Director, Research Science-Inventory Planning and Control
Our Peculiar Ways (and How They Benefit Science and Engineering)
Inventory Planning and Control (IPC) team owns Amazon’s global inventory planning systems. We design the Amazon fulfilment network and decide what, when, where, and how much we should buy to make our customers happy. In order to meet our business goals, we manage the flow of inventory across Amazon’s end-to-end supply chain for millions of items worth billions of dollars of inventory world-wide. The systems managing this supply chain are built entirely in-house. IPC is unique in that we are simultaneously developing the science of supply chain planning and solving some of the toughest computational challenges at Amazon. In this talk, we will first take a look at Amazon “peculiar” culture that has allowed us to focus on customer needs and drive innovation at scale. We will then discuss the culture of establishing “tenets” that reward fast experimentation and allow us to learn from mistakes. The talk will also cover the mix of traditional and non-traditional supply chain challenges that are tackled in IPC. Finally we close with discussing our vision for building the supply chain of tomorrow.
Deepak Bhatia is currently the Director of Research Science at Amazon Inventory Planning and Control (IPC) team. He joined IPC in 2011 and pioneered the adoption of automated decision making systems in the domains of Buying, Inventory Health and Assortment Planning. Deepak has over 20 years of experience working in various leadership roles and in the application of technology and quantitative sciences to different facets of Operations Management. Prior to joining Amazon, Deepak worked in various leadership roles at Applied Materials Inc. He holds a Bachelor’s degree in Mechanical Engineering from Punjab Engineering College India, a Master’s degree in Aerospace Engineering from Purdue University and a Master’s degree Management Science & Engineering from Stanford University.
Global Tech Leader
Global Supply Network Modeling and Optimization at Caterpillar’s Assurance of Supply Center
Caterpillar’s Assurance of Supply Center (ASC) combines world class supply network modeling and monitoring of past, present and future conditions throughout a global network of suppliers, assembly locations, dealers and customers. This gives Caterpillar a strategic advantage through complete situational awareness to issues that would otherwise impact complete, timely, and profitable product delivery. In this session we will take a closer look at the business questions Caterpillar’s network modeling and optimization solution is designed to address, walk through a detailed demonstration around a major world event, and describe our vision for even more powerful capabilities in the future.
Tony Grichnik brings together diverse people, technologies, and points of view to discover solutions that are greater than the sum of their parts.
His educational background includes a BS in Mechanical Engineering from Valparaiso University, and holds Christ College honors in humanities, art, and theology. He completed the New Entrepreneurs Program at the University of Chicago, and more recently completed the Supply Chain Executive’s Program at MIT’s Center for Transportation and Logistics. He is currently Caterpillar’s Visiting Scientist to Bradley University’s Intelligent Systems Laboratory, and teaches in their Masters program in Computer Science.
In his professional life, his work at Caterpillar has produced 40 US patents in a wide range of fields including manufacturing quality, supply chain modeling, signal analysis, pattern recognition, medical diagnostics, and virtual sensing. These technologies are used in the development and production of every current Caterpillar engine and transmission as well as the design of their global supply network. Several other patents support Caterpillar’s Healthy Balance® employee wellness program. Outside Caterpillar, he was one of the founders of smartenergy ltd., a start-up company developing wind energy for home heating and cogeneration.
Personal interests include sailing, virtual aircraft design, and building sports cars in his garage. He teaches applied theology to high school and college students through Redeemer Lutheran Church in Peoria, IL. He’s also a coach for the Eureka, Illinois Digital Hornets – a 4-time award-winning team in the US FIRST Robotics’ LEGO® League program for middle school students – and the founder of Eureka High School’s Combat Robotics Program.
Assistant Professor at the Industrial Engineering and Management Department – Faculty of Engineering
University of Porto
Optimizing Stores’ Delivery Time Windows At A Grocery Retail Chain
This talk will address the following topics:
-The identification of important drivers when defining stores’ delivery time windows at grocery retail chains;
– The process of building and solving an optimization model capable of assigning time windows in large retail chains;
– The difficulties in problem definition and solution validation that come with the involvement of a diverse set of stakeholders and the process designed to smoothen the transition towards the prescribed solution.In today’s high competitive retail market companies strive to reduce logistic costs to remain competitive. In this endeavor, the grocery retailer with which we have developed this project identified the definition of the stores’ delivery time windows (TW) as a major opportunity.
Luis Guimarães’s main area of activity is Operations Research/Computer Science. Most of his research is problem-driven and aims to develop advanced analytic solutions to be applied in real-world problems. In this context, he has collaborated in more than 30 industry-based research and consulting projects with various companies, including Unicer, Europac Viana, BaVidros, Sonae MC, the EDP group (EDP Production, EDP Distribution, EDP Renewables, EDP Gas) and Worten in the areas of process industry, transportation, retail and energy. Author of several publications in international journals in the field of Operations Research. He has given over 40 oral presentations in international conferences and seminars (such as Business Analytics & Operations Research, European Conference on Operational Research, Metaheuristics International Conference, International Conference on Modelling in Industrial Maintenance and Reliability, etc). He was part of a team of finalists of the Daniel H. Wagner Prize in 2016.
Vice President, Data Science
Applications of Machine Learning for Asset Failure and Prediction to Improve Supply Chain Performance
Moveable assets such as locomotives, trucks and other equipment are at the core of any supply chain. The proliferation of sensor technologies has resulted in more connected machines than ever before. This allows us to make real-time predictions on the failure rates of these machines. This talk will give an overview of the state of machine learning as applied to failure prediction for industrial and transportation equipment. It will discuss some of the challenges with current approaches, exciting theoretical advancements and some “lessons learned” from the field. This talk will discuss how supply chains can be improved by incorporating these predictions to make real-time updates and changes based on predicted failure events.
Adam McElhinney is currently the Head of Data Science at Uptake Technologies, where he leads a team of 55 Data Scientists building cutting-edge industrial data analytics tools. Additionally, Adam is an Adjunct Professor in the Computer Science Department at Illinois Institute of Technology. Formerly, he was the Head of Business Analytics and the Head of Marketing Analytics at Enova Financial where he helped grow the company from a small startup to a publicly traded online lending leader that currently employs more than 1,000 associates in the Chicagoland area. He has previously worked as a management consultant as well as an analyst designing simulations for the Department of Defense. Adam is serving his seventh year on the Board of the Chicago Chapter of the American Statistical Association. Adam holds a Masters in Statistics from University of Illinois-Chicago and has undergraduate degrees in Mathematics, Economics and Political Science from Indiana University-Bloomington.
Senior Manager, Operations Research
Aircraft Engine Maintenance Forecasting And Optimization At American Airlines
Engine maintenance represents about 40% of direct maintenance costs for major airlines. To ensure safety, support operation and minimize costs, AA developed advanced analytical models to plan and optimize the engine maintenance operation. We will talk about how we use survival analysis to forecast engine removals, simulation models to plan for spare engines and engine parts, and finally optimization models to minimize maintenance and finance costs.
Mei Zhang received her Ph.D. degree in Industrial and Systems Engineering from Georgia Institute of Technology in 1997, M.S. in Geographic Information Systems from University of Tennessee, Knoxville (UTK) in 1993, B.S. in Mathematics from UTK in 1991. Mei currently is the senior manager of Operations Research and Advanced Analytics at American Airlines supporting the Maintenance and Engineering organization. Mei and her team apply process modeling, optimization, simulation and statistical analysis methodologies to help business units to improve efficiencies in their planning and operations. Mei worked at Sabre and i2 Technology briefly before joining AA in 2001.