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This study aims to propose a novel approach to select and prioritize performance indicators in Lean Manufacturing depending on whether they are influencing or being influenced by others, thereby assisting in the decision-making process for improving overall performance.

The methodology comprises two stages. First, a literature review was conducted to identify the performance indicators, and then their interrelationships were analyzed by means of the decision-making trial and evaluation laboratory (DEMATEL) multi-criteria decision-making (MCDM) method.

The results provide a comprehensive visualization of the performance indicators in Lean Manufacturing, with a total of 50 identified indicators. Among these, 29 were categorized as causal, meaning that their results mainly influence the others, and 21 as influenced, with their results mostly being influenced by others. Among the causal indicators, those related to the human factor (eight indicators) were the most predominant. However, the most-cited performance families in the literature do not stand out as being causal, but rather as mostly influenced.

This study can help managers improve and analyze performance more effectively, while focusing on the importance of choosing causal over influenced indicators.

Performance measurement plays a crucial role for organizations, but because of the increasing number of metrics, there lacks an established framework. This exploratory study thus opens the discussion on relevance to determine a group of coherent and connected indicators that could help measure performance in a more comprehensive manner, rather than in several isolated parts.

The purpose of this paper is to present a methodology for medical robot kinematics design developed using a knowledge‐management approach.

A classification of medical robots is proposed based on their kinematic characteristics and 76 robot specifications were collected in a catalogue. Then, having drawn a generic specifications sheet, rules were proposed to choose a structure from these specifications.

Findings are situated at several levels: the catalogue, the classification of robots with respect to their kinematic characteristics, a generic and specific specifications sheet, and an organigram to choose the most relevant structure from the specifications.

This structural synthesis represents a preliminary step in the design of medical robots which will be completed by an additional dimensional synthesis.

This work offers a new methodology for medical robots design distinct from what is usually done for medical or industrial robots design using intuition, expertise and non‐formal knowledge.

To help decision-makers choose appropriate infrastructure project delivery systems (IPDS) and keep up with the construction industry’s rapid growth, this study aims to develop a goal optimization technique.This looks into team integration, large production and optimum sustainability. The suggested approach for meeting several infrastructure project objectives is flexible and expandable. This research overcomes the significant discrepancy between the construction industry’s progress and the rate at which project delivery methods evolve.

This study examined pertinent literature to choose an appropriate project delivery method and gave information on several elements that affect that decision. After optimization using a genetic algorithm (GA), a Pareto front of solutions has been found. The three construction goals of sustainability, mass production and team integration are all met by the chosen best solution. The four most popular possibilities for studying the suggested approach are five primary categories, each of which has 22 variables, and the weight of each variable was established using Simo’s procedure. This is optimized, demonstrating the accuracy of the optimization model.

Sustainability, mass production and team integration are the major goals of selecting the finest IPDS. The Pareto-optimal solutions discovered through analysis demonstrated that the created GA is reliable and generates solid outcomes. In fact, it enables decisions that were based on a single criterion to be overturned. The process has therefore demonstrated its efficacy in identifying the ideal answer. First integrated project delivery (IPD), second design-build (DB), third design-bid-build (DBB) and last construction manager at risk (CMR) are the best options. The weight of the aims function has found these rankings to be satisfactory.

The findings demonstrate that the suggested strategy can lead to optimization, providing the government with a wide range of options for attaining certain project objectives. The ability of this study to evaluate the combined effects of three objectives in choosing the best IPDS, the production of optimal selection solutions (IPDS), which can help with better decision-making when many objectives are present, and the flexibility and extendibility of the suggested approach for achieving priorities in infrastructure projects are what make it unique. This approach was able to select IPDS to meet developments in the construction project.

To confirm the validity of the GA, the factor of error was calculated, which is equal to 1.7599e-08.

Time-cost trade-off is normal conduct in construction projects when projects are expectedly late for delivery. Existing research on time-cost trade-off strategic management mostly focused on the technical calculation towards the optimal combination of activities to be accelerated, while the managerial aspects are mostly neglected. This paper aims to understand the managerial efforts necessary to prepare construction projects ready for an upcoming trade-off implementation.

A preliminary list of critical factors was first identified from the literature and verified by a Delphi survey. Quantitative data was then collected by a questionnaire survey to first shortlist the preliminary factors and quantify the predictive model with different machine learning algorithms, i.e. k-nearest neighbours (kNN), radial basis function (RBF), multiplayer perceptron (MLP), multinomial logistic regression (MLR), naïve Bayes classifier (NBC) and Bayesian belief networks (BBNs).

The model's independent variable importance ranking revealed that the top challenges faced were the realism of contractual obligation, contractor planning and control and client management and monitoring. Among the tested machine learning algorithms, multilayer perceptron was demonstrated to be the most suitable in this case. This model accuracy reached 96.5% with the training dataset and 95.6% with an independent test dataset and could be used as the contingency approach for time-cost trade-offs.

The identified factor list contributed to the theoretical explanation of the failed implementation in general and practical managerial improvement to better avoid such failure. In addition, the established predictive model provided an ad-hoc early warning and diagnostic tool to better ensure time-cost implementation success.