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- Creators: Arora, Hina
- Creators: Runger, George C.
Description
Supply chains are increasingly complex as companies branch out into newer products and markets. In many cases, multiple products with moderate differences in performance and price compete for the same unit of demand. Simultaneous occurrences of multiple scenarios (competitive, disruptive, regulatory, economic, etc.), coupled with business decisions (pricing, product introduction, etc.) can drastically change demand structures within a short period of time. Furthermore, product obsolescence and cannibalization are real concerns due to short product life cycles. Analytical tools that can handle this complexity are important to quantify the impact of business scenarios/decisions on supply chain performance. Traditional analysis methods struggle in this environment of large, complex datasets with hundreds of features becoming the norm in supply chains. We present an empirical analysis framework termed Scenario Trees that provides a novel representation for impulse and delayed scenario events and a direction for modeling multivariate constrained responses. Amongst potential learners, supervised learners and feature extraction strategies based on tree-based ensembles are employed to extract the most impactful scenarios and predict their outcome on metrics at different product hierarchies. These models are able to provide accurate predictions in modeling environments characterized by incomplete datasets due to product substitution, missing values, outliers, redundant features, mixed variables and nonlinear interaction effects. Graphical model summaries are generated to aid model understanding. Models in complex environments benefit from feature selection methods that extract non-redundant feature subsets from the data. Additional model simplification can be achieved by extracting specific levels/values that contribute to variable importance. We propose and evaluate new analytical methods to address this problem of feature value selection and study their comparative performance using simulated datasets. We show that supply chain surveillance can be structured as a feature value selection problem. For situations such as new product introduction, a bottom-up approach to scenario analysis is designed using an agent-based simulation and data mining framework. This simulation engine envelopes utility theory, discrete choice models and diffusion theory and acts as a test bed for enacting different business scenarios. We demonstrate the use of machine learning algorithms to analyze scenarios and generate graphical summaries to aid decision making.
ContributorsShinde, Amit (Author) / Runger, George C. (Thesis advisor) / Montgomery, Douglas C. (Committee member) / Villalobos, Rene (Committee member) / Janakiram, Mani (Committee member) / Arizona State University (Publisher)
Created2012
Description
This thesis, through a thorough literature and content review, discusses the various ways that data analytics and supply chain management intersect. Both fields have been around for a while, but are incredibly aided by the information age we live in today. Today's ERP systems and supply chain software packages use advanced analytic techniques and algorithms to optimize every aspect of supply chain management. This includes aspects like inventory optimization, portfolio management, network design, production scheduling, fleet planning, supplier evaluation, and others. The benefit of these analytic techniques is a reduction in costs as well as an improvement in overall supply chain performance and efficiencies. The paper begins with a short historical context on business analytics and optimization then moves on to the impact and application of analytics in the supply chain today. Following that the implications of big data are explored, along with how a company might begin to take advantage of big data and what challenges a firm may face along the way. The current tools used by supply chain professionals are then discussed. There is then a section on the most up and coming technologies; the internet of things, blockchain technology, additive manufacturing (3D printing), and machine learning; and how those technologies may further enable the successful use of analytics to improve supply chain management. Companies that do take advantage of analytics in their supply chains are sure to maintain a competitive advantage over those firms that fail to do so.
ContributorsCotton, Ryan Aaron (Author) / Taylor, Todd (Thesis director) / Arora, Hina (Committee member) / Department of Information Systems (Contributor) / Department of Supply Chain Management (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12