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The primary objective of this article is to examine current procurement, inventory control and management practices in modern healthcare, with a particular focus on the procurement and management of surgical supplies in a prominent public, highly specialized healthcare sector.

This study was conducted in three phases. In Phase 1, the study team interacted with various hospital management stakeholders, including the surgical hospital store, examined the current procurement process and identified challenges. Phase 2 focused on selecting items for a detailed study and collected the qualitative and quantitative details of the store department of the healthcare sector chosen. A detailed study analyzed revenue, output/demand, inventory levels, etc. In Phase 3, a decision-making framework is proposed, and inventory control systems are redesigned and demonstrated for the selected items.

It was observed that the demand for many surgical items had increased significantly over the years due to an increase in disposable/disposable items, while inventories fluctuated widely. Maximum inventory levels varied between 50 and 75%. Storage and availability were important issues for the hospital. It is assumed the hospital adopts the proposed inventory control system. In this case, the benefits can be a saving of 62% of the maximum inventory, 20% of the average stock in the system and optimal use of storage space, improving the performance and productivity of the hospital.

This study can help the healthcare sector administration to develop better systems for the procurement and delivery of common surgical items and efficient resource allocation. It can help provide adequate training to store staff. This study can help improve management/procurement policies, ordering and delivery systems, better service levels, and inventory control of items in the hospital business context. This study can serve as a pilot study to further investigate the overall hospital operations.

This study can help the healthcare sector administration develop better systems for procuring and delivering common surgical items and efficient resource allocation. It can help provide adequate training to store staff. This study can help improve management/procurement policies, ordering and delivery systems, better service levels and inventory control of items in the hospital business context. This study can serve as a pilot study to further investigate the overall hospital operations.

This study is an early attempt to develop a decision framework and inventory control system from the perspective of healthcare inventory management. The gaps identified in real hospital scenarios are investigated, and theoretically based-inventory management strategies are applied and proposed.

This paper aims to evaluate the outcome effectiveness of the global reporting initiatives (GRI) transitions by understanding how companies have responded to the changes from G3.1 to G4 and finally to the GRI Standards.

A quality disclosure score is developed that incorporates assessments of both the quality of disclosures and the materiality of Australian companies. To analyse materiality, survey data were collected from 187 companies. Disclosure scores are based on a content analysis of the sustainability reports of 12 mining and metals companies and 12 financial services companies that used the GRI Standards from 2011 to 2019 (a total of 213 reports).

The study found that the GRI transitions have not led to companies improving the quality of their disclosures on areas considered important for them to achieve their social and environmental goals. Instead, the companies tended to use a greenwashing strategy, where the quality of disclosure of material issues declined or fluctuated over time.

From a practical perspective, the disclosure score developed in this paper enables managers of companies to recognize a threshold of completeness and to summarize the areas that are not materially relevant to their business.

The results are potentially helpful for investors, shareholders and other stakeholders, enabling them to better understand sustainability reports.

This study contributes to the body of research in sustainability reporting by providing evidence on the outcome effectiveness of the latest updates in the GRI framework.

This paper aims to systematically analyze the weaknesses of public procurement in Malaysia as reported in the Auditor General’s Reports from 2011 until 2018. Specifically, the study examines the types of weaknesses, the modes of public procurement involved with the weaknesses, the procurement categories involved with the weaknesses and the key recommendations to mitigate the weaknesses in public procurement.

A document analysis was adopted in achieving the objective. The Auditor General’s reports and the reports of activities of federal ministries/departments published by the National Audit Department, Malaysia from the year 2011 until 2018 are the main documents used in this study. The data gathered were analyzed using frequency distribution and displayed with descriptive statistics and relevant graphs.

The findings of the study revealed that the top five reported are non-compliance to scope, specifications and terms of contracts; delayed completion/non-completion of project; poor documentation; low quality of products, service and work; and little or no prior planning. It is also found that direct negotiation mode was reported with the highest issues of public procurement, while the procurement mode with the least public procurement weaknesses is direct purchase. Moreover, it was found that work category is the highest with public procurement issues reported compared to supplies and services. The top recommendations given by the Auditor General were to improve internal control, to enhance monitoring, to establish planning in details, to improve assets management and to take appropriate actions toward contractors and procurement officers when needed.

This is among a few studies that attempted to systematically examine the main issues regarding the public procurement activities in Malaysia. This study highlighted pertinent aspects of the public procurement activities, which need close attention by the relevant authorities to ensure efficient and effective public procurement.

The purpose of this essay is the provision of a conceptual approach for a manager-as-coach to use for instructional purposes with an employee. Using scenistic materials (cases, incidents, stories), the aim of the essay is to assist the practitioner apply a practical and relatively adaptable instructional approach. While it is intended for application with a single employee, the approach, with modifications, may be used in a small group environment.

The methodology was directed to the study of manager-as-coach, application of a class of instructional tools, theories of constructivism and situated cognition and the joint (manager–employee) implementation of an action guide aimed at learning and performance improvement. Review and coordination of these areas resulted in a detailed guide for action.

Use of scenistic materials for instructional purposes adds to the repertoire of tools for a manager desiring to act in a coaching capacity. A step-by-step program of activities is offered for practical application. Created for experimentation and use is a research result-driven practical guide/action plan.

The design of the approach expressed requires the manager-as-coach to prepare for instruction and participate in it to the extent that the manager likely contributes to her/his own knowledge and skills in the areas under study. The step-by-step design not only guides the instructional process, it demands that the participants are fully engaged in creating new knowledge, assumptions and examples of practical implementation of what has been learned.

Currently, there is little information or research available to guide a manager in a coaching capacity in the use of situation-based (scenistic) instructional materials. The approach offered in this essay not only considers employee knowledge apprehension but also aims at performance3 improvement in a particular context. Additionally, the approach presented requires dialog, negotiation and focused application, all of which may help the participants improve the quality of their relationship.

To improve the speed and accuracy of turbine blade film cooling design process, the most advanced deep learning models were introduced into this study to investigate the most suitable define for prediction work. This paper aims to create a generative surrogate model that can be applied on multi-objective optimization problems.

The latest backbone in the field of computer vision (Swin-Transformer, 2021) was introduced and improved as the surrogate function for prediction of the multi-physics field distribution (film cooling effectiveness, pressure, density and velocity). The basic samples were generated by Latin hypercube sampling method and the numerical method adopt for the calculation was validated experimentally at first. The training and testing samples were calculated at experimental conditions. At last, the surrogate model predicted results were verified by experiment in a linear cascade.

The results indicated that comparing with the Multi-Scale Pix2Pix Model, the Swin-Transformer U-Net model presented higher accuracy and computing speed on the prediction of contour results. The computation time for each step of the Swin-Transformer U-Net model is one-third of the original model, especially in the case of multi-physics field prediction. The correlation index reached more than 99.2% and the first-order error was lower than 0.3% for multi-physics field. The predictions of the data-driven surrogate model are consistent with the predictions of the computational fluid dynamics results, and both are very close to the experimental results. The application of the Swin-Transformer model on enlarging the different structure samples will reduce the cost of numerical calculations as well as experiments.

The number of U-Net layers and sample scales has a proper relationship according to equation (8). Too many layers of U-Net will lead to unnecessary nonlinear variation, whereas too few layers will lead to insufficient feature extraction. In the case of Swin-Transformer U-Net model, incorrect number of U-Net layer will reduce the prediction accuracy. The multi-scale Pix2Pix model owns higher accuracy in predicting a single physical field, but the calculation speed is too slow. The Swin-Transformer model is fast in prediction and training (nearly three times faster than multi Pix2Pix model), but the predicted contours have more noise. The neural network predicted results and numerical calculations are consistent with the experimental distribution.

This paper creates a generative surrogate model that can be applied on multi-objective optimization problems. The generative adversarial networks using new backbone is chosen to adjust the output from single contour to multi-physics fields, which will generate more results simultaneously than traditional surrogate models and reduce the time-cost. And it is more applicable to multi-objective spatial optimization algorithms. The Swin-Transformer surrogate model is three times faster to computation speed than the Multi Pix2Pix model. In the prediction results of multi-physics fields, the prediction results of the Swin-Transformer model are more accurate.

The COVID-19 pandemic era has severely hampered the economy over the globe. However, the manufacturing organizations across all the countries have struggled heavily, as they were among the least who worked on online mode. The organizations are adopting various innovative quality methodologies to improve their performance. In this regard, they are adopting the Sustainable Lean Six Sigma (SLSS) concept and Industry 4.0 technologies to develop products at a faster rate. The use of Industry 4.0 technologies may reduce material movement and supply chain disruptions with the help of smart intelligent systems. There is a strong synergy between SLSS and Industry 4.0 technologies, resulting in an integrated approach for adoption. This study aims to develop a framework that practitioners can use to adopt Industry 4.0-SLSS practices effectively.

This study portrays 31 Industry 4.0-SLSS practices and 22 performance metrics identified through a literature review to improve the manufacturing supply chain performance. To compute the weights of these practices, the Robust Best–Worst Method (RBWM) is used. The Pythagorean fuzzy combined compromise solution (PF-CoCoSo) method is used to rank performance metrics.

According to the RBWM results, “Process Development Practices (PDP)” are first among the major criteria, followed by “Organizational Management Practices (OMP)” at second, “Technology Adoption Practices (TAP)” at third, “Strategy Management Practices (SMP)” at fourth and “Executive Management Practices (EMP)” at fifth, whereas the PF-CoCoSo method resulted in the performance metric “On time product delivery” ranking first.

The identified practices have the potential to significantly improve the performance of the manufacturing supply chain. Practices that encourage a sustainable manufacturing supply chain and the usage of emerging technology will benefit organizational effectiveness. Managers can assess performance using prioritized performance metrics.

During the COVID-19 pandemic era, this is one of the unique attempts to provide a framework to improve the manufacturing supply chain performance. This study integrates and identifies Industry 4.0-SLSS practices and performance metrics for enhancing overall performance.

This study aims to investigate the physical environmental problems of two Thai community hospitals’ outpatient departments (OPDs) during the COVID-19 outbreak and to provide design guidelines for respiratory disease prevention.

A three-step method was used. The first step involved walk-through observations at Mae Wang Hospital and Doi Saket Hospital to assess the physical environment. The second step consisted of interviews with 22 medical staff working in the two hospitals. Third, the observation and interview data were used to develop a preliminary design guideline, which was evaluated by two focus groups comprising the same interview participants.

The observational findings showed that the two hospitals had difficulties following the COVID-19 prevention protocol due to physical environmental problems. Four different themes emerged from the analysis of the medical staff interviews. The preliminary design guidelines that were evaluated through two focus group discussions resulted in design recommendations for four areas (the overall building, clinical, support and staff areas) in the Mae Wang and Doi Saket OPD buildings. Three topics of design recommendations were provided to reduce the spread of COVID-19: improving hospital management, space design and air quality.

To the best of the authors’ knowledge, this study is the first to provide design guidelines for COVID-19 and respiratory disease prevention in Thai community hospital OPD buildings.

This paper explores the strategies adopted by purchasing firms to streamline relationships with suppliers amid the COVID-19 outbreak.

This study employed a multiple case study method and conducted 42 semi-structured interviews with procurement managers from six firms in the United Arab Emirates.

This study reveals six helpful strategies that purchasing firms can undertake to streamline supplier relationship management (SRM) in the wake of COVID-19. Precisely, purchasing firms are revising supply chain costs, planning orders in advance, sharing critical information with suppliers, planning major contingencies, developing a robust relationship with suppliers (e.g. partnership) and finally, improving the supplier's visibility.

This study comes with certain limitations. First, the results are based on a limited number of 42 interviewees. Hence, the study’s results cannot be generalized to a broader population. Second, the data were collected based on the cause and effect relationship.

The findings of this study can help purchasing firms learn and use new appropriate strategies to manage the relationship with their suppliers in the midst of the COVID-19 outbreak.

This study contributes to SRM literature by unveiling six distinct strategies (such as revising supply chain costs, planning orders in advance, sharing critical information, etc.) that purchasing firms have employed to develop a robust and healthy working relationship with the suppliers in the midst of the COVID-19 outbreak.

This paper aims to present the methodology for designing a system providing comprehensive data about the UK’s museums and enabling research into the history, status and long-term development of the entire sector.

The authors have devised and applied an iterative methodology to deliver a knowledge base, web application and website through which these and related resources are publicly accessed, allowing incorporation of the requirements of user stakeholders drawn from across the UK museum sector.

The methodology has enabled the elicitation of usage scenarios, research questions and feedback from a broad range of user stakeholders, allowing the system to be successfully delivered within the time and staffing constraints of a single publicly-funded research project. Feedback received from external evaluators and users of the system has been overwhelmingly positive.

The system includes the only comprehensive data set of the UK’s museums and is enabling new research by museum studies scholars and museum professionals. The methodology can inform other projects aiming to create specialist knowledge resources involving a wide range of user stakeholders, particularly within constrained time and staffing resources.

Attention is one of the most important factors to affect the academic performance of students. Effectively analyzing students' attention in class can promote teachers' precise teaching and students' personalized learning. To intelligently analyze the students' attention in classroom from the first-person perspective, this paper proposes a fusion model based on gaze tracking and object detection. In particular, the proposed attention analysis model does not depend on any smart equipment.

Given a first-person view video of students' learning, the authors first estimate the gazing point by using the deep space–time neural network. Second, single shot multi-box detector and fast segmentation convolutional neural network are comparatively adopted to accurately detect the objects in the video. Third, they predict the gazing objects by combining the results of gazing point estimation and object detection. Finally, the personalized attention of students is analyzed based on the predicted gazing objects and the measurable eye movement criteria.

A large number of experiments are carried out on a public database and a new dataset that is built in a real classroom. The experimental results show that the proposed model not only can accurately track the students' gazing trajectory and effectively analyze the fluctuation of attention of the individual student and all students but also provide a valuable reference to evaluate the process of learning of students.

The contributions of this paper can be summarized as follows. The analysis of students' attention plays an important role in improving teaching quality and student achievement. However, there is little research on how to automatically and intelligently analyze students' attention. To alleviate this problem, this paper focuses on analyzing students' attention by gaze tracking and object detection in classroom teaching, which is significant for practical application in the field of education. The authors proposed an effectively intelligent fusion model based on the deep neural network, which mainly includes the gazing point module and the object detection module, to analyze students' attention in classroom teaching instead of relying on any smart wearable device. They introduce the attention mechanism into the gazing point module to improve the performance of gazing point detection and perform some comparison experiments on the public dataset to prove that the gazing point module can achieve better performance. They associate the eye movement criteria with visual gaze to get quantifiable objective data for students' attention analysis, which can provide a valuable basis to evaluate the learning process of students, provide useful learning information of students for both parents and teachers and support the development of individualized teaching. They built a new database that contains the first-person view videos of 11 subjects in a real classroom and employ it to evaluate the effectiveness and feasibility of the proposed model.