Optimal Ordering Policies for Inventory Problems With Dynamic Information Delays

Information delays exist when the most recent inventory information available to the Inventory Manager (IM) is dated. In other words, the IM observes only the inventory level that belongs to an earlier period. Such situations are not uncommon, and they arise when it takes a while to process the demand data and pass the results to the IM. We introduce dynamic information delays as a Markov process into the standard multiperiod stochastic inventory problem with backorders. We develop the concept of a reference inventory position. We show that this position along with the magnitude of the latest observed delay and the age of this observation are sufficient statistics for finding the optimal order quantities. Furthermore, we establish that the optimal ordering policy is of state‐dependent base‐stock type with respect to the reference inventory position (or state‐dependent (s, S) type if there is a fixed ordering cost). The optimal base stock and (s, S) levels depend on the magnitude of the latest observed delay and the age of this observation. Finally, we study the sensitivity of the optimal base stock and the optimal cost with respect to the sufficient statistics.     

Communication With Unknown Perspectives

Consider a group of individuals with unobservable perspectives (subjective prior beliefs) about a sequence of states. In each period, each individual receives private information about the current state and forms an opinion (a posterior belief). She also chooses a target individual and observes the target's opinion. This choice involves a trade‐off between well‐informed targets, whose signals are precise, and well‐understood targets, whose perspectives are well known. Opinions are informative about the target's perspective, so observed individuals become better understood over time. We identify a simple condition under which long‐run behavior is history independent. When this fails, each individual restricts attention to a small set of experts and observes the most informed among these. A broad range of observational patterns can arise with positive probability, including opinion leadership and information segregation. In an application to areas of expertise, we show how these mechanisms generate own field bias and large field dominance.     

The Role of NGOs in Ameliorating Sweatshop‐like Conditions in the Global Supply Chain: The Case of Fair Labor Association (FLA), and Social Accountability International (SAI)

Over the last 20+ years, globalization has made international trade and investment more efficient and productive. In the absence of coordinated global regulatory regimes, it has also made multinational corporations (MNCs) impervious to social concerns in the countries where they operate. There is considerable debate in the academic, political, and business arena as to the causes of the apparently inequitable distribution of benefits between labor and capital. Notwithstanding, the relative merits of this debate, and facing tremendous societal pressure, companies have adopted voluntary codes of conduct to ensure that workers making products for them in poor and developing countries are protected from hazardous working conditions and receive wages that meet local laws and market conditions. These codes, however, suffer from a lack of credibility and proof of substantive compliance. A new type of nongovernmental organization (NGO) has emerged that monitors a company's compliance with prespecified standards and provides assurance to the external stakeholders that the company has fulfilled its voluntary obligations. From an historical perspective, this has been an innovative phenomenon where the monitoring organization purports to act as an honest broker to ensure all concerned that the company/industry has fulfilled its obligations. In this article, we undertake a thorough examination of two such organizations, that is, Fair Labor Association, and Social Accountability International, that have played a pioneering role in bridging the gap between societal expectations and corporate performance. We examine their governance structures, operating procedures, monitoring standards, and public disclosure of findings, and above all, their success in improving the level of corporate compliance with their self‐created codes or standards. Unfortunately, our findings and conclusions are disappointing. In our view, the two groups have mostly failed to meet their avowed goals. Rather than using their NGO status to help companies improve their code compliance, they have suffered from managerial capture and have been reduced to corporate apologists.     

The Efficacy of Neural Networks in Predicting Returns on Stock and Bond Indices*

This paper uses two recently developed tests to identify neglected nonlinearity in the relationship between excess returns on four asset classes and several economic and financial variables. Having found some evidence of possible nonlinearity, it was then investigated whether the predictive power of these variables could be enhanced by using neural network models instead of linear regression or GARCH models. Some evidence of nonlinearity in the relationships between the explanatory variables and large stocks and corporate bonds was found. It was also found that the GARCH models are conditionally efficient with respect to neural network models, but the neural network models outperform GARCH models if financial performance measures are used. In resonance with the results reported for the tests for neglected nonlinearity, it was found that the neural network forecasts are conditionally efficient with respect to linear regression models for large stocks and corporate bonds, whereas the evidence is not statistically significant for small stocks and intermediate-term government bonds. This difference persists even when financial performance measures for individual asset classes are used for comparison.     

Economic Evaluation of Systems that Expedite Inventory Information

Information delays exist when the most recent inventory information available to the Inventory Manager (IM) is dated. Such situations arise when it takes a while to process the demand data, count the inventory, and pass the results to the IM. We show that the optimal total inventory‐related cost decreases when the length of the information delay decreases. The amount of the decrease is an important datum for an IM interested in considering whether or not to invest in reducing the delay. The investment is required to finance design and acquisition of an information (collection and dissemination) system that can reduce the information delay. Such systems include phone calls, business meetings, and the use of information collection mechanisms such as radiofrequency identification tags.     

Reclaiming Self

In this article I use performance poetry pieces to reflect upon my experiences of living with a disability as a person of color, a woman, and an immigrant. It is my firm contention that, although listening to the stories of the clients we meet on our journeys is integral to creating individual and structural changes and raising awareness of mental health issues, as social workers it is just as important to share our own personal and collective stories of mental health, stigma, and recovery. As helpers and change makers, it is unlikely that we can influence mental health policy purely from behind the wall of positivism. Stories of clients as well as stories of our lived experiences are critical to challenging the strong divide that characterizes the social work academy: the us/them, service provider/service user, and the expert/client. Furthermore, my hope is that this article will add to the literature concerning the traumas experienced by immigrant women in their private domains.     

Internal Migration and Rural Inequalities in India

Population Research and Policy Review - The study examines the effects of seasonal and permanent migration on rural inequalities in India. We apply the counterfactual method to estimate income...