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- Application of Benford’s law to detect signs of under-invoicing in companies in the restaurant sector during the COVID-19 pandemicPublication . Martins, Ana Catarina Rodrigues; Alves, Jorge; Vaz, Clara B.The main objective of this study is to detect signs of under-invoicing by applying Benford‘s law to the Portuguese restaurant sector during the COVID-19 pandemic, in the context of government support policies. Between 2020 and 2021, the State adopted several measures to provide additional support to companies that have seen a significant decrease in their activity, namely, a reduction of at least 25% in turnover. A literature review was carried out focusing on the impact of the COVID-19 pandemic on the companies under analysis, the support measures adopted by the State and, finally, a survey of the theoretical component relating to the application of Benford‘s law in accounting. The data were collected from the Iberian Balance Sheet Analysis System database for 2019, 2020, and 2021. After analysing the data, significant deviations are observed in several digits, practically for all the compliance tests, both in the analysis of the first digit test and in the analysis of the first two digits test. The results therefore show signs of under-invoicing in 2020 by the analysed companies, which suffered, on average, a 79% reduction in turnover.
- Analysis of the effect of environmental conditions on the performance of retailing storesPublication . Vaz, Clara B.; Camanho, AnaThe assessment of performance in retailing services has gained considerable attention in recent years. The increased competition motivated the organisations to strive for efficiency in order to cut costs and deliver better customer services. This paper develops a method based on Data Envelopment Analysis (DEA) for the efficiency assessment of retailing stores. The method enables the quantification of inefficiencies taking into account the effect of exogenously factors (i.e., non-discretionary inputs and outputs). The method developed starts with the identification of the factors that affect the DMUs’ performance, including those outside the decision makers’ control. The statistical significance of the effect of the exogenous factors on performance is tested using the Kolmogorov-Smirnov test, such that only the relevant factors are considered in the DEA assessment. An enhanced DEA model is run for each store, ensuring that the definition of the efficient frontier is based exclusively on the discretionary variables, and only comparable stores are allowed as peers, i.e., stores whose area of influence is identical or less favourable than the catchment area of the store under assessment. As a result, the shape of the production possibility set is adjusted for each DMU according to the exogenous conditions where it operates. To avoid the loss of discrimination power of the DEA analysis, the choice of the peers authorized in the assessments is done allowing for trade-offs between the nondiscretionary factors (e.g., a store with more population in the surrounding area than the DMU under assessment may be allowed as peer if its level of competition is also higher). The adjustments required to the standard DEA model imply the definition of a mixed integer DEA formulation. To disentangle technical inefficiency from the effect of environmental conditions, the results of the DEA model that takes into account the environmental conditions are compared with the results of a standard DEA model including only the discretionary inputs and outputs. This analysis showed that the average efficiency estimates of the stores analysed increases when the exogenous factors are taken into account, with the efficiency values for some stores increasing up to 15%. Finally, to explore in more detail the impact of each of the non-discretionary factors (population and competition) on the performance of individual stores, a “step by step” approach was used. This approach consists of adding to the DEA model the environmental factors, one at each time (like a stepwise procedure of regression analysis). The results obtained in the successive models are analysed for each DMU to quantify the effect of each exogenous variable on store performance. The applicability of the approach developed in this paper is illustrated in the context of a real-world efficiency assessment of grocery stores. The assessment adopted an output oriented perspective, consistent with the objective of sales maximization. The managerial implications of the results obtained are explored, comparing the insights gained with the DEA analysis with the results of the approaches for managerial planning and control currently used in the organisation.
- Optimization of heat and ultrasound-assisted extraction of Eucalyptus globulus leaves reveals strong antioxidant and antimicrobial propertiesPublication . Lima, Laíres; Pereira, Ana I.; Vaz, Clara B.; Ferreira, Olga; Dias, Maria Inês; Heleno, Sandrina A.; Calhelha, Ricardo C.; Barros, Lillian; Carocho, MárcioThe extraction of phenolic compounds from eucalyptus leaves was optimized using heat and ultrasound-assisted techniques, and the bioactive potential of the resulting extract was assessed. The independent variables, including time (t), solvent concentration (S), and temperature (T) or power (P), were incorporated into a fivelevel central composite design combined with Response Surface Methodology. Phenolic content was determined by HPLC-DAD-ESI/MS and used as response criteria. The developed models were successfully fitted to the experimental data to identify the optimal extraction conditions. Heat-assisted extraction proved to be the most efficient method for phenolic recovery, yielding 27 ± 2 mg/g extract under optimal conditions (120 min, 76.5 ◦C, and 25 % ethanol, v/v). The extracts exhibited a high concentration of phenolic glycoside derivatives, including gallotannin, quercetin, and isorhamnetin. These findings suggest that the extracts hold promise as natural additives in food technology, owing to their moderate antimicrobial activity and strong antioxidant properties.
- Predicting Retail Store Transaction Patterns: A Comparison of ARIMA and Machine Learning ModelsPublication . Vaz, Clara B.; Sena, Inês; Braga, Ana Cristina; Novais, Paulo; Lima, José; Pereira, Ana I.Retail transactions represent sales of consumer goods, or final goods, by consumer companies. This sector faces security challenges due to the hustle and bustle of sales, affecting employees’ workload. In this context, it is essential to estimate the number of customers who will appear in the store daily so that companies can dynamically adjust employee schedules, aligning workforce capacity with expected demand. This can be achieved by forecasting transactions using past observations and forecasting algorithms. This study aims to compare the ARIMA time series algorithm with several Machine Learning algorithms to predict the number of daily transactions in different store patterns, considering data variability. The study identifies four typical store patterns based on these criteria using daily transaction data between 2019 and 2023 from all retail stores of the leading company in Portugal. Due to data variability and the results obtained, the algorithm that presents the most minor errors in predicting daily transactions is selected for each store. This study’s ultimate goal is to fill the gap in forecasting daily customer transactions and present a suitable forecasting model to mitigate risks associated with transactions in retail stores.
- D.R.E.A.M. App to Promote the Mental Health in Higher Education StudentsPublication . Vaz, Clara B.; Pais, Clarisse; Pinheiro, MarcoThis paper presents the development process of the mobile App D.R.E.A.M., Design-thinking to Reach-out, Embrace and Acknowledge Mental health, which is a tool for self-assessment and self-care in promoting the mental health of higher education students. In Portugal, the program for promoting Mental Health in higher education advocates the development and use of digital tools, such as apps and/or social networks and platforms, aimed at promoting wellbeing and with the potential for use to be more accessible to higher education students. The objective of this app is to promote the mental health and wellbeing of higher educa- tion students. Design Thinking was used as the methodology for building the app, which was developed using a combination of low-code/no-code tools, Flutter/Dart coding, and Google’s Firebase capabilities and database functionalities. In the first semester of the 2023/2024 academic year, 484 students downloaded the app, and 22 emails were received for psychological consultations. A dynamic update of the app is required, with modules on time management and study organization, struc- tured physical activity programs, development of socio-entrepreneurial skills, and vocational area.
- Spare parts inventory management using quantitative and qualitative classificationPublication . Oliveira, Fernanda; Vaz, Clara B.This paper focuses on the spare parts inventory management of a maintenance provider of the health sector, where the commitment to ensure the agreed customer service level and the guarantee of maximum availability of the devices are relevant issues. Spare parts inventory management means handling with unpredictable consumption, since in most cases, it is impossible to know in advance when a specific spare part will be necessary or the needed amount. Determining an adequate inventory management policy for spare parts, specifically for unplanned maintenance operations is essential to provide the contracted service level. Considering that the criticality of a spare part has consequences regarding the availability of an equipment and service level agreement, the spare parts were classified in terms of quantity, value of usage and criticality. Based on this classification, differentiated service levels and inventory management policies were adopted for each group.
- Performance assessment of wind farmsPublication . Vaz, Clara B.This study develops a methodology to provide insights regarding the performance of the wind farms of a European player in the energy sector. The focus of the wind farm performance assessment is on the operating stage which corresponds to the electrical energy generation process. Firstly, the Data Envelopment Analysis is used to measure the performance of wind farms in generating electrical energy from the resources available and exogenous variables, during the period under analysis. This analysis enables the identification of the best practices of the efficient farms which can be emulated by inefficient ones. Secondly, bootstrap procedures are applied to obtain statistical inference on the efficiency estimates. The performance assessment approach proposed will be useful for promoters in the management and control of wind farms and for the installation of wind farms in new areas, but also for repowering processes in existing farms.
- Efficiency analysis accounting for internal and external non-discretionary factorsPublication . Camanho, Ana; Portela, Maria; Vaz, Clara B.This paper develops a method based on data envelopment analysis (DEA) for efficiency assessments taking into account the effect of non-discretionary factors. A typology that classifies the non-discretionary factors into two groups is proposed: the factors that characterize the external conditions where the decision making units (DMUs) operate (external factors), and the factors that are internal to the production process but cannot be controlled by the decision makers (internal factors). This paper proposes an enhanced DEA model that accommodates non-discretionary inputs and outputs and treats them differently depending on their classification as internal or external to the production process. This generalized model integrates the previous approaches for dealing with non-discretionary variables described in the DEA literature. The model defines the efficient frontier based exclusively on the discretionary variables and internal non-discretionary factors, but the potential peers of each DMU are restricted to other units facing comparable external conditions (represented by the external nondiscretionary factors). The peer selection criteria implemented in the DEA model is informed by decision makers' opinion. The applicability of the model developed is illustrated with a real-world assessment of retailing stores.
- Livro de atas do XVI Congresso da Associação Portuguesa de Investigação OperacionalPublication . Oliveira, José F. (Ed.); Vaz, Clara B. (Ed.); Pereira, Ana I. (Ed.)
- Determinants of nursing homes performance: the case of portuguese santas casas da MisericórdiaPublication . Veloso, André Filipe Santos; Vaz, Clara B.; Alves, JorgeThis study aims to evaluate the economic efficiency of Nursing Homes owned by 96 Santas Casas da Misericórdia (SCM) and the determinants that influenced their efficiency in 2012 and 2013. The SCM are the oldest non-profit entíties, which belong to the Third Sector in Portugal, provide this social response and receive significant financial contributions annually from the state. The study is developed in two stages. In the first stage, the efficiency scores were calculated through the non-parametric DEA technique. In the second stage, Tobit regression is used to verify the effect of certain organizational variables on efficiency, namely the number of users and existence of Nursing Home chains. The results of the DEA model show that the efficiency average is 81.9%, and only 10 out of 96 Nursing Homes are efficient. Tobit regression shows that the number of users has a positive effect on the efficiency of Nursing Homes, whereas the existence of Nursing Home chains affects their efficiency negatively.