Abstract: In Malaysia, there is no research done specifically on collecting construction productivity data to serve as a guideline and standards for use by all players in the construction industry. Similar to JKR, as the largest implementers’ of government projects, the current practice on approval the schedule of works that submitted by contractors are mostly based on the engineers ‘gut’ feeling/opinion and past experience. There is no standard procedure and guidelines to review and checking the duration and resources allocation in each activity in contractor’s work program. As an effort, this study will start with focusing on the identification of factors that influencing roofing works productivity in construction project. Those factors can be obtained through a series of statistical analysis based on the data collected from questionnaire and interview with the experts. Data from previous projects also very important in order to get the actual productivity rate for roof works. Finally, the expected finding for this study will be: a) List of factors that influence the productivity rate for roof works, b) The critical factors affecting most to productivity rate for roofing works, c) A metrics of productivity rate for roof works as a standard and guideline for improving JKR projects scheduling.
Managing construction projects always requires constant monitoring of project performance and the updating on project schedule. Project performance data and productivity data from the construction field is a key role in evaluating and predicting project performance in term of cost and schedule. According to Motwani et al. (1995), the productivity in construction has always been very difficult to measure and control. Even though, productivity in construction hard to measure, there is a need to use it for timely decisions and reduce the negative impacts on cost and schedule. Therefore, the integration of historical productivity data with the on-going performance data in construction field are required (Hwang and Liu, 2005).
Hwang and Liu, (2009) presented that an accurate productivity prediction is important for managing construction projects. In fact, a construction project is commonly to be successful when it is delivering within its budget and timeline. The planning and control process also explains the importance of accurate productivity estimate which first, estimating time and cost of construction activities that inevitably requires productivity estimates (Hinze, 1998), and second, integrated management of time and cost must involved productivity factor (Dawood and Molson, 1997).
There are most previous studies focusing on defining factors influence productivity and measuring limited parts of activities at micro level to investigate the relationship between factors and productivity. A standard construction productivity metric system that more reliable is a critical element in construction productivity performance evaluation and improvement process. There need a tool to drive performance improvement through internal and external benchmarking. Park, et al., (2005) present that nowadays, demand from the construction industry on developing acceptable construction productivity metrics and standardized productivity data are the main issues to focus by the industry players and researches.