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In the construction sector, the knowledge-based process outgrows its emphasis on technological aspects. Yet, there is a lack of applied studies showing how a procurement system (PS) could be selected in the digital age. In particular, there is a radical need to establish an innovative process to visualise novel PS decision. Therefore, this paper aims to present a knowledge visualised framework for aiding construction PS decision-making.

This paper describes the construction of process innovation. The framework (process) is supported by four influential decision supporting methods (mean utility values, analytic hierarchy process, fuzzy set theory and Delphi method) and computer programming (Matlab).

There are four stages of this framework: (1) uniform rating for decision alternatives; (2) group decision for determining the decision attribute; (3) determining the final choice; (4) reporting the cognitive computing process. Supported by individual and groups decision dynamics, this framework emphasises how the dashboard aided innovative approach enables the induction of understanding, cognitive computing for decision-making and how the information would precisely be represented, which are vital requirements of modern construction.

The contribution of this paper presents two leverage points that support the modern PS decision. Firstly, this paper provides a holistic view of the decision supporting methods on the basis of how a suitable PS would be systematically sought. Based on the existing studies, this paper upgrades into a visualised knowledge decision supporting process. It helps the participants understand and improve their cognitive learning. Secondly, this framework allows the participants to have a view of the individual and group decisions. It sheds light on the development of the collaborative decision-making process.

This compact case provides a relatively large data set that students explore using visualization and a Tableau dynamic dashboard that they create. Students were asked to describe what the data set contained in relation to employee attrition experience of Baca Beverage Distributors (BBD). The application and managerial questions are set in human resources and a company that is facing high attrition during the pandemic.

BBD shared their data and problem scenario for this compact case. The protagonist, Morgan Matthews, was the authors’ contact and provided significant clarification and guidance about the data. Both the company and the protagonist have been disguised. Some of the job positions have been rephrased. All names of employees, supervisors and managers have been replaced with codes.

During the 2020–2022 pandemic years, BBD experienced, like many companies, a higher than usual employee turnover rate and Morgan Matthews, Director of People, was concerned. Not only was it time-consuming, expensive and disruptive but the company had prided itself on being a good place to work. Were they hiring the right people, people that fit the company culture and people that fit the positions for which they were hired? The company had been using the Predictive Index [1] when on-boarding employees. In addition, there were results from self-reviews and manager reviews that could be used. Morgan wondered if data visualization and visual analytics would be useful in describing their employees and whether it would reveal any opportunities to improve the turnover rate. Before seeking a solution for the high turnover, it was important to step back and learn what the data said about who was leaving and the reasons they gave for leaving.

This compact case can be used in courses that include visualization using Tableau and dashboards. As it is a compact case, it requires less preparation time from the students and less class time for discussion. The case is for students who have been recently introduced to business analytics, specifically visualization and data storytelling with Tableau. For this reason, significant guidance has been provided in the case assignment. The level of the case can be adjusted by the amount of guidance provided in the case assignment. Courses include introduction to business analytics, descriptive analytics and visualization, communication through data storytelling. The case can be used for all modalities – in person, hybrid, online. The authors use it here for visualization and dynamic dashboards but using the same data set and compact case description, exploratory data analysis could be assigned.

Supplementary material for this article can be found online.

Risk management is an essential cornerstone of any effective unit. The maternity dashboard has been found to be an efficient governance tool, but there is no such scorecard in gynaecology. The paper aims to conceptualise and implement an acute gynaecology dashboard in a teaching hospital over a period of two years and review the changes brought in practice as a result of the dashboard.

This acute gynaecology dashboard was designed in line with the existing maternity dashboard. Goals and benchmarks were determined on the basis of available national guidelines, expert opinions and local policies. The dashboard was prospectively implemented, updated monthly and presented in the relevant forums. A retrospective overview of the changes brought in the practice is presented in this paper.

Through the use of the dashboard significant problems related to workforce, training and clinical activity were identified. A number of changes were subsequently executed to improve patient management, service provision and training. This paper provides empirical insights about how positive changes in clinical practice could be brought in by the implementation of the acute gynaecology dashboard. The acute gynaecology dashboard was found to be a valuable governance tool to monitor performance and improve training and patient care.

The acute gynaecology dashboard can be used as an effective clinical governance tool to monitor performance and leads to improvement in clinical practice in other acute gynaecology units.

Though the maternity dashboard is widely in use, there has been no previous description of an acute gynaecology dashboard and this is the first paper in this area. With the increasing demand of acute gynaecology services, the dashboard becomes an essential tool for clinical governance.

This research navigates the confluence of data analytics, machine learning, and artificial intelligence to revolutionize the management of urban services in smart cities. The study thoroughly investigated with advanced tools to scrutinize key performance indicators integral to the functioning of smart cities, thereby enhancing leadership and decision-making strategies. Our work involves the implementation of various machine learning models such as Logistic Regression, Support Vector Machine, Decision Tree, Naive Bayes, and Artificial Neural Networks (ANN), to the data. Notably, the Support Vector Machine and Bernoulli Naive Bayes models exhibit robust performance with an accuracy rate of 70% precision score. In particular, the study underscores the employment of an ANN model on our existing dataset, optimized using the Adam optimizer. Although the model yields an overall accuracy of 61% and a precision score of 58%, implying correct predictions for the positive class 58% of the time, a comprehensive performance assessment using the Area Under the Receiver Operating Characteristic Curve (AUC-ROC) metrics was necessary. This evaluation results in a score of 0.475 at a threshold of 0.5, indicating that there's room for model enhancement. These models and their performance metrics serve as a key cog in our data analytics pipeline, providing decision-makers and city leaders with actionable insights that can steer urban service management decisions. Through real-time data availability and intuitive visualization dashboards, these leaders can promptly comprehend the current state of their services, pinpoint areas requiring improvement, and make informed decisions to bolster these services. This research illuminates the potential for data analytics, machine learning, and AI to significantly upgrade urban service management in smart cities, fostering sustainable and livable communities. Moreover, our findings contribute valuable knowledge to other cities aiming to adopt similar strategies, thus aiding the continued development of smart cities globally.

The textile industry holds immense significance in the economy of any nation, particularly in the production of synthetic yarn and fabrics. Consequently, the pursuit of acquiring high-quality products at a reduced cost has become a significant concern for countries. The primary objective of this research is to leverage data mining and data intelligence techniques to enhance and refine the production process of texturized yarn by developing an intelligent operating guide that enables the adjustment of production process parameters in the texturized yarn manufacturing process, based on the specifications of raw materials.

This research undertook a systematic literature review to explore the various factors that influence yarn quality. Data mining techniques, including deep learning, K-nearest neighbor (KNN), decision tree, Naïve Bayes, support vector machine and VOTE, were employed to identify the most crucial factors. Subsequently, an executive and dynamic guide was developed utilizing data intelligence tools such as Power BI (Business Intelligence). The proposed model was then applied to the production process of a textile company in Iran 2020 to 2021.

The results of this research highlight that the production process parameters exert a more significant influence on texturized yarn quality than the characteristics of raw materials. The executive production guide was designed by selecting the optimal combination of production process parameters, namely draw ratio, D/Y and primary temperature, with the incorporation of limiting indexes derived from the raw material characteristics to predict tenacity and elongation.

This paper contributes by introducing a novel method for creating a dynamic guide. An intelligent and dynamic guide for tenacity and elongation in texturized yarn production was proposed, boasting an approximate accuracy rate of 80%. This developed guide is dynamic and seamlessly integrated with the production database. It undergoes regular updates every three months, incorporating the selected features of the process and raw materials, their respective thresholds, and the predicted levels of elongation and tenacity.

This study aims to focus on providing a computerized classification testing (CCT) system that can easily be embedded as a self-assessment feature into the existing legacy environment of a higher education institution, empowering students with self-assessments to monitor their learning progress and following strict data protection regulations. The purpose of this study is to investigate the use of two different versions (without dashboard vs with dashboard) of the CCT system during the course of a semester; to examine changes in the intended use and perceived usefulness of two different versions (without dashboard vs with dashboard) of the CCT system; and to compare the self-reported confidence levels of two different versions (without dashboard vs with dashboard) of the CCT system.

A total of N = 194 students from a higher education institution in the area of economic and business education participated in the study. The participants were provided access to the CCT system as an opportunity to self-assess their domain knowledge in five areas throughout the semester. An algorithm was implemented to classify learners into master and nonmaster. A total of nine metrics were implemented for classifying the performance of learners. Instruments for collecting co-variates included the study interest questionnaire (Cronbach’s a = 0. 90), the achievement motivation inventory (Cronbach’s a = 0. 94), measures focusing on perceived usefulness and demographic data.

The findings indicate that the students used the CCT system intensively throughout the semester. Students in a cohort with a dashboard available interacted more with the CCT system than students in a cohort without a dashboard. Further, findings showed that students with a dashboard available reported significantly higher confidence levels in the CCT system than participants without a dashboard.

The design of digitally supported learning environments requires valid formative (self-)assessment data to better support the current needs of the learner. While the findings of the current study are limited concerning one study cohort and a limited number of self-assessment areas, the CCT system is being further developed for seamless integration of self-assessment and related feedback to further reveal unforeseen opportunities for future student cohorts.

Higher education institutions (HEIs) face a number of challenges in effectively managing and reporting on sustainability information, such as siloes of data and a limited distribution of information. Business intelligence (BI) can assist in addressing the challenges faced by organisations. The purpose of this study was to propose a BI framework for strategic sustainability information management (the Sustainable BI Framework) that can be used in HEIs.

The research applied the design science research methodology whilst using a South African HEI as a case study. The problems with sustainability information management were identified, and a theoretical framework was proposed. In addition, a practical BI software tool was developed as proof of concept to address these problems and to assist with the management of strategic sustainability information in an HEI.

The proposed sustainability BI tool was evaluated through heuristic and usability evaluations with senior management. The results indicated that the usability of the BI tool was positively rated and that the framework can assist in overcoming the constraints that HEIs face in effectively managing sustainability information.

The research was limited to a single case. However, the theoretical framework was derived from and expanded on existing stakeholder theory, sustainability reporting theory and literature on BI dashboard development. The framework was implemented successfully in the Sustainable BI Tool prototype at the case study, and the results reveal in-depth information regarding information management for sustainability reporting in higher education.

The Sustainable BI Tool is a solution that integrates data from multiple areas of sustainability and provides a single integrated view of the information to stakeholders. The information is provided through performance dashboards, which provide predictive capabilities to enable management to report on sustainability and determine if the institution is meeting its strategic goals. The lessons learnt can also assist other HEIs considering implementing BI for sustainability reporting.

Improved sustainability reporting for HEIs provided by the BI framework can improve the environmental and social impact of the educational community.

This study provides the most comprehensive framework for guiding the design of a BI tool to assist in effectively managing sustainability information in HEIs.

The aim of this paper is to analyse how technological innovations in performance measurement systems make it possible to overcome some of the challenges that public healthcare organizations face where management and control are concerned. The changes that could be applied to the performance measurement system of healthcare organisations were analysed together with an evaluation of the responses developed in order to achieve these changes.

The paper contains an in-depth case-study of a public university hospital which utilises an innovative information system.

The case-study highlights how technological innovations in performance measurement systems impact the management and monitoring information system in a public university hospital, through the implementation of a multidimensional management dashboard.

The limitation of this paper is that only one case-study is analysed, albeit in depth, while it would be interesting to consider more public university hospitals.

The paper highlights the fundamental role of middle management in change processes in the healthcare sector.

The case-study highlights how critical the active involvement of middle management is in performance measurement and management, and how this is achieved thanks to the adoption of a simple, clear method which ensures comprehensible communication of the objectives, as well as the measurement of performance by means of radar plots.

While several warehouses are now technologically equipped and smart, the implementation of real-time analytics in warehouse operations is scarcely reported in the literature. This study aims to develop a practical system for real-time analytics of process monitoring in an internet-of-things (IoT)-enabled smart warehouse environment.

A modified system development research process was used to carry out this research. A prototype system was developed that mimicked a case company’s actual warehouse operations in Indonesia’s manufacturing companies. The proposed system relied heavily on the utilization of IoT technologies, wireless internet connection and web services to keep track of the product movement to provide real-time access to critical warehousing activities, helping make better, faster and more informed decisions.

The proposed system in the presented case company increased real-time warehousing processes visibility for stakeholders at different management levels in their most convenient ways by developing visual representation to display crucial information. The numerical or textual data were converted into graphics for ease of understanding for stakeholders, including field operators. The key elements for the feasible implementation of the proposed model in an industrial area were discussed. They are strategic-level components, IoT-enabled warehouse environments, customized middleware settings, real-time processing software and visual dashboard configuration.

While this study shows a prototype-based implementation of actual warehouse operations in one of Indonesia’s manufacturing companies, the architectural requirements are applicable and extensible by other companies. In this sense, the research offers significant economic advantages by using customized middleware to avoid unnecessary waste brought by the off-the-shelves generic middleware, which is not entirely suitable for system development.

This research’s finding contributes to filling the gap in the limited body of knowledge of real-time analytics implementation in warehousing operations. This should encourage other researchers to enhance and develop the devised elements to enrich smart warehousing’s theoretical knowledge. Besides, the successful proof-of-concept implementation reported in this research would allow other companies to gain valuable insights and experiences.