Browsing by Author "da Silveira, Giovani J. C."

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    Bullwhip effect in the oil and gas supply chain : a multi-case study
    (Elsevier, 2019-11) Zhu, Tianyuan; Balakrishnan, Jaydeep; da Silveira, Giovani J. C.
    The bullwhip effect has been extensively studied in the retail, wholesale and manufacturing industries. However, it has been rarely explored in the context of resource extraction industries such as oil and gas, despite their economic impact and distinct features. This paper investigates the factors that impact the bullwhip effect in the oil and gas supply chain using case study evidence from six companies in North America, which cover refining and marketing, exploration and production, integrated oil and gas, and drilling. For each type of company studied, the operational causes of the bullwhip effect proposed in the literature and other factors of influence are examined. The findings indicate that the existing theories of the bullwhip effect have limitations in explaining the phenomenon in the oil and gas industry. Information sharing, a widely advocated countermeasure of the bullwhip effect may not be relevant in the integrated oil and gas company. Regarding the factors that drive or mitigate the bullwhip effect in different types of companies in the oil and gas supply chain, seven propositions are developed and several additional findings are obtained. All of these results enable better understandings of the bullwhip effect in academia, oil and gas organisations and related industries, and may provide guidance for potential countermeasures in practice.
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    Compensation-based incentives, ERP, and delivery performance: analysis from production and improvement perspectives
    (Emerald, 2013) da Silveira, Giovani J. C.; Snider, Brent; Balakrishnan, Jaydeep
    Purpose – This research investigates the role of compensation-based incentives in relationships between Enterprise Resource Planning (ERP) usage and delivery performance in manufacturing. Design/methodology/approach – We carry out two studies exploring links between ERP, incentives, and performance from alternative perspectives: (i) of incentives tied to regular production activities, and their relationship with delivery performance advantage over competitors, and (ii) of incentives tied to improvement activities, and their relationship with delivery performance improvements. Statistical analysis is carried out on data from 698 metal working manufacturers from 22 countries, giving a broad cross sectional view of a global industry. Findings – The studies indicate that ERP usage relates positively with both delivery advantage and delivery improvements. Furthermore, incentives tied to improvement initiatives may explain delivery improvements both directly and as moderators in the relationship between ERP and performance. Research implications – The results suggest that ERP adoption can be framed as a principal-agency phenomenon where performance outcomes are partially influenced by incentives. Practical implications – The results imply that incentives tied to improvement initiatives may foster employee engagement with the new ERP, leading to stronger delivery performance benefits. Originality/value – To the best of our knowledge, this is the first research to explore ERP usage as a principal-agency problem, and to analyse its relationships with incentives under alternative performance perspectives. The results may significantly contribute to the knowledge of ERP-performance relationships and the role of incentives.
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    Decision analysis framework for predicting no-shows to appointments using machine learning algorithms
    (2024-01-05) Deina, Carolina; Fogliatto, Flavio S.; da Silveira, Giovani J. C.; Anzanello, Michel J.
    Abstract Background No-show to medical appointments has significant adverse effects on healthcare systems and their clients. Using machine learning to predict no-shows allows managers to implement strategies such as overbooking and reminders targeting patients most likely to miss appointments, optimizing the use of resources. Methods In this study, we proposed a detailed analytical framework for predicting no-shows while addressing imbalanced datasets. The framework includes a novel use of z-fold cross-validation performed twice during the modeling process to improve model robustness and generalization. We also introduce Symbolic Regression (SR) as a classification algorithm and Instance Hardness Threshold (IHT) as a resampling technique and compared their performance with that of other classification algorithms, such as K-Nearest Neighbors (KNN) and Support Vector Machine (SVM), and resampling techniques, such as Random under Sampling (RUS), Synthetic Minority Oversampling Technique (SMOTE) and NearMiss-1. We validated the framework using two attendance datasets from Brazilian hospitals with no-show rates of 6.65% and 19.03%. Results From the academic perspective, our study is the first to propose using SR and IHT to predict the no-show of patients. Our findings indicate that SR and IHT presented superior performances compared to other techniques, particularly IHT, which excelled when combined with all classification algorithms and led to low variability in performance metrics results. Our results also outperformed sensitivity outcomes reported in the literature, with values above 0.94 for both datasets. Conclusion This is the first study to use SR and IHT methods to predict patient no-shows and the first to propose performing z-fold cross-validation twice. Our study highlights the importance of avoiding relying on few validation runs for imbalanced datasets as it may lead to biased results and inadequate analysis of the generalization and stability of the models obtained during the training stage.
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    Queueing analysis of two healthcare systems where physician activity is offloaded to supporting health professionals
    (2020-06-30) Tagimacruz, Maria Antonieta; Bischak, Diane P.; Marshall, Deborah A.; Bijvank, Marco; Brennan, Robert William; da Silveira, Giovani J. C.; Willoughby, K. A.
    Patient waiting time to see a specialist and specialist utilization are vital aspects of providing musculoskeletal (MSK) healthcare service. Long waiting times not only subject patients to physical pain and suffering but also detrimentally impacts society. This dissertation uses queuing theory to analyze three models for rheumatologists consultation involving an alternative care provider, osteoarthritis consultation involving a musculoskeletal screener, and a central intake referral system for osteoarthritis patients. The second chapter presents a queueing and simulation approach to analyze the impact of an alternative care provider in a rheumatologist consultation system on waiting time and specialist utilization. Using a multi-class closed queueing network, we look into the boundaries for workload allocation within which performance improvements are realized. Chapter three compares an osteoarthritis consultation system with and without an MSK screener modeled as a network of queues. In addition to the impact of the addition of the MSK screener, we also investigate the impact of the surgical threshold of the screener relative to that of the surgeon to the patient waiting time and surgeon’s utilization. Finally, in the fourth chapter, we model an osteoarthritis referral central intake system and explore the impact of the deterministic and probabilistic routing decisions at the central intake on surgical patient waiting time and surgeon’s utilization.
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    The use of Mass Customization to Improve Environments in Social Housing Neighbourhoods in Brazil
    (2021-01-04) Felix Dalla Vecchia, Luisa Rodrigues; Monteyne, David; Kolarevic, Branko; Brown, John L.; da Silveira, Giovani J. C.
    This research examines how mass customization can contribute to promoting better environments in social housing developments in Brazil, both within the units and for the neighbourhood. It focuses on developments of house units for the lowest income range of social housing programs seeking to propose processes and systems that could facilitate the provision of customized house units, allowing the needs of different families to be met as they change over time, without creating problems for the city. The research analyses the ecology of the system of social housing provision, from the proposal of new developments to post-occupancy renovations, in concert with the concept of mass customization, its tools and processes. From this analysis, a practical solution of how mass customization could be implemented in this context is proposed. This mass customization system considers the interests of the stakeholders, their capabilities, and the need for the least amount of changes to current policy and regulation. As part of the mass customization system, this study outlines the necessary functionality of a co-design system to be used with the families in this context. This co-design system is essential to allow the families to visualize, manipulate and validate the design of their units. From the analysis and solution design for this specific social housing context, broader conclusions are drawn contributing to the advancement of knowledge in the areas of social housing in Brazil and mass customization in housing more generally. This research shows how the concept of mass customization could bring benefits to the context of social housing neighbourhoods of the lowest income range. It also shows how a shift in perception, including post-occupancy construction as an integral part of the process of provision of social housing, could result in significantly better environments in these neighbourhoods over time. More generally, this research contributes to the field of mass customization in housing by showing that it can be advantageous for the mass customization strategy to focus on differentiating the houses post-occupancy. The research also shows that mass customization can be applied with the goal of bringing broader benefits to society by providing individual customization.