Attributes of Big Data Analytics for Data-Driven Decision Making in Cyber-Physical Power Systems

Big data analytics is a virtually new term in power system terminology. This concept delves into the way a massive volume of data is acquired, processed, analyzed to extract insight from available data. In particular, big data analytics alludes to applications of artificial intelligence, machine learning techniques, data mining techniques, time-series forecasting methods. Decision-makers in power systems have been long plagued by incapability and weakness of classical methods in dealing with large-scale real practical cases due to the existence of thousands or millions of variables, being time-consuming, the requirement of a high computation burden, divergence of results, unjustifiable errors, and poor accuracy of the model. Big data analytics is an ongoing topic, which pinpoints how to extract insights from these large data sets. The extant article has enumerated the applications of big data analytics in future power systems through several layers from grid-scale to local-scale. Big data analytics has many applications in the areas of smart grid implementation, electricity markets, execution of collaborative operation schemes, enhancement of microgrid operation autonomy, management of electric vehicle operations in smart grids, active distribution network control, district hub system management, multi-agent energy systems, electricity theft detection, stability and security assessment by PMUs, and better exploitation of renewable energy sources. The employment of big data analytics entails some prerequisites, such as the proliferation of IoT-enabled devices, easily-accessible cloud space, blockchain, etc. This paper has comprehensively conducted an extensive review of the applications of big data analytics along with the prevailing challenges and solutions

Future trends in reputation management

__Abstract__ Companies must redefine their portfolio in such a way that people will perceive it as having added value for their personal lives, says RSM's Professor Cees van Riel, an expert in reputation management – and there's good reason for the world's biggest companies to believe him

Viable framework 1.0, evaluating viability of startups by using a data based framework

Most individuals have at least one business idea that they believe might be great. At the same time governments, academics and the media are also encouraging these individuals to come forth and become entrepreneurs. There is little doubt today that the entrepreneurial sector is critical for the future development and sustainability of the economy (representing 33% of global GDP and 45% of total employment). However, not all the ideas from entrepreneurs are viable. Many times, entrepreneurs do not have enough knowledge and/or skills to evaluate and assess their own business ideas during the early stage. On the other side, investors are often times swamped with hundreds of business plans to evaluate, having to allocate a large percentage of internal, but limited resources to this process often times causing them to miss interesting opportunities. How can be determined if a business venture is viable in advance? If it will be able to survive and reach success? Viable Framework 1.0 aims to provide a data based framework as a tool to evaluate the viability of early stage startups. This framework can help investors (and corporations) and entrepreneurs by creating a common language between both sides of the development process of new fast growing potential ventures. The methodology to create the framework has been structured in four steps. The first step is the literature review with the goal of analysing previous research in similar fields, defining the concepts of viability, success and survival, and collecting a comprehensive list of factors that might determine success. The second step is to shortlist the initial list of success determinants with the criteria of number of appearances in the articles, academic works and books studied in the literature review. The third step is to ask investors about their own definition of viability, success and survival, and about the importance of these collected and shortlisted determinants of success in order to compare the theoretical and the practical points of view. And the fourth step is to analyse Crunchbase, as a defacto standard database on startups in order to detect some insightful information, contrast with the factors collected in the literature review then measured by investors, and try to find predictive models using data mining and machine learning. After executing the four steps, we concluded than the theory and practice meet on the need of strong team capable to lead a brilliant execution . There is a general alignment, among all the sources analysed about the need of a complete, strong and determined team with the ability to design, plan and execute an scalable business model. The contradictions detected between literature-investor inputs and data inputs reveal the need of building better datasets in order to predict viability by looking at information from different investment stages. Other contradictions, for which we did not find a clear explanation, would require further research and that is why we Viable Framework 1.0, evaluating viability of startups by using a data based framework

A Case Study Approach for Managing Risks & Challenges When Expanding to Emerging Markets

Globalization created new opportunities and many companies decide to expand to take advantage of these opportunities to improve their competitiveness. The present study, using a case study methodology, examines the expansion of three large companies (IKEA, Coca Cola and Kellogg's) in emerging markets. Through a critical literature review and review of corporate reports, the study analyzes companies’ adopted strategies and practices, influential factors and risks when expanding abroad, providing the rationale behind their strategic choices. The study findings, applying theory into practice, indicate the factors and practices that are important to be considered by companies operating in a foreign environment in order to address business risks, and concludes that in order to be successful they have to incorporate into their strategy effective risk management policies to mitigate risks and turn challenges into opportunities. The study bridges risk management and strategy development

The significance of deepwater oil drilling for the US energy security: the case of the Gulf of Mexico

This study seeks to quantify and model the significance of the deepwater oil development for domestic US energy supplies in the short-term. It explores the significance and potential contribution deepwater oil supply from the Gulf of Mexico (GoM) can make in providing energy security to the US. The output of this research demonstrates the growth in deepwater oil production and how this latter relates to total US oil production over the next 10 years; and therefore the role it can play in providing energy security to the USA. The literature offers commercial and academic debate on this topic. The research model analyses current available data and make sensible assumptions on the likely future growth of deepwater oil production in the GoM based on a number of scenarios. Our results support the high/best case and suggest that deepwater oil from the GoM can significantly provide energy security to the US on the short term. However, on the long term and in order to maintain its energy security, the US needs to develop and use renewable sources of energy

Lean manufacturing maturity level: a case-study applied in the Brazilian auto-parts industry

Lean Manufacturing known as Toyota Production System is a systematic method of waste reduction within a manufacturing system without sacrificing its productivity, assisting on the identification and steady elimination of waste. It has started in Japan and, besides the culture differences, it has arrived in Brazil and is being successfully used in some companies with engagement and commitment through the employees. As lean manufacturing is composed by many different tools that support the sustainability of the continuous improvement and considering the difficulty in assessing the evolution of this system through the operational maturity level, it is important to have a general overview of how methods are implemented and sustained by the organization in order to reach its targets. So far, the purpose of this article was the application of a business process maturity model questionnaire in a Brazilian auto-parts manufacturing company, specializing in interior electronics, which has fourteen methods related to its business system, then ordinate them to facilitate the gap's visualization between them and the company`s target. The first step was to understand each method, adapt the Business Process Maturity Model (BPMM) to evaluate the methods maturity, and finally propose a connection between them in order to observe the gaps and help the company to develop an action plan to guarantee that the sustainability of the implementation until all the methods reaches the highest lean maturity level