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This research aims to enhance the operational excellence and continuous improvement of the retail supply chain in the Saudi Thobe Factory through an integrated approach of Six Sigma DMAIC (Define-Measure-Analyze-Improve-Control) with artificial intelligence (AI).

The study identified the tailoring department as the department with maximum defects by using voice of customer and critical to quality tools. An AI-integrated Six Sigma approach was applied to identify and eliminate nonproductive stages, and a new facility layout was designed to enhance productivity and customer satisfaction.

The use of the factor rating method and simulation using Arena software led to an improved sigma level from 1.597 to 2.237, representing an increment of about 40%. Additionally, the defects per million opportunities reduced from 461,538 to 230,769. The study can help production industry management to optimize facility layouts and improve overall production line efficiency.

This study addresses the lack of published research on the use of an integrated approach of Six Sigma DMAIC with AI in the retail and distribution sector of Saudi Arabia, particularly for small and medium-sized enterprises (SMEs). The study demonstrates how this approach may significantly boost SMEs’ performance and provides a basis for future research in this area.

This study provides a practical example of how an integrated approach of Six Sigma DMAIC with AI can be used in the retail and distribution sector of Saudi Arabia to enhance operational excellence and continuous improvement. The study highlights the potential benefits of this approach for SMEs in the region and provides a framework for future research.

This study aims to improve the market competitiveness of iron and steel manufacturers in developing countries by reducing their production costs.

The research methodology relies on a case study-based approach. The study relies on six steps. The first is the preparation, then the five steps of the six-sigma – define, measure, analyze, improve, control. The qualitative and quantitative data were considered. The qualitative analysis relies on the experts’ judgment of internal status. The quantitative analysis uses the job floor data from three iron and steel manufacturers. After collecting, screening and analyzing the data, the root causes of the different wastes were identified that increase production costs. Consequently, lean manufacturing principles and tools are identified and prioritized using the decision-making trial and evaluation laboratory method, and then implemented to reduce the different types of waste.

The main wastes are related to inventory, time, quality and workforce. The lean tools were proposed with the implementation plan for the discovered root causes. The performance was monitored during and after the implementation of the lean initiatives in one of the three companies. The obtained results showed an increase in some performance indicators such as throughput (70.6%), revenue from by-products (459%), inventory turnover (54%), operation availability (45%), and plant availability (41%). On the other hand, results showed a decrease of time delay (78%), man-hour/ton (52.4%) and downgraded products (63.3%).

The current case study findings can be utilized by Iron and Steel factories at the developing countries. In addition, the proposed lean implementation methodology can be adopted for any other industries.

The current work introduces an original and practical road map to implement the lean six-sigma body of knowledge in the iron and steel manufacturers.

This work introduces an effective and practical case study-based approach to implementing the lean six-sigma body of knowledge in the iron and steel manufacturers in one of the underdevelopment countries. The consideration of the opinion of the different engineers from different sectors shows significant identification of the major problems in the manufacturing and utility sectors that lead to significant performance improvement after solving them.

Construction defects are one of the primary causes of deficient quality performance and constantly yield project cost and schedule overruns mostly because of rework. This study aims to propose a new method for identifying a bespoke checklist of the most recurrent defects in residential construction work and their correlated causes as a basis for developing a framework for practical improvements in managing defect risks.

A literature review was first used to ground the defects within the existing theory and develop a new framework for ranking the key defects involved. This was demonstrated and tested by an analysis of Queensland Building Construction Commission (QBCC) archival data for 42,000 reported defects in new Queensland residential buildings. A questionnaire survey of 427 construction practitioners from 37 trades was then conducted to determine their causes.

The developed framework consists of preconditions for defective acts, defective supervision and organizational influences and is found to be suitable for analyzing the QBCC archives to rank the 20 most frequent defects. In the demonstration study, a questionnaire survey identified the highest frequency latent causes of defective acts to be workers taking shortcuts to complete tasks, workers’ lack of skill or knowledge level, challenges and limitations of technical constructability, and incorrect material supply; the failure to correct a known problem and poor supervision for defective supervision; and inadequate employee training, low managerial priority for quality and high time pressure and constraints for organizational influences.

The method is a new approach to identifying the key defects in residential construction work and their correlated causes for developing bespoke checklists as an aid to in managing defect risks.

Building defects are becoming recurrent phenomena in most high-rise buildings. However, little research exists on the analysis of defects in high-rise buildings based on data from real-life projects. This study aims to develop dashboards and models for revealing the most common locations of defects, understanding associations among defects and predicting the rectification periods.

In total, 15,484 defect reports comprising qualitative and quantitative data were obtained from a company that provides consulting services for the construction industry in Victoria, Australia. Data mining methods were applied using a wide range of Python libraries including NumPy, Pandas, Natural Language Toolkit, SpaCy and Regular Expression, alongside association rule mining (ARM) and simulations.

Findings reveal that defects in multi-storey buildings often occur on lower levels, rather than on higher levels. Joinery defects were found to be the most recurrent problem on ground floors. The ARM outcomes show that the occurrence of one type of defect can be taken as an indication for the existence of other types of defects. For instance, in laundry, the chance of occurrence of plumbing and joinery defects, where paint defects are observed, is 88%. The stochastic model built for door defects showed that there is a 60% chance that defects on doors can be rectified within 60 days.

The dashboards provide original insight and novel ideas regarding the frequency of defects in various positions in multi-storey buildings. The stochastic models can provide a reliable point of reference for property managers, occupants and sub-contractors for taking measures to avoid reoccurring defects; so too, findings provide estimations of possible rectification periods for various types of defects.

This paper aims to introduce a novel structure for the physical internet (PI)–enabled sustainable supplier selection and inventory management problem under uncertain environments.

To address hybrid uncertainty both in the objective function and constraints, a novel interactive hybrid multi-objective optimization solution approach combining Me-based fuzzy possibilistic programming and interval programming approaches is tailored.

Various numerical experiments are introduced to validate the feasibility of the established model and the proposed solution method.

Due to its interconnectedness, the PI has the opportunity to support firms in addressing sustainability challenges and reducing initial impact. The sustainable supplier selection and inventory management have become critical operational challenges in PI-enabled supply chain problems. This is the first attempt on this issue, which uses the presented novel interactive possibilistic programming method.

The textile industry contributes 2 and 3% to the global and Indian Gross Domestic Product (GDP), respectively. India supplies a quarter of global cotton yarn. Yet, most yarn manufacturing companies use outdated methods and lack organisational skills and strategies. Improvement in processes in India could significantly help the industry worldwide.

The variables that influence the performance of the system were identified. Their interrelationships and impact were identified from the employees in the chosen case study, a yarn manufacturing industry. A System Dynamics (SD) approach was employed to study the benefits of implementing 5S lean strategies. The impact of each variable on various performance measures such as throughput, Work In Progress, processing time, waiting time, idle time, over-processing and scraps was analysed.

Improvement in outcomes reflected an enhanced adoption of leanness in the industry. The decision-makers can utilise this study to optimise the necessary parameters in the system and attain the desired productivity levels. Better resource management and reduced processing time helped increase the despatch rate by 9.735% and decrease the WIP by 23.01%. Time management helped to reduce the inventory, idle time and waiting time. Over-processing, defects and scraps were minimised, indicating a shift towards lean.

This study pioneers the use of SD simulation models for optimising yarn manufacturing using lean strategies. Improvement in performance measures by integrating these strategies opens avenues for future research using multiple approaches to address a problem.

Implementing 5S lean principles and simulations enhances productivity, reduces waste and optimises resource management for the yarn manufacturing industry. Decision-makers can employ simulation to witness the outcomes of their changes without investing cost and time and without associated implementation risks.

The use of a simulation model to witness the benefits of incorporating lean strategies in yarn production has not been explored. This approach could help the managers and policymakers understand their existing system's shortcomings and critical areas that require improvement.

Variations due to errors and mistakes have caused cost overruns in building projects. Therefore, it is undeniable that the gravity of such variations will be a critical factor in deciding the success of any building project. In addition, the design stage of a building project is considered the most suitable stage to identify and mitigate the causes of potential variations. However, there are no proper mechanisms to minimise the frequency or gravity of variations. Many researchers experienced the promising essence of Poka-Yoke, a mistake-proofing method aimed at increasing efficiency by early detection and eradication of the causes of potential errors. However, less attention has been paid so far to implement Poka-Yoke principles to minimise variations in the building project. Therefore, this study aims to develop a framework to minimise variations in building projects through the integration of the Poka-Yoke principles.

An interpretivism stance is adopted, and a qualitative research approach is used. The data collection technique adopted is semi-structured interviews with ten experts, and the data is analysed using code-based content analysis through NVivo12.

Research findings revealed 23 causes of variations, categorised under client-originated, consultant-originated, contractor-originated and other variations. The identified causes were then mapped with the Poka-Yoke principles to develop the framework. The research findings could prove useful to researchers, academics, government agencies and construction professionals in developing nations that have demographic/cultural and socioeconomic characteristics such as Sri Lanka.

The findings benefitted the Sri Lankan construction sector by minimising the causes of variations. To the best of the authors’ knowledge, this study will be the first of its kind in the Sri Lankan construction industry, leading to a better understanding of the “Poka-Yoke” principle within the building construction context.

Innovation is critical for businesses to stay competitive in today's world, as it allows them to constantly look for new ways to differentiate their products or services from their competitors as well as improve cost-effectiveness. This study explore the role of strategic human resource practices in developing organizations' competitive capabilities-differentiation and cost-effectiveness, which, improves their innovation performance to create a competitive advantage.

The authors collected data from 387 employees from 25 knowledge-intensive information technology organizations in India through a questionnaire-based survey. After checking for biases, reliability and validity, the hypothesized relationships were tested by structural equational modeling using AMOS 26.

Strategic HR practices have a significant and positive effect on innovation performance and both competitive capabilities-differentiation and cost-effectiveness. While the differentiation capability had a strong positive effect on innovation performance, cost-effectiveness capability was not significantly related to innovation performance. The differentiation capability mediates the relationship between strategic HR practices and innovation performance link, whereas the cost-effectiveness capability did not have a mediating effect.

This study provides practical insights to HR and knowledge managers to focus on development of human capital and invest in hiring, training, development, strategic performance management practices to enhance employees' knowledge behaviors, which, stimulates innovation performance.

The paper adds to the strategic HRM paradigm by clarifying the underlying process of how strategic HR practices leads to higher innovation. It affirms the vitality of choosing appropriate competitive capabilities, and supporting organizational factor for business's success. It fills an important research gap by providing original empirical evidence from knowledge intensive information technology organizations in the emerging economy of India.

The purpose of this paper is to examine the resistance behavior of smart factories in small and medium-sized enterprises (SMEs). Drawing upon dual factor perspective, this study examines how two types of quality and perceived usefulness impact user resistance as enabling factors and how switching cost, skepticism, habit and inertia contribute to user resistance as inhibiting factors. Additionally, multi-group analysis is employed to compare small and medium enterprises.

Purposive sampling technique was employed to collect 460 Korean SMEs employees, consisting of 235 small enterprises and 225 medium enterprises. Partial least squares structural equation modeling was used for data analysis.

The results reveal that all three inhibiting factors, switching cost, skepticism and habit, are key antecedents of inertia. In small enterprises, skepticism has a greater impact on inertia, which in turn strongly affects resistance. Additionally, system quality is more crucial for small enterprises, whereas information quality holds more importance for medium enterprises in mitigating resistance. Moreover, when the implementation level of a smart factory is high, the effect of perceived usefulness on user resistance diminishes.

This study has revealed the importance of considering both enabling and inhibiting factors for the adoption of smart factory systems in the context of SMEs. Additionally, it has provided evidence that as the level of the smart factory system increases, the effect of perceived usefulness on user resistance decreases, thus making the transition to smart factory systems more challenging.

The success of construction projects can be indicated by measuring their performance. For effective project performance (PP), the successful execution of a construction projects is very important. A systematic review of the literature on performance areas and performance assessment models was undertaken. The purpose of this paper is to develop a mathematical formulation for construction PP areas to suit the Indian context by modifying the current project quarterback rating (PQR) model.

Based on the literature, the PQR model has not been validated for suitability in the Indian context. To validate the PQR model and modify the same for the Indian context, a survey instrument was used to collect data on performance areas and a multivariate data analysis technique was carried out to develop a modified model. Delphi technique was used to assign the weights for each performance metric in performance areas.

This study concluded the importance of three additional performance areas, namely, productivity, stakeholder satisfaction and environment for assessing PP for Indian construction projects. It also identified the interrelationship between the performance areas and the PP.

The developed modified PQR model (MPQR) will guide the concerned stakeholders to take corrective actions for improving the performance of construction projects.

The MPQR proposed in this paper covers ten areas and is a comprehensive single score that can be used to benchmark and compare performance over different projects to achieve continuous improvement.