Performance evaluation of reverse logistics enterprise – an agent-based simulation approach

Reverse logistics (RL) has been applied in many industries and sectors since its conception. Unlike forward logistics, retracing consumer goods from the point of consumption to the point of inception is not a well-studied process. It involves many uncertainties such as time, quality and quantity of returns. The returned products can be remanufactured, have parts harvested, or be disposed safely. It is important to implement these activities in a cost-effective manner. The aim of this research is to measure the performance of the RL enterprise with the help of an agent-based simulation model. The major entities in the RL network are considered as Agents that can act independently. There are several different agents: collector agent, sorting-cum-reuse agent, remanufacturing agent, recycler agent, supplier agent and distributor agent. The individual performances of the agents are measured and recommendations are given to improve their performance, leading to the enhancement of the total performance of the RL enterprise. The approach is applied to a case study involving cell phone remanufacturing

Identifying system-wide contact center cost reduction opportunities through lean, customer-focused IT metrics

Thesis (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management; and, (S.M.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering; in conjunction with the Leaders for Manufacturing Program at MIT, 2009.Includes bibliographical references (p. 71-72).Dell's long-term success depends on its customers' future buying patterns. These patterns are largely determined by customers' satisfaction with the after-sales service they receive. Previously, Dell has been able to deliver high customer satisfaction but has done so at a high expense, further reducing the low margins on their consumer product line. Dell's Global Consumer Services and Support organization (GCSS) is constantly innovating to lower its operating costs while maintaining customer satisfaction. Their task is difficult to achieve in part because of the broad scope of problems that Dell's customer service agents (CSAs) tackle and the grey areas of support boundaries. In order to identify and correct the root-causes of these contact-center costs, Dell needs the ability to measure the specific cost of supporting individual customers. Yet, no such customer-centric data framework exists at Dell, or indeed in the contact center industry. However, it is possible to create just such a customer focused data framework by applying an automated value stream mapping (VSM) analysis to a large sample of contact-center activity data from Dell's data warehouse. The resulting data set is a collection of digital value stream maps representing the end-to-end customer service experience of each contact-center customer. After performing the proposed data transformations, these customer-focused metrics (CustFM) are shown to yield significant insights into previously unidentifiable cost reduction opportunities available across Dell's global contact-center network.by Avijit Sen.S.M.M.B.A

Machine learning applications in operations management and digital marketing

In this dissertation, I study how machine learning can be used to solve prominent problems in operations management and digital marketing. The primary motivation is to show that the application of machine learning can solve problems in ways that existing approaches cannot. In its entirety, this dissertation is a study of four problems—two in operations management and two in digital marketing—and develops solutions to these problems via data-driven approaches by leveraging machine learning. These four problems are distinct, and are presented in the form of individual self-containing essays. Each essay is the result of collaborations with industry partners and is of academic and practical importance. In some cases, the solutions presented in this dissertation outperform existing state-of-the-art methods, and in other cases, it presents a solution when no reasonable alternatives are available. The problems are: consumer debt collection (Chapter 3), contact center staffing and scheduling (Chapter 4), digital marketing attribution (Chapter 5), and probabilistic device matching (Chapters 6 and 7). An introduction of the thesis is presented in Chapter 1 and some basic machine learning concepts are described in Chapter 2

Spartan Daily, September 24, 1993

Volume 101, Issue 19https://scholarworks.sjsu.edu/spartandaily/8447/thumbnail.jp

Spartan Daily, May 2, 2005

Volume 124, Issue 60https://scholarworks.sjsu.edu/spartandaily/10132/thumbnail.jp

Information Outlook, October 1999

Volume 3, Issue 10https://scholarworks.sjsu.edu/sla_io_1999/1009/thumbnail.jp

Spartan Daily, May 8, 1989

Volume 92, Issue 63https://scholarworks.sjsu.edu/spartandaily/7853/thumbnail.jp

Leading in Service Innovation: Three perspectives on service value delivery in a European context

This paper explores the relationships between the shaping of “service value propositions” according to three dimensions: “intangible mix”, “physical support”, and “time”, and the strengthening of Innovative Capability in service organizations. After the first introductory section, we describe a series of related innovative moves experienced in the European context by leading companies. We analyze how JCDecaux addresses service recipients simultaneously as citizens and consumers, focusing especially on the Cyclocity project. With CS2 Lawyers in the UK, we envision how automation and technology adoption in professional services may lead to significant productivity improvement for the good of society. Finally, we study how SNCF in France has succeeded in implementing a permanently strengthening value proposition in public service through the recent launching of the IDTGV initiative. In these situations, the service companies have clearly addressed their market considering three different forms of interrelated, yet distinct, targets: “ultimate beneficiaries”, “paying bodies”, and entities or individuals who somehow “prescribe” the consumption of services. In these three situations, we investigate the robustness of the “value propositions” thus implemented, and analyze the particular role played by technology in the success of the new ventures.Service and innovation; value proposition; technology implementation

Information Outlook, November 2001

Volume 5, Issue 11https://scholarworks.sjsu.edu/sla_io_2001/1010/thumbnail.jp