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This study aimed to bridge the gap between the financial measurement of process improvement ideas and Lean Six Sigma measurements. It was required to increase employee engagement in process improvement initiatives.

Through both a practical and theoretical application of the Design for Lean Six Sigma methodology, the researcher was able to design a process and a benefit measuring methodology that was acceptable by finance and aligns with the benefits expected from the elimination of the Lean wastes.

The project found that benefit measurement methodology is not understood by most employees, which leads to a lack of engagement in working on improvements. The result of the study was a model for employees to identify and quantify these benefits. This has resulted in a model for cost-benefit analysis aligning financial costs with non-value add waste costs and cost of poor-quality costs resulting in increased process improvement ideas and activity.

While this study was limited to one company, applying this methodology could benefit any company experiencing the same difficulties.

This is one of the first studies to try and cost the benefits of LSS projects both from an organisational and generic viewpoint.

This research paper aims to develop and validate an enhanced business process improvement methodology (EBPIM) by integrating the DMAIC (define, measure, analyze, improve and control) and the comprehensive business process management (CBPM) methodologies.

A systematic literature review and analysis were conducted to prove the novelty of the research approach and identify the similarities, differences, strengths and weaknesses of the DMAIC and the CBPM methodologies. The EBPIM was proposed based on the analysis results. Then, a focus group approach was used to evaluate and validate the methodology.

The EBPIM consists of nine activities: preparation, selection, description, quantification, modeling, enactment, improvement opportunities selection, analysis and improvement and monitoring. The proposed methodology adopted the systematic and structured process of the DMAIC methodology by having one tollgate between every two activities to check the progress and authorize the team to go to the next activity. At the same time, it has the ability of the CBPM methodology to enhance the interaction between human activities and business process management systems (BPMS).

The EBPIM was evaluated and validated by a focus group of academic professors. However, the main limitation of the proposed methodology is that it is still theoretical and needs to be empirically tested. Therefore, future work will focus on testing the EBPIM in different industries and organization sizes.

From the theoretical perspective, the proposed methodology adds value to the knowledge in the scope of business processes improvement methodologies (BPIMs) by integrating the DMAIC and the CBPM methodologies. It takes advantage of and combines the strengths of the DMAIC and CBPM methodologies. From the practical perspective, the proposed methodology presents a valuable tool that can facilitate the organization’s mission to improve the areas that need improvement using a systematic improvement methodology that will effectively enhance organizational performance (OP).

The BPIMs literature analysis proved that most of the reviewed methodologies could not support all phases of the business process improvement (BPI) activities. It was concluded that integrating the DMAIC and the CBPM methodologies is a novel approach. The proposed methodology will enhance the efficiency of both methodologies, fill the gaps that may exist in both of them and lead to better results in terms of BPI.

The evaluation of time-based performance is a valued approach in the lean management thinking which is based on delivering value from customer's perspective. This approach contributes for long-term competitiveness and success in today's business environment. The focus of this study is to analyse the cycle time and manufacturing lead time with value stream mapping (VSM) in the preparatory stage of the textile fabric manufacturing process and to identify and improve the non-value adding activities in the value chain (VC). The study presents an insight on the translation of performance improvement across functions and how upstream supply chain (SC) segments can be linked in the performance improvement program. It also covers how the application of VSM improves visibility and planning flexibility in textile fabric manufacturing process.

The time-based performance was evaluated using VSM and recording of the activity times in the existing process. The impact of the quality of supplier's raw material was also measured contributing to identify the strategy for procurement and the means to establish a feedback system to the upstream segments of the SC. The methodology of VSM, observation of the practice and the expertise of the individuals involved with the process were utilised to develop the value stream maps and to identify value adding activities, non-value adding activities, existing gaps and plans for improvement.

The means for improving the time-based performance were identified and their impact was measured. The factors responsible for improvement are related to the production system and with the procurement strategy. The improvement was achieved in terms of available capacity utilisation, balancing the work flow in the preparatory stages, visibility of the process by measuring its capability and flexibility for the planning function. The study revealed that the effectiveness and enhancement of VSM and related tools should be adopted to address the issue of limiting success rates of long term and repeating application of such tools. Continuous improvement, innovations and the systematic embedding of VSM in the process life cycle provide the ways for achieving long-term success.

The paper presents a real and in-depth study on the application of VSM in the textile manufacturing process. The scope of the study is broad; it covers activities across functions with actual estimates of activity times in the manufacturing process for the focused value streams. It offers researchers the opportunity to analyse the translation of productivity improvement across functions and how upstream SC segments can be linked in a performance improvement program.

The study offers useful insight for the managers in textile manufacturing and other sectors for improving the time-based performance and achieving higher utilisation of capacity. It identified the production factors and their impact on warping and sizing cycle time in selected value streams and those which share common activities. It also identified the directions for future research when repeating the application of VSM in the continuous improvement cycle. Furthermore, since the industries need to progress towards advanced systems including Industry 4.0 standards, adoption of advanced VSM tool with relevant technology can align their production systems to develop the required capability. This will also bring a sustainable competitive advantage in the system.

The focused sector is stagnant in terms of productivity and innovation. The adoption of the advanced tools can facilitate the implementation of continuous improvement and innovation strategies.

The main focus of this study is to analyse and improve the cycle time in the preparatory stage of the fabric manufacturing process. This has impact on other important and tangible measures including capacity utilisation and work flow and intangible measures including production planning flexibility and process visibility. The improvement impact is across departments.

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.

When improving business processes, process analysts can use data-driven methods, such as process mining, to identify improvement opportunities. However, despite being supported by data, process analysts decide which changes to implement. Analysts often use process visualisations to assess and determine which changes to pursue. This paper helps explore how process mining visualisations can aid process analysts in their work to identify, prioritise and communicate business process improvement opportunities.

The study follows the design science methodology to create and evaluate an artefact for visualising identified improvement opportunities (IRVIN).

A set of principles to facilitate the visualisation of process mining outputs for analysts to work with improvement opportunities was suggested. Particularly, insights into identifying, prioritising and communicating process improvement opportunities from visual representation are outlined.

Prior work focuses on visualisation from the perspectives – among others – of process exploration, process comparison and performance analysis. This study, however, considers process mining visualisation that aids in analysing process improvement opportunities.

Existing conceptual, empirical and case studies evidence suggests that manufacturing industries find the joint implementation of Kaizen philosophy initiatives. However, the existing practices rarely demonstrated in a single framework and implementation procedure in a structure nature. This paper, therefore, aims to develop, validate and practically test a framework and implementation procedure for the implementation of integrated Kaizen in manufacturing industries to attain long-term improvement of operational, innovation, business (financial and marketing) processes, performance and competitiveness.

The study primarily described the problem, extensively reviewed the current state-of-the-art literature and then identified a gap. Based on it, generic and comprehensive integrated framework and implementation procedure is developed. Besides, the study used managers, consultants and academics from various fields to validate a framework and implementation procedure for addressing business concerns. In this case, the primary data was collected through self-administered questionnaire, and 244 valid questionnaires were received and were analyzed. Furthermore, the research verified the practicability of the framework by empirically exploring the current scenario of selected manufacturing companies.

The research discovered innovative framework and six-phase implementation procedure to fill the existing conceptual gap. Furthermore, the survey-based and exploratory empirical analysis of the research demonstrated that the practice of the proposed framework based on structured procedure is valued and companies attain the middling improvements of productivity, delivery time, quality, 5S practice, waste and accident rate by 61.03, 44, 52.53, 95.19, 80.12, and 70.55% respectively. Additionally, the companies saved a total of 14933446 ETH Birr and 5,658 M² free spaces. Even though, the practices and improvements vary from company to company, and even companies unable to practice some of the unique techniques of the identified CI initiatives considered in the proposed framework.

All data collected in the survey came from professionals working for Ethiopian manufacturing companies, universities and government. It is important to highlight that n = 244 is high sample size, which is adequate for a preliminary survey but reinforcing still needs further survey in terms of generalization of the results since there are hundreds of manufacturing companies, consultants and academicians implementing and consulting Kaizen. Therefore, a further study on a wider Ethiopian manufacturing companies, consultants and academic scale would be informative.

This work is very important for Kaizen professionals in the manufacturing industry, academic and government but in particular for senior management and leadership teams. Aside from the main findings on framework development, there is some strong evidence that practice of Kaizen resulted in achieving quantitative (monetary and non-monetary) and qualitative results. Thus, senior management teams should use this research out to practice and analyze the effect of Kaizen on their own organizations. Within the academic community, this study is one of the first focusing on development, validating and practically testing and should aid further study, research and understanding of Kaizen in manufacturing industries.

So far, it is rare to find preceding studies proposed, validated and practically test an integrated Kaizen framework with the context of manufacturing industries. Thus, authors understand that this is the very first research focused on the development of the framework for manufacturing industries continuously to be competitive and could help managers, institutions, practitioners and academicians in Kaizen practice.

This study aims to investigate how to identify and address production levelling problems in assembly lines utilising an intensive manual workforce when higher productivity levels are urgently requested to meet market demands.

A mixed-methods approach was used in the research design, integrating case study analysis, interviews and qualitative/quantitative data collection and analysis. The methodology implemented also introduces to the literature on operational performance a novel combination of data analysis methods by introducing the use of the Natural Language Understanding (NLU) methods.

First, the findings unveil the impacts on operational performance that transportation, limited documentation and waiting times play in assembly lines composed of an intensive workforce. Second, the paper unveils the understanding of the role that a limited understanding of how the assembly line functions play in productivity. Finally, the authors provide actionable insights into the levelling problems in manual assembly lines.

This research supports industries operating assembly lines with intensive utilisation of manual workforce to improve operational performance. The paper also proposed a novel conceptual model prescriptively guiding quick and long-term improvements in intensive manual workforce assembly lines. The article assists industrial decision-makers with subsequent turnaround strategies to ensure higher efficiency levels requested by the market.

The paper offers actionable findings relevant to other manual assembly lines utilising an intensive workforce looking to improve operational performance. Some of the methods and strategies examined in this study to improve productivity require minimal capital investments. Lastly, the study contributes to the empirical literature by identifying production levelling problems in a real context.

World Health Organizations (WHO) (2023) states that governments and employers have a responsibility to uphold that right by providing work that simultaneously prevents workers from experiencing excessive stress and mental health risks. The business environment continues to produce a lot of stress on workers, which includes internal pressures to achieve results and employees suffer the consequences. Some companies have turned to mindfulness as a technique that helps mitigate these consequences and have joined Kaizen as a process improvement technique in the work environment. Therefore, this study has a research purpose: “to comprehend the possible linkage between Kaizen philosophy from an individual perspective, with Mindfulness ZEN Buddhism technique to understand the individual benefit (well-being) of each employee in organizations.” The answer to this represents the research gap in this article. The research questions governing this study are as follows: RQ1: Does Mindfulness is used as Kaizen technique of personal-individual improvement in 21st-century organizations? RQ2: What elements and characteristics of Kaizen and mindfulness can be found working together? And RQ3: Which qualitative impact of mindfulness and Kaizen in the workplace outcome (well-being, performance of the job (process)) and relationships with other employees)?

This research used a qualitative approach due to the recent phenomenon studied. In a certain way, it was used a mixed-method (combination of qualitative data – web search secondary data analysis and qualitative research-Convergence Model). First, it was done an intensive web search with the aim to identify companies' corporate mindfulness programs, along with companies which have applied mindfulness and Kaizen programs. It was identified a group of big companies with global and international presence (“famous” for their products and services) in diverse industrial and service sectors, country of origin and business locations; with the purpose of getting a holistic vision of all organizations which have practice Kaizen and mindfulness. Therefore, this study explored secondary data related to both practices, analyzing reports or briefings published in management magazines and official WEB pages and/or business magazines.

As a result of the triangulation of the data with its secondary data convergence model and qualitative research, a theoretical framework was reached that shows the benefits of the two combined twin techniques of Kaizen and mindfulness. The worker experiences a path that goes from concentrating on the execution of their processes, following their operating standards (Standardize, Do, Check, Act [SDCA] cycle), going through the evolution to continuous improvement or Plan-Do-Check-Act (PDCA) cycle, experiencing work with concentration-awareness and reducing your daily stress, maintaining high sensitivity to the work process and your environment and finally, discovering an essential life purpose. Finally, worker experiences benefit when there is wide application of both with the SDCA and PDCA cycles such as high motivation, constant learning from your mistakes, day-to-day learning and the Munen Musso (not using the mind).

The main limitation is the qualitative methodological bias and secondary data research. In addition, to have a theoretical sample. However, the richness of the data helps to overcome this limitation. On the other hand, the qualitative research interviews are for a certain geographical area, therefore, the results cannot be generalized.

The results of this research can shed light on operations managers in the use of techniques for continuous improvement and improvement of people's quality of life, such as mindfulness. In Mexico, they are beginning to be used jointly (twin techniques) to comply with Regulation 035 of psychosocial risk, the researchers are sure that in other countries it will be used in the same way to comply with regulations. However, the research findings show the benefits that can be provided to workers in organizations by applying Kaizen and Mindfulness together.

To the best of the authors’ knowledge, according to the literature review, this is the first article that explores the relationship between Kaizen and Mindfulness as twin techniques that help improve the individual quality of life of employees in organizations.

This research paper aims to analyze the scientific and grey literature on Quality 4.0 and zero-defect manufacturing (ZDM) frameworks to develop an integrated quality 4.0 framework (IQ4.0F) for quality improvement (QI) based on Six Sigma and machine learning (ML) techniques towards ZDM. The IQ4.0F aims to contribute to the advancement of defect prediction approaches in diverse manufacturing processes. Furthermore, the work enables a comprehensive analysis of process variables influencing product quality with emphasis on the use of supervised and unsupervised ML techniques in Six Sigma’s DMAIC (Define, Measure, Analyze, Improve and Control) cycle stage of “Analyze.”

The research methodology employed a systematic literature review (SLR) based on PRISMA guidelines to develop the integrated framework, followed by a real industrial case study set in the automotive industry to fulfill the objectives of verifying and validating the proposed IQ4.0F with primary data.

This research work demonstrates the value of a “stepwise framework” to facilitate a shift from conventional quality management systems (QMSs) to QMSs 4.0. It uses the IDEF0 modeling methodology and Six Sigma’s DMAIC cycle to structure the steps to be followed to adopt the Quality 4.0 paradigm for QI. It also proves the worth of integrating Six Sigma and ML techniques into the “Analyze” stage of the DMAIC cycle for improving defect prediction in manufacturing processes and supporting problem-solving activities for quality managers.

This research paper introduces a first-of-its-kind Quality 4.0 framework – the IQ4.0F. Each step of the IQ4.0F was verified and validated in an original industrial case study set in the automotive industry. It is the first Quality 4.0 framework, according to the SLR conducted, to utilize the principal component analysis technique as a substitute for “Screening Design” in the Design of Experiments phase and K-means clustering technique for multivariable analysis, identifying process parameters that significantly impact product quality. The proposed IQ4.0F not only empowers decision-makers with the knowledge to launch a Quality 4.0 initiative but also provides quality managers with a systematic problem-solving methodology for quality improvement.