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This study aims to explore the integration of Industry 4.0 technologies with lean six sigma practices in the manufacturing sector for enhanced process improvement.

This study used a fuzzy decision-making trial and evaluation laboratory approach to identify critical Industry 4.0 technologies that can be harmonized with Lean Six Sigma methodologies for achieving improved processes in manufacturing.

The research reveals that key technologies such as modeling and simulation, artificial intelligence (AI) and machine learning, big data analytics, automation and industrial robots and smart sensors are paramount for achieving operational excellence when integrated with Lean Six Sigma.

The study is limited to the identification of pivotal Industry 4.0 technologies for Lean Six Sigma integration in manufacturing. Further studies can explore the implementation challenges and the quantifiable benefits of such integrations.

Integrating Industry 4.0 technologies with Lean Six Sigma enhances manufacturing efficiency. This approach leverages AI for predictive analysis, uses smart sensors for energy efficiency and adaptable robots for flexible production. It is vital for competitive advantage, significantly improving decision-making, reducing costs and streamlining operations in the manufacturing sector.

The integration of Industry 4.0 technologies with Lean Six Sigma in manufacturing has significant social implications. It promotes job creation in high-tech sectors, necessitating advanced skill development and continuous learning among the workforce. This shift fosters an innovative, knowledge-based economy, potentially reducing the skills gap. Additionally, it enhances workplace safety through automation, reduces hazardous tasks for workers and contributes to environmental sustainability by optimizing resource use and reducing waste in manufacturing processes.

This study offers a novel perspective on synergizing advanced Industry 4.0 technologies with established Lean Six Sigma practices for enhanced process improvement in manufacturing. The findings can guide industries in prioritizing their technological adoptions for continuous improvement.

Realities of food insecurity are more pronounced with a specific focus on women in developing countries. The need to understand the varied food insecurity experiences among female-headed agricultural households in such contexts provided the rationale and motivation for this study.

The study employed a quantitative cross-sectional approach, drawing on the binary logistic regression to determine the influence of socioeconomic status on household coping mechanisms in response to food insecurity in a stratified random sample of 509 female-headed agricultural households in Liberia.

The results revealed that most respondents experienced food insecurity reflected in inadequate food availability, an inability to eat nutritious food and the necessity to skip meals. In response, they employed coping strategies such as borrowing money, selling assets, and reducing health expenses, which were influenced by socioeconomic characteristics such as gender, education, and marital status.

The study illustrates the multi-layered and complex context of food insecurity among women. From these findings, the study proposes the consideration of such dynamics to inform practical and relevant mitigatory policy approaches to the target demographic.

With food insecurity being a social problem, the study identifies its social impact by documenting the participants' lived experiences. Thus, the study contributes to a deeper understanding of food insecurity across different segments of society.

The study draws its originality from understanding how food insecurity impacts female-headed households, highlighting the often-ignored gender dynamics of food insecurity in developing nations and aggregating the coping strategies and food insecurity expenses.