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Full Agenda – Manufacturing June 19-22, 2017

  Day 1: Tuesday, June 20, 2017

All Sessions are in Room: Salon A4


Registration & Networking Breakfast • Room: Salon A


Conference Co-Chairs Welcome

Bala Deshpande

IBM Watson Cognitive Center of Competence

Jon Riley
Vice President, Digital Manufacturing
National Center for Manufacturing Sciences

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Weird Science: How to Know Your Predictive Discovery Is Not BS

"An orange used car is least likely to be a lemon." At least that's what was claimed by The Seattle Times, The Huffington Post, The New York Times, NPR, and The Wall Street Journal. However, this discovery has since been debunked as inconclusive. As data gets bigger, so does a common pitfall in the application of standard stats: Testing many predictors means taking many small risks of being fooled by randomness, adding up to one big risk. John Elder calls this issue vast search. In this keynote, PAW founder Eric Siegel will cover this issue and provide guidance on tapping data's potential without drawing false conclusions.

Eric Siegel
Founding Chair
Predictive Analytics World

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Diamond Sponsor Presentation
The New Era of B2B Growth: Moving to Analytics-based Sales

Seventy-one percent of B2B customers are at risk based on Gallup's latest research. One of the primary drivers of this is B2B salespeople and account managers are not keeping pace with the changing supplier-customer relationship. Historically, B2B salespeople and account managers were the experts, the individuals with the most specialized knowledge concerning their solutions. However, with the increasing digitalization of data and growth in data science and analytics divisions at organizations, customers are more knowledgeable than ever and are expecting more from their suppliers. B2B companies must transition from solution- and relationship-based customer-management strategies to insight- and data-based strategies that demonstrate unique business understanding and help proactively solve customer questions. John H. Fleming will discuss Gallup's latest insights and best practices from leading organizations in how they: build insight-based customer management strategies; align sales, account management and analytics functions; and generate unique insights through advanced analytics, alternative data and effective customer collaboration.

John H. Fleming
Chief Scientist - Marketplace Practice and HumanSigma Consulting


Exhibits & Morning Coffee Break • Room: Salon A


Failure Detection, Fault Prediction, Predictive Maintenance
Case Study: Rolls Royce Company
Predictive Analytics Solution Template for Early Prediction of Assembly Line Failures

We present a novel solution template for manufacturing that combines SME and root cause analysis, data availability (failures at the end of manufacturing pipeline), and a flexible platform that decouples infrastructure (storage, data movement, visualization) from analytics engine which supports modern DS languages like R and Python. By using these components, we can leverage the existing manufacturing infrastructure and use machine learning to build advanced analytics solutions that predict failures before they happen. Early prediction of future failure devices, before they are shipped, allows repairs or even discarding that may be much cheaper than going through recall and warranty cost.

George Iordanescu
Data Scientist


Failure Detection, Fault Prediction, Predictive Maintenance
Case Study: Beet Analytics
Applying Machine Learning to Optimize Manufacturing Operation Cycle Time

Beet Analytics specializes in developing product and services that help manufacturing organizations reach unprecedented levels of system performance and process optimization. In this session Girish Rao will share how Beet has been applying machine learning in its products and services to achieve this objective.

Girish Rao
Director of Core Development
Beet Analytics

Anish Mathew

Soliton Technologies, Inc


Lunch in the Exhibit Hall • Room: Salon A


Panel Discussion
Venture Capital perspectives on Predictive Analytics in Manufacturing

Even though the Industrial Internet is taking substantial hold across the globe, relatively few venture capitalists are focused on advanced manufacturing. We have cultivated a group of venture capitalists from across the country who focus their work on deep analytics in the industrial and manufacturing world.

Adrian Fortino
Mercury Fund
Mike Major
Managing Partner
Data Point Capital

Nathan Oostendorp
Co-founder and CTO
Sight Machine

Saurabh Sharma
Jump Capital

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Solving The Curse of Dimensionality in Process Manufacturing

Often, application of analytics to process manufacturing is straightforward because of its continuous nature. However, large-scale industrial fermentation involves a series of batch reactions of increasing size. The process is flexibly designed to allow fermentation tanks to interconnect to following vessels. While the providing flexibility, it is difficult to analyzes because of the large number of combinations. This case study presents a method to analyze such a system that has more than 485,000 different combinations, but fewer than 1,300 runs. While investigation did not solve the problem, it identified areas for the process engineers to examine.

James Foster
Director of Process Development Research
Archer Daniels Midland Company

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Exhibits & Afternoon Break • Room: Salon A


Case Study: Siemens PLM
Closing the Loop with Predictive Product Performance

In this presentation, we will share customer experiences using Predictive Product Performance with IoT, manufacturing, design, and supply chain data to provide closed loop analytics capabilities. From prerequisites to lessons learned, and tangible ROI, we will cover the key influencing factors on making the most out of your analytics investments.

Richard Semmes
Senior Director, R&D
Siemens PLM

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Case Study: North Carolina State University
Gleaning Insights for Process Improvement from Textual Data

Manufacturing and other companies capture a large amount of text data in their daily operations. Invariably this data is manually entered during operations as "notes" or free-form text, creating several, unit-specific, unstructured data repositories across the organization. This repository of "notes" cannot be meaningfully utilized to glean business insights. This presentation specifically covers the free-form text used in purchase orders. A large manufacturer realized it had a challenge on its hands with its purchase order process in which free-form text was added to POs. The intent of this free-form text was to provide additional information to suppliers.

Dr. Arun Gupta

IBM Watson Cognitive Computing Center of Competence


Networking Reception

Room: Salon A

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  Day 2: Wednesday, June 21, 2017

All Sessions are in Room: Salon A4


Registration & Networking Breakfast • Room: Salon A


Conference Welcome

Bala Deshpande

IBM Watson Cognitive Center of Competence

Jon Riley
Vice President, Digital Manufacturing
National Center for Manufacturing Sciences


Artificial Intelligence: Will it make Smart Manufacturing Smarter?

In this presentation, we will address how AI can increase industrial productivity significantly and how it can power the so called 4th industrial revolution. But also look at what is meant by AI, and where does it stand in its evolution today (with examples of applications) and where it can take us, all from a manufacturing perspective.

Bala Deshpande

IBM Watson Cognitive Center of Competence

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IoT and Industrial Internet
Case Study: Honeywell International
Profit from Insight: Monetizing Data & Analytics in Today's Connected World

From automated buildings to smart cities to intelligent avionics, today’s connected world is data driven. From people to process to industrial data, an-ever smarter environment requires managing, analyzing and transforming the data into useful insight.

Join Honeywell Data & Analytics Leader Sakti Kunz to explore:

  • How can today’s businesses make sense of all the data connected devices are generating?
  • How can data be turned into financial opportunity and meaningful daily advantages?
  • How can you maximize impact while creating a fast time-to-market plan?
  • How can today’s data and analytics practitioners become proactive, connected drivers of data revenue?
Sakti Kunz
Head of Data & Analytics
Honeywell International


Exhibits & Morning Coffee Break • Room: Salon A


Collaboration 4.0

A robust and vibrant manufacturing base forms the foundation for every successful global economy, and competitive manufacturing requires implemented innovation. Data, information and analytics are core to many of the collaborations NCMS leads across the country with manufacturers of all sizes. This presentation will not only discuss a proven collaboration model to transition critical innovations, but also share a couple of examples that include the Department of Defense and a small manufacturer.

Jon Riley
Vice President, Digital Manufacturing
National Center for Manufacturing Sciences

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Lunch in the Exhibit Hall • Room: Salon A


Case Study: Usynaptics
Intermittent Fault Detection & Isolation Enables Cost Effective Readiness

Universal Synaptics is the industry leader in detecting and isolating elusive intermittent faults in compliance with the Department of Defense MIL-PRF 32516. The massive digital testing void that exists today with conventional scanning test equipment, led to the development of the patented Voyager Intermittent Fault Detectors.

Ken Anderson
Vice President
Universal Synaptics


IoT and Industrial Internet
Case Study: Schneider Electric
Reduce unplanned downtime using predictive maintenance

Industrial companies are currently facing an influx of data from the Industrial Internet of Things (IIoT), but without proper context that information is just noise. A predictive analytics solution monitors equipment and uses advanced pattern recognition and machine learning to detect problems before they occur. This empowers organizations to transition to a proactive or predictive maintenance strategy in order to reduce unscheduled downtime and increase asset reliability, availability and performance. This presentation will include examples of early warning notifications and the tens of millions of dollars of avoided costs achieved through implementing predictive maintenance strategies.

Michael Reed
Manager - Analytical Services
Schneider Electric

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Exhibits & Afternoon Break • Room: Salon A


Data Science for Manufacturing
Case study: Magnitogorsk Iron and Steel
Decreasing Steelmaking Costs with Machine Leaning: A Practical Case Study

Sergey will present data analysis initiatives at Magnitogorsk Iron & Steel Works (MMK), including the project "Sniper" Carried out together with Yandex Data Factory, it is aimed at producing steel with the target chemical composition at the lowest possible cost. Based on the analysis of the data on more than 200000 smeltings, a machine learning based recommender model was built to provide operators with real-time recommendations on the use of ferroalloys and additional materials. On average, the service helps reducing the usage of ferroalloys by an average of 5%, which equates to annual savings of more than $4 million.

Sergey Sulimov
Non-Executive Director
Magnitogorsk Iron and Steel Works OJSC


Case Study: Jivoo, Inc.
Advancing Hydroponics Through IoT Analytics

In this session we'll review a case study of smart hydroponics - how we created a connected farm, the data we collected, and the analysis performed to improve yields and make a better product.

Steven Fowler

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