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Skewed pricing is a typical tactic used by tenderers in unit price projects to gain additional advantages at the expense of the owner or other competing tenderers. This paper aims to describe the development of a model for detecting skewed pricing in competitive tendering for unit price contracts.

The model evaluates how much the offered unit rates for work items deviate from the reasonable rate identified from the item’s submitted unit rates. Item rate deviations are integrated into a total deviation score for each submitted tender based on the relative weight of the work item to the total project amount. The model allows for assigning higher weights to work items that are more prone to skewed pricing, such as those that are performed early and those that are expected to experience quantity fluctuations.

The paper presents a detection model that uses only the submitted prices of the competing tenderers to perform the needed calculations, which reduces subjectivity in identifying skewed tenders. Two examples are given to demonstrate how the model may be used to detect skewed tenders.

The model supports tendering officials in the challenging task of identifying skewed tenders, which is required by rules and regulations governing public procurement. The model’s ease of use is expected to make it more widely used as a decision-support tool during the tender evaluation stage of real-world projects.

Assessing front end engineering design (FEED) accuracy is significant for project owners because it can support informed decision-making, including confidence in cost and schedule predictions. A framework to measure FEED accuracy does not exist in the literature or in practice, not does systematic data directly linking FEED accuracy to project performance. This paper aims to focus first on gauging and quantifying FEED accuracy, and second on measuring its impact on project performance in terms of cost change, schedule change, change performance, financial performance and customer satisfaction.

A novel measurement scheme was developed for FEED accuracy as a comprehensive assessment of factors related to the project leadership and execution teams, management processes and resources; to assess the environment surrounding FEED. The development of this framework built on a literature review and focus groups, and used the research charrettes methodology, guided by a research team of 20 industry professionals and input from 48 practitioners representing 31 organizations. Data were collected from 33 large industrial projects representing over $8.8 billion of installed cost, allowing for a statistical analysis of the framework's impact on performance.

This paper describes: (1) twenty-seven critical FEED accuracy factors; (2) an objective and scalable method to measure FEED accuracy; and (3) data showing that projects with high FEED accuracy outperformed projects with low FEED accuracy by 20 percent in terms of cost growth in relation to their approved budgets.

FEED accuracy is defined as the degree of confidence in the measured level of maturity of the FEED deliverables to serve as a basis of decision at the end of detailed scope, prior to detailed design. Assessing FEED accuracy is significant for project owners because it can support informed decision-making, including confidence in cost and schedule predictions.

FEED accuracy has not been assessed before, and it turned out to have considerable project performance implications. The new framework presented in this paper is the first of its kind, it has been tested rigorously, and it contributes to both the literature body of knowledge as well as to practice. As one industry leader recently stated, “it not only helped to assess the quality and adequacy of the technical documentation required, but also provided an opportunity to check the organization's readiness before making a capital investment decision.”

In this paper, a new front end converter (FEC) topology has been proposed for the switched reluctance (SR) motor drive. This study aims to present the performance analysis of FEC-based SR motor drive using various types of control schemes like conventional proportional integral (PI) controller, fuzzy logic controller (FLC) and fuzzy-tuned proportional integral controller (Fuzzy-PI).

The proposed FEC-based SR motor drive with various control strategies is derived for the torque ripple minimization and speed control.

The steady state and the dynamic response of the FEC-based SR motor drive are analyzed using three different controllers under change in speed and loading conditions. The Fuzzy-PI-based control scheme improves the dynamic response of the system when compared with the FLC and the conventional PI controller.

The hardware prototype has been implemented for the FEC-based SR motor drive by using the Xilinx SPARTAN 6 FPGA processor. The experimental verification has been conducted and the results have been measured under steady state and dynamic conditions.

Fashion retailers have increasingly adopted consumer-facing in-store technology (CFIT) to enhance the customer experience/service provision. This paper aims to explore managerial experiences and sociotechnical implications of introducing these technologies into organizational working processes.

This study draws on interpretive analysis of semi-structured interviews with 74 senior fashion retail industry practitioners, technology providers and consultants to understand managerial perspectives on technological innovation issues.

Endogenous and exogenous factors act as drivers or barriers to CFIT adoption and are influenced by strategic and tactical motives. Key issues that retail managers encounter include challenges in aligning technology implementation with retailer brand image to manage risk and reputation, with additional complexity arising from different internal and/or external actors involved, as well as required levels of change in organizational structure.

This study contributes an empirically derived framework identifying reasons for – and the drivers/barriers influencing – fashion retailers' CFIT adoption, classifying three broad approaches to CFIT adoption: embedded, transformative and opportunistic.

Megaproject performance measurement (MPM) has received great attention in the project management community, but it primarily focused on the design of performance measures or frameworks. Yet, whether MPM utilization can improve megaproject performance and how project actors use MPM to improve megaproject performance is less well understood. This study aims to investigate whether and how the use of MPM can contribute to better megaproject performance.

Through the lens of the lever of control, this study conceptualizes MPM utilization as diagnostic use and interactive use. A holistic research model and related hypotheses integrating MPM use, project complexity and megaproject performance were established. The model was validated using a partial square-structural equation modeling method.

Based on 214-megaproject data collected through a questionnaire survey in China, the results show positive effects of diagnostic use and interactive use on megaproject performance. Both, however, have substitutional interaction effects. The moderating results suggest that the higher project complexity weakens the positive effects of MPM utilization on megaproject performance.

This study advances megaprojects performance measurement and management literature by validating the value of MPM utilization on performance. It also presents practical implications for project managers to improve performance by appropriate MPM utilization.

To make the robot that have real autonomous ability is always the goal of mobile robot research. For mobile robots, simultaneous localization and mapping (SLAM) research is no longer satisfied with enabling robots to build maps by remote control, more needs will focus on the autonomous exploration of unknown areas, which refer to the low light, complex spatial features and a series of unstructured environment, lick underground special space (dark and multiintersection). This study aims to propose a novel robot structure with mapping and autonomous exploration algorithms. The experiment proves the detection ability of the robot.

A small bio-inspired mobile robot suitable for underground special space (dark and multiintersection) is designed, and the control system is set up based on STM32 and Jetson Nano. The robot is equipped with double laser sensor and Ackerman chassis structure, which can adapt to the practical requirements of exploration in underground special space. Based on the graph optimization SLAM method, an optimization method for map construction is proposed. The Iterative Closest Point (ICP) algorithm is used to match two frames of laser to recalculate the relative pose of the robot, which improves the sensor utilization rate of the robot in underground space and also increase the synchronous positioning accuracy. Moreover, based on boundary cells and rapidly-exploring random tree (RRT) algorithm, a new Bio-RRT method for robot autonomous exploration is proposed in addition.

According to the experimental results, it can be seen that the upgraded SLAM method proposed in this paper achieves better results in map construction. At the same time, the algorithm presents good real-time performance as well as high accuracy and strong maintainability, particularly it can update the map continuously with the passing of time and ensure the positioning accuracy in the process of map updating. The Bio-RRT method fused with the firing excitation mechanism of boundary cells has a more purposeful random tree growth. The number of random tree expansion nodes is less, and the amount of information to be processed is reduced, which leads to the path planning time shorter and the efficiency higher. In addition, the target bias makes the random tree grow directly toward the target point with a certain probability, and the obtained path nodes are basically distributed on or on both sides of the line between the initial point and the target point, which makes the path length shorter and reduces the moving cost of the mobile robot. The final experimental results demonstrate that the proposed upgraded SLAM and Bio-RRT methods can better complete the underground special space exploration task.

Based on the background of robot autonomous exploration in underground special space, a new bio-inspired mobile robot structure with mapping and autonomous exploration algorithm is proposed in this paper. The robot structure is constructed, and the perceptual unit, control unit, driving unit and communication unit are described in detail. The robot can satisfy the practical requirements of exploring the underground dark and multiintersection space. Then, the upgraded graph optimization laser SLAM algorithm and interframe matching optimization method are proposed in this paper. The Bio-RRT independent exploration method is finally proposed, which takes shorter time in equally open space and the search strategy for multiintersection space is more efficient. The experimental results demonstrate that the proposed upgrade SLAM and Bio-RRT methods can better complete the underground space exploration task.

This paper aims to enrich the 3D urban models with data contributed by citizens to support data-driven decision-making in urban infrastructure projects. We introduced a new application domain extension to CityGML (social – input ADE) to enable citizens to store, classify and exchange comments generated by citizens regarding infrastructure elements. The main goal of social – input ADE is to add citizens’ feedback as semantic objects to the CityGML model.

Firstly, we identified the key functionalities of the suggested ADE and how to integrate them with existing 3D urban models. Next, we developed a high-level conceptual design outlining the main components and interactions within the social-input ADE. Then we proposed a package diagram for the social – input ADE to illustrate the organization of model elements and their dependencies. We also provide a detailed discussion of the functionality of different modules in the social-input ADE.

As a result of this research, it has seen that informative streams of information are generated via mining the stored data. The proposed ADE links the information of the built environment to the knowledge of end-users and enables an endless number of socially driven innovative solutions.

This work aims to provide a digital platform for aggregating, organizing and filtering the distributed end-users’ inputs and integrating them within the city’s digital twins to enhance city models. To create a data standard for integrating attributes of city physical elements and end-users’ social information and inputs in the same digital ecosystem, the open data model CityGML has been used.

Manufacturing firms must embrace smart technologies and develop complex leadership approaches to achieve sustainability. Using the dynamic capability theory, this paper aims to examine the influence of the adoption of industry 4.0 technologies (AT) and paradoxical leadership (PL) on corporate sustainable performance (CSP) of manufacturing small-medium enterprises (SMEs) in Malaysia. Moreover, organisational ambidexterity (OA) is a mediator and strategic flexibility (SF) is a moderator in the study.

The study is a cross-sectional, quantitative study design that collected 395 usable responses through a simple random sampling technique and a close-ended structured questionnaire. Structural equation modelling (SEM) procedures were followed to analyse the data.

The statistical outcome implies that the AT significantly influence CSP and OA and mediate with CSP in the presence of OA. Moreover, PL shows a significant impact on OA, is insignificant on CSP and mediates with OA and CSP. The authors found a significant association between OA and CSP; however, SF did not provide evidence of a moderate effect.

The findings of this study clarify the role that organisational capabilities (OA, AT, PL and SF) play in fostering sustainability. The authors suggest incorporating SMEs from different geographies in other sectors by applying diverse methodologies and relevant constructs.

The result injects new perspectives into policy, managerial and individual levels. Installing OA, AT, PL and SF makes SMEs sustainable.

The empirical validation of the influence of OA and AT on CSP and the interaction of PL and SF enriches the organisational and entrepreneurial literature.

The purpose of this study is to learn how the incorporation and use of leanness, agility and innovation in Indian manufacturing micro, small and medium enterprises (MSMEs) affect their bottom lines and how much these factors contribute to the MSMEs’ ability to meet their long-term sustainability goals.

The suggested model was subjected to data validation and additional empirical validation using a sample of 411 Indian manufacturing MSMEs. The analysis of construct measures is conducted through the utilization of confirmatory factor analysis, a statistical technique that is grounded in the theoretical framework of structural equation modeling (SEM). In addition, path model analysis was applied for the purpose to validate the assumptions that were included in the structural models.

Consistent with the proposed model, the findings of this study demonstrate that leanness, agility and innovation have a substantial favorable impact on the sustainability of a company’s performance. These findings may be helpful in gaining professionals, academics and policymakers to acknowledge the significance of leanness, agility and innovation in enhancing the long-term sustainability of MSMEs and enhancing the overall performance of a particular company. This research excluded the service industries-based research papers.

Many research in the field of manufacturing industries that have adopted leanness, agility, innovativeness and sustainability as individual approaches or as a collective methodology of two or more were considered in the current study. This research excluded the service industries-based research papers.

This literature review has recognized and analyzed various dimensions and roles of leanness, agility, innovativeness and sustainability that are prevalent in manufacturing industries that include the positive and negative effects on the performance of the industries. The research enlightens the path and shows future directions for research to develop efficient, effective and sustainable manufacturing industries.

By promoting the concept of focusing on the “human factor”, namely, stakeholder perspectives, the MSME sector is propagating a strategy that moves away from an excessive focus on technology and toward a more humane one. Through the application of the three key concepts of leanness, agility and innovation, this work aims to create a framework for measuring the sustainability performance of micro-, small- and medium-sized enterprises (MSMEs), with the ultimate goal of assisting the country in achieving the Sustainable Development Goals in the fields of industry, innovation and infrastructure by supporting environmentally friendly and resource-conserving businesses that give back to society and the natural environment.

The objective of this research is to assess the importance and effectiveness of integrating various approaches such as leanness, agility, innovativeness and sustainability within the framework of manufacturing micro, small, and medium enterprises (MSMEs). The authors hope that by going further into these concepts, they will be able to broaden their understanding and get a more comprehensive insight into the role that these concepts play and how they might be successfully used within this environment.

Although Agtech firms have promoted digital platforms for retailing farm supplies (RFS), farmers are sceptical while purchasing them online. As a result, they struggle to generate a sustained demand. Among other approaches, these platforms onboard complementors to become full-stack farming solution providers. Whether platform complementarity can induce farmers' trust remains ambiguous. Literature on network externality theory highlights that complementarity positively affects the perceived value for buyers. The sociotechnical systems literature indicates that perceived value is an antecedent of user trust. In this vein, the authors ask: Does perceived complementarity affect farmers' trust in the RFS platform? Alternatively, the Agtech firms augment the platform's look and feel to make the digital retail setting appear “normal” to farmers. The extant research on the social cognitive theory indicates that a retail setting conforming with the generalised expectancy of buyers harbours their trust. Against this backdrop, the authors ask whether situational normality affects farmers' trust in the RFS platform.

The study is based on a questionnaire survey of 212 Indian farmers using RFS platforms. The data were analysed using structural equation modelling (SEM) analysis.

This study establishes that platforms' complementarity and situational normality ameliorate farmer trust. The authors also identify the socioeconomic factors shaping the farmers' trust in platforms.

The present study has taken all RFS together as a single umbrella category, which can be considered a limitation. Also, the study is based on the cross-sectional survey of RFS platform users; the farmers' attitudes are dynamic in nature and evolve over time; however, the temporal factors shaping the farmer attitudes have not been considered in this study.

The study establishes the epistemological relationship between complementarity, situational normality and farmers' trust in agricultural platforms.