Business analytics

About: Business analytics is a research topic. Over the lifetime, 3593 publications have been published within this topic receiving 84601 citations. The topic is also known as: Business Analytics & business analytics.

Business Analytics for Flexible Resource Allocation Under Random Emergencies

Abstract: In this paper, we describe both applied and analytical work in collaboration with a large multistate gas utility. The project addressed a major operational resource allocation challenge that is typical to the industry. We study the resource allocation problem in which some of the tasks are scheduled and known in advance, and some are unpredictable and have to be addressed as they appear. The utility has maintenance crews that perform both standard jobs each must be done before a specified deadline as well as respond to emergency gas leaks that occur randomly throughout the day and could disrupt the schedule and lead to significant overtime. The goal is to perform all the standard jobs by their respective deadlines, to address all emergency jobs in a timely manner, and to minimize maintenance crew overtime. We employ a novel decomposition approach that solves the problem in two phases. The first is a job scheduling phase, where standard jobs are scheduled over a time horizon. The second is a crew assignment phase, which solves a stochastic mixed integer program to assign jobs to maintenance crews under a stochastic number of future emergencies. For the first phase, we propose a heuristic based on the rounding of a linear programming relaxation formulation and prove an analytical worst-case performance guarantee. For the second phase, we propose an algorithm for assigning crews that is motivated by the structure of an optimal solution. We used our models and heuristics to develop a decision support tool that is being piloted in one of the utility's sites. Using the utility's data, we project that the tool will result in a 55% reduction in overtime hours. This paper was accepted by Noah Gans, special issue on business analytics.

Leveraging big-data for business process analytics

Abstract: Purpose – This paper aims to present a solution that enables organizations to monitor and analyse the performance of their business processes by means of Big Data technology. Business process improvement can drastically influence in the profit of corporations and helps them to remain viable. However, the use of traditional Business Intelligence systems is not sufficient to meet today ' s business needs. They normally are business domain-specific and have not been sufficiently process-aware to support the needs of process improvement-type activities, especially on large and complex supply chains, where it entails integrating, monitoring and analysing a vast amount of dispersed event logs, with no structure, and produced on a variety of heterogeneous environments. This paper tackles this variability by devising different Big-Data-based approaches that aim to gain visibility into process performance. Design/methodology/approach – Authors present a cloud-based solution that leverages (BD) technology to provid...

Visual Analytics for the Strategic Decision Making Process

Abstract: The collection and storage of huge amounts of data is no longer a challenge by itself. However, rapidly growing data repositories are creating considerable challenges in many application areas. Visualizations that worked well with a few data items now produce confusing or illegible displays. Decision-makers struggle to act based on a severely restricted understanding of the situation. The goal of Visual Analytics is to overcome this information overload and create new opportunities with these large amounts of data and information. The key challenge is to intelligently combine visualization techniques and analytic algorithms, and to enable the human expert to guide the decision making process. This chapter covers interesting and relevant previous work on situation awareness, naturalistic decision making, and decision-centred visualization. These concepts are put into the context of Visual Analytics research and are further illustrated by application examples.

IoT data visualization for business intelligence in corporate finance

Abstract: Business intelligence (BI) incorporates business research, data mining, data visualization, data tools,infrastructure, and best practices to help businesses make more data-driven choices.Business intelligence's challenging characteristics include data breaches, difficulty in analyzing different data sources, and poor data quality is consideredessential factors. In this paper, IoT-based Efficient Data Visualization Framework (IoT- EDVF) has been proposed to strengthen leaks' risk, analyze multiple data sources, and data quality management for business intelligence in corporate finance.Corporate analytics management is introduced to enhance the data analysis system's risk, and the complexity of different sources can allow accessing Business Intelligence. Financial risk analysis is implemented to improve data quality management initiative helps use main metrics of success, which are essential to the individual needs and objectives. The statistical outcomes of the simulation analysis show the increasedperformance with a lower delay response of 5ms and improved revenue analysis with the improvement of 29.42% over existing models proving the proposed framework's reliability.