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The purpose of this study is to implement lean six sigma (LSS) methodology to improve the admission process in a higher education institute (HEI).

In this study, case study research methodology is adopted and implemented through an LSS define-measure-analyze-improve-control (DMAIC) framework.

The preliminary investigation showed that the completion of the whole admission process of a new student takes an average of 88 min, which is equivalent to a sigma level of about 0.71 based on the targeted admission cycle time of 60 min. The implementation of the proposed LSS approach increased the sigma level from 0.71 to 2.57, which indicates a reduction in the mean admission cycle time by around 55%. This substantial improvement is expected not only to provide an efficient admission process but also to enhance the satisfaction of students and employees and increase the reputation of the HEI to a significant level.

In this study, the sample size used in the analysis is considered small. In addition, the effectiveness of the proposed approach is investigated using a discrete event simulation with a single-case study, which may limit generalization of the results. However, this study can provide useful guidance for further research for the generalization of the results to wider scopes in terms of different sectors of HEIs and geographical locations.

This study uses several statistical process control tools and techniques through a LSS DMAIC framework to identify and element the root causes of the long admission cycle time at a HEI. The approach followed, and the lessons learned, as documented in the study, can be of a great benefit in improving different sectors of HEIs.

This study is one of the few attempts to implement LSS in HEIs to improve the administrative process so that better-quality services can be provided to customers, such as students and guardians. The project is implemented by a group of undergraduate students as a part of their senior design project, which paves the way for involving students in future LSS projects in HEIs. This study is expected to help to improve understanding of how LSS methodology can be implemented in solving quality-related problems in HEIs and to offer valuable insights for both academics and practitioners.

The purpose of this paper is twofold: first, a case study on applying lean principles in manufacturing operations to redesign and optimize an electronic device assembly process and its impact on performance and second, introducing cardboard prototyping as a Kaizen tool offering a novel approach to testing and simulating improvement scenarios.

The study employs value stream mapping, root cause analysis, and brainstorming tools to identify root causes of poor performance, followed by deploying a Kaizen event to redesign and optimize an electronic device assembly process. Using physical models, bottlenecks and opportunities for improvement were identified by the Kaizen approach at the workstations and assembly lines, enabling the testing of various scenarios and ideas. Changes in lead times, throughput, work in process inventory and assembly performance were analyzed and documented.

Pre- and post-improvement measures are provided to demonstrate the impact of the Kaizen event on the performance of the assembly cell. The study reveals that implementing lean tools and techniques reduced costs and increased throughput by reducing assembly cycle times, manufacturing lead time, space utilization, labor overtime and work-in-process inventory requirements.

This paper adds a new dimension to applying the Kaizen methodology in manufacturing processes by introducing cardboard prototyping, which offers a novel way of testing and simulating different scenarios for improvement. The paper describes the process implementation in detail, including the techniques and data utilized to improve the process.

The purpose of this paper is to assess sub-national business cycle (BC) synchronization's impact on national cycles in four emerging markets economies with inflation targeting (IT-EMEs): Brazil, Colombia, South Korea and Mexico.

The authors use panel data models with fixed-effects and distributed lags.

The authors disclosed that sub-national synchronization increased national cycle amplitudes during expansion and recession phases. The authors also noticed that South Korea exhibited a more pronounced effect compared to Latin American countries, and this seemed to be associated with differences in the homogeneity of the production structures in the regions of these countries.

The authors cautioned that contrasting the findings with prior research on the effects of regional BC synchronization in IT-EMEs or with studies in different geographical contexts, is not possible due to the absence of prior research endeavors with this specific focus.

This study constitutes a first attempt to explain the impact of subnational cycle synchronization on the magnitude of national cycles in four IT-EMEs.

Many problems occur when assigning tasks to work centres, especially in determining the required number of workstations for line balancing which requires a minimum theoretical number of workstations. The most common problem is bottleneck. In this paper, a method is proposed to solve floating tasks problem in single-model line when the actual required number of workstations exceeds the minimum theoretical number, and the standard time of the floating task (work center) exceeds the cycle time. The floating task will represent a critical bottleneck activity in line. The proposed method depends on minimizing the standard time of critical bottleneck and non-critical activities by a minimum free-floating time depends on the average of slack times of the non-critical activities, and it will increase the line efficiency from (77%) to (88%), and balance delay is minimized from (23%) to (12%).

The purpose of this study is to investigate the integrated application of business process modeling and notation (BPMN) and value stream mapping (VSM) to improve knowledge work performance and productivity in police services. In order to explore the application of the hybrid BPMN-VSM approach in police services, this study uses the department of digital crime investigation (DCI) in one Norwegian police district as a case study.

Service process identification was the next step after selecting an appropriate organizational unit for the case study. BPMN-VSM-based current state mapping, including time and waste analyses, was used to determine cycle and lead time and identify value-adding and nonvalue-adding activities. Subsequently, improvement opportunities were identified, and the current state process was re-designed and constructed through future state mapping.

The study results indicate a 44.4% and 83.0% reduction in process cycle and lead time, respectively. This promising result suggests that the hybrid BPMN-VSM approach can support the visualization of bottlenecks and possible causes of increased lead times, followed by the systematic identification and proposals of avenues for future improvement and innovation to remedy the discovered inefficiencies in a complex knowledge-work environment.

This study focused on one department in a Norwegian police district. However, the experience gained can support researchers and practitioners in understanding lean implementation through an integrated BPMN and VSM model, offering a unique insight into the ability to investigate complex systems.

Complex knowledge work processes generally characterize police services due to a high number of activities, resources and stakeholder involvement. Implementing lean thinking in this context is significantly challenging, and the literature on this topic is limited. This study addresses the applicability of the hybrid BPMN-VSM approach in police services with an original public sector case study in Norway.

This paper aims to unearth the essential components of Six Sigma for successful deployment and sustainment of service quality in four different organizations in the United Arab Emirates (UAE). More specifically, the paper is intended to determine the motivation to apply Six Sigma, Voice of Customer, Key Performance Indicators (KPIs), Critical-to-Quality (CTQ), readiness factors, Critical Success Factors, sustainment measures, tools and techniques used, challenges/barriers and performance impact on the company.

An exploratory research methodology with multiple case study analyses was adopted to determine the answers to the research objectives. Four case studies from different service processes of four companies were analyzed. The case studies were collated from these companies via a case study protocol with pre-defined criteria.

The analysis shows that service operation improvement projects are primarily dependent on the voice of the internal customer, with return on investment in savings as the KPI of the process. Most organizations prefer cycle time and errors as the CTQs in the Six Sigma projects. Even novice users can effectively apply the Six Sigma methodology with external experts’ assistance, mentoring and interventions. Across the case studies, it is observed that the projects were successfully deployed due to the support of top management leadership, effective communication and cross-functional teams. Employee resistance to change is the common barrier observed during the case study analysis. Eventually, in all the four case studies, Six Sigma is executed with standard tools and techniques within the define, measure, analyze, improve, control (DMAIC) approach.

The present study’s findings cannot be generalized due to the limited number of case study analyses in different ecosystems in the UAE. The authors would like to analyse and report more case studies in service quality improvement through the Six Sigma methodology to comprehend and develop a generic roadmap for the deployment of Six Sigma in the UAE service industry.

The study’s findings provide insights into commonalities and differences between the essential factors of Six Sigma deployment and sustainability in UAE companies.

The study results might help the policymakers and key decision makers in UAE and other countries understand the effectiveness of Six Sigma in service quality improvement with its essential factors for deployment.

This exploratory study aims to explore the operational and financial constraints faced by small and medium enterprises (SMEs) in India during the COVID-19 pandemic. The paper highlights the role of supply chain finance (SCF) in the uncertain business environment caused by the pandemic.

The study adopts an inductive approach and conducts convergent interviews with 32 SME owners and bank officials who are associated with SME-related financial transactions. The analysis of the interview data has been done through a grounded theory approach.

The findings portray four key themes representing the operational and financial constraints faced by SMEs during the pandemic. Further, the study identifies four drivers of SCF adoption among SMEs, including capital constraints, high inventory turnover cycle time, high order fulfilment cycle time and long debtors’ collection period.

The study provides various insights to the managers and owners of SMEs to deal with the economic crisis and eliminate the financial pressure created by the pandemic. The study enlightens the policymakers about the struggles of the SMEs during the economic turmoil created by the pandemic and guides them to introduce the relevant policies to resolve their problems.

To the best of the authors’ knowledge, this is the first study to identify the factors driving the SMEs to adopt SCF due to the economic chaos created by the pandemic. Also, the study theoretically contributes to the literature by developing a theoretical framework for SCF adoption based on grounded theory.

This research seeks to explore the potential of Lesson Study as a vehicle to support professional development (PD) in a rural, Irish primary school. Lesson Study was utilised to design and implement integrated STEM (Science, Technology, Engineering and Mathematics) lessons with young children (ages 4–7 years).

Three teachers were introduced to and participated in four cycles of Lesson Study over the course of one school year. Qualitative data were generated from interviews, collaborative weekly meetings and observation sheets.

Analysis suggests that Lesson Study supported the development of a culture of collaboration and provided an opportunity for teachers to share their knowledge. Findings also reveal that Lesson Study motivated teachers to reflect on their role within the classroom and enabled them to move away from teacher-led approaches. Although teachers perceived Lesson Study to be a beneficial form of PD, factors constrained their engagement, including practical, cultural and sustainability challenges.

The study explores the adaptability of Lesson Study in first level education in the context of STEM education. It reveals teachers’ first experience of Lesson Study, given its stark contrast to more “traditional” PD experiences they are accustomed to. This article will, therefore, be of interest to teachers, school leaders and policy makers.

This paper contributes initial findings to a currently under-researched area, Lesson Study in a rural context. This study also combines Lesson Study with STEM education, which has not been widely explored.

Assembly lines are widely employed in manufacturing processes to produce final products in a flow efficiently. The simple assembly line balancing problem is a basic version of the general problem and has still attracted the attention of researchers. The type-I simple assembly line balancing problems (SALBP-I) aim to minimise the number of workstations on an assembly line by keeping the cycle time constant.

This paper focuses on solving multi-objective SALBP-I problems by utilising an artificial bee colony based-hyper heuristic (ABC-HH) algorithm. The algorithm optimises the efficiency and idleness percentage of the assembly line and concurrently minimises the number of workstations. The proposed ABC-HH algorithm is improved by adding new modifications to each phase of the artificial bee colony framework. Parameter control and calibration are also achieved using the irace method. The proposed model has undergone testing on benchmark problems, and the results obtained have been compared with state-of-the-art algorithms.

The experimental results of the computational study on the benchmark dataset unequivocally establish the superior performance of the ABC-HH algorithm across 61 problem instances, outperforming the state-of-the-art approach.

This research proposes the ABC-HH algorithm with local search to solve the SALBP-I problems more efficiently.

Rolling element bearings (REBs) are commonly used in rotating machinery such as pumps, motors, fans and other machineries. The REBs deteriorate over life cycle time. To know the amount of deteriorate at any time, this paper aims to present a prognostics approach based on integrating optimize health indicator (OHI) and machine learning algorithm.

Proposed optimum prediction model would be used to evaluate the remaining useful life (RUL) of REBs. Initially, signal raw data are preprocessing through mother wavelet transform; after that, the primary fault features are extracted. Further, these features process to elevate the clarity of features using the random forest algorithm. Based on variable importance of features, the best representation of fault features is selected. Optimize the selected feature by adjusting weight vector using optimization techniques such as genetic algorithm (GA), sequential quadratic optimization (SQO) and multiobjective optimization (MOO). New OHIs are determined and apply to train the network. Finally, optimum predictive models are developed by integrating OHI and artificial neural network (ANN), K-mean clustering (KMC) (i.e. OHI–GA–ANN, OHI–SQO–ANN, OHI–MOO–ANN, OHI–GA–KMC, OHI–SQO–KMC and OHI–MOO–KMC).

Optimum prediction models performance are recorded and compared with the actual value. Finally, based on error term values best optimum prediction model is proposed for evaluation of RUL of REBs.

Proposed OHI–GA–KMC model is compared in terms of error values with previously published work. RUL predicted by OHI–GA–KMC model is smaller, giving the advantage of this method.