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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.

Supplier selection in the supply chain network (SCN) has strategic importance and involves multiple factors. The multi-criteria nature of the problem coupled with environmental uncertainty requires several procedures and considerations. The issue of decision-making in selecting the best among various qualified suppliers remains the major challenge in the pharmaceutical industry. This study investigated the multi-criteria multi-supplier decision-making process and proposed a model for supplier selection problems based on mixed-integer linear programming.

The concept of principal component analysis (PCA) was used to reduce data dimensionality, and the four best criteria have been considered and selected. The result is subjected to decision-makers’ (DMs’) reliability test using the concept of a triangular fuzzy number (TFN). The importance of each supplier to each measure is established using fuzzy technique for order preference by similarity to an ideal solution approach, and the suppliers have ranked accordingly.

This study proposes a mixed integer linear programming model for supplier selection in a pharmaceutical company. The effectiveness of the proposed model has been demonstrated using a numerical example. The solution shows the model's applicability in making a sound decision in pharmaceutical companies in the space of reality. The model proposed is simple. Readily commercial packages such as LINDO/LINGO and GAMS can solve the model.

This research contributed to the systematic manner of supplier selection considering DMs’ value judgement under a fuzzy environment and is limited to the case study area. However, interested researchers can apply the study in other related manufacturing industries. However, the criteria have to be revisited to suit that system and might require varying ratings based on the experts' opinions in that field.

This work suggests more insights practically by considering a realistic and precise investigation based on a real-life case study of pharmaceutical companies with six primary criteria and twenty-four sub-criteria. The study outcome will assist organizations and managers in conducting the best decision objectively by selecting the best suppliers with their various standards and terms among many available contenders in the manufacturing industry.

In this paper, the authors attempted to identify the most critical attributes to be preserved by the top managers (DMs) while selecting suppliers in pharmaceutical companies. The study proposed an MILP model for supplier selection in the pharmaceutical company using fuzzy TOPSIS.

The attributes that influence the selection of applicants and the relevant crediting decisions are naturally distinguished by interactions and interdependencies. A new method of possibilistic discrimination analysis (MPDA) was developed for the second stage to address this phenomenon. The method generates positive and negative discrimination measures for each alternative applicant in relation to a particular attribute. The obtained discrimination pair reflects the interaction of attributes and represents intuitionistic fuzzy numbers (IFNs). For the aggregation of applicant's discrimination intuitionistic fuzzy assessments (with respect to attributes), new intuitionistic aggregation operators, such as AsP-IFOWA and AsP-IFOWG, are defined and studied. The new operators are certain extensions of the well-known Choquet integral and Yager OWA operators. The extensions, in contrast to the Choquet aggregation, take into account all possible interactions of the attributes by introducing associated probabilities of a fuzzy measure.

For optimal planning of investments distribution and decreasing of credit risks, it is crucial to have selected projects ranked within deeply detailed investment model. To achieve this, a new approach developed in this article involves three stages. The first stage is to reduce a possibly large number of applicants for credit, and here, the method of expertons is used. At the second stage, a model of improved decisions is built, which reduces the risks of decision making. In this model, as it is in multi-attribute decision-making (MADM) + multi-objective decision-making (MODM), expert evaluations are presented in terms of utility, gain, and more. At the third stage, the authors construct the bi-criteria discrete intuitionistic fuzzy optimization problem for making the most profitable investment portfolio with new criterion: 1) Maximization of total ranking index of selected applicants' group and classical criterion and 2) Maximization of total profit of selected applicants' group.

The example gives the Pareto fronts obtained by both new operators, the Choquet integral and Yager OWA operators also well-known TOPSIS approach, for selecting applicants and awarding credits. For a fuzzy measure, the possibility measure defined on the expert evaluations of attributes is taken.

The comparative analysis identifies the applicants who will receive the funding sequentially based on crediting resources and their requirements. It has become apparent that the use of the new criterion has given more credibility to applicants in making optimal credit decisions in the environment of extended new operators, where the phenomenon of interaction of all attributes was also taken into account.

The purpose of this study is to investigate the relationship between socioeconomic factors and entrepreneurial orientation (EO) across genders. The study also highlights the impact of cognitive and motivational factors on the EO in Saudi Arabia.

This is a quantitative analysis based on the Adult Population Survey of 2016 conducted by the Global Entrepreneurship Monitor among a sample of 4,053 in the Kingdom of Saudi Arabia.

The results showed significant differences in most EO dimensions between genders. It was found that EO is positively related to gender, with more women intending to start a venture than men in the Kingdom of Saudi Arabia. Self-efficacy, motivation and social welfare motivational factors for EO are positively associated with the gender of the respondents.

This paper illustrates the urgency to establish programs that support self-efficacy, motivation and social welfare to promote entrepreneurship among women in the Kingdom.

The study highlights the impact of cognitive and motivational factors on EO among females in Saudi Arabia.

Entrepreneurship is one of the pillars of economic development and helps to green the economy. This study investigates the factors that affect green entrepreneurship (GE) in small- and medium-sized enterprises (SMEs) in Saudi Arabia.

Using quantitative modes, this study's findings are based on 334 valid cases from Saudi Arabian SME employees.

Using path analysis, this study’s findings reveal that green entrepreneurial skills (GES), green opportunities (GOs), entrepreneurial motivation (EM), green incentives (GIs), availability of capital (AoC) and green entrepreneurial self-efficacy (GESE) have positive and significant effects on GE. Moreover, GESE mediates GE's relationships with GES, GOs, EM, GIs and AoC.

This study's findings support the development of green business practices in terms of green business planning, training and skills. The research provides opportunities for green businesses. Policymakers and SME authorities would benefit from this study's findings in producing green goods and services.

This study empirically confirms the role of various factors such as GESE and GE among Saudi Arabian SMEs.

This study aims to identify the most significant barriers and the stationary barrier to modular construction (MC) implementation and promote MC widespread use. By doing so, the construction industry can leverage the benefits of MC, such as faster construction times, improved quality control, reduced waste and increased sustainability.

This study uses a Gini’s mean analysis approach to identify the stationary barriers hindering the MC adoption in residential projects. The research focuses on the Egyptian context and uses a questionnaire survey to gather data from professionals in the construction industry.

According to the survey findings, the top five significant MC barriers are inability to modify the design; contractors asking for high bidding prices (higher initial cost); scepticism, conservation and resistance of clients to innovation and change; transportation restrictions; and lack of a one-size-fits-all tool for the design. In addition, Gini’s mean of dispersion demonstrated that the stationary barrier that faces MC adoption is the apprehension that architectural creativity will suffer because of MC.

The identified obstacles could be useful for decision makers in countries that have not yet adopted MC and may aid in the planning process to manage the risks associated with MC projects. The paper stresses the significance of devising techniques to overcome these barriers and proposes several methods to tackle these challenges.

This study fills the knowledge gap by identifying the stationary barrier and emphasising the potential risks associated with MC barriers. Furthermore, it suggests several strategies for overcoming and reducing these barriers in developing countries residential projects.

The study aims to qualitatively analyze how faculty can mobilize the intellectual capital of higher education institutions (HEIs), comprising human, structural and relational capital to enable the education and learning of individuals with intellectual and developmental disabilities.

Drawing upon the extant literature, the researcher conducted a qualitative study through written, in-depth interviews with a sample of 40 academic staff/faculty members having prior experience in teaching individuals with intellectual and developmental disabilities. The data was collected through a set of questions formulated as key questions, to be asked to all participants for their responses.

Results of the analysis demonstrated that intellectual capital’s contribution to higher education of individuals with intellectual and developmental disabilities can be best understood in terms of its three components/dimensions. Accordingly, three main themes, with each comprising two sub-themes were uncovered. The first theme, leveraging human capital comprised: faculty acumen and faculty training as sub-themes; the second theme, resourcing structural capital comprised: tangible and intangible structural capital as sub-themes; and the third theme, nurturing relational capital comprised: in-class engagement and the second is ex-class connection as sub-themes.

The paper collects data from 40 faculty having prior experience in teaching individuals with intellectual and developmental disabilities to explore and reveal a completely new perspective of looking at intellectual capital as a means of providing accessible and inclusive higher education to differently-abled students, making them a part of the mainstream.

This study aims to explore the critical application areas of radio frequency identification (RFID) technology for sustainable buildings.

The quantitative research approach was adopted through a structured questionnaire administered to relevant stakeholders of construction projects. The data collected were analysed with the exploratory factor analysis, relative importance index (RII) and fuzzy synthetic evaluation (FSE).

The study’s results have categorised the crucial areas of application where construction industry stakeholders should focus their attention. These areas are divided into four categories: management technologies, production technologies, sensing technologies and monitoring technologies. The findings from the FSE indicate that monitoring technologies represent the most significant category, whereas management technologies rank as the least significant. Moreover, the RII analysis highlights that tools management stands out as the most important application of RFID, while dispute resolution emerges as the least significant RFID application.

The study establishes the core areas of RFID application and their benefits to sustainable buildings. Consequently, it helps stakeholders (consultants, clients and contractors) to examine the RFID application areas and make informed decision on sustainable construction. Furthermore, it provides systematic proof that can aid the implementation of RFID in developing countries.

The study provides an insight into the possible application areas and benefits of RFID technology in the construction industry of developing countries. It also developed a conceptual frame for the critical application areas of RFID technology in the construction industry of developing countries.

The overuse and scarcity of resources emphasize the importance of the circular economy. The technology facilitated by Industry 4.0 stimulates the implementation of the circular economy that aims to reduce resource use and enhance operational efficiency. This study focuses on enhancing delivery efficiency in an online-to-offline (O2O) context from an Industry 4.0 technology-facilitated personal configuration perspective, that is, comparing in-house and crowdsourced delivery efficiency in China's O2O on-demand food delivery context.

The authors collect 128,152 orders from 38 restaurants of an online restaurant chain in China. The authors adopt multiple regression analysis to examine the delivery efficiency gap between in-house and crowdsourced deliverymen and the determinants of this efficiency gap.

The findings of this study reveal that crowdsourced deliverymen exhibit higher delivery efficiency, in terms of a shorter delivery time, than in-house deliverymen. In addition, the authors find that platforms providing monetary incentives or implementing late delivery penalties enlarge this efficiency gap. Furthermore, the authors show that external factors, such as working on weekends and bad weather conditions, contribute to the narrowing of this performance efficiency.

The study's findings suggest that platforms should use advanced technologies facilitated by Industry 4.0 to optimize their personnel configuration to enhance their delivery efficiency and reduce carbon emissions. The effective approaches include using financial incentives and improving working schedules.

The authors' findings contribute to the online fulfillment literature by focusing on delivery efficiency in the O2O context from the Industry 4.0 technology-facilitated personnel configuration perspective. The authors examine how internal and external factors moderate the performance efficiency between these two types of deliverymen.

This study presents a resource framework for reverse logistics operations based on widespread literature and industry experience. A common and comprehensive definition of resources that affect the implementation of reverse logistics is still patchy in the extant literature. Hence, a complete scheme, such as this, for the categorisation of resources that affect the implementation of reverse logistics is essential. This provides a useful information for business managers and extends reverse logistics research.

This study systematically identified, and validated with the help of reverse logistics experts, the resources that affect the implementation of reverse logistics.

The result is a classification scheme, termed as “reverse logistics resource matrix” (RLRM). This matrix offers a holistic overview of the resources that affect the implementation of reverse logistics for both scholars and practitioners.

To best of the researchers' knowledge, the RLRM is the first attempt to comprehensively structure the resources that affect the implementation of reverse logistics in an integrated framework.