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Data Envelopment Analysis (DEA) Approach for Making the Bid/No Bid Decision: A Case Study in a Turkish Construction Contracting Company
Abstract
Construction contracting companies face two critical decisions in competitive bidding environment, which include: The bid/no-bid decision and the mark-up selection decision. Making the right bid/no-bid decision is critical to the survival, sustainability, and success of the contractors in the industry. There are many factors that affect this decision. This makes the bidding decisions complex and complicated. Therefore, it is not an easy task for managers to consider the combined impact of all these factors on the bid/no-bid decision within a limited time frame with limited capacity of information they have for every single bidding opportunity. This study proposes a Data Envelopment Analysis (DEA) approach for making the bid/no-bid decision. DEA is a robust non-parametric linear programming approach, which is mostly used for benchmarking, performance measurement, and decision making problems. The applicability of the proposed approach was demonstrated in a real case study conducted in a Turkish construction contracting company. In the case study, 49 bidding opportunities formerly faced by the studied company were evaluated via the developed DEA model. The accuracy rate of the proposed approach was 92%. Company management found the proposed approach satisfactory and implementable in future bid/no-bid decision problems.
... According to Cheng et al. (2010), contractors typically receive construction contracts through either direct negotiation or competitive bidding. Moreover, Brook (2008) and Polat and Bingol (2017) revealed that most of the work carried out in the construction industry is secured through a process of competitive bidding which is recognised as an unbiased way of selecting a contractor. However, in spite of the technique, bidding for a project can be recognised as one of the key processes of a contractor (Abu-Shaaban, 2008). ...
... Hence, contractors mostly rely upon their experience and intuition when making bid decisions, and such practices will not always assure consistent consequences. (Polat & Bingol, 2017). ...
... According to Abu Shaaban (2008), a construction organisation can either negotiate with the client or successfully compete under competitive bidding to secure a project. According to Polat and Bingol (2017), competitive bidding is the most common method of contractor selection. In competitive bidding, there are a considerable number of bidders competing with each other in order to come up with the most competitive and favourable bidding price (Li et al., 2019). ...
The process of bidders submitting offers for the completion of construction projects is known as construction bidding. Contractors frequently acquire projects through competitive bidding, and the decision to bid or not to bid is a critical step in that process. Errors in this decision-making process will ultimately result in huge losses for contractors. Many theoretical models have been established across the world to aid in the bid/no-bid decision-making process. However, researchers have identified that those models cannot be directly applied to construction projects due to their complexity. The majority of these models are merely academic exercises that are less useful in real-life circumstances. On the other hand, Sri Lankan contractors have less exposure to these models and more commonly commit malpractice when taking bid/no-bid decisions, which can have significant impacts on the contractor. This research was carried out to address these gaps where the main research aim was to offer a hierarchical framework for facilitating bid/no-bid decision-making of Sri Lankan contractors. The research adopted a qualitative research approach, where data was collected through semi-structured interviews. Consequently, code-based content analysis was used with the aid of the QSR NVivo software to capture significant findings. The research revealed three different bid/no-bid decision-making processes that are currently used in Sri Lanka. Furthermore, the drawbacks of each existing approach were compared and contrasted. Subjectivity, forces from the upper levels, and documentation errors have been identified as common drawbacks inherent in all three approaches. It is further evident that maintaining a database, contacting relevant parties to take the decision, and doing material and competitor analysis will be beneficial for contractors to enhance bid/ no-bid decision making. The most popular and least specious approach among identified approaches was used as the basis to propose the new hierarchical framework. Keywords: Bid/No Bid Decision, Decision-Making Approaches, Hierarchical Framework
... According to Cheng et al. (2010), contractors typically receive construction contracts through either direct negotiation or competitive bidding. Moreover, Brook (2008) and Polat and Bingol (2017) revealed that most of the work carried out in the construction industry is secured through a process of competitive bidding which is recognised as an unbiased way of selecting a contractor. However, in spite of the technique, bidding for a project can be recognised as one of the key processes of a contractor (Abu-Shaaban, 2008). ...
... Hence, contractors mostly rely upon their experience and intuition when making bid decisions, and such practices will not always assure consistent consequences. (Polat & Bingol, 2017). ...
... According to Abu Shaaban (2008), a construction organisation can either negotiate with the client or successfully compete under competitive bidding to secure a project. According to Polat and Bingol (2017), competitive bidding is the most common method of contractor selection. In competitive bidding, there are a considerable number of bidders competing with each other in order to come up with the most competitive and favourable bidding price (Li et al., 2019). ...
The research revealed three different bid/no bid decision-making processes that are currently used in Sri Lanka. Furthermore, the drawbacks of each existing approach were compared and contrasted. The most popular and least specious approach among identified approaches was used as the basis to propose the new hierarchical framework.
... Additional factors were introduced in the process, leading to an increase in complexity, highlighting the need for more structured and objective approaches [9,10]. ...
... Refs. [10,37] applied DEA to the context of bid/no-bid decision, using a CRS DEA model, oriented to inputs, without weight restrictions. Similarly, to the current application, each DMU represents a bidding opportunity. ...
... Similarly, to the current application, each DMU represents a bidding opportunity. Similarly to [10], ref. [37] considered the qualitative measures only before the DEA model application, while incorporated linguistic judgments to DEA measures. Ref. [37] discussed the limitations of DEA discrimination power and pointed out that the model of [37] as a solution, though, as well as [38], did not discuss the incorporation of tie-breaking methods. ...
In Brazil, the National School Feeding Program (PNAE) seeks to contribute to the socio-economic development of smallholder farmers, prioritizing them in supplying their products for preparing daily meals in public schools. However, farmers face challenges in determining which school calls to bid for and the potential benefits from their participation, due to the multiple quantitative and qualitative decision criteria involved. This paper presents a novel Data Envelopment Analysis (DEA)-based method for bidding priority setting, to support the decision making. The model was applied for a case study in Brazil. The academic contribution lies in the innovation of using a Double-Frontier Slack-Based Measure (SBM) DEA model for Hierarchical Network systems, i.e., applied to multiple levels and followed by a tie-breaking method. The practical contribution lies in the decision support of farmers by presenting the results at three levels, the first of which is a ranking by the town or urban cluster priority, the second by the school, and the third by the products. Thus, using the rankings of calls, farmers can make informed decisions regarding the feasibility of bidding for each PNAE public call. At the same time, the objective rankings can alleviate friction and conflict within co-operatives during the decision-making process.
... The first decision such actors face in the bidding process is whether to submit a bid or not (Engwall, 1975). Traditionally, the common practice was to base the 'bid/no-bid' decision on subjective intuitions, derived from a combination of gut feelings, experience and guesses (Cheng et al., 2011;Egemen & Mohamed, 2008;Irtishad, 1990;Polat & Bingol, 2017;Sonmez & Sözgen, 2017;Wanous et al., 2003). However, as such decisions increased in complexity, with additional items factored into the decision-making process, the need for more structured and objective approaches emerged (Egemen & Mohamed, 2008;Irtishad, 1990;Wanous et al., 2003), as systematic models are likely to improve the quality of decision-making (Polat & Bingol, 2017). ...
... Traditionally, the common practice was to base the 'bid/no-bid' decision on subjective intuitions, derived from a combination of gut feelings, experience and guesses (Cheng et al., 2011;Egemen & Mohamed, 2008;Irtishad, 1990;Polat & Bingol, 2017;Sonmez & Sözgen, 2017;Wanous et al., 2003). However, as such decisions increased in complexity, with additional items factored into the decision-making process, the need for more structured and objective approaches emerged (Egemen & Mohamed, 2008;Irtishad, 1990;Wanous et al., 2003), as systematic models are likely to improve the quality of decision-making (Polat & Bingol, 2017). ...
... DEA is able to incorporate qualitative and quantitative measures in a single efficiency score for each potential bid, automatically attributing weights to different measures in order to reduce ambiguity and subjectivity (El-Mashaleh, 2013). While embedded in mathematical programming, DEA still requires the definition of multiple variables, with the most common approach prescribing the identification of factors affecting the bid positively and negatively (El-Mashaleh, 2010;Polat & Bingol, 2017). This requirement of identifying positive and negative impacts of the factors has been simplified in a piece of work separating the 19 criteria adopted simply into inputs to be minimised and outputs to be maximised, according to the general framework of DEA (El-Mashaleh, 2013). ...
Smallholder farmers are among the most vulnerable communities in developing countries, lacking a stable income due to inconsistent access to markets. Aiming to tackle rural poverty, the Brazilian government established institutional markets for smallholder farmers to supply their produce to schools through a non-competitive bidding mechanism. However, participation of farmers is still limited due to the challenging decision-making process.
Aspiring to contribute towards increasing their participation, this study aims to support farmers into two key decisions they face during sequential stages of the bidding process, namely whether to bid for each available school and product combination and whether subsequently to accept the awarded bids once the bids’ outcome is known. A decision support system, based on two sequential MILP optimisation models, was developed and applied to the case study of Canudos settlement, guiding farmers on the optimal bidding and contract acceptance strategy.
This study contributes to the decision support systems field by applying OR methods to a real-life problem within a new context. It is the first application of an OR-based decision support system in the non-competitive bid/no-bid literature, defining an optimal bidding strategy through the application of optimisation methods to maximise profitability while removing subjectivity from the decision-making process. Moreover, it is the first decision support system within the bid/no-bid decision-making field being applied to the agricultural and institutional market context. The proposed approach could have a significant social impact for smallholder farmers in Brazil, improving their living conditions by providing security of income and strengthening inclusive agricultural growth.
... Project owners expect a capable contractor that can ensure the effective delivery of project and meet all project requirements. Bidders hope to get profits from the project and they often pay attention to owners' financial capabilities and their reputation in the industry (Polat and Bingol, 2017). ...
... Risk perception is another key factor impacting risk decision-making (Sitkin and Pablo, 1992;Chan and Au, 2008;Ellis et al., 2011). When faced with risk, decision-makers with heightened Hypotheses Sources H1: Contractual governance by owners' bidding documents positively affects contractors' bid/no-bid decision-making Le� sniak (2015), Le� sniak and Plebankiewicz (2015) H2: Contractors' trust in owners mediates the relationship between contractual governance and bid/no-bid decisionmaking H2a: Contractual governance outlined in bidding documents positively impacts contractors' trust H2b: Contractors' trust in owners positively impacts bid/nobid decision-making Ryciuk (2016), Polat and Bingol (2017) H3: Contractors' risk perception plays a mediating role between the contractual governance and bid/no-bid decisionmaking H3a: Contractual governance outlined in bidding documents negatively impacts contractors' risk perception H3b: Contractors' risk perception negatively impacts bid/nobid decision-making Thevendran and Mawdesley (2004), Chan and Au (2008), Chen et al. (2015) H4: Contractual governance of bidding documents influences contractors' bid/no-bid decision-making through the mediating roles of trust and risk perception H4a: Contractors' trust in owners negatively impacts contractors' risk perception (Keil et al., 2000). Thevendran and Mawdesley (2004) introduced the concept of risk perception to the project risk management area. ...
Purpose
The sustainable development of contractor organizations depends highly on bidding decision-making of projects. This current study, leveraging the risk decision-making theory, attempts to elucidate the process of contractors’ bid/no-bid decision-making and reveal how the process is influenced by their perception of risk. In particular, this study aims to explore the multiple mediating effects of contractors’ trust in owners and risk perception in explaining the relationship between contractual governance outlined in owners’ bidding documents and the bid/no-bid decisions.
Design/methodology/approach
A questionnaire survey was used to obtain data from the Chinese construction industry. The PLS-SEM technique was employed to analyze a dataset of 557 available questionnaires.
Findings
The findings indicate that (1) the contractual governance provided by owners’ bidding documents positively impacts contractors’ bid/no-bid decisions; (2) both risk perception and trust serve as multiple mediators in this relationship and (3) trust mediates the relationship between contractual governance and contractors’ risk perception.
Originality/value
Drawing upon the risk decision-making theory, this study proposes a multiple mediation model for understanding contractors’ bid/no-bid decision-making processes. It contributes to a better understanding of contractors’ bidding decision-making mechanisms, thereby offering theoretical guidance for contractors to make reasonable and informed risk decisions.
... Project type refers to the nature of the construction project that will be executed. This is important since the contractor's experience and expertise in a particular project type can be a deciding factor on whether to bid for a project [20]. Project size refers to the magnitude of the construction project that will be executed. ...
... Finally, the complexity of the project refers to the severity of the complexities and challenges that are associated with the construction project. Contractors need to be technically capable and have enough experience and knowledge to tackle complex projects [20]. ...
The environmental harms that the construction industry has caused are significantly detrimental and apparent. These harms include the emittance of a substantial amount of carbon dioxide and inducing ground and water contamination. Fortunately, these adverse environmental impacts can be minimized and counteracted by carrying out sustainable construction projects. As sustainable construction projects gain more popularity and are increasingly in demand, it is crucial for those who will execute these projects to be knowledgeable about the nature of sustainable construction projects and be able to determine whether to bid for these projects. The bidding decision is one of the critical decisions that contractors have to make due to the complexity and uncertainty surrounding this choice. Given the above challenge, this research aims to identify the most significant factors that affect the bidding decision in sustainable construction. The methodology adopted in this research is a mix of qualitative and quantitative analysis. An extensive literature review was first conducted to determine the bidding decision factors. After that, a survey was distributed to United Arab Emirates (UAE) construction professionals, where they were asked to rank the significance of the 40 extracted factors. The weighted average approach was then used to prioritize the most significant factors of the bidding decision in sustainable construction. The results showed that the top 10 ranking factors were as follows: client’s financial capabilities, client’s payment history, client’s reputation, project risks, contractor’s financial capabilities, project complexity, experience in similar projects, project type, contractor’s access to technologies required to execute sustainable projects, and material availability. The findings of this research can benefit contractors and subcontractors by increasing their understanding of the major factors affecting the bidding decision in sustainable construction. Contractors armed with such invaluable information will be better equipped to reach more effective better bidding decisions.
... More studies can be cited with the use of AHP in the automobile industry for manufacturing performance and production flow [15]. In the best use of equipment, eliminate bottlenecks, and enable training of operators [34]. ...
... The AHP method allowed for the assessment of both qualitative and quantitative criteria [34,35,41]. This aspect is important in the agricultural industry, as some additional qualitative criteria considered relevant by management could be included in the analysis. ...
Competition among companies is growing globally, with the need to increase productivity and efficiency in the product sector. However, there is also a growing concern about global warming and the depletion of natural resources, as well as their effects on human health. In this context, all human activities that involve intense usage of resources must take into account sustainability as one of the decision criteria. This work presents the application of decision-making methods to define the best product mix in the agricultural machinery industry. With this objective, the current schedule of the production line was identified, along with the production flow, by performing an inventory analysis and an environmental impact study (endpoint). A total of seven alternatives for the production mix of grain trailers were defined, considering different materials and production processes. The selection of the best schedule according to the different criteria was performed through the analytic hierarchy process (AHP) and data envelopment analysis (DEA) to evaluate the managerial implications for decision making. The results obtained through AHP identified a single alternative as being the best, which facilitates the decision making. The DEA method identified two alternatives as the most efficient, and in this case the manager can choose between a product mix that generates lesser environmental impact or greater profitability. Although applied to agricultural industry, the presented methodology can be easily adapted to other activities related to the built environment, such as construction industry.
... According to Brook (2016), contractor organizations need to make several critical decisions when taking part in competitive bidding processes. The decision to 'bid or no-bid' and the decision on the 'optimum bid mark-up percentage' are two of them (Polat and Bingol 2017). Generally, the term mark-up refers to an allowance for profit plus general overhead (Tavakoli and Utomo 1989). ...
... Generally, the term mark-up refers to an allowance for profit plus general overhead (Tavakoli and Utomo 1989). According to Polat and Bingol (2017), an optimum mark-up is essential for a contractor organization for its survival, success and sustainability in the construction industry. This is even more significant in the case of contractor organizations who engage in bidding for infrastructure projects due to complex nature, high project/contract prices and high risks associated with infrastructure project delivery (Flyvbjerg 2007). ...
Tender estimates play a vital role in achieving bid successes. A bid price which has an optimum markup is governed by many factors. The research aims to identify the significant factors influencing the bid markup decision of infrastructure projects in Sri Lanka. Using a literature review, semi-structured interviews and a questionnaire survey, the study identifies nine significant factors influencing the markup decision of infrastructure projects. The identified significant factors are 'estimated direct cost', 'competitiveness of other bids', 'type of work', 'project duration', 'the ability to predict pre-tender estimate', 'project location', 'reliability of the company estimate', 'number of bidders' and 'need for work', respectively. The study also reveals potential relationships between the identified significant factors and bid markup based on case studies. Amongst, 'reliability of the company estimate', 'number of bidders' and 'type of work' have shown positive relationships with markup. The contents should be of interest to bidders and researchers in construction.
... The characteristics of building construction differ considerably from those of infrastructure construction; thus, the factors influencing bid/no-bid decisions are compared according to the project type. This is significant because a contractor's past performance and level of experience with a given type of project may influence their decision to submit a bid [55]. The project type term describes the nature of the proposed construction project. ...
While contractors may experience financial failure if they bid on an inappropriate project, bidding on the right project may allow them to profit substantially. Therefore, understanding the various factors that influence the bid/no-bid decision is crucial for construction companies in determining whether to pursue a project. The present study aims to identify the critical factors influencing contractors’ bid/no-bid decisions. A total of 112 responses were collected from a questionnaire survey to rate the relative importance of 22 factors, and these factors were then analyzed based on the type of project and the contractor’s years of experience. The results indicate that the client’s ability to pay, clarity of the scope of work, project cash flow, the need for work, and availability of qualified labor are the most critical factors influencing contractors when making bid/no-bid decisions. The factor “previous experience in similar projects” was statistically significant among building and infrastructure projects, while “project location” was statistically significant among contractors with different years of experience. Finally, factor analysis identifies the six major underlying groups: client-related factors, bidding-related factors, contractor-related factors, market-related factors, economy-related factors, and project-related factors. The study’s findings can help contractors better understand the factors influencing their bidding-related decisions.
... However, sometimes the DM does not have access to all this data. El-Mashaleh (2013) and Polat and Bingol (2017) propose approaches that use data envelopment analysis (DEA) to decide whether to participate, while Mehrabani et al. (2020) consider a model that ranks the projects and in bidding decisions, prioritizes the first in the rank. In these methods, both quantitative and qualitative criteria are considered, but they do not incorporate the imprecision of the DM's judgment for qualitative bidding criteria. ...
For construction companies, the decision about whether to bid for a project is a strategic decision. This paper provides a model for assessing project attractiveness levels and deciding whether to submit a proposal. The model introduces a classification model that incorporates fuzzy preferential information to evaluate project attractiveness under multiple dimensions and enables the decision‐maker to make more effective decisions. Projects are assigned to ordinal categories of attractiveness based on an adapted fuzzy ELECTRE TRI‐C procedure. The main advantages of this model are that the attractiveness level can be used as an input for the bidding strategies, the attractiveness evaluation can support the contractor in prioritizing the projects to bid on, and the model captures the decision process's subjective assessments and situations of imprecision observed on early phases of the project life cycle.
... Another idea is developing a benchmark based on expert knowledge and historical information and subsequently providing a ranking of different projects to bid in reference to the benchmark for bid project selection. Fuzzy-TOPSIS (Al-Humaidi, 2016) and DEA methods (El-Mashaleh, 2013;Polat & Bingol, 2017) can be adopted under this circumstance. The last idea is estimating project award prices with the help of artificial intelligence (AI) models, such as artificial neural networks (ANN) and general regression neural network (GRNN) (Shi et al., 2016). ...
Decision making is a key to business or project success in any sectors, especially in construction that requires handling numerous information and knowledge. Multiple criteria decision making (MCDM) is an important tool for decision problem solving due to simultaneous consideration of multiple criteria and objectives. Various MCDM methods are continually emerging and tend to be increasingly adopted to address the real-world construction problems. Therefore, it is urged to systematically review the existing body of literature to demonstrate the evolution of the mainstream MCDM methods in general and their application status in construction. A total of 530 construction articles published from 2000 to 2019 are selected in this study and then categorized into seven major application areas using a novel systematic literature review (SLR) methodology. The bibliometric analysis is then used to describe the research trend. Subsequently, the qualitative discussion by themes is conducted to analyze the application of MCDM methods in construction. A further discussion makes it possible to identify the potential challenges (e.g. applicability, robustness, postpone effect, dynamic and prospective challenges and scale problem) to existing research. It also contributes to the recommendation of future directions for the development of MCDM methods that would benefit construction research and practice.
... The mark-up decision should be linked to the final tender review with senior management and preceded by an estimation of costs in cooperation with potential subcontractors and risk assessment [17,38]. Setting an optimum mark-up should be considered crucial to the contractor's survival and success in the construction market [39]. According to [40], the estimated direct costs, competitiveness of other bids and the type of work are the three most significant factors affecting the bid mark-up decision in infrastructure projects. ...
A contractor’s ability to prepare a competitive bid for a construction tender is crucial for its survival on the market. The bid price estimation strategy should promote the probability of winning a sufficient amount of tenders but, at the same time, ensure the economic stability and development of the company. This paper aims to address this issue in the area of Czech public construction procurement. The opinions, experiences and practices of contractors were collected through a questionnaire survey, and the data were evaluated with the support of statistical methods. This revealed that Czech contractors mostly base their multicriteria bidding strategy on cost-oriented pricing while considering various aspects such as the risks and attractiveness of the tender. The Czech construction market is generally perceived as oriented toward low costs, and with a relatively common occurrence of abnormally low bids. The findings presented in this paper may help contractors improve their current bidding strategies in public construction procurement.
İnşaat projelerinde süresel gecikmelerin yönetimi, dünya genelinde araştırmacılar arasında büyük ilgi görmektedir. Bu konudaki geniş literatür, iş süresini etkileyen çok sayıda faktör olduğunu öne sürmektedir. Bu faktörlerle iş süresini belirmeye yönelik tahmin yöntemleri, daha güvenilir araçlar ve etkin zaman performansı sağlamak açısından önceki araştırmalarda kullanılmıştır. İş süresi hesaplama tekniklerinin önemli potansiyeli olmasına rağmen, bu yöntemler sınırlı sayıdaki çalışmada ihale aşamasında ve konut projelerinde uygulanmıştır. Ayrıca Türkiye’de inşaat süresi ile ilgili araştırmalar, konut projelerinde önemli gecikmeler olduğunu göstermiştir. Bu nedenle “İdeal İş Süresi”ne ulaşmak amacıyla yeni bir hesaplama yöntemi önermek için sadece konut projelerinde inşaat süresini etkileyen faktörlerin araştırılmasına karar verilmiştir. Konut projelerine ilişkin veriler, Türkiye'de konut projeleri inşa etmede temel kurum olan Türkiye Cumhuriyeti Toplu Konut İdaresi Başkanlığı'ndan (TOKİ) elde edilmiştir. İstatistiksel veri analizinde çoklu regresyon, CHAID ve CART analizleri kullanılmıştır. Çalışmanın bulguları, her bir istatistiksel yöntem için İdeal İş Süresini önemli ölçüde etkileyen birkaç faktörün olduğunu göstermiştir. Her üç istatistiksel yöntemin de geçerliliğini test etmek için kestirim değerleri ve standart hatalar hesaplanmıştır. Regresyon formülü, önerilen hesaplama yönteminin sınanmasında istatistiksel anlamlılık göstermiştir. Yöntemin farklı konut projelerine de uygulanması, geciken proje sayısının önemli ölçüde azaldığını kanıtlamıştır.
Purpose
This paper aims to analyze the economic-financial performance (EFP) and value creation (VC) in the Brazilian construction industry.
Design/methodology/approach
Based on the theories of strategy and finance, a quantitative-qualitative, descriptive and explanatory and applied study was carried out, contrasting the performance of the Direcional company and the civil construction industry – both listed on the Brazilian Stock Exchange and the Over-the-Counter Market (B3)
Findings
The analysis of the EFP in the Brazilian construction industry shows that EZTEC, Helbor, Trisul and Direcional were the companies with the best EFP in the period. The analysis of the Economic Value Added (EVA®, henceforth EVA), as a VC metric and basis for assessing the relative technical efficiency score by Data Envelopment Analysis (DEA®, henceforth DEA), revealed that the companies Direcional, EZTEC, MRV and CR2 were considered efficient throughout the period covered. The multicriteria methodology for empirical testing of the EFP and VC allowed not only contrasts Direcional's results with the other companies of the construction industry but also offered a complementary tool for comparative analysis of enterprises of different sizes, structures and realities.
Research limitations/implications
Regardless of any contextual limitations, from a theoretical point of view, the research not only helps fill the research gap aforementioned but also expands knowledge on the topic and demonstrates how this multi-criteria methodology (integrating DEA and EVA) can be used to assess EFP and VC in addition to traditional tools. However, this new approach evaluates, at the same time, corporate and sectorial effectiveness by contrasting the efficiency and efficacy (simultaneously) in the generation of performance and value of a company in relation to the industry.
Practical implications
Significant implications for managerial practice could be noted by offering a tool to improve company performance and creating a competitive benchmarking process for analysts, investors, managers, financing agencies, shareholders, policymakers and business owners, as well as organizations and sectors in similar situations – who need to assess the EFP and VC holistically and improve their decision-making processes.
Originality/value
The uniqueness and innovation of this research come from the original multi-criteria methodology developed, applied and validated for analysis of EFP and VC. This methodology was operationalized through DEA applied to the companies' EVA, making it possible to compare corporate results and those of the whole industry in a balanced way – an unexplored issue in the literature, especially in emerging economies, opening several avenues for future research.
As a pivotal industry in national economy, increasing the efficiency of construction industry means a lot to economy development and people’s living standard. Based on the trend and variance analysis on status of China’s construction industry, this paper employs there-stage data envelopment analysis (DEA), which effectively eliminated the impact of external environment and statistical noise, to measure the efficiency of construction industry in 31 provinces of China from 2011 to 2015. According to the overall results in the first stage, the average efficiency of construction industry is above 85%, and the efficiency of scale shows an uptrend for most provinces. When the impacts of external environment and statistical noise are removed through stochastic frontier analysis (SFA), the overall efficiency goes down due to the decrease of technical efficiency. Besides, the results also reveal that the efficiency varies greatly among provinces and the provincial development is uneven. Finally, suggestions and policy implications are discussed.
Purpose – The logic followed for making decisions and selecting projects significantly influences construction companies’ success. This research investigates project selection in the context of business management with the specific aim of understanding the role of business models in project selection.
Design/methodology/approach – The research objective is pursued by conducting a multiple-case study. Managers acting in key decision-making roles from eight construction companies are interviewed. A conceptual framework is developed for analysing the interview data and the prevailing project selection practices in construction.
Findings – The findings suggest that project selection is not guided by any specific business model, but that the decision-making process is dominated more by short-term factors such as need of work and profitability. Thus, estimation know-how largely determines the kind of projects companies are willing to consider, regardless of their competence to deliver them.
Research limitations/implications – The study produces a hypothesis that ignorance of business models in project selection and their general underutilization in management have negative effects on performance of the construction industry. More consistent management practice would enable the development of business models and processes contributing to performance and help companies to distinguish themselves from each other.
Originality/value – As opposed to previous studies that have produced bidding models that emulate the current industry practices, this research analyses the prevailing logic of project selection from a more critical perspective. In addition, the project selection practices of Finnish construction companies have not been investigated previously.
Purpose
– Making the right bid/no-bid decision is critical to the success and development of construction contracting enterprises. Decision makers’ personal characteristics, such as risk perception and propensity, have great impact on bid/no-bid decisions, which is the major concern of this research. The purpose of this paper is to explore the relationship among decision makers’ risk perception, risk propensity, and their bid/no-bid decision making of construction projects, as well as the factors influencing the risk perception and propensity.
Design/methodology/approach
– In total, four hypotheses were proposed based on an extensive literature review. Experimental questionnaires were distributed to employees working in Chinese construction contracting enterprises with knowledge of construction bidding, and 134 valid questionnaires were obtained. Multivariate statistical analysis through SPSS 19.0 was used to analyze the acquired data.
Findings
– Data analysis shows that in the context of international construction contracting: risk perception has a negative influence on bid/no-bid decision making; while risk propensity produces a positive influence and the probability and magnitude of potential gain or loss both have significant impacts on risk perception, and the probability plays a more important role.
Originality/value
– This research studied the bid/no-bid decision making of construction projects from the new perspectives of risk perception and risk propensity of the decision makers.
It is widely accepted in the construction industry that contract documents, specifying the responsibility and risk of each participant, are the basis for project managers' and superintendents' decision making (DM). However, in practice strict adherence to the formal procedures and chains of command would not always be possible without an unacceptable expenditure of time and money. Although much attention is given to the decisions at the project manager and superintendent level, the underlying rules and mechanisms for the moment-to-moment DM at the site management level has not been documented. In this paper, a social network (SN)–based data envelopment analysis (DEA) benchmarking procedure (SDBP), which combines DEA (assessing the relative efficiency of DM units) and SN (concentrating on the relationships amongst DM units) to identify the benchmarks for the inefficient specialty trades (STs). This paper also uses a case study to illustrate how to implement the SDBP. This research contributes to the body of knowledge because it combines the DEA and SN analysis for the first time to propose a new method of identifying the improvement direction (benchmarks) for STs of a project. This technique is beneficial to project managers because it (1) untangles the role and effectiveness of the existing interactions and inter-dependencies among the STs, (2) evaluates the STs' potential for being benchmarks, and (3) outlines how an inefficient ST can improve its performance through learning from the practices of other good performers (social learning).
Purpose
– The purpose of this paper is to identify and rank the factors that affect the bid/no bid decision according to their relative importance from the perspective of the contracting parties operating in the Gaza Strip, Palestine.
Design/methodology/approach
– The objectives of this research were investigated through a postal questionnaire, which covered a randomly selected sample of 63 contractors, 29 clients and 13 consultants operating in the construction industry in the Gaza strip. The questionnaire was structured based on related literature, the pilot study and actual factors affecting bidders' decisions to bid or not that arise from special conditions in the Gaza Strip. A total of 78 factors that affect the bid/no bid decision were identified. These factors were then ranked according to their relative importance to contracting parties operating in the Gaza Strip, Palestine.
Findings
– The results illustrate that the financial capability of the contractors, the financial capability of the clients, the financial values of the project, the due date of the payments, the availability of construction raw materials in local markets, and the stability of the construction industry were the most critical factors affecting the bid/no bid decision, as agreed by all respondents.
Originality/value
– The paper provides supportive practical solutions for contractors, clients and consultants to enhance and improve bidding decisions. It is recommended that clients and consultants consider the financial capabilities, technical capabilities and staff competencies of the contractors during the awarding stage, and not simply focus on the lowest bid.
Two critical decisions faced by bidders in competitive bidding include, firstly, whether or not to submit a bid, and secondly (if the answer to the first is ‘yes’) what markup value should be used on the submitted bid. In the construction industry, government agencies and private sector clients typically adopt competitive bidding to determine contract awards. Contractors also apply the same approach to bidding decisions. There are many variables that affect contractor decisions regarding whether to bid and the markup scale, which complicate the bidding decision process. This study proposes a Multi-Criteria Prospect Model for Bidding Decision (BD-MCPM) to assist contractors to make decisions on bid/no bid and markup scale. Key factors of influence that impact bidding decisions were identified first. Second, Fuzzy Preference Relations (FPR) was employed to assess factor weights and determine bid/no bid. Finally, if a decision to bid is given, then the Multi-Criteria Prospect Model (MCPM), which links Fuzzy Preference Relations (FPR) and Cumulative Prospect Theory (CPT), is deployed to determine the markup scale that best conforms to primary decision maker (PDM) preferences. The applicability of this model was demonstrated in a real case study.
Santrauka
Ketinantiems dalyvauti konkurse reikia priimti du lemiamus sprendimus: ar teikti pasiūlymą ir kokį antkainį nurodyti pasiūlyme. Statybų sektoriuje valstybės įstaigos ir klientai iš privačiojo sektoriaus paprastai konkursus skelbia siekdami išrinkti laimėtoją, su kuriuo pasirašoma sutartis. Rangovai sprendimus dalyvauti konkurse vertina taip pat. Rangovų apsisprendimą dalyvauti konkurse ir pasirinktą antkainį lemia ne vienas kintamasis, ir tai apsunkina apsisprendimo dalyvauti konkurse procesą. Šiame tyrime pristatomas daugiakriterinis perspektyvų modelis apsisprendimui dalyvauti konkurse (BD-MCPM), kuris rangovams padės apsispręsti, ar dalyvauti konkurse ir kokį antkainį pasirinkti. Pirmiausia buvo nustatyti pagrindiniai veiksniai, kurie daro įtaką apsisprendimui dalyvauti konkurse. Po to, naudojant neraiškiuosius prio- ritetinius ryšius (angl. Fuzzy Preference Relations, FPR), buvo įvertinti veiksnių reikšmingumai ir nustatyta, dalyvauti ar nedalyvauti konkurse. Pagaliau, jeigu nusprendžiama dalyvauti konkurse, naudojant daugiakriterinį perspektyvų modelį (MCPM), kuris neraiškiuosius prioritetinius ryšius susieja su kaupiamaja perspektyvos teorija (angl. Cumulative Prospect Theory, CPT), nustatomas antkainio lygis, kuris labiausiai atitinka pagrindinio sprendimus priimančio asmens pageidavimus. Praktinis šio modelio naudingumas pademonstruotas atliekant atvejo tyrimą.
Reikšminiai žodžiai: apsisprendimas dalyvauti arba nedalyvauti konkurse, antkainio lygis, pasiūlymų teikimo strategija, daugiakriterinis perspektyvų modelis, kaupiamoji perspektyvos teorija, neraiškieji prioritetiniai ryšiai
Bid/no-bid decision-making is a critical activity for a contractor. Limited by both the nature of and the competition for bid opportunities, bid/no-bid decision is associated with uncertainty and complexity. Because subjective considerations such as nature, competition, value of the bid opportunity, resource capabilities, and the reputation of the company are relevant to the bid/no-bid decision, a fuzzy logic approach is adopted. In this approach assessments are described subjectively in linguistic terms, while screening criteria are weighted by their corresponding importance using fuzzy values. As an illustration, a practical example for evaluating a bid opportunity of an international co-development commercial airplane project in Taiwan is cited, which shows how this method can provide the analyst with more convincing and reliable results and cost saving for the company.
The purpose of this paper is to utilize data envelopment analysis (DEA) to benchmark safety performance of construction contractors. DEA has been recognized as a robust tool that is used for evaluating the performance of business organizations. The proposed approach is deployed based on empirical data collected from 45 construction contractors. On a scale of 0–1.0, DEA analysis assesses the relative efficiency of every contractor relative to the rest of the contractors in terms of safety performance. For inefficient contractors, DEA analysis provides quantitative guidance on how to become efficient.
The bid/no bid decision requires an understanding of a company's assessment in relation to factors affecting the decision. Different companies might have different assessment values. The aim is to investigate how bid/no bid decisions are influenced by different characteristics of contractors. Various factors are identified and then analysed in order to investigate their influence and relative significance. A questionnaire survey was used to identify and rank the factors affecting the bidding decision and then analyse them in terms of differences between the returned responses with respect to the differing respondent characteristics. The findings have established the ranking order of the factors affecting the bid/no bid decision and identified their weights of importance. In addition, the influence of these characteristics upon the different weights of importance given by the survey respondents is found to be statistically significant. The most influential characteristics that affected their assessment of the weight of importance are contractor size, classification status of the contractor and the main client type. Different contractors' characteristics should be reflected in the way that the bid/no bid decisions are modelled. Also, the data collected should be categorized with regards to contractors' characteristics before starting the data analysis and modelling processes.
This paper presents the findings of a survey of the bidding practices of Canadian civil engineering construction contractors. The results of the survey provide insight into the most important factors that contractors consider in making four bid decisions: the decision to bid, the risk allowance, the opportunity allowance, and the markup-size decision. The survey methodology is described to illustrate its effectiveness. Common practices in assessing risks and opportunities, the competition, and markup are discussed. A major conclusion of this paper is that the decision-making process used in bidding is largely subjective and based on experienced judgement. The assessment of the competition is done on an informal basis in most cases, with little use of historical competitor data. Risk and opportunity assessment is subjective and largely based on experience. Although the markup-size decision is critical to the success of a company in achieving its objectives and realizing a profit, markup setting is usually based on experience, with little or no formal methods of analysis.
To bid or not to bid for a certain project is considered to be a crucial decision for contractors' survival. The objective of this paper is to identify and rank the factors that affect the bid/no bid decision according to their relative importance from the perspective of the local contractors operating in the Gaza Strip, Palestine. A total of seventy-eight factors were identified through a comprehensive literature review to evaluate the most important factors that affect the contractors' decisions to bid or not in the Gaza strip. A survey was conducted on a sample of seventy three contractors classified under the building category, and sixty five of them responded with response rate 89%. The results illustrate that, the financial capability of the contractors, the reputation of the clients, the financial capability of the clients, the financial values of the project, the availability of construction raw materials in the local markets, and the stability of the construction industry were the most critical factors affecting the contractors bid-no bid decisions. This study suggests that contractors and clients should improve their financial systems and capabilities in order to stay in business.
Project selection is a fundamental decision in construction companies. Regarding the high complexity and dynamic nature of construction projects, the level of uncertainty in this industry is very high. Portfolio selection strategy can be considered as one of the best ways for mitigating the risk of project selection. With respect to the considerable importance of project portfolio selection in reducing the risk to a company, developing research in this area is crucial. The main focus of this paper is to present a hybrid model according to the fuzzy case-based reasoning for prescreening of projects according to the factors generating risk in the construction industry and the historical records of the company, also allocating the most appropriate prescreened projects to the portfolio of company with the use of zero-one linear goal programming. A real case study has been presented for model implementation and for more understanding.
The service economy is now the largest portion of the industrialized world's economic activity. This development has dramatically raised the importance of maximizing productivity excellence in service organizations. The need for productivity excellence has led service organization managers to use benchmarking techniques to identify and adopt best practices in their organizations. Benchmarking has enabled service organizations to continuously improve by allowing service units to learn from methods that prove the most efficient and effective. Service Productivity Management systematically explores complex service issues and suggests the most appropriate methods to improve service productivity, quality, and profitability. The book provides insights and methods to answer questions on a range of productivity issues: How do you manage profitability of a network of hundreds or thousands of branch offices disbursed over several states and countries? How can managed-care organizations manage the quality and cost of hundreds of physicians providing health services to millions of plan members? What methods would enable a government to ensure that the multiple offices serving citizens across a country are operating at low cost while meeting the required service quality? Each of these service settings are examples of the many service providers that deliver a complex set of services to a widely diversified set of customers. Service Productivity Management is an in-depth guide to using the most powerful available benchmarking technique to improve service organization performance-Data Envelopment Analysis (DEA). The underlying concepts that drive DEA and enable it to increase productivity and profitability of service organizations are explained in non-technical language. It describes how DEA: (1) Identifies best practice service units; (2) Exposes high cost inefficient service units; (3) Identifies specific changes to each service unit to elevate performance to the best practice level that provides high quality service at low cost; and most important, (4) Guides the improvement process. Use of basic and advanced DEA methods are all supported by case-study applications of organizations that have successfully improved their performance. The techniques discussed in the book are accessible to all managers with access to Microsoft® Excel spreadsheet software (Excel). The book provides step-by-step guidance to enable the reader to apply DEA and Excel software to their organization. DEAFrontier is a Microsoft® Excel Add-in designed to run DEA analyses on any set of organizations of interest to the reader. For a free trial version of the DEAFrontier software, please visit www.deafrontier.com. © 2006 by Springer Science+Business Media, LLC. All rights reserved.
Bidding is a strategic decision that helps contractor firms to survive. Traditionally, bidding behaviors are highly unstructured in construction companies. This paper discusses the issues involved in evaluating a tender to assist the managers of a firm in making reliable and sound decisions. The emphasis of the work is on developing a logical decision making framework that covers organizational, environmental, risky and financial considerations. The model takes advantage of well-known decision making methods such as the analytical hierarchy process and the simple additive weighting. The model's input would be the linguistic description of each project's value with respect to the decision criteria, and the output would be the value of each project proposed to decision makers considering its correlation with existing projects. By means of a case study, the model has been implemented. The proposed decision making model is found to yield substantially improved solutions when large size contractors are concerned.
A nonlinear (nonconvex) programming model provides a new definition of efficiency for use in evaluating activities of not-for-profit entities participating in public programs. A scalar measure of the efficiency of each participating unit is thereby provided, along with methods for objectively determining weights by reference to the observational data for the multiple outputs and multiple inputs that characterize such programs. Equivalences are established to ordinary linear programming models for effecting computations. The duals to these linear programming models provide a new way for estimating extremal relations from observational data. Connections between engineering and economic approaches to efficiency are delineated along with new interpretations and ways of using them in evaluating and controlling managerial behavior in public programs.
Recently, the award of overseas construction projects to Korean construction companies has substantially increased, and the number of Korean bids to win such projects has grown significantly also. Overseas project bidding characteristically requires a higher resource input cost in the initial preparation phase than does domestic project bidding. However, the bidding process of construction companies is frequently suspended for various reasons, and hence the opportunity cost associated with the bidding process becomes a sunk cost which, in turn, puts the bidder at a further loss. Therefore, by identifying projects with the highest chance of proceeding up to the final bid submission in the early phase of bidding, the sunk cost that results from a bid drop or suspension can be reduced and the projects with high bid probabilities can be targeted to enhance order possibility and performance capability. Unfortunately, many contractors tend to rely on qualitative assessments based on their past experiences and intuition, or on the Chief Executive Officers’ (CEO) subjective instructions, when making a bid decision. In this respect, this study proposes a model utilizing the logistic regression method, analyzing the correlation between various factors of project and bid decision making to increase the effectiveness of future decision making. If factors relating to the internal decision-making process of a construction company and overseas bidding process are coordinated, the bidders can be expected to enhance the reliability of their decisions using this model, and the cost incurred during a bidding process may be reduced.
In several countries, it is a common practice for construction owners to require contractors to submit several types of surety bonds shifting the risk of contractors default to sureties. The process of evaluating contractors’ applications for bonding is complicated and time-consuming. Surety underwriters are faced with the challenge of considering tens of objective and subjective criteria to make the bonding recommendation. These criteria include a contractor’s financial strength, past performance, and other related aspects. Literature review revealed that there is little guidance to surety underwriters in this regard. As a result, the purpose of this paper is to equip surety underwriters with a decision making model for evaluating contractors’ applications for bonding. The proposed model is based on data envelopment analysis (DEA). The DEA is a robust nonparametric linear programming approach that is widely used for benchmarking, performance measurement, and decision making. The proposed DEA model guides surety underwriters throughout the decision making process. The model offers surety underwriters the flexibility to accommodate any number of variables in the evaluation process. The proposed model is deployed based on a database that includes 49 contractors. The validity and applicability of the proposed model are scored positively by industry practitioners in terms of methodology used, evaluation criteria, ease of use, performance results, and overall usefulness compared to existing methods.
To bid or not to bid for construction projects is a crucial strategic decision for contractors' sustainability in the industry. The high complexity and uncertainty associated with such a decision-making process, which involves several multitudes and reflection of numerous internal and external determinants, necessitate further exploration of the prominent factors underpinning contractors' decisions. Following the announcement of awarding the "Federation Internationale de Football Association" (FIFA) 2022 World Cup hosting rights to the State of Qatar on December 2, 2010, the State is pledging to spend tens of billions of dollars on hundreds of new projects. Thus, the objective of this case study is to identify, explore, and rank the relative importance of the critical factors determining contractors' decisions to bid or not bid for local construction projects. To achieve this objective, a structured questionnaire survey comprising 43 factors that were shortlisted based on relevant previous investigations on bidding practices in the construction industry, and the input of local industry experts and professionals, was distributed to a statistically representative sample of contractors. Using the "Relative Importance Index" technique, the following critical factors are identified: (1) previous experience with the employer, (2) need for work, (3) current workload, (4) previous experience in similar projects, (5) size of project, (6) identity and reputation of the employer in the industry, (7) financial stability of the employer, (8) availability of other projects, (9) promptness of the employer in the payment process, and (10) tender documents quality level. The outcomes reported in this study can be used to provide employers, consultants, contractors, and policymakers, guidance for focusing, acting upon, and controlling the cardinal factors perceived to impact contractors' bidding behaviors in an environment on the brink of undergoing an unprecedented boom in demand for faster delivery and lower cost of constructed facilities. (C) 2014 American Society of Civil Engineers.
Deciding whether to take part in a bidding has a significant influence on the condition of a construction company. Not bidding deprives the contractor of the possibility of completing a given building project, yet the contractor has to consider whether, for example, he or she has the capability of carrying out a particular project. This paper presents a brief description of some chosen models aiding the bid/no bid decision that have been proposed in previous studies. Moreover, the authors designed their own model based on the fuzzy set theory. The basis of the decision model became the previous selection of factors influencing the decision. To achieve this, the authors conducted a study among contractors in 2010-2011 in Southern Poland. One hundred and fifty surveys were sent, providing 41% of answers. Based on the analysis of the surveys, according to the contractors, the top three most vital factors influencing the bid/no bid decision are (1) the type of work, (2) experience in similar projects, and (3) contractual terms. Both the evaluation of the factors and their influence on the decision to be made in a given enterprise are largely subjective and hard to unambiguously assess; therefore, the proposed model incorporates the fuzzy set theory, which is exceptionally convenient for finding solutions to the problems resulting from human subjectivity. The results are presented and commented on in this paper. (C) 2014 American Society of Civil Engineers.
Deciding whether or not to bid on a particular project is regarded as one of the major challenges faced by construction contractors. This decision-making problem involves the consideration of several factors. These factors are related to project, client, consultant, competitors, and market. Although several researchers have targeted the subject of bidding in construction, few of them have tackled the bid/no-bid problem in particular. The purpose of this paper is to propose an empirical framework for making the bid/no-bid decision. The proposed framework consists of two consecutive components. Component 1 determines key bidding factors that are considered by contractors when evaluating bids. Senior managers at top contracting organizations were interviewed to identify factors that affect the bid/no-bid decision. Component 2 utilizes data envelopment analysis (DEA) to make the bid/no-bid decision. The strengths of utilizing DEA include its input-output framework, which facilitates incorporating many factors in the decision-making process. The proposed framework offers construction contractors a systematic approach for making the bid/no-bid decision. The model is practically illustrated based on 39 bids that were collected from eight contractors.
Bid/no bid decisions are crucial for the business continuity of a contractor. Thus, the decision should be made carefully within the context of the short- and long-term strategy of the organization. Despite the availability of several models, this decision commonly is made based on past experience and subjective judgments. This study aims to show how ANFIS can be used as a decision support tool by contractors during the bid/no bid decision process for international construction projects. Review of the literature revealed that 52 factors may affect a contractor's bid/no bid decision. These factors were categorized into six main groups, which are bidding documents-related factors, contractor-related factors, project-related factors, contract-related factors, host country-related factors, and opportunity-related factors. Having identified these factors, a questionnaire was designed to identify each factor's relative importance. The questionnaire was applied to a large-scale contractor, and actual data from 151 international projects were obtained. An ANFIS model was developed via MATLAB software program using the collected data. The proposed ANFIS model integrates the learning advantage of neural networks with fuzzy logic that represents the human reasoning mechanism. The statistical indicators revealed that the performance of the proposed model is satisfactory.
To be successful in a competitive environment, contractors have to prepare their bids wisely. The two main decisions they have to make are the project selection decision and the markup selection decision. This paper proposes a new optimization model that combines these two main decisions. It also takes into account the importance of eminence and previous works as the most important non-monetary evaluation criterion used by owners for evaluating bids. These factors combined with the two decisions make this model complex and nonlinear. To solve this model, a customized Genetic Algorithm is developed. Using Monte Carlo simulation, the result of the model is compared to the results of conventional models that only consider bidding markups. The comparison shows that considering eminence can increase the expected profit of the contractor to more than 25% under some evaluation criteria mindsets of the owner.
This paper presents a framework to facilitate the selection of the most appropriate company to be contracted among competitive bids. This framework is intended to be integrated in e-marketplaces to comply with the major technological advances in the construction industry. A novel feature of the system is that it allows bilateral evaluations between companies to better understand general contractor-subcontractor relationships and to improve the level of transparency within the construction sector. The performance assessment system incorporates other innovative features, such as the ability to specify a set of performance indicators suitable for inclusion in e-marketplaces covering three different perspectives: company reliability, operation performance, and bid attributes. The system also allows the integration of the preferences of the decision maker concerning the selection of the best company for a given work.
Decision-making involves a process by which a choice is selected from a number of options. Bid decisions by contractors are complex due to uncertainty about many factors affecting their outcomes. This study was able to identify, through a questionnaire survey, 55 factors characterizing the bid decision-making process. The questionnaire was mailed in August 1990, to 300 top contractors in the UK. The results indicate that several factors are considered equally important for bid/no bid and markup size decisions. Other factors are seen to have considerable importance for one decision but not for another. The need for work, the number of competitors tendering, and the amount of experience on such projects are identified as the top three factors that affect a contractor's decision to bid for a project. The degree of difficulty, the risk involving owing to the nature of the work, and the current work load are the highest ranked factors affecting markup size decisions.
In today's highly competitive construction industry, there is a critical need for managers to continuously improve their firm's efficiency and effectiveness. More specifically, managers need to know which performance measures are most critical in determining their firm's overall success. Benchmarking, when done properly, can accurately identify both successful companies and the underlying reasons for their success. However, rigorous benchmarking within the construction industry still remains an embryonic field. This paper analyzes and critiques both the performance measures and metrics used traditionally in the construction industry and the benchmarking models developed to date for the industry. Based on the results of this analysis, benchmarking models using data envelope analysis are proposed that offer significant improvements over current models. The proposed models measure construction firm performance on a company-wide basis, foster trade-off analyses among various performance metrics, and tie the resources expended by construction firms to how well those firms perform overall. The models also provide managers guidance in determining how specific company resources can be reallocated to improve overall company performance.
Bid decisions are heuristic in nature as they are made on the basis of experience, judgment, and perception. In an attempt to uncover the underlying factors that characterize the bidding decision‐making process, a questionnaire survey was conducted among general contractors. This paper contains results based on the response obtained from 400 of the top general contractors in the United States. Characteristics of the group, factors affecting bid/no‐bid and percent‐markup decisions, and policies and practices of the contractors are reported. The study reveals that bidding decisions are greatly influenced by subjectively evaluated criteria, such as type of job, location, size of job, need for work, Owner, subcontractors, degree of hazard, and degree of difficulty. Competition and profitability, although significant, are not the topranked factors.
Previous studies have aimed to develop effective methods to derive baseline productivity (BP) for labor-intensive activities in construction sites. However, there are two different definitions of BPs: one is defined as a performance benchmark of best practice and the other as a standard reflecting a contractor's normal operating performance. It is necessary to clarify the difference between the two definitions and their corresponding BPs. This research introduces data envelopment analysis (DEA) as a new method for deriving BP and compares DEA with the other four BP deriving methods. DEA is concluded as the best method in terms of objectivity, effectiveness, and consistency to find BP that represents the best performance a contractor can possibly achieve. With the capability of deriving productivities of multi-input and multi-output activities, the proposed DEA has raised the scale of labor productivity from the level of single factor productivity to total factor productivity which will help construction researchers and managers to evaluate performances of interests in a much more effective way.
The web benchmarking systems broadly used in the construction industry (CI) are designed to provide results based on key performance indicators (KPIs). No insights concerning organization overall performance and improvements targets are available. This research aims to fulfill this gap using data envelopment analysis (DEA) as a method to complement the information provided by a set of KPIs. The methodology proposed is useful to all organizations involved in benchmarking routines. To enable a more realistic assessment of CI companies, two types of DEA models were used, one allows factor weights to vary freely and the other includes weight restrictions. These models assign an efficiency score to each organization, identifying efficient organizations and providing performance improvements targets for the others. To enable suggesting targets for all organizations, expert opinion was used to specify virtual units which were included in the efficiency assessment to define a practical frontier located beyond the productivity levels of the original DEA frontier. Based on a sample of 20 Portuguese leading contractors, the Portuguese web benchmarking system for CI, icBench, was used to demonstrate the advantages of integrating the DEA method with KPIs benchmark scores.
Selecting an appropriate competition strategy in bidding is the ambition of most contractors. The multiple requirements of clients encourage contractors to consider other strategies to deliver additional benefits besides offering a low-price bid. Offering low bids will reduce contractors' profits and potentially make development less attractive. Contractors need to understand their specific resources that generate competitive advantage and accordingly develop strategies to win contracts. This paper reports the findings from a recent survey on competition strategies in the Hong Kong construction industry. Thirteen typical bidding strategies, their used frequency in bidding, and their effectiveness for winning contracts of different types and between different groups of contractors are studied. The analysis of findings is explored to provide local contractors and clients with new insights into competition strategies in bidding.
Data envelopment analysis (DEA) measures the relative efficiency of decision-making units and avoids any functional specification to express production relationship between inputs and outputs. DEA-based Malmquist productivity index (MPI) measures the productivity change over time. In this paper, the MPI is used to measure the productivity changes of Chinese construction industry from 1997 to 2003. The results of analyses indicate that productivity of the Chinese construction industry experienced a continuous improvement from 1997 to 2003 except for a decline from 2001 to 2002. It is found that there are gaps in productivity development level among western, midland, eastern, and northeastern regions in the Chinese construction industry. The DEA-based MPI approach provides a good tool to support setting up policies and strategic decisions for improving the performance of the Chinese construction industry and promoting the sustainable development of the industry between different regions.
This paper, which is written to both researchers and practitioners, examines the impact of information technology (IT) on construction firm performance. Based on data collected from 74 construction firms, regression analysis is used to test the relationship between performance and IT. Analysis provides empirical evidence that IT is positively associated with firm performance, schedule performance, and cost performance. Firm performance is a composite score of several metrics of performance: schedule performance, cost performance, customer satisfaction, safety performance, and profit. The regression analysis shows that for every 1 unit increase in IT utilization, there is an increase of about 2, 5, and 3% in firm performance, schedule performance, and cost performance, respectively. No relationship is found between IT use and customer satisfaction, safety performance, and profitability.
A structured methodology for modeling the bid/no-bid decision problem is presented. The method is based on the techniques of decision analysis. The advantages of explicating the decision-making process is emphasized in the paper. Explication of the process brings numerous advantages, including improved communication and documentation of the underlying technique and assumptions. Suggestions are made about how to explicate the decision-making process. A procedure for quantifying the subjective evaluation and the aspiration level of a bidder is outlined. The model is based on a set of attributes obtained as a result of a questionnaire survey conducted among 400 of the top general contractors of the United States. It was assumed that these attributes are independent of each other, implying an additive model. The structured decision-making process, as suggested, is suitable for implementation in a computerized decision-support system. The method is demonstrated with the help of a hypothetical example.
The bidding decision is a complex problem affected by numerous factors. The current study collected a list of determining factors from the results of past research and opinions of six experienced practitioners in competitive bidding. Based on these factors, a bid reasoning model was established to go deeply into the bid decision process. Differing from other earlier work, this model-oriented study focuses on the effects of the determining factors on four reasoning subgoals: competition, risk, company's position in bidding, and need for work. Their different contributions to each reasoning subgoal were reviewed under three main construction procurement methods, namely, unit rate contract, lump sum contract, and design/build contract. The Analytic Hierarchy Process technique was applied in this study. A survey questionnaire was developed with four hierarchies being formulated respectively for the four reasoning subgoals. The survey was conducted in two steps, the pilot survey among the six experienced practitioners and the subsequent one among 153 top contractors in Singapore. From this survey, a set of top key factors and their relative importance weights were identified. This result together with the bid reasoning model can serve as the framework for the further development of the bid decision support system.
Since contractors' bidding behaviors are affected by numerous factors related both to the specific features of the project and dynamically changed situations, bidding decision problems are highly unstructured. No clear rules can be found in delivering a bidding decision. In this problem domain, decisions are commonly made based upon intuition and past experience. Case-based reasoning (CBR) is a subbranch of artificial intelligence. It solves new problems by matching against similar problems that have been encountered and resolved in the past. It is a useful tool in dealing with complex and unstructured problems, which are difficult if not impossible to be theoretically modeled. This paper presents a case-based reasoning bidding system that helps contractors with the dynamic information varying with the specific features of the job and the new situation. In this system, bid cases are represented by sets of attributes derived from a preliminary survey of several experienced bidders, focusing, respectively, on two reasoning subgoals: (1) Risk; and (2) competition. Through the system, similar cases can be retrieved to assess the possible level of competition and risk margin. A hypothetical example is explained and evaluated to demonstrate the feasibility of the method. The effectiveness of this system is tested by a Monte Carlo simulation in comparison to the conventional statistical method.
Contractor prequalification is essential in most construction projects, and the process has been performed by many different methods in practice. Data envelopment analysis (DEA) had been recognized as a useful technique to prequalify contractors by assigning relative efficiency scores. Data envelopment analysis, however, usually requires a large amount of data and has not been fully developed to achieve reliable results. An enhanced contractor prequalification model using DEA was developed together with a methodology for determining a "practical frontier" of best contractors. The established practical frontier can be used as a regional performance standard for the owner in prequalification and as improvement guidelines for contractors.Key words: contractor prequalification, construction engineering, data envelopment analysis, practical frontier.
Very little is known about the financial well-being of contractors, in part because they are generally privately held companies. The goals of this work were to develop a model based on data envelopment analysis to assess contractor performance and to use the model to provide a set of financial benchmarks for the industry. As the efficiency score of contractors decreased, the following trends were evident: decreasing current ratio, increasing accounts receivable and payable times, increasing debt to equity, increasing fixed assets to equity, increasing gross profits to sales, increasing administrative expenses to net worth, decreasing net income to sales, and decreasing net income to equity.Key words: DEA, benchmark, efficiency, peer group, DMU, building contractor, heavy civil contractor, specialty contractor, distinct cultural environment, frontier.
One of the most crucial decisions that is regularly exercised by construction contractors is to determine whether to bid or not to bid on a certain project. The purpose of this paper is to propose a data envelopment analysis (DEA) approach for the bid–no-bid decision. DEA is a robust non-parametric linear programming approach that is used for benchmarking performance and for making selection decisions. Based on a contractor's database of previous considerations of bidding opportunities, DEA creates a “favorable frontier” that consists of favorable bidding opportunities. New bidding opportunities are evaluated in reference to this “favorable frontier” and the bid–no-bid decision is consequently made. The proposed approach incorporates subjective management expertise and deals systematically with bidding situations to guide contractors in their bid–no-bid determination. A major strength of the proposed DEA approach is that it is deployable by organizations facing the bid–no-bid problem regardless of size, country of operation, number and type of factors considered in bidding, or even industry.
Purpose
The “multi‐criteria selection” has become popular practice in selecting contractors, especially for public works, which involve multiple stakeholders. In line with this development, contractors need to consider more factors apart from offering a lower bidding price in formulating a bidding strategy. The purpose of this paper is to introduce a quantitative method, namely the fuzzy competence requirement (FCR) model, for assisting a contractor to formulate a better bidding strategy by better utilising its competence for meeting the best multiple criteria imposed by clients.
Design/methodology/approach
The fuzzy linear programming with multiple objectives technique is applied in this paper and an illustrative example is introduced to demonstrate the application of the proposed model.
Findings
The FCR model can assist in generating a range of bidding strategies by assuming different confidence levels that contractors may perceive. These strategies could be valuable references for contractors to develop their competitive bidding strategy, which enables contractors to efficiently utilise their competence to meet clients' multiple selection criteria.
Research limitations/implications
The FCR model can provide contractors with useful information for formulating their bidding strategy. However, other factors, such as the sense of the market and estimation of rivals' bidding strategy, should also be considered in the bidding decision.
Practical implications
The FCR model can help contractors make better decisions in bidding by considering their competence and meeting client's multiple criteria.
Originality/value
This paper introduces a new model – FCR model – for helping contractors improve the efficiency of their bidding decision.
This paper presents a hybrid framework that integrates value analysis and decision making for eliciting and evaluating design alternatives. Value analysis approach relies on the integration of the functional analysis through the systematic use of the functional analysis system technique and quality function deployment. This value analysis methodology enables customer requirements to be linked to specific design alternatives during the project design stage. The degree of project complexity will affect the number of design alternatives to be evaluated. As such, the data envelopment analysis DEA is incorporated as a decision support tool to evaluate the degree to which each design alternative satisfies the customer requirements. DEA is used to calculate a customer requirement efficiency index for each alternative. This index is a measure of how well a particular alternative achieves the requirements taking into account its overall cost. The framework was successfully used on a high-tech research facility construction project.
Bidding strategies vary from contractor to contractor, each of which will have different degrees of sensitivity towards the factors affecting their bidding decisions. A competitor analysis using a linear mixed model is proposed for use by contractors as part of a more informed approach in identifying key competitors, and as a basis for formulating bidding strategies. The competitiveness between bids is examined according to: (i) project size, (ii) work sector; (iii) work nature; and (iv) number of bidders. The model was tested empirically by application to a bidding dataset obtained from a large Hong Kong contractor. Allowing for different degrees of sensitivity towards the four bidding variables across competing contractors (i.e. with the model parameters that varied across competing contractors), the results indicate that competitiveness in bidding of this contractor is generally greater than the majority of its competitors.
In management contexts, mathematical programming is usually used to evaluate a collection of possible alternative courses of action en route to selecting one which is best. In this capacity, mathematical programming serves as a planning aid to management. Data Envelopment Analysis reverses this role and employs mathematical programming to obtain ex post facto evaluations of the relative efficiency of management accomplishments, however they may have been planned or executed. Mathematical programming is thereby extended for use as a tool for control and evaluation of past accomplishments as well as a tool to aid in planning future activities. The CCR ratio form introduced by Charnes, Cooper and Rhodes, as part of their Data Envelopment Analysis approach, comprehends both technical and scale inefficiencies via the optimal value of the ratio form, as obtained directly from the data without requiring a priori specification of weights and/or explicit delineation of assumed functional forms of relations between inputs and outputs. A separation into technical and scale efficiencies is accomplished by the methods developed in this paper without altering the latter conditions for use of DEA directly on observational data. Technical inefficiencies are identified with failures to achieve best possible output levels and/or usage of excessive amounts of inputs. Methods for identifying and correcting the magnitudes of these inefficiencies, as supplied in prior work, are illustrated. In the present paper, a new separate variable is introduced which makes it possible to determine whether operations were conducted in regions of increasing, constant or decreasing returns to scale (in multiple input and multiple output situations). The results are discussed and related not only to classical (single output) economics but also to more modern versions of economics which are identified with "contestable market theories."
A model for measuring the efficiency of Decision Making Units (=DMU's) is presented, along with related methods of implementation and interpretation. The term DMU is intended to emphasize an orientation toward managed entities in the public and/or not-for-profit sectors. The proposed approach is applicable to the multiple outputs and designated inputs which are common for such DMU's. A priori weights, or imputations of a market-price-value character are not required.
A mathematical programming model applied to observational data provides a new way of obtaining empirical estimates of extrernal relations—such as the production functions and/or efficient production possibility surfaces that are a cornerstone of modern economics. The resulting extremal relations are used to envelop the observations in order to obtain the efficiency measures that form a focus of the present paper.
An illustrative application utilizes data from Program Follow Through (=PFT). A large scale social experiment in public school education, it was designed to test the advantages of PFT relative to designated NFT (=Non-Follow Through) counterparts in various parts of the U.S. It is possible that the resulting observations are contaminated with inefficiencies due to the way DMU's were managed en route to assessing whether PFT (as a program) is superior to its NFT alternative. A further mathematical programming development is therefore undertaken to distinguish between “management efficiency” and “program efficiency.” This is done via procedures referred to as Data Envelopment Analysis (=DEA) in which one first obtains boundaries or envelopes from the data for PFT and NFT, respectively. These boundaries provide a basis for estimating the relative efficiency of the DMU's operating under these programs. These DMU's are then adjusted up to their program boundaries, after which a new inter-program envelope is obtained for evaluating the PFT and NFT programs with the estimated managerial inefficiencies eliminated.
The claimed superiority of PFT fails to be validated in this illustrative application. Our DEA approach, however, suggests the additional possibility of new approaches obtained from PFT-NFT combinations which may be superior to either of them alone. Validating such possibilities cannot be done only by statistical or other modelings. It requires recourse to field studies, including audits (e.g., of a U.S. General Accounting Office variety) and therefore ways in which the results of a DEA approach may be used to guide such further studies (or audits) are also indicated.
The paper describes a simulation approach based on the Analytic Hierarchy Process (AHP) to assess the probability of winning in a competitive bidding process where competing bids are evaluated on a multiple criteria basis, assuming the point of view of the contractor. The model allows the contractor to define his bidding strategy on the basis of the information currently available concerning the owner, the competitors, and the profile of his own bid. The model is applied to an auction, where a number of contractors compete for the design and construction of a process plant in a developing country.
This paper aims at exploring quantitative methods that are appropriate for project location selection. One of the major considerations of whether to undertake a construction project is to determine if the location is valuable for investment. Location selection may be simply based on past experience, rudimentary, “gut-feeling”, or a combination of them. Alternatively, it may involve scientific methods. The paper introduces both deterministic and dynamic approaches and presents some of the basic quantitative methods, including data envelopment analysis model and binary integer linear program models, serving as a base for both academics and practitioners. To expand the contribution of the paper, illustrative examples are given.
In an effort to uncover the main factors that characterize the two distinct but sequential stages of bidding decisions, namely bid/no bid and mark-up size decisions, this study identifies the key determining factors and their importance weights by presenting survey findings of 80 contracting organizations from Northern Cyprus and Turkish construction markets regarding this issue. The framework presented in this study will serve as a basis for a knowledge-based system model which will guide the contracting organizations in reaching ‘strategically correct’ bid/no bid and mark-up size decisions. The proposed framework used a reasoning model, which went deeply into the heart of the decision-making process and helped to clarify the complex picture regarding the two sequential decisions. The results revealed that there is a distinct and significant difference in the importance assigned to the same key factors for the two separate decision processes. Furthermore, the study confirmed the fact that the factors related to ‘strategic considerations’ have a significant role in both of these decision processes. One of the most striking findings of this research was the distinct difference in approach of varying sizes of contractors to the overall issue. Hence, these results suggest that any model regarding bidding and mark-up size decisions should definitely differentiate among different sizes of contractors to reflect the approaches of the related contractors in a better manner. Furthermore, it was interesting to find out that the correlation between different sizes of contractors’ approaches was higher in the mark-up decision process compared to the bidding decision process. In spite of the fact that this study was based on input provided by certain types of contractors in Northern Cyprus and Turkish construction markets only, we believe that the overall approach, reasoning and findings of the proposed framework are of good value to contracting organizations in different construction markets throughout the world as well.
Data envelope analysis (DEA) is currently applied to ongoing organizations such as hospitals, police stations, schools, etc. This paper investigates the possibility of using the DEA approach for evaluating the performances of projects in a multi-project environment, where each project is usually a one-time non-repeated event. Projects are evaluated by the earned value management system (EVMS) and the multidimensional control system (MPCS) methods. In this environment, it is usually necessary to reduce the number of outputs and inputs not to exceed the number of projects. This paper deals with a method for their reduction. The paper illustrates a detailed example on how to apply and interpret the DEA method in a multi-project environment.
This paper presents the development of a practical knowledge-based system software called SCBMD, which deals systematically with different bidding situations and assists the contractors in reaching ‘strategically correct’ bid/no bid and mark-up size decisions. The proposed framework differentiates among different sizes of contractors and uses a reasoning model that goes deeply into the heart of the decision making process. Hence, it helps to clarify the complex picture regarding the two sequential decisions and allows the users to follow the methodology used and the steps followed in reaching the final decisions. In order to see the degree to which SCBMD mirrors actual industry practices, it was implemented to 100 different real bidding situations. An accuracy of 86% was reached in simulating actual bid/no bid decisions of the contractors while the average difference in markup percentages proposed by the software and the contractors was found as 1.75%. Moreover, the software was evaluated for its timeliness under varying bidding conditions and found very effective especially for the cases where the contractor would like to reach decision alternatives for different possible scenarios of the same bidding situation.
Current initiatives for e-commerce deployment in the construction industry are related to the establishment of business-to-business (B2B) marketplaces, which allow large communities of buyers and suppliers to meet and trade with each other. Although these efforts have included aspects of interoperability, database management and security protocols, trading mechanisms in the supply chain have not been properly evaluated. Buyers of construction materials interact with material suppliers in a marketplace environment, thus striving for the best possible spot buy offer. This paper addresses the purchase of construction materials as the last component in the supply chain. A tool for optimizing purchasing decisions in B2B construction marketplaces is presented.
Data Envelopment Analysis (DEA) has become an accepted approach for assessing efficiency in a wide range of cases. The present paper suggests a systematic application procedure of the DEA methodology in its various stages. Attention is focused on the selection of ‘decision making units’ (DMUs) to enter the analysis as well as the choice and screening of factors. The application of several DEA models (in different versions and formulations) is demonstrated, in the process of determining relative efficiencies within the compared DMUs.
Private finance initiative (PFI), as a form of public/private partnership (PPP), helps to contract the private sector to governmental projects. In contrast to traditional public financed projects, PFI projects are procured by allowing a private sector entity to take the responsibility to design, build, finance, and operate (DBFO) an asset for a contract period of up to several decades. Moreover, banks are perceived to take the leading role in financing PFI projects. Since project financing involves credit assessment of loan applicants, banks have employed popular credit scoring models to assess their creditworthiness. Although the existing models are useful for credit scoring, new models have to emerge in response to ever-changing business practices. This paper therefore aims at introducing the application of data envelopment analysis (DEA) as an alternative credit-scoring model. Unlike traditional credit-scoring building on a formula where weights to a set of criteria are assigned subjectively, DEA will automatically generate the relative weights for analysis. However, incorporating DEA demands additional considerations, which are discussed in this paper. Finally, examples are demonstrated for illustrating this alterative approach to credit scoring by DEA.