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Facilities management (FM), which is crucial for the operation and sustainability of buildings and infrastructure across the world, covers a wide range of competencies that may vary across regions. Focusing on Sri Lanka and Hong Kong, this study aims to reveal and compare the importance and current competency levels of FM competencies in these regions, based on which appropriate education and training can be formulated to enhance the growth of their FM sectors.

After an extensive literature review, 12 FM competency attributes (CAs) were identified. A questionnaire survey in Sri Lanka and Hong Kong solicited the industry practitioners’ perceived importance and current levels of the CAs. The survey responses, 126 from Sri Lanka and 148 from Hong Kong, were analysed by a modified Importance-Competency Analysis matrix, followed using the Mann−Whitney U test to identify any differences in the responses between the two regions.

Between Sri Lanka and Hong Kong, significant differences were found to exist in the importance and current competency levels of over half of the CAs. Among the CAs requiring priority attention, “operation and maintenance” and “leadership” are the top two in Sri Lanka while the top two in Hong Kong are “technology” and “leadership”.

This study yielded insightful results on the importance of FM competencies and the current competency levels in a developing region and a developed region, which are useful for the development of FM education and research.

This paper aims to assess the combined application of Lean Six Sigma and agile approach for optimizing operational processes of order management in the seed industry.

This study is based on an action research case conducted in a multinational Brazilian Seeds Business enterprise. This paper reports on the application of the Lean Six Sigma define-measure-analyze-improve-control (DMAIC), using the steps of DMAIC cycle as a sprint of agile approach. The methodology involves outlining an operational process through sequential activities, each associated with a cycle time, equivalent number of full-time employee and number of orders. Performance metrics for the order management process include continuous monitoring of these activities, using monitoring systems, management software and manual records to collect data.

The findings reveal significant improvements in critical-to-quality measures related to customer care, planning and logistics. The implementation of the DMAIC methodology and agile approach resulted in tangible enhancements in cycle time, defects per opportunities and overall process efficiency. The results allow the classification of defects, the identification of their causes and, consequently, the presentation of a control plan to mitigate these problems. Furthermore, the study identifies key causes of operational issues and proposes a prioritized action plan.

The limitation of this research is its restriction to a single case. The external validity of the results and generalizability to other organizational contexts may be compromised due to the lack of case diversity. The fact that the research focuses on a single company, even if it is a large multinational company, may limit the applicability of the findings to different sectors, sizes and organizational structures, which may be an opportunity for future research.

The findings suggest that the integrated approach of DMAIC and agile methodology contributes to a culture of continuous improvement and operational efficiency. The systematic collection and analysis of data enhance evidence-based decision-making, providing a robust foundation for strategic and operational choices. Moreover, the successful integration of methodologies presents a comprehensive framework applicable to diverse organizational challenges.

The paper applies action research to understand and address operational challenges, emphasizing practical solutions. The integration of DMAIC and agile enhances the depth of process analysis, enabling the identification, implementation and control of improvements. This study offers a significant contribution both to practitioners, providing practical implications, and to academics, enriching the Lean Six Sigma and agile body of knowledge.

Like a well-designed procurement strategy, a well-designed rolling stock strategy promotes the development of a railroad company and contributes to its competitive advantage. Effective implementation of the rolling stock strategy enhances the likelihood of enjoying the benefits of the strategy’s design. A successful implementation relies on the strategy being known, understood, controlled and communicated to the entire organization.

The research consists of an extensive literature review as well as a series of workshops with rolling stock suppliers, railway authorities and representatives and rail users.

This paper presents a collection of solutions for passenger rail rolling stock procurement. The authors offer guidance to the strategic procurement of rolling stock in the European Union (EU) and propose key performance indicators to measure the effectiveness of a rolling stock strategy.

While this study presents a thorough evaluation of rolling stock procurement practices, it is limited by its geographic isolation. Future studies should compare the procurement practices discussed here to others in the EU, as well as in North America, South America and Asia. Additionally, this study focused on the passenger rail segment. Passenger transport is an important function of the rail network in the EU, but future research should expand the assessment of rolling stock procurement strategies to freight rail as well.

Overall, this work provides visibility into common rolling stock procurement practices in complex EU railway markets. This work expands the literature through a review of these practices and through enabling research into best practices or a commonly accepted procurement model.

The objective of the study is to explore the mediatory role of the environmental performance of organisations on their economic and social performances. It demonstrates that implementing environmental management techniques should not only be done to comply with environmental regulations, but also as a means of improving social and economic performance.

The data were gathered from the manufacturing industry of Zimbabwe, and 302 useable responses were received. Data analysis was performed through structural equation modelling (SEM) using SMART PLS 3.

Improvement in environmental performance led to improvements in both social and economic performances. Also, environmental performance contributes the greatest total effect; hence, it deserves attention, not only for compliance but also for economic reasons.

Our goal is to quantify the extent to which environmental performance might improve the social and, more importantly, the economic performance of organisations. The study also explores the relative importance and performance of lean manufacturing (LM), green manufacturing (GM), social performance and environmental performance for purposes of prioritisation of organisational improvement initiatives.

The purpose of this paper is to investigate the determinants of capital expenditures (CAPEX) in the theme park industry, particularly following the financial stresses induced by the COVID-19 pandemic. CAPEX drivers are poorly understood due to the idiosyncratic nature of this industry, which is dominated by a few large players. It also aims to identify the variables influencing both the growth and maintenance components of CAPEX among U.S. theme park operators.

The paper uses contingency theory to analyze both financial and nonfinancial data from U.S. theme park operators between 2009 and 2021. The paper also uses partial least squares structural equation modeling to manage issues of multicollinearity and to ensure robustness in the findings.

The analysis identifies several key determinants of CAPEX. Resources and the presence of competing theme parks in proximity to an operator’s parks positively affect CAPEX. Conversely, higher leverage, dividend payouts, intellectual property (IP) dominance and population density in areas with their active parks correlate with reduced CAPEX. The paper also notes distinct trends in maintenance versus growth CAPEX post-COVID-19, with maintenance CAPEX increasing as operators invest in existing assets while growth CAPEX trending downwards.

The study’s scope is confined to U.S.-based theme park operators, limiting the generalizability of the findings internationally. Moreover, data limitations restrict the sample size due to the consolidation of the industry players, potentially affecting the statistical power of the analysis.

This research offers significant insights for theme park operators, industry analysts and policymakers. Understanding the factors influencing CAPEX can aid operators in strategic planning and investment decisions, especially in a post-pandemic economic environment where efficient capital allocation will be crucial for recovery and growth. A major contribution of this research is the development of a new measure for IP dominance, which allows theme park operators to quantify the impact of IP on their investment strategies.

This study contributes uniquely by incorporating both financial and nonfinancial determinants in analyzing CAPEX within the theme park industry, a sector significantly impacted by the pandemic. It introduces novel metrics for assessing the impact of IP on CAPEX and differentiates between the factors driving maintenance and growth expenditures. The findings enrich the existing literature on hospitality management and provide actionable insights that could guide the strategic financial decisions of theme park operators.

Although the previous generations of the Industrial Revolution have brought many advantages to human life, scientists have been looking for a substantial breakthrough in creating technologies that can improve the quality of human life. Nowadays, we can make our factories smarter using new concepts and tools like real-time self-optimization. This study aims to take a step towards implementing key features of smart manufacturing including  preventive self-maintenance, self-scheduling and real-time decision-making.

A new bi-objective mathematical model based on Industry 4.0 to schedule received customer orders, which minimizes both the total earliness and tardiness of orders and the probability of machine failure in smart manufacturing, was presented. Moreover, four meta-heuristics, namely, the multi-objective Archimedes optimization algorithm (MOAOA), NSGA-III, multi-objective simulated annealing (MOSA) and hybrid multi-objective Archimedes optimization algorithm and non-dominated sorting genetic algorithm-III (HMOAOANSGA-III) were implemented to solve the problem. To compare the performance of meta-heuristics, some examples and metrics were presumed and solved by using the algorithms, and the performance and validation of meta-heuristics were analyzed.

The results of the procedure and a mathematical model based on Industry 4.0 policies showed that a machine performed the self-optimizing process of production scheduling and followed a preventive self-maintenance policy in real-time situations. The results of TOPSIS showed that the performances of the HMOAOANSGA-III were better in most problems. Moreover, the performance of the MOSA outweighed the performance of the MOAOA, NSGA-III and HMOAOANSGA-III if we only considered the computational times of algorithms. However, the convergence of solutions associated with the MOAOA and HMOAOANSGA-III was better than those of the NSGA-III and MOSA.

In this study, a scheduling model considering a kind of Industry 4.0 policy was defined, and a novel approach was presented, thereby performing the preventive self-maintenance and self-scheduling by every single machine. This new approach was introduced to integrate the order scheduling system using a real-time decision-making method. A new multi-objective meta-heuristic algorithm, namely, HMOAOANSGA-III, was proposed. Moreover, the crowding-distance-quality-based approach was presented to identify the best solution from the frontier, and in addition to improving the crowding-distance approach, the quality of the solutions was also considered.

This paper aims to propose a framework to assess the readiness of Brazilian small and medium service companies to use Industry 4.0 technologies to improve the efficiency of processes, reinforcing the idea that evaluating and planning the implementation is as important as the use, without the need for big investments.

The paper presents two applications on the readiness level of small and medium enterprises, with support from researchers and stakeholders. The literature seems to focus more on the maturity level than on providing a sequence of operational steps or decision support. It would lead to better implementation of I4.0 technologies in assessing the company’s readiness level to adopt them.

The authors analyze the relevant case studies in Brazilian small and medium service enterprises based on the proposed framework to investigate the main challenges and hardships for Industry 4.0 readiness in the current scenario and outline directions to increase their readiness for adopting key Industry 4.0 technologies. Moreover, the case studies consolidate a fundamental advantage to small and medium enterprise service: a guided implementation provides more knowledge about products, processes and I4.0 technologies, even in unsuccessful case studies.

The need was identified when studying market trends with the digital transformation provided by Industry 4.0 technologies and, in parallel, the difficulties of small and medium service companies in adapting to this new scenario. Through the proposed framework, an action plan can be drawn up with well-defined objectives, goals and activities so that the service enterprises can increase their Industry 4.0 readiness level within three months.

A relevant aspect is that this study is based on feedback from stakeholders and managers, which makes the analyses and conclusions from empirical points of view without relevant theoretical knowledge of the relationship between I4.0 technologies. Therefore, the proposed framework made it possible to diagnose the organization’s external and internal environments, allowing the ideal condition and the current situation of the companies to be compared, as argued in the Industry 4.0 readiness literature review.

The purpose of this study is to explore the increased uptake of Artificial Intelligence (AI) technology by Facility Management (FM) organizations for enhanced operational efficiency and competitive advantage. While AI adoption in FM has been widely reported, limited attempts have been made to assess its impact on the social well-being of FM employees. To contribute towards addressing this gap, this study established the essential employee social well-being factors mostly impacted by the adoption of AI in South African FM organizations.

A four-stage design comprising a comprehensive review of literature, expert interviews, questionnaire census and focus group discussion sessions was used to elicit data from a sample of participants drawn from 22 South African FM organizations. The data was analyzed using a combination of content analysis, relative importance index and interpretative structural modeling for various data sets toward achieving the study’s objectives.

Sixteen employee social well-being factors, classified under job satisfaction, social relationship and knowledge development categories, respectively, were identified as being impacted by AI adoption in FM organizations. Furthermore, it was established that job security, job autonomy and professional status, which belong to the job satisfaction social well-being factor category, were deemed by FM employees as being mostly impacted by AI adoption.

The enhanced understanding of the impact of AI adoption on FM employees’ social well-being factors will contribute to the development of a collaborative intelligence framework for managing AI adoption in FM organizations toward engendering optimal AI–FM employee relationships for improved productivity.

Besides being one of the foremost studies to investigate the impact of AI adoption on FM employees’ social well-being, this study introduces a hierarchical framework of understanding employee social well-being factors based on multi-stakeholder perspectives.

The paper aims to develop a useful tool that can reliably and accurately find the critical paths of high-rise buildings and provide optimal solutions considering the uncertainty based on Monte Carlo simulation (MCS) to enhance project implementation performance by assisting site workers and project managers in high-rise building engineering.

This research proposes an approach integrating the improved nondominated sorting genetic algorithm II (NSGA-II) considering uncertainty and delay scenarios simulated by MCS with the technique for order preference by similarity to an ideal solution.

The results demonstrate that the proposed approach is capable of generating optimal solutions, which can improve the construction performance of high-rise buildings and guide the implementation management for shortening building engineering project schedule and cost under the delay conditions.

In this study, only the construction data of the two floors was focused due to the project at the construction stage, and future work can analyze the whole construction stage of the high-rise building to examine the performance of the approach, and the multi-objective optimization (MOO) only considered two factors as objectives, where more objectives, such as schedule, cost and quality, can be expanded in future.

The approach proposed in this research can be successfully applied to the construction process of high-rise buildings, which can be a guidance basis for optimizing the performance of high-rise building construction.

The innovations and advantages derived from the proposed approach underline its capability to handle project construction scheduling optimization (CSO) problems with different performance objectives under uncertainty and delay conditions.

Public-private partnerships (PPPs) have become integral in delivering public services and infrastructure, particularly in the context of megaprojects. This paper focuses on the interplay between stakeholder management, challenges, critical success factors (CSFs), and the overall success of PPP-arranged civil infrastructure megaprojects.

Using the PRISMA methodology, we comprehensively analyze challenges and critical success factors (CSFs) influencing stakeholder engagement within PPP megaprojects. A focused search equation identified 595 papers, which were distilled down to 34 relevant papers and case studies. Qualitative analysis of these papers revealed 48 CSFs categorized into 11 challenges from a stakeholder management perspective, which were further delineated across public, private, and combined sectors, and then mapped along the PPP megaproject lifecycle.

Informed by a diverse amalgam of civil and project management literature, this research reveals the intricate dynamics of PPP megaprojects across the globe that emphasize the critical nature of stakeholder engagement, analysis, and management practices. Key findings highlighted conflicting interests between public and private stakeholders, manifesting in challenges like project performance versus profitability. The literature emphasized instances where neglect of local community culture led to adverse social outcomes. A universal conclusion underscored the context-specific nature of challenges and CSFs, stressing the need for a holistic understanding of stakeholders and project dynamics.

The paper acknowledges that it focused on 34 selected papers out of 595 identified. This sample focuses on civil engineering megaprojects which may not fully represent the breadth of research in the field, potentially missing out on valuable insights from excluded studies.

We believe that the compiled list of CSFs, organized according to stakeholder relationships and the project lifecycle, serves as a potent tool for managers and planners. By enabling the identification of complexity from diverse perspectives, this research allows elucidating the challenges faced by the management team in PPP megaproject.

This research identifies several social outcomes related to PPP megaprojects. Critical Success Factors identified as such should allow the project managers to maximize benefits for society and minimize risk and negative externalities.

This study contributes valuable insights for policies and practices by systematically describing challenges and related CSFs throughout the PPP megaproject lifecycle. Additionally, it addresses the nuanced aspects of internal and external stakeholder management, thereby contributing to the overall understanding and best practices required to confront complex megaprojects involving a wide range of stakeholder groups.