For multi-site organisations, informed decision making on capital investment aimed closing the energy efficiency gap, cutting carbon emissions and improving network performance across a global site base is a complex problem. This paper presents the systematic development and implementation of a novel methodology to reach optimal energy efficiency in multi-site organisations across their network whilst reducing carbon footprint. The methodology, a Global Energy Management System, is based on the following strategic pillars: (1) Site Characterization (2) Performance Evaluation via key performance indicators and energy benchmarking (3) Energy Strategy (4) Shared learnings and dissemination. These pillars are underpinned by essential foundations: (a) Global energy team and communication forum, (b) Knowledge base at site and global level, and (c) Corporate Energy Policy. The methodology culminates with a simplified, understandable, systematic, repeatable and scalable decision support framework addressing the complexities unique to decision-making on capital investments in global multi-site organisation. A case study is presented for a multi-national corporation in the life sciences industry. The proposed approach increased the visibility of energy and related carbon emissions issues and triggered unprecedented levels of funding and support for energy efficiency measures, leading to entering the energy efficiency continuous improvement journey towards optimal network performance.
While some researchers have suggested that the self-employment (SE) sector is a haven during a financial Crisis, others believe that SE is not necessarily the desired outcome, but an indicator that the labor market is tightening for some groups. Few researchers have compared the SE sector before and after the occurrence of a significant financial Crisis, especially in developed countries. This paper analyzes the determinants of entry into self-employment during the 2008 Spanish Crisis. Using data from the Encuesta de Presupuesto Familiar (EPF), results show that although the rate of SE did not experience a significant change during this time the Crisis affected people differently based on gender, with being females more affected than males. Results also suggest differences between Comunidades Autonomas in how the self-employment sector behaved during the Crisis.
Even though machine learning (ML) applications are not novel, they have gained popularity partly due to the advance in computing processing. This study explores the adoption of ML methods in marketing applications through a bibliographic review of the period 2008-2022. In this period, the adoption of ML in marketing has grown significantly. This growth has been quite heterogeneous, varying from the use of classical methods such as artificial neural networks to hybrid methods that combine different techniques to improve results. Generally, maturity in the use of ML in marketing and increasing specialization in the type of problems that are solved were observed. Strikingly, the types of ML methods used to solve marketing problems vary wildly, including deep learning, supervised learning, reinforcement learning, unsupervised learning, and hybrid methods. Finally, we found that the main marketing problems solved with machine learning were related to consumer behavior, recommender systems, forecasting, marketing segmentation, and text analysis-content analysis.
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