Stability of Multiple Seasonal Holt-Winters Models Applied to Hourly Electricity Demand in Spain

[EN] Electricity management and production depend heavily on demand forecasts made. Any mismatch between the energy demanded with respect to that produced supposes enormous losses for the consumer. Transmission System Operators use time series-based tools to forecast accurately the future demand and set the production program. One of the most effective and highly used methods are Holt-Winters. Recently, the incorporation of the multiple seasonal Holt-Winters methods has improved the accuracy of the predictions. These forecasts, depend greatly on the parameters with which the model is constructed. The forecasters need to deal with these parameters values when operating the model. In this article, the parameters space of the multiple seasonal Holt-Winters models applied to electricity demand in Spain is analysed and discussed. The parameters stability analysis leads to forecasters better understanding the behaviour of the predictions and managing their exploitation efficiently. The analysis addresses different time windows, depending on the period of the year as well as different training set sizes. The results show the influence of the calendar effect on these parameters and if it is necessary or not to update them in order to obtain a good accuracy over time.The authors would like to thank the Spanish Ministry of Economy and Competitiveness for the support under project TIN2017-8888209C2-1-R.Trull, Ó.; García-Díaz, JC.; Troncoso, A. (2020). Stability of Multiple Seasonal Holt-Winters Models Applied to Hourly Electricity Demand in Spain. Applied Sciences. 10(7):1-16. https://doi.org/10.3390/app10072630S11610

Initialization Methods for Multiple Seasonal Holt-Winters Forecasting Models

[EN] The Holt-Winters models are one of the most popular forecasting algorithms. As well-known, these models are recursive and thus, an initialization value is needed to feed the model, being that a proper initialization of the Holt-Winters models is crucial for obtaining a good accuracy of the predictions. Moreover, the introduction of multiple seasonal Holt-Winters models requires a new development of methods for seed initialization and obtaining initial values. This work proposes new initialization methods based on the adaptation of the traditional methods developed for a single seasonality in order to include multiple seasonalities. Thus, new methods to initialize the level, trend, and seasonality in multiple seasonal Holt-Winters models are presented. These new methods are tested with an application for electricity demand in Spain and analyzed for their impact on the accuracy of forecasts. As a consequence of the analysis carried out, which initialization method to use for the level, trend, and seasonality in multiple seasonal Holt-Winters models with an additive and multiplicative trend is provided.Trull, O.; García-Díaz, JC.; Troncoso, A. (2020). Initialization Methods for Multiple Seasonal Holt-Winters Forecasting Models. Mathematics. 8(2):1-17. https://doi.org/10.3390/math8020268S11782Weron, R. (2014). Electricity price forecasting: A review of the state-of-the-art with a look into the future. International Journal of Forecasting, 30(4), 1030-1081. doi:10.1016/j.ijforecast.2014.08.008Taylor, J. W. (2003). Short-term electricity demand forecasting using double seasonal exponential smoothing. Journal of the Operational Research Society, 54(8), 799-805. doi:10.1057/palgrave.jors.2601589Taylor, J. W. (2010). Triple seasonal methods for short-term electricity demand forecasting. European Journal of Operational Research, 204(1), 139-152. doi:10.1016/j.ejor.2009.10.003Holt, C. C. (2004). Forecasting seasonals and trends by exponentially weighted moving averages. International Journal of Forecasting, 20(1), 5-10. doi:10.1016/j.ijforecast.2003.09.015Bowerman, B. L., Koehler, A., & Pack, D. J. (1990). Forecasting time series with increasing seasonal variation. Journal of Forecasting, 9(5), 419-436. doi:10.1002/for.3980090502Initializing the Holt–Winters Methodhttps://robjhyndman.com/hyndsight/hw-initialization/Rasmussen, R. (2004). On time series data and optimal parameters. Omega, 32(2), 111-120. doi:10.1016/j.omega.2003.09.013Trull, Ó., García-Díaz, J., & Troncoso, A. (2019). Application of Discrete-Interval Moving Seasonalities to Spanish Electricity Demand Forecasting during Easter. Energies, 12(6), 1083. doi:10.3390/en12061083Segura, J. V., & Vercher, E. (2001). A spreadsheet modeling approach to the Holt–Winters optimal forecasting. European Journal of Operational Research, 131(2), 375-388. doi:10.1016/s0377-2217(00)00062-xMakridakis, S., & Hibon, M. (1991). Exponential smoothing: The effect of initial values and loss functions on post-sample forecasting accuracy. International Journal of Forecasting, 7(3), 317-330. doi:10.1016/0169-2070(91)90005-gWilliams, D. W., & Miller, D. (1999). Level-adjusted exponential smoothing for modeling planned discontinuities. International Journal of Forecasting, 15(3), 273-289. doi:10.1016/s0169-2070(98)00083-

Bi-objective optimization of a multihead weighing process

[EN] A multihead weighing process is a packaging technology that can be of strategic importance to a company, as it can be a key to competitive advantage in the modern food industry. The improvement in the process quality and sensory quality of food packaged in a multihead weighing process investigated in this paper is relevant to industrial engineering. A bi-objective ad hoc algorithm based on explicit enumeration for the packaging processes in multihead weighers with an unequal supply of the product to the weighing hoppers is developed. The algorithm uses an a priori strategy to generate Pareto-optimal solutions and select a subset of hoppers from the set of available ones in each packing operation. The relative importance of both aforementioned objectives is dynamically managed and adjusted. The numerical experiments are provided to illustrate the performance of the proposed algorithm and find the optimum operational conditions for the process.García-Díaz, JC.; Pulido-Rojano, A.; Giner-Bosch, V. (2017). Bi-objective optimization of a multihead weighing process. European J of Industrial Engineering. 11(3):403-423. doi:10.1504/EJIE.2017.084882S40342311

Anxiety towards Statistics and Its Relationship with Students' Attitudes and Learning Approach

[EN] Many university students have difficulties when facing statistics related tasks, leading to an increase in their levels of anxiety and poor performance. Researchers have identified negative attitudes towards statistics, which have been shaped through students' secondary education experience, as a major driver for their failure. In this study we want to uncover the causal recipes of attitudes leading to high and low levels of anxiety in secondary education students, and the role that the learning approach plays in these relationships. We used fuzzy sets comparative qualitative analysis (fsQCA) in a sample of 325 students surveyed on the multifactorial scale of attitudes toward statistics (MSATS) and the revised two factor study process questionnaire (R-SPQ-2F). The results indicate that, respectively, a high or a low level of self-confidence is the most important and a sufficient condition by itself for achieving a low or a high level of anxiety, while the learning approaches and other attitudes are only present in other causal combinations that represent a small number of cases.Peiró Signes, A.; Trull, O.; Segarra-Oña, M.; García-Díaz, JC. (2021). Anxiety towards Statistics and Its Relationship with Students' Attitudes and Learning Approach. Behavioral Sciences. 11(3):1-13. https://doi.org/10.3390/bs11030032S11311

Packaging Process Optimization in MultiheadWeighers with Double-Layered Upright and Diagonal Systems

[EN] In multihead weighers, packaging processes seek to find the best combination of passage hoppers whose product content provides a total package weight as close as possible to its (nominal) label weight. The weighing hoppers arranged in these machines dispense the product quantity that each package contains through computer algorithms designed and executed for this purpose. For its part, in the packaging process for double-layered multihead weighers, all hoppers are arranged in two levels. The first layer comprises a group of weighing hoppers, and the second comprises a set of booster hoppers placed uprightly or diagonally to each weighing hopper based on design of the machine. In both processes, the initial machine configuration is the same; however, the hopper selection algorithm works differently. This paper proposes a new packaging process optimization algorithm for double-layer upright and diagonal machines, wherein the hopper subset combined has previously been defined, and the packaging weight is expressed as actual values. As part of its validation, product filling strategies were implemented for weighing hoppers to assess the algorithm in different scenarios. Results from the process performance metrics prove that the new algorithm improves processes by reducing variability. In addition, results reveal that some machine configurations were also able to improve their operation.We express our gratitude for the support from Universidad Simon Bolivar, and Universitat Politecnica de Valencia.Garcia-Jimenez, R.; García-Díaz, JC.; Pulido-Rojano, ADJ. (2021). Packaging Process Optimization in MultiheadWeighers with Double-Layered Upright and Diagonal Systems. Mathematics. 9(9):1-20. https://doi.org/10.3390/math9091039S1209

Attitudes towards statistics in secondary education: Findings from fsQCA

[EN] Students report a high degree of anxiety and reduced self-confidence when facing statistical subjects, especially in secondary education. This anxiety turns into poor academic performance. Most studies have used linear models for studying the interrelation between different attitudes and proving their impact on performance or related variables. This study uses a different approach to explain and better understand the causal patterns of factors stimulating lower levels of anxiety in students when facing statistics in secondary education. We employed the Multi-factorial Scale of Attitudes Toward Statistics (MSATS) and fuzzy-set qualitative comparative analysis (fsQCA) on a sample of 95 secondary school students in Spain. We identified the recipes or causal combination of factors, leading to low and high levels of anxiety. The results indicate that self-confidence and motivation are important factors in these recipes, but there is no single necessary condition that ensures lower levels of anxiety.Peiró Signes, A.; Trull, Ó.; Segarra-Oña, M.; García-Díaz, JC. (2020). Attitudes towards statistics in secondary education: Findings from fsQCA. Mathematics. 8(5):1-17. https://doi.org/10.3390/math8050804S11785Gal, I., & Ginsburg, L. (1994). The Role of Beliefs and Attitudes in Learning Statistics: Towards an Assessment Framework. Journal of Statistics Education, 2(2). doi:10.1080/10691898.1994.11910471Cashin, S. E., & Elmore, P. B. (2005). The Survey of Attitudes Toward Statistics Scale: A Construct Validity Study. Educational and Psychological Measurement, 65(3), 509-524. doi:10.1177/0013164404272488Garfield, J., & Ben-Zvi, D. (2007). How Students Learn Statistics Revisited: A Current Review of Research on Teaching and Learning Statistics. International Statistical Review, 75(3), 372-396. doi:10.1111/j.1751-5823.2007.00029.xChiesi, F., & Primi, C. (2009). Assessing statistics attitudes among college students: Psychometric properties of the Italian version of the Survey of Attitudes toward Statistics (SATS). Learning and Individual Differences, 19(2), 309-313. doi:10.1016/j.lindif.2008.10.008Beins, B. (1985). Teaching the Relevance of Statistics through Consumer-Oriented Research. Teaching of Psychology, 12(3), 168-169. doi:10.1207/s15328023top1203_16Rojo Robas, V., Madariaga, J. M., & Villarroel, J. D. (2020). Secondary Education Students’ Beliefs about Mathematics and Their Repercussions on Motivation. Mathematics, 8(3), 368. doi:10.3390/math8030368Roberts, D. M., & Bilderback, E. W. (1980). Reliability and Validity of a Statistics Attitude Survey. Educational and Psychological Measurement, 40(1), 235-238. doi:10.1177/001316448004000138Wise, S. L. (1985). The Development and Validation of a Scale Measuring Attitudes toward Statistics. Educational and Psychological Measurement, 45(2), 401-405. doi:10.1177/001316448504500226Schau, C., Stevens, J., Dauphinee, T. L., & Vecchio, A. D. (1995). The Development and Validation of the Survey of Antitudes toward Statistics. Educational and Psychological Measurement, 55(5), 868-875. doi:10.1177/0013164495055005022Roberts, D. M., & Saxe, J. E. (1982). Validity of a Statistics Attitude Survey: A Follow-Up Study. Educational and Psychological Measurement, 42(3), 907-912. doi:10.1177/001316448204200326ZEIDNER, M. (1991). STATISTICS AND MATHEMATICS ANXIETY IN SOCIAL SCIENCE STUDENTS: SOME INTERESTING PARALLELS. British Journal of Educational Psychology, 61(3), 319-328. doi:10.1111/j.2044-8279.1991.tb00989.xBaloğlu, M. (2003). Individual differences in statistics anxiety among college students. Personality and Individual Differences, 34(5), 855-865. doi:10.1016/s0191-8869(02)00076-4Schram, C. M. (1996). A Meta-Analysis of Gender Differences in Applied Statistics Achievement. Journal of Educational and Behavioral Statistics, 21(1), 55-70. doi:10.3102/10769986021001055Clute, P. S. (1984). Mathematics Anxiety, Instructional Method, and Achievement in a Survey Course in College Mathematics. Journal for Research in Mathematics Education, 15(1), 50. doi:10.2307/748987Froelich, A. G., Stephenson, W. R., & Duckworth, W. M. (2008). Assessment of Materials for Engaging Students in Statistical Discovery. Journal of Statistics Education, 16(2). doi:10.1080/10691898.2008.11889561Onwuegbuzie, A. J., & Daley, C. E. (1999). Perfectionism and statistics anxiety. Personality and Individual Differences, 26(6), 1089-1102. doi:10.1016/s0191-8869(98)00214-1Benson, J. (1989). Structural Components of Statistical Test Anxiety in Adults. The Journal of Experimental Education, 57(3), 247-261. doi:10.1080/00220973.1989.10806509CARMONA, J. (2005). MATHEMATICAL BACKGROUND AND ATTITUDES TOWARD STATISTICS IN A SAMPLE OF SPANISH COLLEGE STUDENTS. Psychological Reports, 97(5), 53. doi:10.2466/pr0.97.5.53-62Macher, D., Paechter, M., Papousek, I., & Ruggeri, K. (2011). Statistics anxiety, trait anxiety, learning behavior, and academic performance. European Journal of Psychology of Education, 27(4), 483-498. doi:10.1007/s10212-011-0090-5Musch, J., & Broder, A. (1999). Test anxiety versus academic skills: A comparison of two alternative models for predicting performance in a statistics exam. British Journal of Educational Psychology, 69(1), 105-116. doi:10.1348/000709999157608Onwuegbuzie, A. J., & Seaman, M. A. (1995). The Effect of Time Constraints and Statistics Test Anxiety on Test Performance in a Statistics Course. The Journal of Experimental Education, 63(2), 115-124. doi:10.1080/00220973.1995.9943816Tremblay, P. F., Gardner, R. C., & Heipel, G. (2000). A model of the relationships among measures of affect, aptitude, and performance in introductory statistics. Canadian Journal of Behavioural Science / Revue canadienne des sciences du comportement, 32(1), 40-48. doi:10.1037/h0087099Hardy, L., & Hagtvet, K. A. (1996). Anxiety and performance: Measurement and modelling issues. Anxiety, Stress & Coping, 9(1), v-viii. doi:10.1080/10615809608249388King, N. J., Ollendick, T. H., & Gullone, E. (1991). Test anxiety in children and adolescents. Australian Psychologist, 26(1), 25-32. doi:10.1080/00050069108258829Seipp, B. (1991). Anxiety and academic performance: A meta-analysis of findings. Anxiety Research, 4(1), 27-41. doi:10.1080/08917779108248762Sesé, A., Jiménez, R., Montaño, J. J., & Palmer, A. (2015). Can Attitudes toward Statistics and Statistics Anxiety Explain Students’ Performance? // ¿Pueden las actitudes hacia la estadística y la ansiedad estadística explicar el rendimiento de los estudiantes? Revista de Psicodidactica / Journal of Psychodidactics, 20(2), 285-304. doi:10.1387/revpsicodidact.13080Mondéjar-Jiménez, J., & Vargas-Vargas, M. (2010). Determinant factors of attitude towards quantitative subjects: Differences between sexes. Teaching and Teacher Education, 26(3), 688-693. doi:10.1016/j.tate.2009.10.004Nasser, F. M. (2004). Structural Model of the Effects of Cognitive and Affective Factors on the Achievement of Arabic-Speaking Pre-service Teachers in Introductory Statistics. Journal of Statistics Education, 12(1). doi:10.1080/10691898.2004.11910717Onwuegbuzie, A. J. (2003). Modeling Statistics Achievement among Graduate Students. Educational and Psychological Measurement, 63(6), 1020-1038. doi:10.1177/0013164402250989Lalonde, R. N., & Gardner, R. C. (1993). Statistics as a second language? A model for predicting performance in psychology students. Canadian Journal of Behavioural Science/Revue canadienne des sciences du comportement, 25(1), 108-125. doi:10.1037/h0078792Onwuegbuzie, A. J. (2000). Statistics Anxiety and the Role of Self-Perceptions. The Journal of Educational Research, 93(5), 323-330. doi:10.1080/00220670009598724Katz, B. M., & Tomazic, T. J. (1988). Changing Students’ Attitudes toward Statistics through a Nonquantitative Approach. Psychological Reports, 62(2), 658-658. doi:10.2466/pr0.1988.62.2.658Vanhoof, S., Castro Sotos, A. E., Onghena, P., Verschaffel, L., Van Dooren, W., & Van den Noortgate, W. (2006). Attitudes Toward Statistics and Their Relationship with Short- and Long-Term Exam Results. Journal of Statistics Education, 14(3). doi:10.1080/10691898.2006.11910588Bandalos, D. L., Finney, S. J., & Geske, J. A. (2003). A model of statistics performance based on achievement goal theory. Journal of Educational Psychology, 95(3), 604-616. doi:10.1037/0022-0663.95.3.604Bandalos, D. L., Yates, K., & Thorndike-Christ, T. (1995). Effects of math self-concept, perceived self-efficacy, and attributions for failure and success on test anxiety. Journal of Educational Psychology, 87(4), 611-623. doi:10.1037/0022-0663.87.4.611Schutz, P. A., Drogosz, L. M., White, V. E., & Distefano, C. (1998). Prior knowledge, attitude, and strategy use in an introduction to statistics course. Learning and Individual Differences, 10(4), 291-308. doi:10.1016/s1041-6080(99)80124-1Usher, E. L., & Pajares, F. (2008). Sources of Self-Efficacy in School: Critical Review of the Literature and Future Directions. Review of Educational Research, 78(4), 751-796. doi:10.3102/0034654308321456Comas, C., Martins, J. A., Nascimento, M. M., & Estrada, A. (2017). Estudio de las Actitudes hacia la Estadística en Estudiantes de Psicología. Bolema: Boletim de Educação Matemática, 31(57), 479-496. doi:10.1590/1980-4415v31n57a23Walsh, J. J., & Ugumba-Agwunobi, G. (2002). Individual differences in statistics anxiety: the roles of perfectionism, procrastination and trait anxiety. Personality and Individual Differences, 33(2), 239-251. doi:10.1016/s0191-8869(01)00148-9Ribes Giner, G., Perelló Marín, M. R., & Pantoja Díaz, O. (2017). Revisión sistemática de literatura de las variables clave del proceso de co-creación en las instituciones de educación superior. Literature review of the key variables of the co-creation process in higher education institutions. TEC Empresarial, 11(3), 41. doi:10.18845/te.v11i3.3365Müller-Merbach, H. (2008). Knowledge management: a program for education and leadership. Knowledge Management Research & Practice, 6(4), 350-356. doi:10.1057/kmrp.2008.25Carnell, L. J. (2008). The Effect of a Student-Designed Data Collection Project on Attitudes Toward Statistics. Journal of Statistics Education, 16(1). doi:10.1080/10691898.2008.11889551Slootmaeckers, K., Kerremans, B., & Adriaensen, J. (2013). Too Afraid to Learn: Attitudes towards Statistics as a Barrier to Learning Statistics and to Acquiring Quantitative Skills. Politics, 34(2), 191-200. doi:10.1111/1467-9256.12042Elmore, P. B., & Vasu, E. S. (1986). A Model of Statistics Achievement Using Spatial Ability, Feminist Attitudes and Mathematics-Related Variables as Predictors. Educational and Psychological Measurement, 46(1), 215-222. doi:10.1177/0013164486461025Fiss, P. C. (2011). Building Better Causal Theories: A Fuzzy Set Approach to Typologies in Organization Research. Academy of Management Journal, 54(2), 393-420. doi:10.5465/amj.2011.60263120Fiss, P. C. (2007). A set-theoretic approach to organizational configurations. Academy of Management Review, 32(4), 1180-1198. doi:10.5465/amr.2007.26586092Braumoeller, B. F. (2004). Hypothesis Testing and Multiplicative Interaction Terms. International Organization, 58(04). doi:10.1017/s0020818304040251Rihoux, B. (2006). 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Monitoring and control of the multihead weighing process through a modified control chart

Modified control charts are used to monitor and control manufacturing processes which are considered to be six-sigma processes. The use of these charts is based on the idea that the cost of identifying and correcting special causes is much higher than the cost of off-target products. Therefore, the process mean is essentially acceptable as long as it is anywhere within the specification limits. These concepts are applied to the packaging process in multihead weighers. The weight of the packed product, seen as the quality characteristic to be monitored, must be as close to a specified target weight as possible and comply with applicable regulations. The packaging process was previously optimized and improved using a packaging strategy, which was evaluated through a proposed packing algorithm. In this way, a set of numerical experiments were conducted to examine the solutions generated, which were subsequently monitored.Los gráficos de control modificados se utilizan para el seguimiento y control de procesos de fabricación que son considerados como procesos seis-sigma. El uso de estos gráficos se basa en la idea de que el costo de identificar y corregir causas especiales de variación es mucho más alto que el costo de productos alejados de su valor nominal. Por lo tanto, la media del proceso es esencialmente aceptable siempre que esté dentro de los límites de especificación. Estos conceptos han sido aplicados al proceso de envasado en pesadoras multicabezal. El peso del producto envasado, visto como la característica de calidad a ser monitoreada, debe ser lo más cercano posible a un peso objetivo y cumplir con la normativa. El proceso ha sido previamente optimizado y mejorado mediante una estrategia de envasado, la cual es evaluada a través de un algoritmo de envasado. De esta manera, un conjunto de experimentos numéricos fueron realizados para examinar las soluciones generadas, las cuales son posteriormente monitoreadas

Estrategias de optimización Bi-Objetivo para el proceso de pesaje multicabezal

[EN] Multihead weighing processes are considered an important strategy in packaging companies. Multihead weighers are used for dosing a wide range of products, from granules to large products. The packaging process consists of choosing a subset of hoppers in the multihead weigher to form a product package. This paper proposes a set of filling strategies of hoppers to reduce the variability in the weight of the produced packages. The strategies are evaluated through a bi-objective optimization approach which aims to minimize the absolute difference between the target weight and the actual weight of the packages, while trying to maximize the selection of those hoppers with more time in the packaging system. In the bi-objective approach, the considered objectives are dynamically adjusted and managed in each packaging operation. In addition, the mathematical model and the packing algorithm are developed and presented. The results of the process performance parameters are obtained and analyzed to show the effectiveness of the proposed strategies. Also, conditions of minimum variability are identified and those can be used by the packaging industry where multihead weighers are used.[ES] Los procesos de pesaje multicabezal son actualmente considerados una estrategia importante en empresas de envasado. Maquinas pesadoras multicabezal son utilizadas para la dosificación de amplia gama de productos, desde granulados a productos de gran tamaño. El proceso de envasado consiste en la selección de un subconjunto de tolvas en la pesadora multicabezal para formar un paquete de producto. La presente investigación propone un conjunto de estrategias de llenado de tolvas para reducir la variabilidad en el peso de los paquetes producidos. Las estrategias son evaluadas mediante un enfoque de optimización bi-objeivo que busca minimizar la diferencia absoluta entre el peso objetivo y el peso real de los paquetes, al tiempo que intenta maximizar la selección de aquellas tolvas con mayor tiempo en el sistema de envasado. En el enfoque biobjetivo, los objetivos considerados son dinámicamente ajustados y gestionados en cada operación de envasado. Además, el modelo matemático que representa nuestro problema y el algoritmo de envasado son desarrollados y presentados. Los resultados de los parámetros de rendimiento del proceso son obtenidos y analizados como una medida de la efectividad de las estrategias propuestas. Asimismo, las condiciones de mínima variabilidad son identificadas para motivar su uso en la industria de envasado de producto en donde se utilicen máquinas de pesaje multicabezal.Pulido-Rojano, AD.; García-Díaz, JC. (2019). Estrategias de optimización Bi-Objetivo para el proceso de pesaje multicabezal. Investigación Operacional. 40(3):362-373. http://hdl.handle.net/10251/157765S36237340

Parameterization, Analysis, and Risk Management in a Comprehensive Management System with Emphasis on Energy and Performance (ISO 50001: 2018)

[EN] The future of business development relies on the effective management of risks, opportunities, and energy and water resources. Here, we evaluate the application of best practices to identify, analyze, address, monitor, and control risks and opportunities (R/O) according to ISO 31000 and 50000. Furthermore, we shed light on tools, templates, ISO guides, and international documents that contribute to classifying, identifying, formulating control, and managing R/O parameterization in a comprehensive management system model, namely CMS QHSE3+, which consists of quality (Q), health and safety (HS), environmental management (E), energy efficiency (E2), and other risk components (+) that include comprehensive biosecurity and biosafety. By focusing on the deployment of R/O-based thinking (ROBT) at strategic and operational levels, we show vulnerability reduction in CMS QHSE3+ by managing energy, efficiency, and sustainability.We express our gratitude for the support from Cajacopi Atlantico, QUARA Technology, ASTEQ Technology, Universidad Simon Bolivar, Universitat Politecnica de Valencia and to all the personnel and companies who offered us their contributions and their valuable points of view.Poveda-Orjuela, PP.; García-Díaz, JC.; Pulido-Rojano, A.; Cañón-Zabala, G. (2020). Parameterization, Analysis, and Risk Management in a Comprehensive Management System with Emphasis on Energy and Performance (ISO 50001: 2018). Energies. 13(21):1-44. https://doi.org/10.3390/en13215579S1441321SDBS Business Demography Indicatorshttps://stats.oecd.org/index.aspx?queryid=70734The World Economy on a Tightrope. 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