A systems approach to reducing utility billing errors
Name
857790055-MIT.pdf
Description
Full printable version
Size
6.09 MB
Format
Adobe PDF
Checksum (MD5)
ab6b6741199054af3f1f086d5360545a
Author(s)
Ogura, Nori
Advisor(s)
Mort Webster and Georgia Perakis.
Date Issued
2013
Publisher
Massachusetts Institute of Technology
Abstract
Many methods for analyzing the possibility of errors are practiced by organizations who are concerned about safety and error prevention. However, in situations where the error occurrence is random and difficult to track, the rate of errors at a particular instant in time is not a practical metric of hazardous conditions (or whether a system may be vulnerable to errors). Qualitative indicators (such as stress levels) that are easier to observe, but difficult to measure, may be linked to the dynamic behavior of quantitative indicators that are easier to measure using System Dynamics models. In this work, we propose a method to find an appropriate metric for error analysis, by determining the direct quantitative triggers associated with the qualitative indicators of hazardous conditions. A System Dynamics model is generated for determining the measurable quantitative indicator behaviors linked to more apparent qualitative factors for determining the health of a system. Used in concert with other system methodologies, it gives insight into triggers and policies for developing and implementing improvement processes. The context of this research is in reducing billing errors at a utility company which for confidentiality reasons we refer to as United Energy. We use several system methodologies including System Dynamics and Safety System Analysis, to assess the billing operation system and process, to develop a project management plan for the development and implementation of a tool to reduce billing errors.
Description
Thesis (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management; and, (S.M.)--Massachusetts Institute of Technology, Engineering Systems Division; in conjunction with the Leaders for Global Operations Program at MIT, 2013.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 63-64).
Subjects
Sloan School of Management.
Engineering Systems Division.
Leaders for Global Operations Program.
MIT Department
Leaders for Global Operations Program at MIT
Massachusetts Institute of Technology. Engineering Systems Division
Sloan School of Management
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