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Full Agenda – Manufacturing June 9-10, 2015

  Day 1: Tuesday, June 09, 2015

8:00-8:45am

Registration & Networking Breakfast


8:45-8:50am

Conference Co-Chairs Welcome

Bala Deshpande
Founder
SimaFore

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

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Dell Software

8:50-9:10am

Diamond Sponsor Presentation
Manufacturing Intelligence: Bringing Together the Shop Floor
and the C-Suite Through Predictive Analytics


Manufacturing has long been closely measured and monitored. Operations research was the original Business Intelligence starting 50 years ago (or more). It is time to update the measurement of manufacturing and to integrate the wide range of data sources and feeds to enable a new type of integrated intelligence that empowers predictive analytics. Originating at the shop floor and continuing all the way to the executive management level, predictive analytics can predict machine maintenance, quality levels, out of control conditions, process yields and more. These types of insights are now easily within our grasp, but we need to connect and manage the data sources and flows to enable the production of these new insights and predictions. We will discuss how to make this vision a reality.

John Thompson
General Manager for Advanced Analytics
Dell Software

9:10-10:00am

Keynote
Predictive Analytics and Big Data in Manufacturing: Future perspective

Is mfg lagging behind in applying predictive analytics compared to other verticals? How can we successfully convert mfg business executives to champions of predictive analytics? We will explore the history of data in mfg and what the future holds for analytics in mfg. We will discuss the biggest contributors of data to mfg going forward and the key challenges that need to be overcome to convert these challenges into meaningful business opportunities.

Bala Deshpande
Founder
SimaFore

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10:00-10:30am

Exhibits & Morning Coffee Break


10:30-11:15am

Predictive Analytics in Manufacturing
Case Study: Archer Daniel Midlands Company
Training Manufacturing Engineers in Analytics

Analytics is a powerful tool for exploring process manufacturing unfortunately there are not enough data scientists to work on all applications. To resolve this, Archer Daniels Midland Co. decided to train statistically savvy manufacturing engineers and scientists in basics analytics. Experience data scientists ran the training which included data preparation, cleaning, and analysis with nonlinear techniques such as CART. The data scientists serves as a resource for the members. After the training, conference call occur every two weeks to discuss projects and to teach new techniques.

James Foster
Director of Process Development Research
Archer Daniels Midland Company

11:20am-12:05pm

Track 2: Online Marketing
Case Study: SmarterHQ
The Revolution in Retail Customer Intelligence

In this new era of Big Data, retailers collect data in ever-increasing volume and variety. In the midst of Big Data, a revolution is taking place in how retailers gain insights about customers, whether they interact with the brand online, in stores, or both. This session will describe the transition from reporting to data-driven decisions using predictive analytics. Success requires collecting the right data, creating informative derived attributes, making this data accessible in a timely manner, and building predictive models. Examples, drawn from real-world retailers, will include shopping cart funnel management, shopping cart abandonment, marketing attribution, churn, and purchase propensity.

Dean Abbott
Co-Founder and Chief Data Scientist
SmarterHQ

12:05-1:30pm

Lunch in the Exhibit Hall


1:30-2:15pm

KEYNOTE
Future Vehicle Technologies

While quality and safety remain critical focus areas for automakers, consumers are increasingly placing greater emphasis on the inclusion of emerging technologies in their vehicles. According to J.D. Power research, within the past year alone, there has been an 11% increase in the proportion of consumers who say that the lack of the latest technology features is one of the most important reasons why they avoided a specific model or brand. For automakers, suppliers, and technology companies to succeed in anticipating and meeting consumer demand for leading-edge technologies, they must first identify which design and technological advances consumers most prefer.

Robert Preston
Senior Manager, Global Automotive
J.D. Power

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2:15-2:35pm

Gold Sponsor Presentation
Applying Design Thinking in Predictive Analytics

Cognizant

Crisp problem definition for Analytics professionals is the key to a successful outcome. Yet, a number of analytics driven features offered to the customers do not carry sufficient value proposition. This leads to a waste of effort, time and energy not to mention the infrastructure costs and motivation.

This talk will focus on learning and experiences in applying Design Thinking framework to the front end of innovation. Specifically where IoT related cases are being developed.

Prasad Satyavol
Global Head- Innovation, Manufacturing Practice
Cognizant Technology Solutions

2:40-3:25pm

Failure Detection
Case Study: Bosch
Manufacturing Analytics at Scale: Data Mining and Machine Learning inside Bosch

Bosch is a global engineering products and services company with over 200 manufacturing sites world-wide. From anti-lock brakes, to noiseless dishwashers to gearboxes for wind turbines, Bosch manufactures a wide range of innovative products. Over the last several years, Bosch has established a centralized data mining team to leverage machine learning and 'big data' technologies in solving problems across the entire spectrum of products and operations. In this talk, I will share successful case studies from the manufacturing domain including: in-line defect reduction, test time reduction, and a centralized dashboard for globally distributed assembly lines.

Jeffrey Thompson
Senior Data Scientist
Robert Bosch, LLC

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3:25-3:55pm

Exhibits & Afternoon Break


3:55-4:40pm

Fault Prediction
Staying Ahead of Failure: Parametric Data and Analytics in High-Tech Manufacturing

High tech manufacturers generate millions of data points during the design, manufacturing, testing, and servicing of devices. Traditionally, this parametric data is analyzed by engineers reactively when issues occur, often looking at patterns in isolation. However, as devices push the bounds into new horizons of electromechanical engineering, manufacturers must also push beyond their traditional data analysis techniques and pay attention to previously unremarkable parameters to maintain high yield and to ensure rapid time to market for these cutting-edge devices. This case study shows how one manufacturer is using parametric data analysis at the component level to identify predictors.

Field Cady
Senior Data Scientist
Think Big Analytics

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4:45-5:30pm

Predictive Maintenance
Predicting Automobile Fault Codes Using Aggregated Vehicle Parameter Data

Data are collected from control units in an automobile using custom built data acquisition device and is processed to summarize normal operating conditions and to identify anomalous events. Predictive models such as Neural Networks, Logistic Regression, etc are built using this data-set to predict potential faults and their performances are compered. These are initially developed in-memory, and big data framework is used for larger data-sets. The business value includes understanding of normal operating conditions for hundreds of parameters, early detection of anomalies, and potentially alerting vehicle owners for predictive maintenance.

Vishnu Prasad
Project Leader
Soliton Technologies

Bala Deshpande
Founder
SimaFore

Mark Goerlich
Automotive Consultant


5:30-7:00pm

Networking Reception

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  Day 2: Wednesday, June 10, 2015

8:00-8:45am

Registration & Networking Breakfast


8:45-8:50am

Conference Welcome

Bala Deshpande
Founder
SimaFore

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

Datawatch

8:50-9:10am

Diamond Sponsor Presentation
Advanced Analytics for Any Data at Real-time Speed

Today, data is flowing into organizations at unheard-of speeds from a variety of sources, including streaming in motion data. This session will demonstrate how the ability to prepare, incorporate, enrich and visualize multi-structured data for advanced visual analysis is essential to make more meaningful and timely business decisions.

Dan Potter
Chief Marketing Officer
Datawatch Corporation


9:10-10:00am

Expert Panel
Machine Learning for Manufacturing: Challenges and Opportunities

Machine learning has been in the news quite a bit recently. Most of the applications are centered around the technology, e-commerce and marketing space. How can manufacturing benefit from the advancements in this area? Which are some of the natural applications of machine learning to manufacturing? What are the challenges to their successful adoption? What new opportunities are likely to emerge which are unique to manufacturing in applied machine learning? A panel of experts will debate this very interesting topic.

Terry Flood
Partner
SimaFore

Juergen Heit
Senior Research Engineer
Robert Bosch, LLC

Michael Zeller, Zementis

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10:00-10:45am

Predictive Analytics on Unstructured Data
Video and Images in Predictive Analytics: Next Frontier?

Camera is emerging as the next biggest sensor. With more than 40 mn images on Instagram and 300 mn on Facebook every day, everyone seems to be taking advantage of this democratization of "photography as a profession". Also, a clear trend is visible where embedded camera as a sensor is throwing up big possibilities in transportation (e.g., Trucking, Cars) in promoting safety, security and newer perspectives in understanding consumer behavior. Also, with Drones becoming a part of the overall execution in many industries, the significance of video analytics and its integration/leverage in business processes will become the next challenge for the Analytics professionals.

This talk will focus on experiences and learnings from cases in fusion analytics with Video/Image as the main input along with Open & Enterprise data sources.

Prasad Satyavolu
Global Head Innovation
Cognizant Technology Solutions

10:45-11:15am

Exhibits & Morning Coffee Break


11:15am-12:00pm

Big Data in Manufacturing
Utilizing On Board Technologies To Improve Maintenance Practices in Airlines

PwC Advisory Team recently developed an innovative analytical approach for one of the largest US carrier to use existing on-board technologies in aircraft to predict maintenance needs in advance and thus prevent potential maintenance delays and cancellation of the flights.

Kumar Satyam
Manager, Advisory
PricewaterhouseCoopers LLP

Alex Mannella
Principal
PricewaterhouseCoopers LLP

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12:00-1:00pm

Lunch in the Exhibit Hall



1:00-1:10pm

Lightning Round

  

1:30-2:15pm

Keynote
Big Context: Earlier Predictions with IoT Sensing and Operational Mapping

A subtle change in heat, pressure, and vibration allows us to predict machine failure three days in advance. But before those faint signals became detectable in the physical world, there were operational precursors--the root cause of the problem. 2 weeks ago, Anne ran that machine too aggressively during 1st shift. Phil failed to lubricate the machine properly last week. If we tracked and mapped these events, couldn't we predict failure days or weeks earlier? Better yet, couldn't we suggest actions to avoid failure altogether? We think so. We're combining IoT sensing and advanced mapping techniques to see cause before effect.

Julian Loren
Director, Industrial Internet Applications
GE

2:15-3:00pm

Big Data in Manufacturing
Putting Predictive Models for Applications in Manufacturing into Operational Use

Products with a strong emphasis on safety, like in the automotive industry, undergo stringent quality checks before they are released for further use. Today, different types of manufacturing and operations data are combined to create statistical models to anticipate issues and to increase product quality and production efficiency. In the context of the Internet of Things (IoT), standards-based processes and platforms are imperative to tackle today's challenges in data analytics. This talk will give a brief overview of common issues, standards, and approaches that have proven useful in our work.

Juergen Heit
Senior Research Engineer
Robert Bosch, LLC

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3:00-3:30pm

Exhibits & Afternoon Break


3:30-4:15pm

IoT and Predictive Analytics
High Performance Computing and Big Data Synergies

In the last 50 years, computing has seen tremendous changes with an impact on the computing solutions we used today. What is little known however is the fact that a lot of thishas been driven by Supercomputing (aka High Performance Computing). Often we see technologies which were first pioneered in HPC - then appear in the general domain and now in the world of Big Data. We will point out several technologies which will help Big Data solutions get implemented correctly and thus make visible the importance of infrastructure in the overall picture.

Sharan Kalwani
Computer Systems Architect
Fermi National Accelerator Labs

4:20-5:05pm

Predictive Maintenance
Predictive Analytics and Its Relationship with Prognostic and Health Management of Machinery

Anticipation of the needs for machinery maintenance and replacement is of great value to manufacturers of all types and many other businesses. Hence, the field of Prognostic and Health Management (PHM) developed over the past few decades . The PHM Society serves such work: www.phmsociety.org. Predictive Analytics (PA) has emerged in recent years [2,3], and has many similarities to PHM. The developed field of PHM and the newer field of PA share common methodologies and goals. The purpose of this study is to examine the similarities and differences between PHM of machinery and PA.

Anasse Bari
Visiting Assistant Professor of Computer Science
George Washington University

David Nagel
Research Professor
George Washington University

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Co-located with
PAW Chicago
The eMetric Summit

2015 Sponsors

Datawatch
Dell Statsoft

Cognizant

Cyient Insights
Decision Systems Inc. Predixion
SimaFore

Nutonian
Sight Machine
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Program by: Elder Research, Inc.
Elder Research, Inc.

Produced by Prediction Impact, Inc. and Rising Media, Inc.

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