted the classic reactions to many traumatic events in business; it has been used extensively by consultants to track responses to significant change.

Figure 6.2 Bereavement Curve

      6.3 Review Process

      I was given the task of unraveling this mystery, and identifying the issues and charting a way forward. Because this was my first brush with benchmarking, my first step was to understand the benchmarking process as a totality. I needed to understand how it worked, and what both the terminology and the definitions meant. The second step was to scrutinize the input process which we had used.

      The benchmarking input document took the form of an extremely detailed and structured questionnaire which was sent to a number of refineries in the area. Each completed the questionnaire for their own facility. There was some degree of validation built into these questionnaires, but anyone completing them properly needed a good understanding of the benchmarking firm’s terminology and definitions. Teasing out the required information from many sources in a refinery was not easy. It was made more difficult because the in-house information was presented in many different formats and with many inconsistent definitions often slanted to the needs of particular users of this information. For example, we found about four different definitions of overtime, with different variants focusing on hours worked and hours paid and added complexities being introduced if the work was done by shift workers on national holidays.

      Handling this data gathering process effectively was not easy; it required a good understanding not only of a number of underlying management and financial measurement concepts, but also the concepts and assumptions behind the benchmarking firm’s definitions. In retrospect, and knowing the importance of correct inputs, a person of high competence with a good overview of the business should have been allocated to the job. However, completion of this sort of questionnaire is not the most glamorous of jobs and it certainly wasn’t seen as career enhancing. So, unsurprisingly, we found that the task had been given to a fairly inexperienced individual.

      Once an awareness of all of the above had been gained (understanding would only come later), the next step was to confirm that the input data was accurate, or at least reasonably so. This is where things started to become even more difficult. Although the location was reasonably sophisticated in the use of computer systems, the data sought did not seem to be retrievable in any sort of straightforward way. There were no consistent definitions between any of the computer systems or the various manual systems used in the location. Indeed, definitions were often totally absent; many individuals had concocted definitions as required in an ad-hoc way.

      Certainly it was not easy to get the input information required. The sort of information being sought included details of plant utilization, availability, reliability, reasons for downtime or failure, overtime, and costs. It became clear that inspired (and some not-very-inspired) guesses had been made to feed the questionnaire. Indeed, there had been a large number of errors in answering the questionnaire; the data input contained significant inaccuracies. But it was impossible without a lot more effort to make more than guesses as to whether the inputs painted a black or a charitable picture of our performance.

      6.4 Characteristics of Refining Industry Top Performers

      Information from our benchmarking company and scrutiny of top performers showed that some of the excuses we were toying with as partial justifications for poor performance were invalid. It became clear that the following aspects of a refinery had little impact on performance:

      •Age

      •Size

      •Geographical location

      •Feedstock

      •Extent of use of contractors

      •Organization (functional or business unit)

      •Unionization

      There were top performers (and poor performers) of all sorts of shapes, ages, sizes, etc. However, what was apparent was that a move away from the traditional command and control regimes of the past would be beneficial.

      The “Characteristics of Refining Industry Top Performers” were identified as:

      •Clear organizational goals

      •Flat organization with increased span of supervision

      •Data-based self-management systems

      •Good management systems with small management staff

      •Emphasis on improved operational reliability

      •Intelligent risk taking

      •More collaboration and teamwork

      •Emphasis upon value-added aspect of each position or policy

      6.5 Results of the Review

      We had many manual information systems, a Computerized Maintenance Management System (CMMS), and a lot of other computer systems. But these made up islands of isolated data with incompatible definitions. It was common practice to use different terminology to discuss activities around the refinery. We could not easily access such factual data as who was doing what and why. Thus, we had a high technology refinery run by well-educated and competent staff, but groups in the refinery were each speaking their own language. There were some common business objectives defined by senior management, but by the time they had gone through the translation filters of the disparate groups,