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Supply Chain and Logistics Institute Seminar Series

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https://hdl.handle.net/1853/70972
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The New Frontier of Data-Driven Price Optimization

2017-11-15 , Wang, He

In practice, firms are often faced with pricing challenges including high demand uncertainty, limited inventory, and restrictions to conduct price experimentation. In this talk, I will discuss models and algorithms that combine machine learning and price optimization. The key idea of these algorithms is to use real-time sales data to improve pricing decisions. I will report simulation and field experiment results that show significant revenue improvement using these methods.

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Advances in Last Mile Logistics

2017-04-26 , Erera, Alan L. , Savelsbergh, Martin W. P.

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Additive Supply Chains

2017-10-25 , Das, Suman

Additive manufacturing (AM) technologies build parts directly from digital data without any specialized or custom tooling. In the AM approach, a 3-D blueprint for an item can be downloaded from the cloud, and the item can be constructed immediately on-site, using 3-D printing equipment and feedstock materials. AM eliminates multiple time-consuming and expensive steps while significantly simplifying goods transport. AM will thus eliminate or reduce multiple supply chain tiers. The global supply base and the manufacturing landscape will be dramatically impacted through the increasing industrial adoption of AM. In this session, we will discuss the evolution of AM, some recent applications, and its impact as it relates to logistics and supply chain.

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Predictive Analytics within the Service Supply Chain

2017-03-29 , Gebraeel, Nagi

The Stewart School of Industrial and Systems Engineering at Georgia Tech established the Center for Predictive Analytics and Real-Time Optimization. (PARO). The center focuses on two main thrust areas. The first thrust area focuses on developing Predictive Analytic tools capable of synthesizing and extracting information from multi-stream sensor signals to predict future performance of complex engineering systems. The second thrust area deals with the development of real-time enhanced optimization models that compute optimal decision by leveraging the information embedded in the data. The development of modern methodologies allow for efficient updating when information changes as well as automatic model calibration using techniques from machine learning, information theory, and statistics. Housed in the Supply Chain and Logistics Institute, the Center for Predictive Analytics and Real-Time Optimization brings together experts from various disciplines. Drs. Gebraeel, Kvam, Paynabar, Pokutta, Ramudhin and Shi provide expertise in Data Mining and Statistical Analysis, Optimization, Diagnostics and Prognostics, Supply Chain, and Reliability, with domain expertise in the following industrial sectors; Automotive, Energy, Logistics, Airlines, Steel, Nanomanufacturing, Wind Power, and others. We provide various types of industries with a vehicle for addressing their problems through a single point of contact using a problem-driven approach.

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Applications of Simulation in Supply Chain Facility Analysis and Design

2017-08-23 , Goldsman, David , Alexopoulos, Christos

We will discuss the use of simulation as a tool for analyzing and improving supply chain performance. In particular, we will show how simulation can be used to (i) evaluate the effectiveness and robustness of a particular supply chain implementation, and (ii) compare the performance of competing supply chain strategies.

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Physical Internet: Concept, Research and Innovation

2017-02-22 , Montreuil, Benoit

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