Characteristics of the Construction Industry from the Marketing Viewpoint: Challenges and Solutions

Marketing management plays a crucial role in successful companies. However, marketing has been either misunderstood or entirely neglected in numerous construction companies because it is difficult to apply conventional marketing strategies due to specific characteristics of the industry. This study systematically investigates the characteristics of the construction industry and their effects on marketing management. A systematic literature review covering scientific papers from 1995 to 2015 (556 scientific papers) identifies 16 characteristics, which are organized into two main categories—the construction industry and construction product-related industry. 'Fragmentation' was the most highlighted characteristic.A total of 10 identified characteristics, according to the experts' opinions gathered through the questionnaire, may affect the construction marketing, of which "competitive bidding mechanism" and "project-based nature of the industry" have the greatest effect. Through a combination of literature review and focus group study, the challenges resulting from each of these 10 particularities regarding various dimensions of marketing are examined and practical solutions are suggested for overcoming these challenges. The main suggestion is the modification of the traditional marketing mix (4Ps) by eliminating “place” and adding four elements—profile, pooling, phase, and presenter. Therefore, a tailored marketing mix for construction companies is developed, with seven elements (7Ps). Moreover, marketing research and marketing intelligence systems are vital marketing tools for construction companies and should allocate adequate resources and responsible staffing levels for these activities

Predicting Project Success in Residential Building Projects (RBPs) using Artificial Neural Networks (ANNs)

Due to the urban population’s growth and increasing demand for the renewal of old houses, the successful completion of Residential Building Projects (RBPs) has great socioeconomic importance. This study aims to propose a framework to predict the success of RBPs in the construction phase. Therefore, a 3-step method was applied: (1) Identifying and ranking Critical Success Factors (CSFs) involving in RBPs using the Delphi method, (2) Identifying and selecting success criteria and defining the Project Success Index (PSI), and (3) Developing an ANN model to predict the success of RBPs according to the status of CSFs during the construction phase. The model was trained and tested using the data extracted from 121 RBPs in Tehran. The main findings of this study were a prioritized list of most influential success criteria and an efficient ANN model as a Decision Support System (DSS) in RBPs to monitor the projects in advance and take necessary corrective actions. Compared with previous studies on the success assessment of projects, this study is more focused on providing an applicable method for predicting the success of RBPs. Doi: 10.28991/cej-2020-03091612 Full Text: PD

Exploring Critical Success Factors in Urban Housing Projects Using Fuzzy Analytic Network Process

Population growth and increasing trend towards urbanization have caused housing demand to exceed its supply, particularly in urban areas in developing countries. Furthermore, housing industry motivates many subsidiary industries and plays a leading socio-economic role in such countries. Therefore, successful completion of housing projects is of great significance quantitatively and qualitatively.This study aims to propose a framework to evaluate the critical success factors (CSFs) in housing projects considering the interrelationship among factors and criteria. The factors were initially identified through literature review and then refined and categorized using a two-round Delphi method and finally prioritized using fuzzy analytic network process (FANP). To demonstrate the implementation of the proposed model, a case study was carried out on an urban residential building project in Tehran. The framework proposed in this study can be applied as a decision support system for decision makers, project managers and practitioners involved in the housing sector

Evaluating delay factors in the construction and operation of port operational areas (case study: Shahid Rajaee port complex)

A significant part of the annual budget of developing countries is allocated to civil projects and the construction industry. In Iran, between 30% and 40% of the total budget of the country `is allocated to this industry. However, the implementation of these projects is often faced with several problems that cause delays and increase costs. The main objective of this research is to identify, analyze, and prioritize factors effective in delays in the construction of port operational area and to offer suggestions for preventing or reducing these delays. The statistical population of the study consists of employers,investors, consultants and contractors involved in the construction of port operational areas in Iran. Data were collected through a questionnaire and were then analyzed using structural equation modeling in VPLS software. Results showed the most effective factors of the delay in the construction of port operational areas to be inadequate monitoring(11%), poor planning and time scheduling (19%), improper allocation of resources (24%), cash flows changes(28%), failure to fund the projects on time (16%)and other factors (27%). These results can assist companies and legal authorities involved in the construction of port operational areas in Iran in making the right decisions based on the importance and effectiveness of each delay factor.Keywords: Delay Factors, Port Construction Projects, Project Management

Branch Client Behavior Analysis Using RFM Method

In today's competitive world, applying new techniques to business development has a great impact. The restaurant industry is no exception. Therefore, in this research, using new methods of knowledge discovery and data mining, customer data of chain restaurant is investigated. The purpose of this study was to explore customer behavior patterns using data mining methods.In this study, one million and five hundred thousand customer records were reviewed in five branches of a chain restaurant and two stages of clustering modeling using RFM method and then classification modeling were performed on the data and the behavior rules chain restaurant customers were extracted. The results of this study have helped to identify the loyal and profitable customers of the chain restaurant which has led to the improvement of the profitability of the chain restaurant. One of the innovations of this research has been the communication between clustering and classification results

A state-of-the-art review on the integration of Building Information Modeling (BIM) and Geographic Information System (GIS)

The integration of Building Information Modeling (BIM) and Geographic Information System (GIS) has been identified as a promising but challenging topic to transform information towards the generation of knowledge and intelligence. Achievement of integrating these two concepts and enabling technologies will have a significant impact on solving problems in the civil, building and infrastructure sectors. However, since GIS and BIM were originally developed for different purposes, numerous challenges are being encountered for the integration. To better understand these two different domains, this paper reviews the development and dissimilarities of GIS and BIM, the existing integration methods, and investigates their potential in various applications. This study shows that the integration methods are developed for various reasons and aim to solve different problems. The parameters influencing the choice can be summarized and named as "EEEF" criteria: effectiveness, extensibility, effort, and flexibility. Compared with other methods, semantic web technologies provide a promising and generalized integration solution. However, the biggest challenges of this method are the large efforts required at early stage and the isolated development of ontologies within one particular domain. The isolation problem also applies to other methods. Therefore, openness is the key of the success of BIM and GIS integration

Combining Monte-Carlo and hyper-heuristic methods for the multi-mode resource-constrained multi-project scheduling problem

Multi-mode resource and precedence-constrained project scheduling is a well-known challenging real-world optimisation problem. An important variant of the problem requires scheduling of activities for multiple projects considering availability of local and global resources while respecting a range of constraints. A critical aspect of the benchmarks addressed in this paper is that the primary objective is to minimise the sum of the project completion times, with the usual makespan minimisation as a secondary objective. We observe that this leads to an expected different overall structure of good solutions and discuss the effects this has on the algorithm design. This paper presents a carefully-designed hybrid of Monte-Carlo tree search, novel neighbourhood moves, memetic algorithms, and hyper-heuristic methods. The implementation is also engineered to increase the speed with which iterations are performed, and to exploit the computing power of multicore machines. Empirical evaluation shows that the resulting information-sharing multi-component algorithm significantly outperforms other solvers on a set of “hidden” instances, i.e. instances not available at the algorithm design phase