Construction Productivity: From measurement to improvement

Transcription

1 Construction Productivity: From measurement to improvement Andreas Malisiovas* (Civil Engineer, University of Texas at Austin, Austin, TX, USA) Abstract The subject of productivity is one of the broadest, most complicated, and therefore vague subjects related with constructions. Much research has been done for creating techniques which can efficiently measure productivity, and even more suggestions for its improvement can now be found in related literature. This paper aims to introduce current techniques and methods for measuring construction productivity along with their critique, and potential ways to improve their use. In order for the present work to be completed, people with great experience from construction industry and academia were interviewed and an extended literature review has been conducted. The data analysis reveals some unique issues that can lead to productivity loss, and also provide engineers with the current industry and academia trends on improving productivity in constructions. The results of that research can help the engineering community understand the seriousness of the construction productivity problem, and provide engineers with techniques and recommendations to face that problem. Keywords Construction Productivity, Productivity Loss, Productivity Measurement, Improvement Techniques 1. Introduction Construction people have to improve their productivity in order to survive in the highly competitive environment of the industry. Hence, productivity has been generating significant interest in both the construction industry and academia (Park et al. (2005)). Although through the years there have been made many suggestions and efforts for measuring and improving productivity (Borcherding (2008), Liberta et al. (2003), Oglesby et al. (1989)) the advancement of that task is relatively slow because of the various factors that affect it and the uncertainty of how to effectively measure it with the most efficient way (CII (2000)). Moreover, the amount of existing techniques globally used for measuring productivity is extremely extended and there are many uncertainties around the accuracy and applicability of many methods. Developed measurement techniques extend from time-lapse photography and video in combination with statistics (Oglesby et al. (1989)), to models using historic data (Song and AbouRizk (2008)), Neural Networks (Chao and Skibniewski (1994), Ezeldin and Sharara (2006)), and techniques from other industries like manufacturing (Lean Construction Institute, Alarcon et al. (2003)). Finally, not all of the proposed measurement and improvement techniques are broadly applied to real construction projects while many are theoretically developed and evaluated. The previously mentioned reason makes it difficult to form an actual list of the widely used techniques, their advantages and disadvantages and to present more suggestions on their applicability, and use for productivity improvement. The purpose of this report is to * for correspondence: -1-

2 present a summary of the most widely used productivity measurement techniques and productivity improvement methods. In addition an effort is made to identify limitations and uncertainties on those methods, and also make some suggestions about productivity improvement. 2. Method The first step of the method followed was that of an extensive bibliographic research, and after establishing a solid background of the academic efforts on construction productivity measurement and improvement, interviews with participants of the construction industry and academia followed. More than 50 experienced engineers working from small to multinational level construction firms, experienced project managers, construction directors, and faculty of academic and research institutes were interviewed. Most of them are employed in the US, and European construction industries, while others are appointed at Universities like the University of Texas at Austin, University of Alaska at Fairbanks, Virginia Tech, University of New Hampshire, and others. The participants were asked questions about how do they perceive productivity terms, the importance of productivity knowledge, the ways and frequency of measuring construction productivity, critique of the measurement techniques they use, the causes of low productivity and the ways to improve it. The method preferred was of interviews with open ended questions because of the complexity and vagueness of the subject of productivity. For the above reason, it was considered to be more practical, and ethical to leave the participants free to express their views and not to direct their options (and consequently the results) by giving them multiple choice questionnaires. After the interviews and the literature review concluded, the most acceptable and broadly used measurement techniques were selected and analyzed. A critique of those techniques is presented along with their presentation and analysis. Furthermore, recommendations for improving construction productivity starting from its measuring were introduced along with the analysis of issues that can cause productivity loss and a discussion on recommendations for facing that problem. 3. Construction productivity measurement techniques Some of the techniques used are designed to measure the productivity of specific crafts at different kinds of construction work (Song et al. (2003)), while others measure productivity at firm or site level and include every participant involved in construction (Alarcon and Rodrigo (2003)). Furthermore, related literature reveals that no universally accepted productivity measurement standard exists (CII (2000), Park et al. (2005)), something which is considered to be the main reason for the existence of so many measurement methods. The methods and techniques presented are, according to the interviews, the most widely used in an effort to measure, and evaluate productivity in a construction site or in a firm level. Some of those methods are experience based while others are applied with the use of mathematical and statistical models, technological tools, and computer-based applications. By recording industry trends in measuring construction productivity it was observed that the majority of independent engineering companies, and smaller construction firms tend to use experience based models for conducting measurements, or they do not even track productivity at all, while large firms, companies and construction organizations (mostly in the US) tend to use measure productivity by using more complex, computer based models and work sampling with the help of technology tools. Moreover, for the same activity, productivity may - 2 -

3 be measured by different people in different ways, and so the resulting productivity values may not be directly comparable. 3.1 Input/ Output (man hours per unit) Input/output ratio (for example work hours per square feet of wall painted), is mostly used for measuring productivity at an activity level (labor productivity), and its lower values indicate better productivity-performance. Furthermore, when measured by that method labor productivity often depicts how efficiently labor is combined with other factors of production, a clue which can be very useful for activity planning and scheduling. Therefore, if productivity is reported as work hours per unit, the cost engineer can easily determine project costs by multiplying productivity times the estimated quantity and the wage rate. The present form of measurement has the disadvantage of being very simplistic, and it cannot depict the real on-site situation by not taking into account any of the factors affecting site activities. That model could be helpful for having an estimation of labor productivity, but the same could not be claimed for the productivity of organizational and off-site staff, management staff (whose works input and output cannot easily be defined), nor for productivity at a firm level. 3.2 Experience-based models Probably the first attempts to measure and understand productivity in the construction industry were totally based on the experience of engineers and constructors in general. In the times where advanced technology and measurement techniques were not available, the actual productivity in a construction site was perceived and evaluated by experience-based estimations based on daily observations on the job site. Despite the industry s technological advancement and the numerous measuring techniques now existing, surveys revealed that more than 20% of contractors still rely on estimators experience and notions for the majority of their estimates. Obviously, the accuracy and reliability of this approach are influenced by personal prejudice and can be highly subjective. When using models and techniques highly related on experience, the most reliable estimate can be made by combining that experience with past project data. However, such empirical practices do not guarantee a consistent estimate due to the lack of an efficient binding mechanism that could relate the present case to past patterns. 3.3 Measuring productivity using project milestones Another broadly used method is measuring productivity by using project milestones. At the beginning of a project, construction managers and other administrative personnel of a project s general contractor, define some project milestones, which need to be completed by specific deadlines. By the end of a predefined time spam (one or two weeks), staff meetings are held in order to review the work progress and discuss about the completion percentage of the milestones. During those meetings, an evaluation of the general work progress plan takes place and the productivity of the whole project is defined by examining the completion percentages of project tasks usually with the help of a project planning software. Despite being easy to follow, the milestone method does not provide any outcomes that could help defining the root of a possible productivity loss. Furthermore, it is not useful for defining on-site productivity and it does not give any numerical results which could enable construction managers to compare each week s productivity with previously recorded data. It can be claimed that this method is - 3 -

4 more a broadly used, experienced based technique than a broadly accepted scientific method which can generate safe and accurate outcomes. 3.4 Activity model (Work sampling) Activity model is based on work sampling, a method that employs statistical sampling theory to measure the utilization of labor, measured in time. Work sampling is based on probability theory. The ratio of the number of observations of a given activity to the total number of observations of all activities approximates the percentage of time that the work process spends on that activity. If the number of random observations from a large group of craft activities on a project is large enough, the percentage of time found by work sampling spent on an activity will differ little from the actual time spent on that activity in the work process developed on the project site. The needed data can be collected with ways varying from observation tours, to the use of video-recording, time lapse photography, and others. It is important that sampling is conducted randomly, and without bias by trained construction or maintenance analysts. Covering the entire labor workforce on-site, each worker is counted as one sampling observation. Usually, most data is recorded in three categories: Direct work, support work, and delays. Work sampling provides the researcher with ratio estimates (waiting/total) which are a very convenient way to measure productivity. Furthermore, the method is easy to administer and fairly cheap and in the same time collects useful facts during project execution that are not normally collected by other methods, while it has no, or minimal, interference with the worker s normal activities. On the other hand, some major disadvantages are the human errors that may occur, and the possible limited accuracy of results. Furthermore, work sampling does not differentiate rework from original work and is frequently reviewed with great suspicion by craftsmen and foremen. 3.5 Factor models Factor models are multi-variant approaches to modeling the productivity of a crew rather than of an individual, based on the factors that affect it. The quantification of factors involves the statistical analysis of crew productivity and related factors (Thomas and Yiakoumis (1987)). Their applicability and accuracy makes them valuable tools for measuring and predicting productivity at site level. The many factor models now existing have implemented many mathematical and statistical methods and software in order to generate more accurate results and simultaneously take under consideration many factors. The key limitation of those models is that they do not address interactions within, and across projects and hence they are not applicable at firm level. 3.6 Cost reporting method Many construction companies, which do not use any statistical or mathematical techniques and software, try to make estimations about their productivity rates by monitoring and comparing project costs. The method they follow for productivity estimation is usually based on the simplistic approach that when the cost is increased, work is unproductive. In order to have the ability to compare costs of similar projects, a database containing historical data of costs for materials, wages, and others is needed. Such data can be collected from previous projects and are used not only for measuring productivity of a specific project but also for predicting future productivity trends. This kind of method has the advantage of being very - 4 -

5 simple and easy to use. On the other hand, it does not pinpoint the root of possible low productivity, and data collection may become a costly and time consuming procedure, with high possibilities for human errors in estimating input and output, when compared to other available techniques for measuring productivity. 4. Factors causing productivity loss Except from their opinions on productivity improvement, construction industry experts were asked questions about issues leading to productivity loss. Their valuable experience combined with the research outcomes of productivity researchers (Borcherding (2008), Fosberg and Saukkoriipi (2007), Ofori (2005)) validated some of the major factors causing low construction productivity, and in the same time revealed reasons that have an important role to a possible performance decline. As it is widely accepted construction productivity loss is mostly related with on-site activities and situations like worker low motivation and dissatisfaction, poor worker skills, insufficient equipment and tools, excessive overtime and material handling, rework and worker fatigue (Borcherding et al. (1972), Borcherding and Garner (1981)). In addition, more than seventy five per cent of the people interviewed seemed to be highly aware of the importance of administrative, and support staff skills. Their views combined with the current literature (Borcherding (1972), Liberta et al. (2003)) indicated as very important factors for productivity loss low planning level, poor pre-planning, lack of leadership and skills in management team, and poor on-site management. Industry trends on productivity improvement After carefully examining the factors that can have a negative impact on productivity, it becomes more efficient to move forward to productivity improvement. Interviews have shown that the majority of the industry professionals (more than eight out of ten) continue to show some preference on traditional techniques like craft motivation enhancement programs, extensive project pre-planning, and the selection of better equipment. Another very popular notion among engineering professionals is productivity improvement through advanced technology use. Many construction firms try to promote the use of hardware technologies, such as optical scanning, optical mark recognition, bar coding, video and camcorders which with the help of software can analyze the data collected and give recommendations for productivity improvement. Furthermore, and according to the related literature (Borcherding (2008), Chao and Skibniewski (1994), LeMenager (1992), Hewage and Ruwanpura (2009)) construction productivity can be boosted with the use of information technology advancements which enable project participants to collect and share important field data in a timely and accurate manner. Examples of such technology applications are mobile computing, 3D Laser Scanning, digital close-range photogrametry, GPS, sensors, and wireless communication (Eldin and Egger (1990), LeMenager (1992), Song et al. (2004)). Moreover, many researchers (Alarcon and Calderon (2003), Forsberg and Saukkoriipi (2007), Salem et al. (2006)) are trying to utilize methods from other industries (like manufacturing) to improve productivity. Lean construction seems to be the most popular of that kind of methods. It is the application of manufacturing s lean production process, where costs are reduced through elimination of wasteful activities and inefficient procedures, in construction by setting up self-sufficient or lean work areas. Despite the fact that many lean construction tools and elements are still in an embryonic state, lean construction - 5 -

6 techniques (Last Planner, Six Sigma) are gaining popularity because they can affect the bottom line of projects. Discussion It is quite obvious that not every measurement method can efficiently be applied to any project s situation, and that the use of each method/technique has advantages and disadvantages. The use of an unsuitable measurement method may not only cause productivity loss, but also falsely indicate such a situation. For the above reasons, and in order to improve the use of measurement techniques, construction managers first need to carefully evaluate the available methods and find the most applicable to their project and needs. Furthermore, people handling the selected measurement tool should be well trained, have knowledge of measurement analysis, vision to relate measurements with the on-site reality, and decisiveness to take actions towards productivity improvements. For improving the broadly used methods the industry should probably rely more on those that give numerical data for productivity, not only because those methods are outcomes of scientific research, but also because they help firms keep a history record of productivity data. Moreover, as the literature review revealed, numerous productivity measurement methods exist. Some are still on experimental level while others have been used for several years. In addition, method selection is frequently based on the size and economic power of the construction firm. Consequently, small firms which depend on individuals with low budgets, tend to use methods based on personal experience and not on proved scientific research. Those experience based techniques may prove to be very efficient, but they have the disadvantage that their efficiency could not be easily measured, nor the firm s productivity trends can be recorded because of the absence of productivity numerical values. As for the industry trends in improving construction productivity literature review has shown that the industry tends to move forward by following the advancement of technology. Most of the new methods proposed are based on the use of IT, computer based systems and software. The question is if small construction firms struggling to survive through today s world economic crisis can afford the use of such applications. The trend of such firms is not to generally measure productivity by using any particular technique, but to have an overview of it through the use of a cost reporting method. That trend in combination with the unwillingness of small firms to make investments in new tools make productivity measurement and improvement a very challenging task for the majority of the industry members. In order to face that challenge, construction community needs to motivate individuals to measure and improve productivity of their firms and companies, and also motivate researchers to create inexpensive and easy to operate scientific tools, as for everyone to have the ability to measure construction productivity effectively. 6. Conclusions Construction is a constantly evolving industry, heavily affected by the rapid evolution of technology which makes situations change dramatically from one day to the other. People involved in constructions have diverse opinions on the subject of productivity measurement, mostly because the term productivity is not easily defined and understood. Furthermore, causes of productivity loss are not always predictable and easy to track. It usually requires much effort to define those causes - 6 -

7 and much more effort to adequately face them. Research also showed that there is a difference in the way of productivity measurement between large and smallindependent firms which do not frequently measure or record productivity of their projects, and thus have difficulties in improving performance. Moreover, the majority of techniques/ tools used for measuring productivity have key limitations, something that should alert industry participants before deciding the selection of a specific tool. In addition, technology use as a measure for boosting productivity is usually a privilege of firms with great economical power, a situation that needs to be faced with much attention. In order to expand the conducted research, further interviews on a very large scale with diverse background participants should be conducted. Some of those participants should be key people working for developers and manufacturers of software and hardware used for productivity measurement and analysis, mostly because they can reveal the conceptual background behind the development of a measurement tool and the industry s needs and wills for such tools. Further research and mostly more interviews and surveys must be conducted in order to create a complete catalogue of measurement techniques used, and the ways to improve them. Finally, the correlation between recorder productivity loss and the technique/tool used to define that loss should be re-examined, and with the help of industry experts and researchers the path leading from productivity measurement to productivity improvement will be shaped. 7. References Alarcon Luis F., and Rodrigo Calderon (2003), Implementing Lean Production Strategies in Construction Companies Research, ASCE, Construction Research , 38 Borcherding John D. (1972), An Exploratory Study of Attitudes that Affect Human Resources in Building and Industrial Construction, Technical Report, Department of Civil Engineering, Stanford University, Stanford California Borcherding John D. (2008), Construction Productivity, Course Package, School of Civil, Architectural, and Environmental Engineering, the University of Texas at Austin, Austin, TX Borcherding, John D. and Garner, D.F. (1981) Work force motivation and productivity on large jobs, ASCE Journal of the Construction Division, 107(3), Chao Li-Chung, and Skibniewski Miroslaw J. (1994), Estimating Construction Productivity: Neural-Network-Based Approach, Paper, Journal of Computing in Civil Engineering, 8, 234 CII Cumulative Change Order Impact Research Team (2000), Quantifying the Cumulative Impact of Change Orders for Electrical and Mechanical Contractors, Research Summary, Construction Industry Institute, the University of Texas at Austin, Austin, TX Eldin Neil N., and Egger Stephan (1990), Productivity Improvement Tool: Camcorders, Paper, Journal of Construction Engineering and Management, 116,

8 Ezeldin Samer A., and Sharara Lakman (2006), Neural Networks for Estimating the Productivity of Concreting Activities, Paper, Journal of Construction Engineering and Management, 132, 650 Forsberg Azam, and Saukkoriipi Lasse (2007), Measurement of Waste and Productivity in Relation to Lean Thinking, Paper, Proceedings of 15 th Annual Conference of the International Group for Lean Construction Jiukun Dai, Paul M. Goodrum and William F. Maloney (2007), Analysis of craft workers and foremen s perceptions of the factors affecting construction labour productivity, Construction Management and Economics 25, Kasun N. Hewage and Janaka Y. Ruwanpura (2009), A novel solution for construction on-site communication the information booth, Canadian Journal of Civil Engineering 36, Lean Construction Institute, Seminars, LeMenager Paul A. (1992), Technology is Here-Are You Ready?, Paper, Journal of Management in Engineering, 8, 261 LePatner Barry B. (2005), Strategies Increase Construction Productivity, Article, Real Estate Weekly, July 27, 2005 Liberda Markus, Ruwanpura Janaka, and Jergeas George (2003), Construction Productivity Improvement: A Study of Human, Management and External Issues, Research, ASCE Construction Research, 120, 5 Ofori George (2005), Productivity of the Construction Industry, Research, Department of Building, School of Design and Environment, National University of Singapore Oglesby Clarkson H., Parker Henry W., and Howell Gregory A. (1989), Productivity Improvement in Construction, Book, McGraw-Hill Book Co., New York, NY Park Hee-Sung, Thomas Stephen R., and Tucker Richard L. (2005), Benchmarking of Construction Productivity, Paper, Journal of Construction Engineering and Management, 131, 772 Salem O., Solomon J., Genaidy A., and Minkarah I. (2006), Lean Construction: From Theory to Implementation, Paper, Journal of Management in Engineering, 22, 168 Song Lingguang, Allouche Michael, and AbouRizk Simaan (2003), Measuring and Estimating Steel Drafting Productivity, Research, ASCE Construction Research, 120, 9 Song Lingguang, and AbouRizk Simaan M. (2008), Measuring and Modeling Labor Productivity Using Historical Data, Paper, Journal of Construction Engineering and Management, 134, 786 Thomas H. R., and Yiakoumis I. (1987), Factor Model of Construction Productivity, Paper, Journal of Construction Engineering and Management, Vol. 113 No. 4, pp