An Adaptable and Scalable Cloud Based Kanban Decision Support System for Operations Engineering

dc.contributor.advisorChen, F. Frank
dc.contributor.authorKrishnaiyer, Krishnan
dc.contributor.committeeMemberWan, Hung-Da
dc.contributor.committeeMemberCastillo, Krystel K.
dc.contributor.committeeMemberXu, Kefeng
dc.creator.orcidhttps://orcid.org/0000-0002-3096-8042
dc.date.accessioned2024-02-12T14:41:39Z
dc.date.available2024-02-12T14:41:39Z
dc.date.issued2018
dc.descriptionThis item is available only to currently enrolled UTSA students, faculty or staff. To download, navigate to Log In in the top right-hand corner of this screen, then select Log in with my UTSA ID.
dc.description.abstractFor several decades, organizations in operations engineering and supply chain management have used lean manufacturing methodologies for enterprise-wide improvement. As these improvements evolve, so does the complexity and the size of data. With the ubiquity of data and the scale of machine automation, abilities for rapid decision making and handling of ever-increasing system complexity become necessary. Various applications in the literature infer Kanban as a tool to control inventory or to manage software user stories. In this research, we propose an EAT (Estimated-Actual-Total) Kanban framework that has practical use whenever dashboard-type monitoring of processes is desirable. The purpose of this investigation is to demonstrate how a cloud-based Decision Support System (DSS), combined with a robust continuous improvement methodology, can help operation managers to make efficacious decisions. The study addresses three research questions: (1) How can a robust Cloud-based Kanban Decision Support System (CKDSS) work for a service industry, particularly in scheduling and resource management? (2) How can an evolutionary algorithm, specifically Ant Colony Optimization (ACO), augment a CKDSS? (3) Can the proof of concept implementation in operations engineering be scalable to financial engineering? Preliminary Web-based Kanban Decision Support System (WKDSS) implementation shows promising results in two action research studies (1) direct mail marketing and (2) education services industry. To address the first research question, the results from the first action research demonstrate that the WKDSS helped to reduce the scheduling time from 180 minutes to three minutes, and in the education services, an operations decision support system contributed to consolidate 175 excel files into one single database. The success of two implementations lays the groundwork to address the remaining two research questions via the enhancement of a web-based system to a cloud-based system – Cloud Kanban (CK). CK is developed and implemented for a generic Service Operations Management (SOM) organization, utilizing the power and innovative cloud platform Microsoft® Azure™. CK provides the following advantages: (1) flexibility to scale up the hardware resources, (2) subscription based capital-expenditure free pay-as-you-go model, (3) automatic software updates minimizing system down-time, (4) enterprise class technology for lower Total Cost of Ownership (TCO), (5) built-in meta-heuristics for augmenting human decision support , (6) higher mobility for accesses from any internet connected device, (7) reliable business continuity with built-in backups resilient to natural disasters and power failures, (8) robust security of data, (9) strategic value with competitive advantage with the nimble management of organization’s resource demands, and (10) collaborative decision making.
dc.description.departmentMechanical Engineering
dc.format.extent197 pages
dc.format.mimetypeapplication/pdf
dc.identifier.isbn9780355957297
dc.identifier.urihttps://hdl.handle.net/20.500.12588/4137
dc.languageen
dc.subjectAction Research Operations Management
dc.subjectCloud-based Decision Support System
dc.subjectContinuous Improvement
dc.subjectKanban
dc.subjectLeanSigma
dc.subjectService Operations Management
dc.subject.classificationIndustrial engineering
dc.subject.classificationSystems science
dc.subject.classificationManagement
dc.titleAn Adaptable and Scalable Cloud Based Kanban Decision Support System for Operations Engineering
dc.typeThesis
dc.type.dcmiText
dcterms.accessRightspq_closed
thesis.degree.departmentMechanical Engineering
thesis.degree.grantorUniversity of Texas at San Antonio
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy

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