Mechanical Engineering - Research Publications
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ItemImpact of Industry 4.0 adoption on workload demands in contact centers(WILEY, 2022-09)Abstract This paper examines the impact of Industry 4.0 (I4.0) technologies on employees workload in contact centers. For that, we adopted the NASA task load index questionnaire to assess the workload of 100 employees from different contact centers in India that have been adopting I4.0 technologies. The collected data is analyzed through multivariate techniques. This study is grounded on concepts from the multiple resource theory. Our findings indicate positive and negative effects of I4.0 on employees workload, conditioned on the adopted technologies (i.e., Internet‐of‐Things, cloud computing, big data, machine learning/artificial intelligence, remote monitoring, and wireless sensors) and workload dimensions considered (i.e., mental demand, physical demand, temporal demand, overall performance, effort, and frustration level). Identifying I4.0's impacts on employees workload allows planning of managerial efforts to mitigate potential issues while setting clear expectations related to the digital transformation of contact centers' processes and services.
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ItemLean and Green Product Development in SMEs: A Comparative Study between Small- and Medium-Sized Brazilian and Japanese Enterprises(Elsevier BV, 2022-09-01)Facing the new challenges in production processes, companies should adopt lean and green practices in product development. In SMEs, the application of these practices is more complex. This work explores the maturity of lean–green methodologies in the product development process in Brazilian and Japanese SMEs. The methodology used is multicriteria, combining the analytic hierarchy process (AHP) and TOPSIS 2-tuple method, applied to four Japanese SMEs and four Brazilian SMEs in the metalworking sector. The criteria for evaluating SMEs are company flexibility, difficulties with NPD, innovation, limited resources, and personnel authority high. The TOPSIS method alternatives refer to 18 lean–green enablers. In the AHP method, the prioritisation of criteria between Japanese and Brazilian specialists presented divergences. In the Japanese context, the incidence of innovation is predominant, while in the Brazilian context, the most important is the limited resources. In the TOPSIS 2-tuple method, the results showed a higher level of maturity in lean–green methodologies in Japanese companies than in Brazilian ones. Lean practices are more evolved compared to sustainable practices in both countries. The study also addressed how open innovation adoption may contribute to innovation and NPD practices. Policymakers need to understand the heterogeneity of innovators within SMEs and how they differently innovate, developing distinct internal and external activities.
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ItemEffects of Lean Interventions Supported by Digital Technologies on Healthcare Services: A Systematic Review(MDPI, 2022-08)Despite the increasing utilization of lean practices and digital technologies (DTs) related to Industry 4.0, the impact of such dual interventions on healthcare services remains unclear. This study aims to assess the effects of those interventions and provide a comprehensive understanding of their dynamics in healthcare settings. The methodology comprised a systematic review following the PRISMA guidelines, searching for lean interventions supported by DTs. Previous studies reporting outcomes related to patient health, patient flow, quality of care, and efficiency were included. Results show that most of the improvement interventions relied on lean methodology followed by lean combined with Six Sigma. The main supporting technologies were simulation and automation, while emergency departments and laboratories were the main settings. Most interventions focus on patient flow outcomes, reporting positive effects on outcomes related to access to service and utilization of services, including reductions in turnaround time, length of stay, waiting time, and turnover time. Notably, we found scarce outcomes regarding patient health, staff wellbeing, resource use, and savings. This paper, the first to investigate the dual intervention of DTs with lean or lean-Six Sigma in healthcare, summarizes the technical and organizational challenges associated with similar interventions, encourages further research, and promotes practical applications.
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ItemLean layout design: a case study applied to the textile industry(FapUNIFESP (SciELO), 2021-01-01)
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ItemPaper-based thesis and dissertations: analysis of fundamental characteristics for achieving a robust structure(FapUNIFESP (SciELO), 2021-01-01)
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ItemForecasting the length-of-stay of pediatric patients in hospitals: a scoping review(BMC, 2021-09-08)BACKGROUND: Healthcare management faces complex challenges in allocating hospital resources, and predicting patients' length-of-stay (LOS) is critical in effectively managing those resources. This work aims to map approaches used to forecast the LOS of Pediatric Patients in Hospitals (LOS-P) and patients' populations and environments used to develop the models. METHODS: Using the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) methodology, we performed a scoping review that identified 28 studies and analyzed them. The search was conducted on four databases (Science Direct, Scopus, Web of Science, and Medline). The identification of relevant studies was structured around three axes related to the research questions: (i) forecast models, (ii) hospital length-of-stay, and (iii) pediatric patients. Two authors carried out all stages to ensure the reliability of the review process. Articles that passed the initial screening had their data charted on a spreadsheet. Methods reported in the literature were classified according to the stage in which they are used in the modeling process: (i) pre-processing of data, (ii) variable selection, and (iii) cross-validation. RESULTS: Forecasting models are most often applied to newborn patients and, consequently, in neonatal intensive care units. Regression analysis is the most widely used modeling approach; techniques associated with Machine Learning are still incipient and primarily used in emergency departments to model patients in specific situations. CONCLUSIONS: The studies' main benefits include informing family members about the patient's expected discharge date and enabling hospital resources' allocation and planning. Main research gaps are associated with the lack of generalization of forecasting models and limited reported applicability in hospital management. This study also provides a practical guide to LOS-P forecasting methods and a future research agenda.
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ItemNo Preview AvailableHospital Investment Decisions in Healthcare 4.0 Technologies: Scoping Review and Framework for Exploring Challenges, Trends, and Research Directions(JMIR PUBLICATIONS, INC, 2021-08-26)BACKGROUND: Alternative approaches to analyzing and evaluating health care investments in state-of-the-art technologies are being increasingly discussed in the literature, especially with the advent of Healthcare 4.0 (H4.0) technologies or eHealth. Such investments generally involve computer hardware and software that deal with the storage, retrieval, sharing, and use of health care information, data, and knowledge for communication and decision-making. Besides, the use of these technologies significantly increases when addressed in bundles. However, a structured and holistic approach to analyzing investments in H4.0 technologies is not available in the literature. OBJECTIVE: This study aims to analyze previous research related to the evaluation of H4.0 technologies in hospitals and characterize the most common investment approaches used. We propose a framework that organizes the research associated with hospitals' H4.0 technology investment decisions and suggest five main research directions on the topic. METHODS: To achieve our goal, we followed the standard procedure for scoping reviews. We performed a search in the Crossref, PubMed, Scopus, and Web of Science databases with the keywords investment, health, industry 4.0, investment, health technology assessment, healthcare 4.0, and smart in the title, abstract, and keywords of research papers. We retrieved 5701 publications from all the databases. After removing papers published before 2011 as well as duplicates and performing further screening, we were left with 244 articles, from which 33 were selected after in-depth analysis to compose the final publication portfolio. RESULTS: Our findings show the multidisciplinary nature of the research related to evaluating hospital investments in H4.0 technologies. We found that the most common investment approaches focused on cost analysis, single technology, and single decision-maker involvement, which dominate bundle analysis, H4.0 technology value considerations, and multiple decision-maker involvement. CONCLUSIONS: Some of our findings were unexpected, given the interrelated nature of H4.0 technologies and their multidimensional impact. Owing to the absence of a more holistic approach to H4.0 technology investment decisions, we identified five promising research directions for the topic: development of economic valuation methodologies tailored for H4.0 technologies; accounting for technology interrelations in the form of bundles; accounting for uncertainties in the process of evaluating such technologies; integration of administrative, medical, and patient perspectives into the evaluation process; and balancing and handling complexity in the decision-making process.
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ItemNo Preview AvailableDesign of lean manufacturing-based strategies to improve the production process of a metalworking company(Inderscience Publishers, 2021-01-01)
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ItemNo Preview AvailableLean production teaching methods and learning assessment: a literature review(Inderscience Publishers, 2021)