Optimizing the credit term decisions in supply chain finance

We study a credit term determination problem in the context of a supplier-buyer supply chain. The supplier's credit term decision is simultaneously made with its production and inventory decisions, and most importantly, it is impacted by the buyer's order quantity. We present a new game-theoretic framework to model this problem, which captures the interaction between the supplier's credit term decision and the buyer's order decision in a multi-period setting. An exact method based on nonlinear programming is implemented to obtain the optimal solutions. We apply our methodologies on a real world case. The computational results show that our approach significantly outperforms the heuristics with fixed credit terms, and either a short or a long credit term can be sub-optimal for the supplier in profitability. Our work offers the first data-driven model and solution approach that assists purchasing and supply managers to make optimal dynamic credit term decision in conjunction with production, ordering and inventory decisions in a game-theoretic setting.     

Analytics in empirical/archival financial accounting research

In this article, we describe various analytics in empirical/archival financial accounting research. We focus the discussion on research questions that are central to accounting research, analytics used to test hypotheses, and evidence. We also describe voice and text analysis, which is generating interesting new datasets and hypothesis tests, offering promising potential for future studies. Our study seeks to (1) inform business professionals about state-of-the-art analytics in accounting research and (2) trigger academics to pursue creative new research opportunities.     

Applications of business analytics in healthcare

The American healthcare system is at a crossroads, and analytics, as an organizational skill, figures to play a pivotal role in its future. As more healthcare systems capture information electronically and begin to collect more novel forms of data, such as human DNA, how will we leverage these resources and use them to improve human health at a manageable cost? In this article, we argue that analytics will play a fundamental role in the transformation of the American healthcare system. However, there are numerous challenges to the application and use of analytics: the lack of data standards, barriers to the collection of high-quality data, and a shortage of qualified personnel to conduct such analyses. There are also multiple managerial issues, such as how to get end users of electronic data to employ it consistently to improve healthcare delivery and how to manage the public reporting and sharing of data. In this article, we explore applications of analytics in healthcare, barriers and facilitators to its widespread adoption, and ways in which analytics can help us achieve the goals of the modern healthcare system: high-quality, responsive, affordable, and efficient care.     

Enabling supply chain analytics for enterprise information systems: a topic modelling literature review and future research agenda

ABSTRACT

This paper provides a literature review of the research within the framework of 1) analytics,2) supply chain management, and 3) enterprise information systems, and relate the findings to competitive enablers. The findings are used to construct a future research agenda. The methodology is a systematic two-stage approach, based on a Smart Literature review framework using topic modelling. The research agenda proposes future research within the themes of 1) context, 2) cross-functional analytics, 3) cross-planning level analytics, 4) implementation and assimilation of analytics in EIS, 5) analytics and big data for SCM, 6) managerial aspects of analytics, and 7) data and system heterogeneity.     

Wage against the machine: A generalized deep-learning market test of dataset value

How can you tell whether a particular sports dataset really adds value, particularly with regard to betting effectiveness? The method introduced in this paper provides a way for any analyst in almost any sport to attempt to determine the additional value of almost any dataset. It relies on the use of deep learning, comprehensive historical box score statistics, and the existence of betting markets. When the method is applied as an illustration to a novel dataset for the NBA, it is shown to provide more information than regular box score statistics alone, and appears to generate above-breakeven wagering profits.     

Financial crises and the composition of cross-border lending

We examine the composition and drivers of cross-border bank lending between 1995 and 2012, distinguishing between syndicated and non-syndicated loans. We show that on-balance sheet syndicated loan exposures, which account for almost one third of total cross-border loan exposures, increased during the global financial crisis due to large drawdowns on credit lines extended before the crisis. Our empirical analysis of the drivers of cross-border loan exposures in a large bilateral dataset leads to three main results. First, banks with lower levels of capital favor syndicated over other kinds of cross-border loans. Second, borrower country characteristics such as level of development, economic size, and capital account openness, are less important in driving syndicated than non-syndicated loan activity, suggesting a diversification motive for syndication. Third, information asymmetries between lender and borrower countries became more binding for both types of cross-border lending activity during the recent crisis.     

融合Google Analytics完善中小型B2C电子商务网站BI功能

本文先分析了中小型B2C电子商务网站BI功能现状,指出造成当前BI应用功能较弱的原因。在此基础上,介绍了中小型B2C电子商务网站适用的免费BI工具GoogleAnalytics的性质、获取方法以及在网站管理、网站营销和网站设计技术改进三方面的应用功能,着重介绍了"跟踪电子商务交易"功能的现实意义,为中小型B2C电子商务网站完善BI功能提供了一个低成本、低技术门槛的选择。