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Theory of Constraints (TOC), though a well-established process improvement methodology in manufacturing, is still a novel philosophy for healthcare and an exhaustive review of literature is needed to summarize the key findings of various researchers. Such a review can provide a direction to the researchers and academicians interested in exploring the application of TOC in the healthcare sector. This paper aims to review the existing literature of TOC tools and techniques applied to the healthcare environment, and to investigate motivating factors, benefits and key gaps for identifying directions for future research in the domain of healthcare.

In this paper, different electronic repositories were searched using multiple keywords. The current study identified 36 articles published between January 1999 to mid-2021 to conceptualize and summarize the research questions used in the study. Descriptive analysis along with pictorial representations have been used for better visualization of work.

This paper presents a thorough literature review of TOC in healthcare and identifies the evolution, current trends, tools used, nature of services chosen for application and research gaps and recommends future direction for research. A variety of motivating factors and benefits of TOC in healthcare are identified. Another key finding of this study is that almost all implementations listed in literature reported positive outcomes and substantial improvements in the performance of the healthcare unit chosen for study.

This paper provides valuable insight to researchers, practitioners and policymakers on the potential of TOC to improve quality of services, flow of patients, revenues, process efficiency and cost reduction in different health care settings. A number of findings and suggestions compiled in the paper from literature study can be used for diagnosing, learning and making substantial changes in healthcare. The methodologies used by different researchers were analysed and combined to propose a generic step by step procedure to apply TOC. This methodology will guide the practising managers about the appropriate tools of TOC for their specific need.

Good health is always the first desire of all men and women around the globe. The global aim of healthcare is to quickly cure more patients and ensure healthier population both today and in future. This article will work as a foundation for future applications of TOC in healthcare and guide upcoming applications in the booming healthcare sector. The paper will help the healthcare managers in serving a greater number of patients with limited available resources.

This paper provides original collaborative work compiled by the authors. Since no comprehensive systematic review of TOC in healthcare has been reported earlier, this study would be a valuable asset for researchers in this field. A model has been presented that links various benefits with one another and clarifies the need to focus on process improvement which naturally results in these benefits. Similarly, a model has been presented to guide the users in implementation of TOC in healthcare.

In recent decades, the student population in dormitories has increased notably, primarily attributed to the growing number of international students. Dormitories serve as pivotal environments for student development. The coordination and compatibility among students can significantly influence their overall success. This study aims to introduce an innovative method for roommate selection and room allocation within dormitory settings.

In this study, initially, using multi-attribute decision-making methods including the Bayesian best-worst method and weighted aggregated sum product assessment, the incompatibility rate among pairs of students is calculated. Subsequently, using a linear mathematical model, roommates are selected and allocated to dormitory rooms pursuing the twin objectives of minimizing the total incompatibility rate and costs. Finally, the grasshopper optimization algorithm is applied to solve large-sized instances.

The results demonstrate the effectiveness of the proposed method in comparison to two common alternatives, i.e. random allocation and preference-based allocation. Moreover, the proposed method’s applicability extends beyond its current context, making it suitable for addressing various matching problems, including crew pairing and classmate pairing.

This novel method for roommate selection and room allocation enhances decision-making for optimal dormitory arrangements. Inspired by a real-world problem faced by the authors, this study strives to offer a robust solution to this problem.

Goa, a tiny state located along the western coast of India, is rich in cultural heritage and biodiversity. It boasts of a good network of roads and also some rustic experiences. However, the entry of new technology in terms of transport is difficult due to the strong links of people with nature and the desire to retain the natural setting for future generations. Thus, the devices or machines must also be resilient and sustainable in the long run. Good governance and infrastructural support can work wonders in the long run if this is supported.

Conversely, smart mobilities must be powered up through hydro, wind, solar, hydrogen, coal and lithium batteries. While the former four are ideal as they are renewable, the latter, which is coal-generated energy and lithium batteries, can deter nature as they have a large carbon footprint. While hydrogen if created through green systems will be more feasible and can be more sustainable to run electric vehicles (EVs). To top it up, the start-up India mission also has played a significant role in helping smart mobilities businesses to thrive in India. Therefore, the need to adopt smart mobilities dependent on green energy is important for this sector to succeed. This chapter also enlightens the reader through a case study about how BLive, an Indian EV start-up introduced smart mobilities in Goa, their mode of implementation, operations and challenges faced.

Robotic arms’ interactions with the external environment are growing more intricate, demanding higher control precision. This study aims to enhance control precision by establishing a dynamic model through the identification of the dynamic parameters of a self-designed robotic arm.

This study proposes an improved particle swarm optimization (IPSO) method for parameter identification, which comprehensively improves particle initialization diversity, dynamic adjustment of inertia weight, dynamic adjustment of local and global learning factors and global search capabilities. To reduce the number of particles and improve identification accuracy, a step-by-step dynamic parameter identification method was also proposed. Simultaneously, to fully unleash the dynamic characteristics of a robotic arm, and satisfy boundary conditions, a combination of high-order differentiable natural exponential functions and traditional Fourier series is used to develop an excitation trajectory. Finally, an arbitrary verification trajectory was planned using the IPSO to verify the accuracy of the dynamical parameter identification.

Experiments conducted on a self-designed robotic arm validate the proposed parameter identification method. By comparing it with IPSO1, IPSO2, IPSOd and least-square algorithms using the criteria of torque error and root mean square for each joint, the superiority of the IPSO algorithm in parameter identification becomes evident. In this case, the dynamic parameter results of each link are significantly improved.

A new parameter identification model was proposed and validated. Based on the experimental results, the stability of the identification results was improved, providing more accurate parameter identification for further applications.

This study aims to analyze the effects of socialization mechanisms (belief system and peer mentoring) on managers’ job engagement and their desire to have a significant impact through work, that is, the desire to substantially improve or facilitate the lives of others by performing their work. The study also examines the moderating role of organizational identification.

A survey was conducted with middle and lower-level managers at one of the largest banks in Brazil, the BankCo. The authors obtained a sample of 201 respondents and tested the research hypotheses with structural equation modeling. The authors also performed a complementary data analysis with fuzzy-set qualitative comparative analysis.

The results suggest that belief systems and peer mentoring directly promote job engagement and indirectly promote desire to have a significant impact to a better world through work (through full mediation of job engagement). The effects of job engagement on desire to have a significant impact through work are even greater when managers have high organizational identification. Finally, several causal combinations are sufficient for high levels of desire to have a significant impact through work.

Beyond studies that examine how organizational mechanisms influence employee outcomes (e.g. performance), this study explores how socialization mechanisms can promote desire to have a significant impact through work. Thus, the authors demonstrate how organizational core values, mission statement and peer mentoring collaborate for managers to develop altruistic behavior, that is, directly related to other human values, such as empathy and ethics, being able to contribute to a world better.

This study developed and empirically tested a model that connects socialization mechanisms, job engagement, organizational identification and managers’ desire to have a significant impact through their work. Therefore, the paper provides insights into the relevance of socialization mechanisms for orchestrating managers’ proactive and altruistic behaviors.

In a dual-robot system, the relative position error is a superposition of errors from each mono-robot, resulting in deteriorated coordination accuracy. This study aims to enhance relative accuracy of the dual-robot system through direct compensation of relative errors. To achieve this, a novel calibration-driven transfer learning method is proposed for relative error prediction in dual-robot systems.

A novel local product of exponential (POE) model with minimal parameters is proposed for error modeling. And a two-step method is presented to identify both geometric and nongeometric parameters for the mono-robots. Using the identified parameters, two calibrated models are established and combined as one dual-robot model, generating error data between the nominal and calibrated models’ outputs. Subsequently, the calibration-driven transfer, involving pretraining a neural network with sufficient generated error data and fine-tuning with a small measured data set, is introduced, enabling knowledge transfer and thereby obtaining a high-precision relative error predictor.

Experimental validation is conducted, and the results demonstrate that the proposed method has reduced the maximum and average relative errors by 45.1% and 30.6% compared with the calibrated model, yielding the values of 0.594 mm and 0.255 mm, respectively.

First, the proposed calibration-driven transfer method innovatively adopts the calibrated model as a data generator to address the issue of real data scarcity. It achieves high-accuracy relative error prediction with only a small measured data set, significantly enhancing error compensation efficiency. Second, the proposed local POE model achieves model minimality without the need for complex redundant parameter partitioning operations, ensuring stability and robustness in parameter identification.

There is growing empirical evidence that firm heterogeneity is technologically non-neutral. This chapter extends the Gandhi, Navarro, and Rivers (2020) proxy variable framework for structurally identifying production functions to a more general case when latent firm productivity is multi-dimensional, with both factor-neutral and (biased) factor-augmenting components. Unlike alternative methodologies, the proposed model can be identified under weaker data requirements, notably, without relying on the typically unavailable cross-sectional variation in input prices for instrumentation. When markets are perfectly competitive, point identification is achieved by leveraging the information contained in static optimality conditions, effectively adopting a system-of-equations approach. It is also shown how one can partially identify the non-neutral production technology in the traditional proxy variable framework when firms have market power.

The purpose of the research has been the primary consideration and evaluation of a cost effective, reliable, robust and simple process of radio frequency identification (RFID)-based stock control, asset management and monitoring of concrete safety bollards used in the road environment. Likewise, the consideration of the use of the same system and technology to other items in and around the general road infrastructure.

The research approach undertaken has been an evaluation of the use of currently available RFID technology, with a key emphasis on low cost, ease of use, reliability and convenience. Practical field exercises completed in considering the relevant RFID tags and readers and associated software and apps and necessary software integration and development have been undertaken. At the same time, evaluating the specific limits created in the specific environment is being applied. Of particular interest has been the use of a moving scan in a vehicle drive-through or pass-bye, type reading system. This has been determined to be viable and completely practical, drastically reducing the key issue of time-taken. Practical application of the system from idea to real life application has been undertaken. The integration of the use of the RFID tag and reader system with necessary and related software to database upload and storage has been established. The creation of an online facility to allow the appropriate use of the data and to include the convenient output of an asset report has been undertaken.

The findings have provided the necessary insight confirming the use of RFID technology as a simple yet reliable, cost effective and adaptable stock control, asset management and geo-locating system in the road environment. The use of such systems in this particular environment is in its infancy, and is perhaps novel and original in the specific aspect of using the system to stock control, manage and monitor road safety concrete bollards and other roadside objects in the road environment.

To establish if in fact, stock control geo-locating can be reliably undertaken with the use of RFID tags and readers in the specific road and road construction environment, particularly with the use of moving RFID reading of passive tags. To establish the minimum requirements of a field usable RFID tag and reader, specifically applicable to the concrete safety bollards, however to other roadside furniture. To identify the minimum requirements of a function, simple app to minimise general requirements of the overall stock control and monitoring of the RFID-tagged objects. To establish the possibility of reading the tag data, global positioning system (GPS) location and video imaging footage as a single operation function. To determine the basic parameters or limits of the GPS geo-locating, on the proposed products selected and overall system. To determine the current best practice in respect of reasonable accuracy and detail in relation to price considerations to a fully function stock control and monitoring system. To identify the minimum requirements of an online database to receive, house and provide ongoing access to and report on the data. To identify the key differences and benefits between traditional stock control and monitoring systems, against that of proposed RFID tag, read and geo-locating system.

The purpose is to identify and evaluate the safety risk factors in the coal mine construction process.

The text mining technique was applied in the stage of safety risk factor identification. The association rules method was used to obtain associations with safety risk factors. Decision-Making Trial and Evaluation Laboratory (DEMATEL) and Interpretative Structural Modeling (ISM) were utilized to evaluate safety risk factors.

The results show that 18 safety risk factors are divided into 6 levels. There are 12 risk transmission paths in total. Meanwhile, unsafe behavior and equipment malfunction failure are the direct causes of accidents, and inadequate management system is the basic factor that determines the safety risk status.

Due to the limitation of the computational matrix workload, this article only categorizes numerous lexical items into 18 factors. Then, the workshop relied on a limited number of experts; thus, the findings may be potentially biased. Next, the accident report lacks a universal standard for compilation, and the use of text mining technique may be further optimized. Finally, since the data are all from China, subsequent cross-country studies should be considered.

The results can help China coal mine project managers to have a clear understanding of safety risks, efficiently carry out risk hazard identification work and take timely measures to cut off the path of transmission with risks identified in this study. This helps reduce the economic losses of coal mining enterprises, thus improving the safety standards of the entire coal mining industry and the national standards for coal mine safety policy formulation.

Coal mine construction projects are characterized by complexity and difficulties in construction. Current research on the identification and assessment of safety risk factors in coal mine construction is insufficient. This study combines objective and systematic research approaches. The findings contribute to the safety risk management of China coal mine construction projects by providing a basis for the development of safety measures.

The main purpose of this study is to test for the interaction effect of digital literacy in the relationship between financial technologies (FinTechs) of biometrics and mobile money and digital financial inclusion among the unbanked poor women, youth and persons with disabilities (PWDs) in rural Uganda.

Covariance-based structural equation modeling was used to construct the interaction effect using data collected from the unbanked poor women, youth and PWDs located in the four regions in Uganda as prescribed by Hair et al. (2022).

The findings from this study are threefold: first; the results revealed a positive interaction effect of digital literacy between FinTechs of biometrics and mobile money and digital financial inclusion. Second; the results also confirmed that biometrics identification positively promotes digital financial inclusion. Lastly; the results showed that mobile money positively promotes digital financial inclusion. A combination of FinTechs of biometrics and mobile money together with digital literacy explain 29% variation in digital financial inclusion among the unbanked poor women, youth and PWDs in rural Uganda.

The data for this study were collected mainly from the unbanked poor women, youth and PWDs. Further studies may look at data from other sections of the vulnerable population in under developed financial markets. Additionally, the data for this study were collected only from Uganda as a developing country. Thus, more data may be obtained from other developing countries to draw conclusive and generalized empirical evidence. Besides, the current study used cross sectional design to collect the data. Therefore, future studies may adopt longitudinal research design to investigate the impact of FinTechs on digital financial inclusion in the presence of digital literacy across different time range.

The governments in developing countries like Uganda should support women, youth, PWDs and other equally vulnerable groups, especially in the rural communities to understand and use FinTechs. This can be achieved through digital literacy that can help them to embrace digital financial services and competently navigate and perform digital transactions over digital platforms like mobile money without making errors. Besides, governments in developing countries like Uganda can use this finding to advocate for the design of appropriate digital infrastructures to reach remote areas and ensure “last mile connectivity for digital financial services' users.” The use of off-line solutions can complement the absence or loss of on-line network connectivity for biometrics and mobile money to close the huge digital divide gap in rural areas. This can scale-up access to and use of financial services by the unbanked rural population.

This paper sheds more light on the importance of digital literacy in the ever complex and dynamic global FinTech ecosystem in the presence of rampant cyber risks. To the best of the authors' knowledge, limited studies currently exist that integrate digital literacy as a moderator in the relationship between FinTechs and digital financial inclusion, especially among vulnerable groups in under-developed digital financial markets in developing countries. This is the novelty of the paper with data obtained from the unbanked poor women, youth and PWDs in rural Uganda.