Quality in construction management: an exploratory study

A mathematical framework for estimating prototyping cost considering transition of quality states under Markovian setting

Purpose

This research aims to aid product development managers to estimate the expected cost associated with the development of cost-intensive physical prototypes considering transitions associated with pertinent states of quality of the prototype and corresponding decision policies under the Markovian setting.

Design/methodology/approach

The authors evolve two types of optimization-based mathematical models under both deterministic and randomized policies. Under the deterministic policy, the product development managers take certain decisions such as “Do nothing,” “Overhaul,” or “Replace” corresponding to different quality states of prototype such as “Good as new,” “Functional with minor deterioration,” “Functional with major deterioration” and “Non-functional.” Under the randomized policy, the product development managers ascertain the probability distribution associated with these decisions corresponding to various states of quality. In both types of mathematical models, i.e. related to deterministic and randomized settings, minimization of the expected cost of the prototype remains the objective function.

Findings

Employing an illustrative case of the operator cabin from the construction equipment domain, the authors ascertain that randomized policy provides us with better decision interventions such that the expected cost of the prototype remains lower than that associated with the deterministic policy. The authors also ascertain the steady-state probabilities associated with a prototype remaining in a particular quality state. These findings have implications for product development budget, time to market, product quality, etc.

Originality/value

The authors’ work contributes toward the development of optimization-driven mathematical models that can encapsulate the nuances related to the uncertainty of transition of quality states of a prototype, decision policies at each quality state of the prototype while considering such facets for all constituent subsystems of the prototype. As opposed to a typical prescriptive study, their study captures the inherent uncertainties associated with states of quality in the context of prototype testing, etc.

Barriers to implementing digitalization in the Indian construction industry

Purpose

This research paper aims to analyze the critical barriers to implementing digitalization in the Indian construction industry as Indian construction companies are lagging in the implementation of digital technologies in the work environment.

Design/methodology/approach

In this research paper, a qualitative research approach is adopted, and multiple detailed interviews are conducted with industry and academic experts. Further, multi-criteria decision-making (MCDM) techniques are used to finalize the prioritization among various alternatives. The fuzzy-decision-making trial and evaluation laboratory (Fuzzy-DEMATEL) and interpretive structural modeling (ISM) techniques are employed to find the exact relationship among the identified alternatives.

Findings

This study identifies 14 critical barriers from an extensive literature review and multiple interviews with industry professionals, and further driving and critical barriers are identified.

Research limitations/implications

In this research paper, an exploratory study with a limited number of respondents from a large Indian construction company is carried out. Further, a detailed longitudinal analysis can be done to assess the subjectivity of the participants with more advanced statistical tools. However, this research discusses several points pertaining to the implementation of digitalization in the construction industry. The research further identifies the critical barriers to digitalization in the Indian construction industry.

Practical implications

The finding of the study has two-pronged implications. First, it provides a road-map to the construction industry by highlighting the engagement of top management as the key focus area for successful digitalization. Second, the finding also shows similarity of the digitalization process to the adoption of process improvement techniques like lean and total quality management (TQM), wherein the top management plays a crucial role in ushering in the implementation of a disruptive change.

Originality/value

The research is unique in two ways. First, this is one of the very few attempts to understand digitalization in the Indian context. Second, the research also demonstrates that the combination of fuzzy DEMATEL and ISM techniques can be successfully employed in the emerging field of construction digitalization research.

A framework for Crosby’s quality principles using ISM and MICMAC approaches

Purpose

The purpose of this paper is to examine Philip B. Crosby’s 14 quality principles and analyze the interaction between them. Hitherto no research has been published on the implementation of total quality management (TQM) using Crosby’s 14 principles. To fill this gap, interpretive structural modeling (ISM) and Matrix Impact Cross-Reference Multiplication Applied to a Classification (MICMAC) analysis have been designed to prioritize, sequence and categorize variables to find both the dependence and driving power of these variables.

Design/methodology/approach

At the initial stage experts from industry as well as from academia were contacted to provide an input for ISM methodology and examine interactions between identified variables. In this approach, interpretations of the interrelationships among variables have been discussed, whereas MICMAC analysis is used to discover dependence and driving power.

Findings

The results of the investigation revealed that “Management Commitment,” “Quality Improvement Team,” “Quality Awareness,” “Supervisor Training,” “Goal Setting” and “Cost of Quality Evaluation” are strategic requirements; “Corrective Action,” “Zero Defects Day” and “Error Cause Removal” are tactical requirements. “Recognition,” “Quality Measurement,” “Quality Councils” and “Do It Over Again” are operational requirements for TQM applications.

Originality/value

ISM is used as a part of this research to provide valuable insights into interrelationships among Crosby’s quality principles through a systematic framework. The research opens up a new focus area on the implementation of TQM for services as well as for the manufacturing industry.

Organizational culture and total quality management practices in Indian construction industry

Purpose

The purpose of this paper is to establish the relationship between organizational culture (OC) and total quality management (TQM) practices in the Indian construction industry.

Design/methodology/approach

A questionnaire survey was conducted to draw valid empirical data from 200 construction firms in India. The dominant culture was identified using cluster analysis.

Findings

The findings of cluster analysis show four emergent clusters, namely, internal, flexible, comprehensive and control. The OC profile of the Indian construction organizations is dominated by internal focus characterized by the features of both group and hierarchical cultures of Competing Values Framework (CVF). Furthermore, the results revealed that the comprehensive focus culture is the most suitable culture in addition to the internal and flexibility focus cultures for the implementation of TQM in India.

Practical implications

Before implementing the TQM practices, managers in construction organizations need to be aware of cultural orientation emphasized in their organizations to facilitate the effective implementation of TQM.

Originality/value

This paper contributes to the existing literature by providing empirical evidence that leads to the association between OC and TQM practices. The study proposes besides the internal and flexibility focus cultures, the comprehensive focus culture within the Indian construction industry are key drivers for the successful implementation of TQM practices.

A Big Data and FMEA-based construction quality risk evaluation model considering project schedule for Shanghai apartment projects

Purpose

The purpose of this paper is to mine information on the construction process of previous projects to develop a construction plan that meets both quality requirements and schedule constraints.

Design/methodology/approach

This paper uses a failure mode and effect analysis to evaluate the construction quality of 311 apartments in Shanghai. The authors also evaluate construction-scheduling control using the earned value management technique and implement an artificial neural network to correlate the results. The authors then develop a quality risk and schedule correlation model based on Big Data. The model can predict the relationship between the planned schedule and the project quality risk using multiple variables such as the number of layers, the schedule performance index and budget costs.

Findings

The methodology offers an innovative approach for assessment on the relationship between quality risk and project schedule. The authors have also built a multiple regression analysis model for comparative purposes with the model. The results show that the proposed model can better describe the relationship. The model can provide a quantitative quality risk value that changes with the planned schedule, as well as help project managers to understand the relationship between quality risk and project scheduling more accurately.

Research limitations/implications

The research approach only focuses on quality risk under the impact of scheduling. Future efforts might focus on developing a model that connects failure models with project schedules and costs in order to improve the effort of quality management.

Practical implications

The model based on Big Data in this paper is developed using real projects and reflects the relationship between project quality risk and scheduling in real environments. The created application provides support for project managers to develop and adjust quality plans and schedules, thereby reducing deviations in quality and scheduling objectives.

Originality/value

The authors make full use of historical project data from the perspective of both quality and schedule management, and provide a novel method to intelligently and objectively analyze the relationship between quality risk and scheduling.

Continuous improvement: challenges for the project-based organization

Purpose

There are limited studies of continuous improvement (CI) from the perspective of a project-based organization (PBO). Hence, the purpose of this paper is to explore challenges that PBOs may encounter when applying CI.

Design/methodology/approach

An exploratory and qualitative approach has been used, involving six management teams in six different PBOs, using focus groups interviews as data collecting method.

Findings

A high degree of autonomy among project managers seems to limit a collective approach to project management in PBOs. As a consequence the overall PBO performance becomes subordinate to the individual project performance—an approach opposite to that of CI. Further, the management teams themselves seem to uphold a project focus, also complicating improvement initiatives from a PBO-perspective.

Research limitations/implications

The management teams have been the unit of analysis, where the PBOs mainly conduct projects in an engineering and construction context, and are located in the same country and region. This approach enables the thorough study of a phenomenon, while preconditions for generalization are limited. However, the findings could be used by researchers as a basis for more in-depth studies of specific challenges, and for making surveys to obtain generalization of results.

Practical implications

The results can induce awareness and understanding of different challenges if applying CI in a PBO, hence a starting point for finding ways to overcome these challenges.

Originality/value

The article contributes to an increased understanding of challenges that PBOs may encounter when applying CI, confirming and presenting additional findings compared to previous studies.