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The purpose of this paper is to explore the information that manufacturing companies have available when competitively bidding for service contracts.

A semi-structured interview study was undertaken with industrialists in various sectors, which are currently facing the issue of servitisation.

One of the main findings was that, despite the novelty of the process, the decision makers at the competitive bidding stage have an understanding of the involved uncertainties. In particular, the uncertainty arising from the customer as the user of the product and evaluator of the competitive bids in addition to the uncertainty connected to the competitors were identified as the main influences on the pricing decision.

The research implications show the influences and considerations during the decision-making process at the competitive bidding stage for service contracts. These include the customer and the competitors.

Shortcomings in the current industrial practice were identified such as the approaches used to communicate the cost estimate for the service contract. The approaches currently used contradict research findings in the area of communicating uncertainty information, which means that further research is to be done to identify optimal approaches to displaying the uncertainty connected to the communicated information.

This paper offers a basis for research to understand the challenges industry faces when competitively bidding for service contracts. This can be used to develop novel approaches in supporting the decision maker such as a model that presents the probability of winning in comparison to the probability of making a profit.

Factors like bid price, submission time, and number of bidders influence the procurement process in public projects. These factors and the award criteria may impact the project’s financial compliance. Predicting budget compliance in construction projects has been traditionally challenging, but Machine Learning (ML) techniques have revolutionised estimations.

In this study, Portuguese Public Procurement Data (PPPData) was utilised as the model’s input. Notably, this dataset exhibited a substantial imbalance in the target feature. To address this issue, the study evaluated three distinct data balancing techniques: oversampling, undersampling, and the SMOTE method. Next, a comprehensive feature selection process was conducted, leading to the testing of five different algorithms for forecasting budget compliance. Finally, a secondary test was conducted, refining the features to include only those elements that procurement technicians can modify while also considering the two most accurate predictors identified in the previous test.

The findings indicate that employing the SMOTE method on the scraped data can achieve a balanced dataset. Furthermore, the results demonstrate that the Adam ANN algorithm outperformed others, boasting a precision rate of 68.1%.

The model can aid procurement technicians during the tendering phase by using historical data and analogous projects to predict performance.

Although the study reveals that ML algorithms cannot accurately predict budget compliance using procurement data, they can still provide project owners with insights into the most suitable criteria, aiding decision-making. Further research should assess the model’s impact and capacity within the procurement workflow.

Previous research predominantly focused on forecasting budgets by leveraging data from the private construction execution phase. While some investigations incorporated procurement data, this study distinguishes itself by using an imbalanced dataset and anticipating compliance rather than predicting budgetary figures. The model predicts budget compliance by analysing qualitative and quantitative characteristics of public project contracts. The research paper explores various model architectures and data treatment techniques to develop a model to assist the Client in tender definition.

The purpose of this paper is to provide a multi‐criteria bidding evaluation model based on Istanbul 2010 PR selection problem to strike a balance among conflicting criteria and to aggregate opinions held by a group of decision makers.

In the study, analytic hierarchy process (AHP) methodology was used to settle the conflict properly. The evaluation criteria were transformed into a hierarchical form and their relative weights were calculated and synthesized for the final ranking of the bidders. Then a linear interpolation‐based spreadsheet model was combined with findings of the AHP to fairly select best bidders.

The paper demonstrates that the hierarchical structure of the AHP methodology can successfully resolve the conflict among evaluation criteria and measure relative importance of the criteria by taking into account the preference of the decision makers. Moreover, a linear interpolation methodology can evaluate quoted bid prices fairly and can help to make the best decision.

In all areas of business management, there is a great need for fair bid evaluation systems. The method presented in the paper will help future studies in designing more intriguing systems and resolving conflicts in the area of bid evaluation.

Planning phase of a project results to series of crucial decisions which determine the path to objectives achievement. At the same time, in this phase, project encounters the highest level of uncertainty in comparison of all phases of project lifecycle. This paper aims to support early decisions of project based on the progress forecasting.

The scope of study is limited to downstream projects of petroleum industry in Iran, and the proposed model is trained and tested based on 75 Iranian completed petroleum projects. First, types of progress curve functions are investigated, and various types are studied and the most appropriate ones are selected through curve fitting. In the next step, using questionnaire, dependent and independent variables are recognized. Finally, using historical data and s-curve generator functions, a fuzzy inference system (ANFIS) based model have been developed to support early phases decision-making processes.

Based on the analysis of received questionnaires, six functional criteria in two groups as dependent variables and 25 independent variables, in two groups and four clusters are determined and categorized. Eventually, performance prediction model of a project has been developed by using Adaptive Nero Fuzzy Inference System.

The main contribution of this study to construction management knowledge is categorizing two groups of variables, which first one defines the project dynamic and the other calculates the key effects on previous one. Also, this investigation improves the current knowledge by analyzing the project system from the dynamic behavior perspective and modeling the defined variables using ANFIS tools.

Compiled by K.G.B. Bakewell covering the following journals published by MCB University Press: Facilities Volumes 8‐18; Journal of Property Investment & Finance Volumes 8‐18; Property Management Volumes 8‐18; Structural Survey Volumes 8‐18.

Index by subjects, compiled by K.G.B. Bakewell covering the following journals: Facilities Volumes 8‐18; Journal of Property Investment & Finance Volumes 8‐18; Property Management Volumes 8‐18; Structural Survey Volumes 8‐18.

Compiled by K.G.B. Bakewell covering the following journals published by MCB University Press: Facilities Volumes 8‐18; Journal of Property Investment & Finance Volumes 8‐18; Property Management Volumes 8‐18; Structural Survey Volumes 8‐18.

Compiled by K.G.B. Bakewell covering the following journals published by MCB University Press: Facilities Volumes 8‐18; Journal of Property Investment & Finance Volumes 8‐18; Property Management Volumes 8‐18; Structural Survey Volumes 8‐18.

Due to the increasing size and complexity of construction projects, construction engineering and management involves the coordination of many complex and dynamic processes and relies on the analysis of uncertain, imprecise and incomplete information, including subjective and linguistically expressed information. Various modelling and computing techniques have been used by construction researchers and applied to practical construction problems in order to overcome these challenges, including fuzzy hybrid techniques. Fuzzy hybrid techniques combine the human-like reasoning capabilities of fuzzy logic with the capabilities of other techniques, such as optimization, machine learning, multi-criteria decision-making (MCDM) and simulation, to capitalise on their strengths and overcome their limitations. Based on a review of construction literature, this chapter identifies the most common types of fuzzy hybrid techniques applied to construction problems and reviews selected papers in each category of fuzzy hybrid technique to illustrate their capabilities for addressing construction challenges. Finally, this chapter discusses areas for future development of fuzzy hybrid techniques that will increase their capabilities for solving construction-related problems. The contributions of this chapter are threefold: (1) the limitations of some standard techniques for solving construction problems are discussed, as are the ways that fuzzy methods have been hybridized with these techniques in order to address their limitations; (2) a review of existing applications of fuzzy hybrid techniques in construction is provided in order to illustrate the capabilities of these techniques for solving a variety of construction problems and (3) potential improvements in each category of fuzzy hybrid technique in construction are provided, as areas for future research.

The purpose of this paper is to provide a perspective of computer‐aided material and process selection (MPS) software tools for product development purpose and present a practical approach for manufacturers and other decision makers involved in MPS.

A multi‐criteria deductive approach for MPS is applied to a case study by taking into account the technical performances and environmental constraints. A resource‐based cost modeling is also deployed to examine the implication of selected material and process on overall product cost.

The paper demonstrates the capabilities and shortcoming of existing computerized MPS software tools in assisting product managers and designers for handling the growing volume of material/process data.

Applying computer‐aided MPS approach to complex shape products with multiple features is not a straightforward task and requires further development in existing MPS software tools.

Computer‐aided MPS systems can assist decision makers in solving many material/process selection problems by following a systematic process.

Given today's rapid technological changes, it is important for decision makers to understand the capabilities of computer‐aided MPS software tools in handling a growing volume of data. Very limited research has been done to explore the capabilities and limitations of existing material/process selectors. It is the first in the literature that demonstrates the application of multi‐criteria deductive approach in MPS using a software tool.