Smart Asset Management for Electric Utilities: Big Data and Future

This paper discusses about future challenges in terms of big data and new technologies. Utilities have been collecting data in large amounts but they are hardly utilized because they are huge in amount and also there is uncertainty associated with it. Condition monitoring of assets collects large amounts of data during daily operations. The question arises "How to extract information from large chunk of data?" The concept of "rich data and poor information" is being challenged by big data analytics with advent of machine learning techniques. Along with technological advancements like Internet of Things (IoT), big data analytics will play an important role for electric utilities. In this paper, challenges are answered by pathways and guidelines to make the current asset management practices smarter for the future.Comment: 13 pages, 3 figures, Proceedings of 12th World Congress on Engineering Asset Management (WCEAM) 201

Choosing how to choose : Institutional pressures affecting the adoption of personnel selection procedures

The gap between science and practice in personnel selection is an ongoing concern of human resource management. This paper takes Oliver´s framework of organizations´ strategic responses to institutional pressures as a basis for outlining the diverse economic and social demands that facilitate or inhibit the application of scientifically recommended selection procedures. Faced with a complex network of multiple requirements, practitioners make more diverse choices in response to any of these pressures than has previously been acknowledged in the scientific literature. Implications for the science-practitioner gap are discussed

PREDICTIVE BUSINESS PROCESS MONITORINGWITH CONTEXT INFORMATION FROM DOCUMENTS

Predictive business process monitoring deals with predicting a process’s future behavior or the value of process-related performance indicators based on process event data. A variety of prototypical predictive business process monitoring techniques has been proposed by researchers in order to help process participants performing business processes better. In practical settings, these techniques have a low predictive quality that is often close to random, so that predictive business process monitoring applications are rare in practice. The inclusion of process-context data has been discussed as a way to improve the predictive quality. Existing approaches have considered only structured data as context. In this paper, we argue that process-related unstructured documents are also a promising source for extracting process-context data. Accordingly, this research-in-progress paper outlines a design-science research process for creating a predictive business process monitoring technique that utilizes context data from process-related documents to predict a process instance’s next activity more accurately

An operational support approach for Mining Unstructured Business Processes

The refined process mining framework contains a set of activities that use extracted information from event logs, discovered models and normative ones. Among these activities, we find those dealing with running events in a Structured Business Process (SBP) context, which are the Detect, the Predict and the Recommend activities. These three activities are nominated as an operational support system that aims at detecting deviations, predicting events and recommending actions. In this regard, operational support systems perform well on SBP while, it stills a challenging task for an Unstructured Business Process (UBP). This puts forward the difficulty of predicting events and recommending actions for UBP, because of its complex structure. In this context, simplification and structuring operations must be applied. Therefore, the intervention of other process mining activities is required for business process simplification and structuring. To this end, we present an operational support approach dealing with UBP, using the refined process mining framework activities

Using big data for customer centric marketing

This chapter deliberates on “big data” and provides a short overview of business intelligence and emerging analytics. It underlines the importance of data for customer-centricity in marketing. This contribution contends that businesses ought to engage in marketing automation tools and apply them to create relevant, targeted customer experiences. Today’s business increasingly rely on digital media and mobile technologies as on-demand, real-time marketing has become more personalised than ever. Therefore, companies and brands are striving to nurture fruitful and long lasting relationships with customers. In a nutshell, this chapter explains why companies should recognise the value of data analysis and mobile applications as tools that drive consumer insights and engagement. It suggests that a strategic approach to big data could drive consumer preferences and may also help to improve the organisational performance.peer-reviewe