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The purpose of this study is to evaluate the efficiency of a Brazilian steelmaking company’s reheating process of the hot rolling mill.

The research method is a quantitative modeling. The main research techniques are data envelopment analysis, TOBIT regression and simulation supported by artificial neural networks. The model’s input and output variables consist of the average billet weight, number of billets processed in a batch, gas consumption, thermal efficiency, backlog and production yield within a specific period. The analysis spans 20 months.

The key findings include an average current efficiency of 81%, identification of influential variables (average billet weight, billet count and gas consumption) and simulated analysis. Among the simulated scenarios, the most promising achieved an average efficiency of 95% through increased equipment availability and billet size.

Additional favorable simulated scenarios entail the utilization of higher pre-reheating temperatures for cold billets, representing a large amount of savings in gas consumption and a reduction in CO2 emissions.

This study’s primary innovation lies in providing steelmaking practitioners with a systematic approach to evaluating and enhancing the efficiency of reheating processes.

Business Process Reengineering (BPR) eliminates non-value-added (NVA) and essential non-value-added (ENVA) waste through radical process redesign to improve organizational operations. Comprehensive research integrating BPR tools is needed to understand their benefits for manufacturing firms. This research presents an integrated BPR-simulation framework tailored to the manufacturing sector to maximize process improvements and operational excellence.

The BPR design methodology adopts a systematic, multi-stage approach. The first phase involves identifying a specific improvement process aligned with BPR's core objectives. This phase analyses and redesigns workflows to optimize task sequences, roles, and stakeholder interactions while eliminating redundancies and inefficiencies via Workflow Process Reengineering. Visual process mapping tools, including VSM and simulation, pinpoint areas of waste, delay, and potential enhancement. The second phase follows the workflow analysis and aims to improve efficiency and effectiveness by redefining roles, rearranging tasks, and integrating automation and technology solutions. The redesigned process undergoes evaluation against key performance indicators to ensure measurable improvements are achieved. The final phase validates the proposed changes through simulation models, assesses the impact on key performance metrics, and establishes the necessary infrastructure for successful implementation. The proposed model is empirically validated through a case study of a leading apparel company in Vietnam, confirming its effectiveness.

The findings reveal that NVA activities are being eliminated, and ENVA activities in key departments are significantly reduced. This yielded a substantial improvement, reducing 25 out of 186 combined ENVA and NVA operations in the sewing facility, involving a decrease of 15 ENVA operations and the removal of 10 NVA operations. Consequently, this led to an 8.5% reduction in the proportion of ENVA operations, accompanied by a complete 100% elimination of NVA activities.

The single case study limits generalizability; thus, expanded implementation across diverse manufacturing sub-sectors is required to establish validity and broader applicability of the integrated framework.

The experimental results highlight the proposed model's effectiveness in optimizing resource utilization and its practical implementation potential. This structured BPR methodology enables organizations to validate, evaluate, and establish proposed process changes to enhance operational performance and productivity.

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.

The aim of this paper is to evaluate the intensity and success of the digitalization process, by measuring the efficiency of the use of information and communication technologies in business in the Republic of Serbia (RS) in the period from 2006 to 2019.

The data envelopment analysis method is applied and due to the sensitivity of the results to measurement errors, the robustness analysis of the obtained values of average efficiency is performed, using the bootstrapping method.

The results show an intensive, expansive and relatively efficient process of digital business transformation in the RS. The results indicate inefficient use of software packages, While the efficiency of e-commerce in companies in most years is over 80%.

The research is limited to the RS, so the conclusions cannot be generalized in a broader context.

The biggest problem in the implementation of digital business transformation in the RS is the understanding of management and employees in organizations that digital business transformation will take place only if software solutions are purchased and installed, with less attention paid to their proper application and low use of their maximum capabilities.

Digital transformation measurement is the subject of a very small number of studies. Through a review of the literature, the authors of this paper do not find the use of data envelopment analysis to measure the efficiency of digital business transformation in the way they present it in this paper.

Healthcare organizations have been facing challenges due to high costs and low efficiency in health services. The growth of costs and losses caused by avoidable mistakes lead to the search for solutions, and Health Lean Management appears as a potential solution to help in solving service quality problems, as well as reducing risks. This study aims to analyse the state of the art in the literature centred on the Lean approach in the context of risk management in healthcare organizations, and to identify new research opportunities, highlighting possible lines of future research.

Following a systematic literature review approach, 51 papers were considered relevant for this research, and reviewed to explore the development of literature in this area.

Based on the results, five main research streams were identified: (1) risk management oriented towards patient safety; (2) risk management oriented towards employee safety; (3) importance of attitudes and behaviours in risk reduction projects; (4) Lean tools used in healthcare risk management and (5) Integration of approaches. Moreover, several shortcomings were identified in literature.

Identified shortcomings represent significant opportunities for further research development.

The altitude and weather conditions affect directly fuel consumption and engine efficiency of the aircraft engines. The thermo-physical properties of the weather of altitude play a significant role in this process. Unfortunately, engine performance based on altitude conditions also causes waste heat and environmental pollution due to engine entropy generation. However, environmental impact assessment is needed to improve environmental sustainability. This study aimed to analyse the energy and environmental performance of a piston engine based on altitude conditions.

This study is based on the entropy approach, and it aims to assess the environmental impact of the engine. Exergy analysis with together two new indices to evaluate the environmental effects caused by the engine under altitude conditions was used.

The analysis reveals that the exergy efficiency of the piston engine is 23.9% on average for the three referenced altitudes, while the exergy efficiency difference between altitude boundary conditions is 11%. In addition, the entropy production of the engine is on average 10.55 kW/K. In this case, the environmental pollution potential resulting from the entropy production of the engine is on average 3.29 times higher due to reversible conditions, while the improvement rate was found to be 58%.

This analysis shows that engine efficiency increases as altitude increases. Similarly, it can also be said that the environmental impacts are reduced and the improvement of the engine has opportunities for operational processes. Besides, in the study, some suggestions for motor performance impact analysis were presented.

With the recent proliferation of AI, organisations are transforming not only their organisational design but also the input and output operational processes of the hiring process. The purpose of this paper is to explore the organisational and operational dimensions resulting from the deployment of AI during talent acquisition process.

The authors conducted semi-structured interviews and meetings with human resources (HRs) professionals, recruiters and AI hiring platform providers in Sweden. Using an inductive data analysis rooted in the principles of grounded theory, the study uncovered four aggregate dimensions critical to understanding the role of AI in talent acquisition.

With insights from algorithmic management and ambidexterity theory, the study presents a comprehensive theoretical framework that highlights four aggregate dimensions describing AI’s transformative role in talent recruitment. The results provide a cautionary perspective, advising against an excessive emphasis on operational performance driven solely by algorithmic management.

The study is limited in scope and subject to several constraints. Firstly, the sample size and diversity are restricted, as the findings are based on a limited number of semi-structured interviews and meetings with HRs professionals, recruiters, and AI hiring platform providers. Secondly, the rapid evolution of AI technologies means that the study’s findings may quickly become outdated as new advancements and applications emerge.

The results provide managers with actionable information that can lead to more precise and strategic management practices, ultimately contributing to improved organizational performance and outcomes. Plus, enhancing their ability to make informed decisions, optimize processes and address challenges effectively.

The results signal both positive and negative impacts on employment opportunities. On the positive side, AI can streamline recruitment processes, making it easier for qualified candidates to be identified and hired quickly. However, AI systems can also perpetuate existing biases present in the data they are trained on, leading to unfair hiring practices where certain groups are systematically disadvantaged.

By examining the balance between transactional efficiency and relational engagement, the research addresses a crucial trade-off that organizations face when implementing AI in recruitment. The originality lies in its critique of the prevailing emphasis on e-recruiting.

The purpose of this study is to quantify and evaluate the efficiency of knowledge services public libraries (PLKS) in China and to analyze the factors affecting the efficiency of PLKS. The focus of this study is to explore the characteristics and changing trends of PLKS and how to improve the efficiency of PLKS. The conclusions drawn in this paper are expected to provide important references for academic research and practical exploration in related fields.

This study constructs a three-stage process of PLKS. On this basis, the efficiency is analyzed by using the Super-NSBM model, and the influencing factors of PLKS are analyzed by using OLS. Finally, through the comprehensive interpretation of the measured data and the existing social development conditions, the authors put forward relevant recommendations.

In the first stage, the PLKS in China meets the basic requirements, but there is an obvious waste of resources in the second stage. In addition, China’s public libraries show obvious regional differences in knowledge service efficiency. Finally, it was found that economic and technological factors can increase the efficiency of PLKS, but the increase in the number of university students will reduce the efficiency of PLKS.

In this study, the authors provide an exhaustive deconstruction and explanation of PLKS, which leads to the proposal of a three-stage PLKS efficiency conversion process. Furthermore, the authors identify a diverse set of quantifiable metrics that are readily accessible for open collection. Notably, the authors correlated the efficiency of PLKS with the influencing factors, exploring PLKS from a new perspective and enriching the research related to PLKS.

This paper aims to provide a reference for the development of digital transformation from the perspective of manufacturing process links.

This paper applies canonical correlation analysis based on digital technology patents in the key links of manufacturing industries (product design, procurement, product manufacturing, warehousing and transportation, and wholesale and retail) and the related indicators of economic benefits of regions in China.

(1) The degree of digitalization of manufacturing process links is significantly correlated with economic benefits. (2) The improvement of the degree of digitalization in the “product design” link, the “warehousing and transportation” link, the “product manufacturing” link and the “wholesale and retail” link has significant impacts on the economic benefits of manufacturing industry. (3) The digital degree of the “procurement” link has no obvious influence on the economic benefits of manufacturing industry.

The research results can provide reference for the formulation and implementation of micro policies. The strategy of improving the level of digital transformation of key links of manufacturing industry is put forward to better promote both the digital transformation of manufacturing industry and economic development.

This paper innovatively studies the relationship between digitalization of manufacturing process links and economic benefits. The findings can provide theoretical and empirical support for the digital transformation of China's manufacturing industry and high-quality development of economy.

This study aims to implement Lean and Green principles within the agribusiness sector, focusing specifically on employing Environmentally Sustainable Value Stream Mapping (Sus-VSM) to assess critical indicators for both present and future states of an orange farm. The objective is to enhance value while simultaneously mitigating environmental impacts.

Employing a mixed-method research design, this study integrates both qualitative and quantitative methodologies. It adapts Sus-VSM and introduces inventory analysis frameworks for application within the agricultural domain, conducting a case study on an orange farm located in Sergipe, Brazil. This research seeks to provide actionable insights into the application of Lean and Green principles in agribusiness and introduces the Overall Lean-Green Effectiveness (OLGE) as an innovative decision-making tool for managers.

The study underscores the efficacy of Sus-VSM in the agricultural sector, albeit noting the necessity for certain process modifications to ensure successful implementation. Upon comparison of the two states, it is observed that the current state incurs 70.55% higher impacts on climate change (0.47 tCO₂eq./ha), whereas the future state could yield a 4.08% increase in aggregated value. Improvements from the current to the future scenario can primarily be achieved through enhanced management of in-field inventory of inputs, given that OLGE in this case study is significantly influenced by efficient inventory management.

The adaptation of VSM for agricultural operations, coupled with the integration of environmental sustainability indicators, represents an innovative strategy for enhancing agricultural processes while minimizing environmental impacts. The proposition of a new Lean and Green indicator, the OLGE, aims to facilitate the interpretation of results and guide improvements.