Development of a decision support system for improving the effectiveness of preventive maintenance scheme

Pao Yue-kong Library Electronic Theses Database

Development of a decision support system for improving the effectiveness of preventive maintenance scheme

 

Author: Fung, Man-kit Daniel
Title: Development of a decision support system for improving the effectiveness of preventive maintenance scheme
Degree: M.Sc.
Year: 1998
Subject: Hong Kong and China Gas Co. Ltd. -- Management
Decision support systems -- China -- Hong Kong -- Case studies
Gas appliances -- China -- Hong Kong -- Maintenance and repair -- Case studies
Gas industry -- China -- Hong Kong -- Management -- Case studies
Hong Kong Polytechnic University -- Dissertations
Department: Dept. of Computing
Pages: 72 leaves : ill. ; 30 cm
Language: English
InnoPac Record: http://library.polyu.edu.hk/record=b1439163
URI: http://theses.lib.polyu.edu.hk/handle/200/2648
Abstract: The success of a public utility relies on how well its products and services can meet the needs of the customers, the reliability of the supply and the company image that customers perceive. Additionally, safety is of paramount importance to a gas company in sustaining its business growth and retaining the existing customers. Hong Kong has been able to maintain its world-class standards in gas safety through stringent rules and regulations, and the willingness of the industry players to improve continuously in this aspect by introducing innovative ideas and practices in the recent years. The Hong Kong and China Gas Company Limited, well known as 'Towngas', has been leading in this direction. In order to ensure that customers will actively maintain their gas appliances to good operating and safety standards, a mandatory maintenance scheme is practiced in Hong Kong to encourage people to use the service when it is necessary. Each customer is required to pay a monthly maintenance charge of $ 9 (in 1997) which includes a regular safety inspection (i.e. a preventive maintenance visit) in every 18 months and unlimited number of on-demand maintenance. The scheme helps to reduce economic concerns of customers during raising a request for repair as there will be no extra charges except for replacement of spare parts. Moreover, the additional regular safety inspection (RSI) has effectively removed some of the potential equipment failures or breakdowns before they come out. This proactive approach safety scheme has earned a lot of praises from the general public and has helped to create a good company image. However, as the number of customers is increasing rapidly at 5% each year and the types of appliances are becoming more varied and complicated, the cost of providing the service has also increased rapidly. While the aim of the service provision is to ensure high gas safety standards, a quality customer service and a break-even operating profit and loss account, the increasing cost will impose a higher pressure on price increase to customers if a better solution cannot be worked out. In view of this, the management has agreed to implement a decision support system (DSS) to improve the effectiveness of the preventive maintenance scheme (i.e. RSI) so as to lower down the operating cost of the activity. The ultimate goal is to achieve ZERO on-demand service through quality and effective RSI. That is, customer does not need to request for maintenance service in the future since all the potential problems and defects are rectified in the RSI. This will result in a Win-Win situation because the cost will be lowered down and customers will not face a high increase in price and there will be apparently no down-time in their gas appliances. The development of the DSS consists of: i. Linking the data mart with DSS ii. Identifying the data and tools for DSS iii. Identifying the user requirement and deriving specification iv. System integration and trial run on DSS v. Pilot test of the identified results from the DSS on the daily operations The DSS runs on a Window 95 personal computer platform by linking the relevant data mart in the newly developed data warehouse of the Company. The extracted data from the data mart is stored on an MS Access version 7.0 database which can be accessed by the statistical tool: SPSS and the data mining tool: DataMind Data Cruncher. An initial trial was performed on 5,000 records and some useful findings were obtained. These findings were then translated to meaningful instructions for a pilot test to be carried out on some selected districts. The effectiveness of the implemented instructions will be measured after a period of time. Since the effect, which is delayed, cannot be recognized when this dissertation is presented, therefore it is not contained in this report.

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