Applications of quadrivariate exponential distribution to a three-unit warm standby system with dependent structure

Two-unit warm standby systems have been elaborately dealt within the literature. However, the study of standby systems with more than two units, though very relevant in state-of-the-art practical situations, has received little attention because of mathematical intricacies involved in analyzing them. Also, such systems have been studied assuming: (i) the lifetime or repair time of the units to be exponential, or (ii) the lifetime and repair time to be independent. The present contribution is an improvement in the state-of-the-art in the sense that three-unit warm standby system with dependent structure is shown to be capable of comprehensive analysis.     

Causality in Contemporary American Sociology: An Empirical Assessment and Critique

Using a unique data set of causal usage drawn from research articles published between 2006–2008 in the American Journal of Sociology and American Sociological Review, this article offers an empirical assessment of causality in American sociology. Testing various aspects of what we consider the conventional wisdom on causality in the discipline, we find that (1) “variablistic” or “covering law” models are not the dominant way of making causal claims, (2) research methods affect but do not determine causal usage, and (3) the use of explicit causal language and the concept of “mechanisms” to make causal claims is limited. Instead, we find that metaphors and metaphoric reasoning are fundamental for causal claims‐making in the discipline. On this basis, we define three dominant causal types used in sociology today, which we label the Probabilistic, Initiating and Conditioning types. We theorize this outcome as demonstrating the primary role that cognitive models play in providing inference‐rich metaphors that allow sociologists to map causal relationships on to empirical processes.     

Modeling of Distributed Generators Resilience Considering Lifeline Dependencies During Extreme Events

This article derives distributed generators resilience models considering lifeline dependencies during extreme events. The effects on power resilience of storage capacity, fuel delays, and fuel order placements are analyzed. Results indicate that storage capacity has an important role in improving overall power supply resilience as seen by loads. In addition, the presented models provide a quantifiable approach in evaluating fuel delivery resilience. The models facilitate studying fuel scheduling policies and local fuel storage sizing for specified resilience requirements. It is observed that tank autonomy greatly affects the flexibility in employing scheduling policies supplying fuel to generators. Resilience dependence on buffer autonomy is high during the first few days of extreme events, and this could have considerable effects on managing evacuations and rescue operations.     

Offshoring Business Process Services and Governance Control Mechanisms: An Examination of Service Providers from India

As emerging markets increasingly rely on service businesses through offshore outsourcing, we examine the role of governance control mechanisms in improving performance among business process outsourcing (BPO) service providers in India. Using data collected from 205 emerging market‐based BPO service providers in India, we examine the antecedents and consequences of establishing governance control mechanisms in BPO service providers. Specifically, we examine how structural (use of contracts with the client), administrative (effective allocation and demarcation of responsibilities within the firm), and relational (collaboration and information sharing with the client) mechanisms drive the performance of a BPO service provider operating in an emerging market. We also examine how key task‐related (task connectivity and task security) and client‐related (end customer orientation and global control) antecedents influence the use of different governance control approaches in this environment. Our analysis finds that both task connectivity and task security significantly impact use of structural and administrative mechanisms, whereas end customer orientation is significantly associated with the strength of the relational mechanisms governing the emerging market‐based BPO service provider and its client. Further global control significantly influences the strength of the structural mechanisms between the client and the BPO service provider. Finally, the three mechanisms have a complementary influence in driving the BPO service provider's performance.     

Strategic Purchases of Bundled Products in a Health Care Supply Chain Environment*

Recent articles in the Wall Street Journal summarize the state of business practice in American hospitals by shedding light on the state of supply chain management practices and foci in today's health care supply chains. In health care, the single largest cost after labor is materials, and it has been documented that health care facilities can reduce the environmental impacts of the products and services they consume before regulatory problems arise or waste disposal costs increase by focusing on their upstream activities. Health care systems around the country consume significant quantities and varieties of products within the health delivery processes. Solving these environmental problems requires a much broader view involving collaborative efforts of professionals from different areas of health care to meet these challenges. The purchasing function bridges the gaps by providing a healthy dialogue on key environmental attributes within the health care supply chain. The concept of bundling new with refurbished products is gaining a lot of attention in the health care supply chain. This research describes a health care purchasing problem for bundling new and refurbished products of the type facing a growing number of large health care providers, and then proposes a methodology for evaluating the complex tradeoffs involved in bundling decisions for refurbished health care products. By exploiting some useful properties of the problem structure, our results provide buyers with useful insights for examining and selecting suppliers who are willing to offer bundles of new and refurbished products.     

Does Security Impact E‐procurement Performance? Testing a Model of Direct and Moderated Effects

Despite the widespread adoption of e‐procurement and the critical role of information security in these situations, academic research examining the relationship between information security and e‐procurement performance has been surprisingly minimal. This study represents an interdisciplinary approach to present and test a theoretical model that links security in e‐procurement processes to e‐procurement performance. E‐procurement performance is measured as a combination of cost savings, order quality, and satisfaction of fulfillment as perceived by buyers. The model also posits that two aspects of the procurement process will enhance the value of security in e‐procurement, namely, process complexity and process interdependence. We empirically examine these relationships using data collected from procurement managers. Our results have important implications for managing the e‐procurement process.     

A Data‐Driven Inventory Control Policy for Cash Logistics Operations: An Exploratory Case Study Application at a Financial Institution

A number of market changes are impacting the way financial institutions are managing their automated teller machines (ATMs). We propose a new class of adaptive data‐driven policies for a stochastic inventory control problem faced by a large financial institution that manages cash at several ATMs. Senior management were concerned that their current cash supply system to manage ATMs was inefficient and outdated, and suspected that using improved cash management could reduce overall system cost. Our task was to provide a robust procedure to tackle the ATM's cash deployment strategies. Current industry practice uses a periodic review system with infrequent parameter updates for cash management based on the assumption that demand is normally distributed during the review period. This assumption did not hold during our investigation, warranting a new and robust analysis. Moreover, we discovered that forecast errors are often not normally distributed and that these error distributions change dramatically over time. Our approach finds the optimal time series forecaster and the best‐fitting weekly forecast error distribution. The guaranteed optimal target cash inventory level and time between orders could only be obtained through an optimization module that was embedded in a simulation routine that we built for the institution. We employed an exploratory case study methodology to collect cash withdrawal data at 21 ATMs owned and operated by the financial institution. Our new approach shows a 4.6% overall cost reduction. This reflects an annual cost savings of over $250,000 for the 2,500 ATM units that are operated by the bank.     

An Efficient Heuristic Procedure for Practical-Sized Capacitated Warehouse Design and Management*

The purpose of this paper is to refine and test a distribution system design model and evaluate its performance with respect to solution quality, model validation, and algorithm performance. The paper reports on the development of a multiproduct warehouse logistics model. The logistics model is used to predict the performance of distribution companies that typically have to deal with product costs, the cost to distribute the products from a set of warehouses to customer outlets, the level of service provided to its customers, and flexibility for each product group and market segment.     

Exploring the Equivalence of Two Common Mixture Models for Duration Data

The beta-geometric (BG) distribution and the Pareto distribution of the second kind (P(II)) are two basic models for duration-time data that share some underlying characteristics (i.e., continuous mixtures of memoryless distributions), but differ in two important respects: first, the BG is the natural model to use when the event of interest occurs in discrete time, while the P(II) is the right choice for a continuous-time setting. Second, the underlying mixing distributions (the beta and gamma for the BG and P(II), respectively), are very different—and often believed to be noncomparable with each other. Despite these and other key differences, the two models are strikingly similar in terms of their fit and predictive performance as well as their parameter estimates. We explore this equivalence, both empirically and analytically, and discuss the implications from both a substantive and methodological standpoint.     

Measuring Seat Value in Stadiums and Theaters

We study how the seat value perceived by consumers attending an event in a theater/stadium depends on the location of their seat relative to the stage/field. We develop a measure of seat value, called the Seat Value Index, and relate it to seat location and consumer characteristics. We implement our analysis on a proprietary data set that a professional baseball franchise in Japan collected from its customers, and provide recommendations. For instance, we find that customers seated in symmetric seats on left and right fields might derive very different valuations from the seats. We also find that the more frequent visitors to the stadium report extreme seat value less often when compared with first‐time visitors. Our findings and insights remain robust to the effects of price and game‐related factors. Thus, our research quantifies the significant influence of seat location on the ex‐post seat value perceived by customers. Utilizing the heterogeneity in seat values at different seat locations, we provide segment‐specific pricing recommendations based on a service‐level objective that would limit the fraction of customers experiencing low seat value to a desired threshold.