Using Predictive Analytics to Reduce Uncertainty in Enterprise Risk Management

Traditional economic and business forecasting about corporate credit has relied on statistics from government agencies, annual reports and financial statements. These statistics are often published with significant delay, which limits their usefulness for predicting changes in creditworthiness. Yet, a delay in responding to changes in a company’s credit rating can have significant financial and risk consequences. With the widespread adoption of search engines, social media and related information technologies, it is possible to obtain data on literally trillions of economic decisions almost the instant that they are made. In this study, we investigated the power of these online activity data combined with data on firms’ business ecosystem to predict the likelihood of counterparty credit downgrade risk. The research offers a novel approach that contributes to the fields of information systems, finance, and social science by providing new insights on the role of these data types on firms’ financial risk

Big data analytics:Computational intelligence techniques and application areas

Big Data has significant impact in developing functional smart cities and supporting modern societies. In this paper, we investigate the importance of Big Data in modern life and economy, and discuss challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explore the powerful combination of Big Data and Computational Intelligence (CI) and identify a number of areas, where novel applications in real world smart city problems can be developed by utilizing these powerful tools and techniques. We present a case study for intelligent transportation in the context of a smart city, and a novel data modelling methodology based on a biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). We further discuss various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment

A System-of-Systems Approach to Enterprise Analytics Design: Acquisition Support in the Age of Machine Learning and Artificial Intelligence

Acquisition Research Program Sponsored Report SeriesSponsored Acquisition Research & Technical ReportsSystem-of-Systems (SoS) capability emerges from the collaboration of multiple systems, which are acquired from independent organizations. Even though the systems contribute to and benefit from the larger SoS, the data analytics and decision-making about the independent system is rarely shared across the SoS stakeholders. The objective of this work is to identify how the sharing of datasets and the corresponding analytics among SoS stakeholders can lead to an improved SoS capability. Our objective is to characterize how the sharing of connected data sets may lead to deployment of different predictive (predicting an outcome from data) and prescriptive (determining a preferred strategy) analytics and lead to better decision outcomes at the SoS level. We build and demonstrate a framework for this objective based on extensive literature review and generating appropriate predictive and prescriptive methodologies that can be used for SoS analysis: Additionally, we propose to utilize machine learning techniques to predict the SoS capability achievable by sharing pertinent datasets and to prescribe the information links between systems to enable this sharing. Two case studies demonstrate the use of the framework and prospects for meeting the objective. Highlights of our study are summarized next.Approved for public release; distribution is unlimited.Approved for public release; distribution is unlimited

Grabbing the Air Force by the Tail: Applying Strategic Cost Analytics to Understand and Manage Indirect Cost Behavior

Recent and projected reductions in defense spending are forcing the military services to develop systematic approaches to identify cost reduction opportunities and better manage financial resources. In response, the Air Force along with her sister services are developing strategic approaches to reduce front-line mission resources, commonly referred to as the Tooth . However, an underemphasized contributing source of costs are mission support activities, commonly referred to as the Tail . With the tail historically representing a sizable portion of the annual Air Force budget, strategically managing cost behavior of these indirect activities has the opportunity to generate significant cost reductions. However, very little applied or academic research have focused on advancing the knowledge behind the economics of, or the analytic techniques applied to, these activities for cost management purposes. To address this concern, this dissertation investigates i) how organizations use analytic methodologies and data sources to understand and manage cost behavior, ii) how to identify underlying cost curves of concern across tail activities, iii) how to distinguish historical relationships between the tooth and tail, iv) how to improve the performance assessment of tail activities for improved resource allocation, and v) how to provide a decision support tool for tooth-to-tail policy impact analysis

Continuous maintenance and the future – Foundations and technological challenges

High value and long life products require continuous maintenance throughout their life cycle to achieve required performance with optimum through-life cost. This paper presents foundations and technologies required to offer the maintenance service. Component and system level degradation science, assessment and modelling along with life cycle ‘big data’ analytics are the two most important knowledge and skill base required for the continuous maintenance. Advanced computing and visualisation technologies will improve efficiency of the maintenance and reduce through-life cost of the product. Future of continuous maintenance within the Industry 4.0 context also identifies the role of IoT, standards and cyber security

BUSINESS INTELLIGENT AGENTS FOR ENTERPRISE APPLICATION

Fierce competition in a market increasingly crowded and frequent changes in consumer requirements are the main forces that will cause companies to change their current organization and management. One solution is to move to open architectures and virtual type, which requires addressing business methods and technologies using distributed multi-agent systems. Intelligent agents are one of the most important areas of artificial intelligence that deals with the development of hardware and software systems able to reason, learn to recognize natural language, speak, make decisions, to recognize objects in the working environment etc. Thus in this paper, we presented some aspects of smart business, intelligent agents, intelligent systems, intelligent systems models, and I especially emphasized their role in managing business processes, which have become highly complex systems that are in a permanent change to meet the requirements of timely decision making. The purpose of this paper is to prove that there is no business without using the integration Business Process Management, Web Services and intelligent agents.business intelligence, intelligent agents, intelligent systems, management, enterprise, web services

A System-of-Systems Approach to Enterprise Analytics Design: Acquisition Support in the Age of Machine Learning and Artificial Intelligence

Excerpt from the Proceedings of the Nineteenth Annual Acquisition Research SymposiumSystem-of-Systems (SoS) capability emerges from the collaboration of multiple systems, which are acquired from independent organizations. Even though the systems contribute to and benefit from the larger SoS, the data analytics and decision-making about the independent system is rarely shared across the SoS stakeholders. The objective of the research presented in this paper is to identify how the sharing of datasets and the corresponding analytics among SoS stakeholders can lead to an improved SoS capability. Our objective is to characterize how appropriate use of data sets may lead to deployment of different predictive (predicting an outcome from data) and prescriptive (determining a preferred strategy) analytics and lead to better decision outcomes at the SoS level. We build and demonstrate a framework for this objective, based on extensive literature review, which utilizes appropriate predictive and prescriptive methodologies for SoS analysis. Additionally, we propose to utilize machine learning techniques to predict the achievable SoS capability and identify sources of uncertainty derived by sharing partial datasets. A case study demonstrates the use of the framework and prospects for future improvements.Approved for public release; distribution is unlimited