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Article

Jemima Antwiwaa Ottou, Bernard Kofi Baiden, Gabriel Nani and Martin Morgan Tuuli

This research investigates the implementation of Six Sigma in competitive tendering processes to address persistent delays by identifying the potential benefits and…

Abstract

Purpose

This research investigates the implementation of Six Sigma in competitive tendering processes to address persistent delays by identifying the potential benefits and challenges of implementing Six Sigma in construction competitive tendering processes. The results seek to encourage practitioners to implement Six Sigma in addressing competitive tendering process delays.

Design/methodology/approach

Literature was reviewed to identify the benefits and challenges of Six Sigma implementation in construction processes and categorized under broad headings. Three case studies were used to authenticate the literature findings by applying Define-Measure-Analyse-Improve-Control to their construction competitive tendering processes. Furthermore, quality tools and techniques together with documentary analysis, content analysis and determination of frequencies of quantitised qualitative data were employed to identify potential benefits and challenges.

Findings

The most common Six Sigma benefits achievable in construction competitive tendering are Time Related benefits. Other benefits likely to emanate as ripple effects are Customer Focus Related, Quality Related, Process Improvement Related, Human Resource Related, Finance Related and Decision Related. However, implementation challenges should be expected.

Practical implications

Six Sigma implementation in construction competitive tendering promotes time efficiency. It is expected that this will encourage quantity surveyors, procurement practitioners and their institutions to implement Six Sigma in addressing persistent delays in their competitive tendering processes.

Originality/value

This paper demonstrates the use of merged approach under mixed method to identify the benefits and challenges of Six Sigma implementation in construction competitive tendering process within the Ghanaian context.

Details

Built Environment Project and Asset Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2044-124X

Keywords

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Article

Jemima Antwiwaa Ottou, Bernard Kofi Baiden and Gabriel Nani

Project management tools and techniques have been widely adopted in the construction industry; however, its combination with Six Sigma and application in construction…

Abstract

Purpose

Project management tools and techniques have been widely adopted in the construction industry; however, its combination with Six Sigma and application in construction procurement has not been widely researched. This paper explores the use of Six Sigma Project Procurement (SSPP) and its potential applications in public sector construction procurement.

Design/methodology/approach

The role of Six Sigma and project management in construction is critically evaluated using three case studies to demonstrate their application in public procurement.

Findings

The use of SSPP by public sector organisations creates efficient and effective construction procurement processes by addressing delays. The proposed timelines for competitive tendering in the Ghanaian Public Procurement Manual are not realistic.

Research limitations/implications

This paper contributes to and broadens the limited body of evidence and knowledge of applying SSPP to public procurement processes and identifies areas for further research.

Practical implications

Project management will continue to expand in the global construction industry. However, what will eventually determine if SSPP is embraced by public sector construction depends on the leadership and success of its execution. The application of SSPP to public construction procurement will address delays and lead to significant time reduction of the process. This will eliminate the major issue (delay) accounting for deficiencies in the process.

Originality/value

The paper yields immense value to construction project management researchers and practitioners, especially in the public sector. It recommends the inclusion of Six Sigma to promote focus on actual instead of perceived problems and advocates for decisions-making based on facts which will ensure continuous improvement.

Details

International Journal of Quality & Reliability Management, vol. 38 no. 2
Type: Research Article
ISSN: 0265-671X

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Article

Isaac Mensah, Theophilus Adjei-Kumi and Gabriel Nani

Determining the duration for road construction projects represents a problem for construction professionals in Ghana. The purpose of this paper is to develop an artificial…

Abstract

Purpose

Determining the duration for road construction projects represents a problem for construction professionals in Ghana. The purpose of this paper is to develop an artificial neural network (ANN) model for determining the duration for rural bituminous surfaced road projects.

Design/methodology/approach

Data for 22 completed bituminous surfaced road projects from the Department of Feeder Roads (rural road agency) were collected and analyzed using the principal component analysis (PCA) and ANN techniques. The data collected were final payment certificates which contained payment bill of quantities (BOQ) of work items executed for the selected completed road projects. The executed quantities in the BOQ were the total quantities of work items for site clearance, earthworks, in-situ concrete, reinforcement, formwork, gravel sub-base/base, bitumen, road line markings and furniture, length of road and actual durations for each of the completed projects. The PCA was first employed to reduce the data in order to identify a smaller number of variables (or significant quantities) that constitute 81.58 percent of the total variance of the collected data. The ANN was then used to develop the network using the identified significant quantities as input variables and the actual durations as output variables.

Findings

The coefficient of correlation (R) and determination (R2) as well as the mean absolute percentage error (MAPE) obtained show that construction professionals can use the developed ANN model for determining duration. The study shows that the best neural network is the multi-layer perceptron with a structure 3-38-1 based on a back propagation feed forward algorithm. The developed network produces good results with an MAPE of 17.56 percent or an average accuracy of 82.44 percent.

Research limitations/implications

Apart from the fact that the sample size was small, the developed model does not incorporate the implications of other likely factors that may affect contract duration.

Practical implications

The outcome of this study is to help construction professionals to fix realistic contract duration for road construction projects before signing a contract. Such realistic contract duration would help reduce time overruns as well as the payment of liquidated and ascertained damages by contractors for late completion.

Originality/value

This paper proposes an alternative way of determining the duration for road construction projects using the total quantities of work items in a final payment BOQ. The approach is based on the PCA and ANN model of quantities of work items of completed road projects.

Details

Engineering, Construction and Architectural Management, vol. 23 no. 5
Type: Research Article
ISSN: 0969-9988

Keywords

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Article

Richard Ohene Asiedu, Nana Kena Frempong and Gabriel Nani

Time overruns are commonplace within the construction industry. These result in deception because project managers critically assess the economic and financial viability…

Abstract

Purpose

Time overruns are commonplace within the construction industry. These result in deception because project managers critically assess the economic and financial viability of a project before implementation. Forecasting the likelihood of time overruns will not only lead to a reconsideration on the decision to build but also help put in place the necessary control measures – exactly what this research achieved.

Design/methodology/approach

The paper argues that rather than depending on the critical failure factors that are unknown at the pre-contract stage to forecast the likelihood of occurrence, it will be more useful to rely on project attributes that are known before contract signing. A multiple linear regression analysis is used for the model development based on ten independent variables.

Findings

About 86.6 per cent of all the projects experienced time overruns. The mean time overrun is 106.5 per cent. Initial contract sum, initial duration, gross floor area, contractor class D2K2, competitive tendering, sole sourcing and single-storey buildings explained about 44.7 per cent of the variations within time overruns, with a mean absolute percentage error of 60.7 per cent.

Research limitations/implications

The predictive accuracy of the model can, in practice, be tested after the completion of a project by comparing the actual project schedule with the planned schedule. Any disparity in the expected outputs should result in a reassessment of the significant independent variables to improve the forecasting abilities of the model.

Practical implications

The model is expected to be very useful at the pre-contract stage when detailed designs are unavailable. As a decision support system, it will help the practitioners and decision-makers make informed decisions while minimizing the time and resources spent to arrive at these decisions.

Originality/value

This research presents a unique opportunity to forecast the likelihood of time overruns within the building sector based on project attributes that are known before the contract-signing phase.

Details

Journal of Financial Management of Property and Construction, vol. 21 no. 3
Type: Research Article
ISSN: 1366-4387

Keywords

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Article

Gabriel Nani, Isaac Mensah and Theophilus Adjei-Kumi

A major concern for construction professionals at the rural road agency in Ghana is the problem of fixing contract duration for bridge construction projects in rural…

Abstract

Purpose

A major concern for construction professionals at the rural road agency in Ghana is the problem of fixing contract duration for bridge construction projects in rural areas. The purpose of the study was to develop a tool for construction professionals to forecast duration for bridge projects.

Design/methodology/approach

In all, 100 questionnaires were distributed to professionals at the Department of Feeder Roads to ascertain their views on the work items in a bill of quantities (BOQ) that impact significantly on the duration of bridge construction projects. Historical data for 30 completed bridge projects were also collected from the same Department. The data collected were executed work items in BOQ and actual durations used in completing the works. The qualitative data were analysed using the relative importance index and the quantitative data, processed and analysed using both the stepwise regression method and artificial neural network (ANN) technique.

Findings

The identified predictors, namely, in-situ concrete, weight of prefabricated steel components, gravel sub-base and haulage of aggregates, used as independent variables resulted in the development of a regression model with a mean absolute percentage error (MAPE) of 25 per cent and an ANN model with a feed forward back propagation algorithm with an MAPE of 26 per cent at the validation stage. The study has shown that both regression and ANN models are appropriate for predicting the duration of a new bridge construction project.

Research limitations/implications

The predictors used in the developed models are limited to work items in BOQs only of completed bridge construction projects as well as the small sample size.

Practical implications

The study has developed a working tool for practitioners at the agency to forecast contract duration for bridge projects prior to its commencement.

Originality value

The study has quantified the relationship between the work items in BOQs and the duration of bridge construction projects using the stepwise regression method and the ANN techniques.

Details

Journal of Engineering, Design and Technology, vol. 15 no. 6
Type: Research Article
ISSN: 1726-0531

Keywords

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