How managers can use predictive analysis and mathematical models as decision making tools

Cet article propose une mesure simple (un modèle mathématique) évaluant la performance de différentes entités de vente (comme les vendeurs, les territoires de vente, les bureaux de vente régionaux ou l'ensemble des ventes de l'organisation) est proposée. Cette mesure est facile à estimer et elle peut facilement être comprise par les gestionnaires. De fait, elle peut être utilisé pour comparer les performances des différentes entités de vente, en tenant compte des conditions prévalant dans les différents marchés (tels que l'efficacité de la concurrence, la pénétration des ventes, ou les fluctuations du marché local). Les résultats de la mise en oeuvre de cette mesure dans une grande entreprise d'assurance dommage nord-américaine sont présentés

Adding Value to Bank Branch Performance Evaluation Using Cognitive Maps and MCDA: A Case Study

The performance evaluation of bank branches is a difficult task. One of the main reasons for this difficulty is the complexity inherent in the variety of aspects to be considered in the evaluation, and the multiple and conflicting interests of the different stakeholders involved. In this paper we aim to show how cognitive mapping and the MACBETH approach can be used to support the evaluation of bank branches through the development of multidimensional performance evaluation systems, and to deal explicitly with the trade-offs between the different dimensions of performance and interests of different stakeholders. A case study is discussed where these techniques are used in a constructive way, making the learning activity easier and introducing transparency in the decision making process. The strengths and weaknesses of the integrated use of these two operational research techniques in this context are also discussed.

Intelligent Detection and Recovery from Cyberattacks for Small and Medium-Sized Enterprises

Cyberattacks threaten continuously computer security in companies. These attacks evolve everyday, being more and more sophisticated and robust. In addition, they take advantage of security breaches in organizations and companies, both public and private. Small and Medium-sized Enterprises (SME), due to their structure and economic characteristics, are particularly damaged when a cyberattack takes place. Although organizations and companies put lots of efforts in implementing security solutions, they are not always effective. This is specially relevant for SMEs, which do not have enough economic resources to introduce such solutions. Thus, there is a need of providing SMEs with affordable, intelligent security systems with the ability of detecting and recovering from the most detrimental attacks. In this paper, we propose an intelligent cybersecurity platform, which has been designed with the objective of helping SMEs to make their systems and network more secure. The aim of this platform is to provide a solution optimizing detection and recovery from attacks. To do this, we propose the application of proactive security techniques in combination with both Machine Learning (ML) and blockchain. Our proposal is enclosed in the IASEC project, which allows providing security in each of the phases of an attack. Like this, we help SMEs in prevention, avoiding systems and network from being attacked; detection, identifying when there is something potentially harmful for the systems; containment, trying to stop the effects of an attack; and response, helping to recover the systems to a normal state

Supplier Selection and Relationship Management: An Application of Machine Learning Techniques

Managing supply chains is an extremely challenging task due to globalization, short product life cycle, and recent advancements in information technology. These changes result in the increasing importance of managing the relationship with suppliers. However, the supplier selection literature mainly focuses on selecting suppliers based on previous performance, environmental and social criteria and ignores supplier relationship management. Moreover, although the explosion of data and the capabilities of machine learning techniques in handling dynamic and fast changing environment show promising results in customer relationship management, especially in customer lifetime value, this area has been untouched in the upstream side of supply chains. This research is an attempt to address this gap by proposing a framework to predict supplier future value, by incorporating the contract history data, relationship value, and supply network properties. The proposed model is empirically tested for suppliers of public works and government services Canada. Methodology wise, this thesis demonstrates the application of machine learning techniques for supplier selection and developing effective strategies for managing relationships. Practically, the proposed framework equips supply chain managers with a proactive and forward-looking approach for managing supplier relationship

Sustainability Analysis and Environmental Decision-Making Using Simulation, Optimization, and Computational Analytics

Effective environmental decision-making is often challenging and complex, where final solutions frequently possess inherently subjective political and socio-economic components. Consequently, complex sustainability applications in the “real world” frequently employ computational decision-making approaches to construct solutions to problems containing numerous quantitative dimensions and considerable sources of uncertainty. This volume includes a number of such applied computational analytics papers that either create new decision-making methods or provide innovative implementations of existing methods for addressing a wide spectrum of sustainability applications, broadly defined. The disparate contributions all emphasize novel approaches of computational analytics as applied to environmental decision-making and sustainability analysis – be this on the side of optimization, simulation, modelling, computational solution procedures, visual analytics, and/or information technologies

Essays on Entrepreneurship in Ecuador: Assessing nonpecuniary effects of access to credit for heterogeneous entrepreneurs

This thesis aims to provide empirical evidence about heterogeneity among entrepreneurs and to explore more in depth the multidimensional concept of entrepreneurship in Ecuador. The thesis is structure in four empirical chapters. Chapter I provides an empirical framework to explore heterogeneity among enterprises and shows that microenterprises in Ecuador are highly heterogeneous. Chapter II explore the presence of mission-drift and trade-offs between social and financial. The results show vary depending on the type of microfinance institution. Chapter III explores gender differences among female and male entrepreneurs in the work-family interface. This chapter shows that female and male entrepreneurs make mostly autonomous entrepreneurial decision-making and are more likely to share decisions about household allocation resources but gender differences appear in decision-making over childbearing and child-rearing. Finally, Chapter IV includes the effect of access to credit over the satisfaction with life of entrepreneurs and shows that having access to a credit has a positive but modest effect of the life satisfaction of entrepreneurs but heterogeneity among female entrepreneurs mask the effects of microcredit programs

Measuring organisational performance using a mix of OR methods

Performance measurement has become an increasingly important issue in recent years. In spite of the remarkable progress that has been achieved in this area of research, many performance measurement initiatives fall short of their potential in supporting decision-making. This paper argues that adopting a multi-method approach to assessing performance has the potential to result in more comprehensive and effective performance measurement systems. To support this assertion, the paper discusses the development of a performance measurement system for a Business Tax Department, which combined the use of several operational research (OR) techniques including qualitative system dynamics, data envelopment analysis and multiple criteria decision analysis. The use of these OR techniques was influential in developing and implementing the performance measurement system and has the potential to be transferred to other contexts

A systematic review of empirical methods for modelling sectoral carbon emissions in China

© 2019 Elsevier Ltd A number of empirical methods have been developed to study China's sectoral carbon emissions (CSCE). Measuring these emissions is important for climate change mitigation. While several articles have reviewed specific methods, few attempts conduct a systematic analysis of all the major research methods. In total 807 papers were published on CSCE research between 1997 and 2017. The primary source of literature for this analysis was taken from the Web of Science database. Based on a bibliometric analysis using knowledge mapping with the software CiteSpace, the review identified five common families of methods: 1) environmentally-extended input-output analysis (EE-IOA), 2) index decomposition analysis (IDA), 3) econometrics, 4) carbon emission control efficiency evaluation and 5) simulation. The research revealed the main trends in each family of methods and has visualized this research into ten research clusters. In addition, the paper provides a direct comparison of all methods. The research results can help scholars quickly identify and compare different methods for addressing specific research questions