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Full Agenda – Manufacturing June 17-18, 2014

  Day 1: Tuesday, June 17, 2014

8:00-8:45am • Room: Foyer

Registration & Networking Breakfast

8:45-8:50am • Room: S104AB

Conference Chairs Welcome

Speaker: Eric Siegel, President, Prediction Impact & Dr. Bala Deshpande, Founder, SimaFore

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Dell Software

8:50-9:10am • Room: S104AB

Gold Sponsor Presentation
From Quality Control and Reacting to Predictive Quality Control and Pre-Acting

Forward looking manufacturers have long recognized the value of data and statistical analysis techniques. In data-driven organizations, conversations about quality and best practices have moved from opinions and ad-hoc trouble-shooting to insights rooted in compelling results derived from empirical data. However, the exponential growth in the available data volume and velocity requires new methods, but also opens up new opportunities for using modern predictive modeling techniques to drive quality and efficiency. This presentation will review the basic difference between statistical analyses and modern predictive modeling techniques. The discussion will then explore typical use cases and approaches how to implement process monitoring and quality control for high-dimensional data, considerations for predictive quality control, and technologies and system requirements to enable a sustainable system that will generate repeatable ROI.

Speaker: Thomas Hill, Ph.D., Executive Director Analytics, Dell Software Group / StatSoft

9:10-10:00am • Room: S104AB

The Prediction Effect, the Data Effect, and the Persuasion Effect

What are the underlying principles that make predictive analytics effective? Why is data predictive, why is imperfect prediction valuable, and what type of prediction succeeds to persuade? You have heard of the butterfly, Doppler, and placebo effects. In this session, PAW founder Eric Siegel covers the Prediction, Data, and Persuasion Effects. Each of these Effects encompasses the fun part of science and technology: an intuitive hook that reveals how it works and why it succeeds.

Attendee's receive a free copy of the related book by Eric Siegel, Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die.

Speaker: Eric Siegel, Founding Chair, Predictive Analytics World

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10:00-10:30am • Room: Foyer

Exhibits & Morning Coffee Break

10:30-11:15am • Room: S102A

Case Study: National Center for Manufacturing Sciences
The Test of Time(lines): Building a Digital Manufacturing Empire

The Test of Time(lines): Building a Digital Manufacturing Empire A new society is on the horizon: a digital manufacturing empire built from the foundations of yesterday and driven by the promise of tomorrow. Viewed through the lens of time's passage, everything becomes predictable, and every past point on a timeline becomes a lesson for the future. Drawing best practices from an unlikely source, Jon Riley reveals an interdependent collective of opportunities in collaboration, manufacturing, PLM, and workforce development: key building blocks for a digital manufacturing empire built to stand the test of time.

Speaker: Jon Riley, VIce President, Digital Manufacturing, NCMS

11:20am-12:00pm • Room: S102A

Tool Wear & Failure Prediction
Case Study: UI Labs
Digital Manufacturing and Design Innovation Institute

Dr. Bartles will discuss the newly established "Digital Manufacturing and Design Innovation Institute" which is a "Public-Private Partnership" between the USG Department of Defense and a brand new startup company called UI LABS who is leading a collaborative team made up of over 40 companies, 23 universities, and several state and local governments. The USG is putting up $70 million over five years and the UI LABS' Industry/University/State & local government team is putting up $250 million over five years to do manufacturing innovation research in the area of digital manufacturing and design. A state of the art facility that can be used to showcase the results of the research and provide training and workforce development opportunities for small and medium size manufacturers will be constructed on Goose Island in downtown Chicago.

Speaker: Dean Bartles, Executive Director, Digital Manufacturing & Design Innovation Institute, UI LABS

12:00-1:10pm • Room: Foyer

Lunch in the Exhibit Hall

1:10-2:10pm • Room: S102A

Mining Big Data for Improving Launch Quality and Customer Satisfaction

Due to competitive pressure, OEM's are increasingly using global architectures to launch similar products worldwide. The customer expectation is often very different across regions making it more challenging to deliver the 'best' quality for each specific market. With vehicle features and technologies becoming more standard, OEM's must differentiate themselves by improving launch quality performance and understanding their customer's needs clearly. This session examines how the automotive big data analytics can enable competitive advantage by improving launch quality and customer satisfaction.

Speaker: Soumen De, EGM - Advanced Quality Analytics (AQA), GM Technical Center India

2:10-2:15pm • Room: S102A

Silver Sponsor Presentation
Predictive Analytics with Humans- in- Loop

The interconnected world has thrown enormous possibilities for Analytics and more specifically targeted micro/unitary level predictions. So the question is what role humans will play in the fast evolving world of Predictive Analytics? Are all decisions will be automated? Will machine learning and fusion of multi-format, multi-source data evolve to eliminate human decisions?

Where are the boundaries? Where are the limitations for everyday businesses? What about the time horizon of these decisions- Immediate, medium and long term? Can we eliminate Uncertainty since there is lesser Information deficiency or latency and the human intervention is/will be minimal?

While these innovations are sure to ring- in huge efficiencies and eliminate waste in the social and business system, it may not be possible to automate or predict all possible scenarios. How will the roles of individuals, a multitude of leaders and decision makers integrate in the bold new Predictive World?

Speaker: Prasad Satyavolu, Global Head- Innovation, Manufacturing Practice, Cognizant

2:15-2:20pm • Room: S102A

Silver Sponsor Presentation
Do you sweat your high value assets without burning them out?

Capital intensive production equipment have to 'sweat it out' to generate the return on the capital invested. However, higher usage results in higher wear and tear of the equipment leading to a possibility of an early failure of the equipment. So what could be the cause and where would be the correlation ? What are the linkage between usage and failure? What environment factors can accelerate or decelerate the rate of equipment failure. More critically, what are the steps that the maintenance engineer be helped to take to extend the life of the asset, while maintaining high production levels.

Speaker: Carrie Ware, Business Development Manager, Technosoft

2:25-3:00pm • Room: S102A

Data Generation, HPC
Smart Manufacturing Getting Smarter: CAE as a Service, On Demand, in the Cloud

This presentation critically analyzes the three options for engineers to use computing power for their CAE design and development simulations: first, the majority of engineers is just using desktop workstations, and many of them are regularly in need of more. Second, a small percentage is buying compute servers, and quite a few are not aware of the high total cost of ownership. And third, very recently, some of the engineers are exploring computing power on demand, in the Cloud, facing quite a few challenges. We compare all three approaches, present benefits and roadblocks, and describe an approach towards identifying the best suited solution depending on the individual scenario. We conclude with several engineering case studies recently obtained from the large number of UberCloud CAE Experiments.

Speaker: Wolfgang Gentzsch, Co-Founder, The UberCloud

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3:00-3:30pm • Room: Foyer

Exhibits & Afternoon Break

3:30-4:10pm • Room: S102A

Panel Discussion:
Predictive Analytics Projects: To Outsource or Not To Outsource?

Many companies are faced with demands from lines of business (LOB) to build predictive analytics solutions. For companies without a data science team, analytics projects often are tossed to the CIO or the CFO (or both) to figure out. Should a company develop a "shared services" model for analytics or leave it to the LOBs? What are the pros and cons of outsourcing an analytics project vs. building an in-house service capability around data science? What are the key criteria to consider when deciding whether to outsource the platform development rather than keep it in-house? When does it make sense to augment current staff with specific expertise vs. hiring FTEs?

Panelists: Terry Flood, SimaFore
Prasad Satyavolu, Global Head - Innovation, Manufacturing, Cognizant
Steven Reagan, Computational Modeling Manager, L&L Products
Jon Riley, Vice President, Digital Manufacturing, NCMS

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4:15-4:50pm • Room: S102A

Forecasting, Cost & Price Modeling
Case Study: L&L Products
Cost Forecasting Using Advanced Analytics

This presentation walks you through a learning path of unexpected relationships, research, and implementation of solutions to data mining challenges experienced at L&L Products. Specifically, the speed and accuracy with which a component price can be delivered has been seen to have great value to L&L's global OEM customers. This session highlights a particular instance of price quote estimation using early design stage information previously thought of little value to deliver pricing quotes within 5% of final offering. Artificial neural networks are developed using up-front design information and ultimately deployed onto desktop environments.

Speaker: Steve Reagan, Computational Modeling Supervisor, L&L Products

4:55-5:30pm • Room: S102A

Fault Detection & Failure Prediction
The New Normal in Maintenance: Predicting Equipment Health using Device Data

As Original Equipment Manufacturers (OEMs) compete in the race to launch new products to target emerging segments, Predictive Asset Maintenance is growing in importance. This is an essential strategy to improve customer satisfaction by minimizing downtime while reducing service and repair costs. The commonality of the business problem across diverse asset classes, from automobiles, construction equipment and printing equipment to storage devices, telecom networks and aircrafts, and the replicability of the data science solution approach, has attracted widespread attention

Speaker: Sumit Kumar Chand, Principal Scientist, Technosoft

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5:30-7:00pm • Room: Foyer

Networking Reception

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  Day 2: Wednesday, June 18, 2014

8:00-8:45am • Room: Foyer

Registration & Networking Breakfast

8:45-8:50am • Room: S102A

Conference Welcome

Speaker: Jon Riley, Vice President, Digital Manufacturing, NCMS

Decision Systems

8:50-9:10am • Room: S102A

Sponsor Presentation
How leading manufactures today are improving product safety, quality, warranty and reliability using analytics and big data

Analytics savvy manufactures such as HP, Lenovo, Trane, Emerson and Trimble are improving product safety, quality, warranty and reliability using analytics and big data. Attend this session if you're interested in better understanding the benefits and how your organization can implement using existing data, a limited budget and your current personnel. Applied analytics is at the heart of the digital manufacturing revolution. This session will highlight how "self-service" analytics used by engineers and analysts facilitates actionable business insight.

Speaker: Reid Karabush, President, Decision Systems Inc.

9:10-10:00am • Room: S102A

Predictive, Accessible and Achievable Solutions to Short- and Long-Term Challenges

As the emergence of big data from machines descends upon large organizations across the globe, most of the focus to date has been on external particular, service management, remote monitoring, predictive parts failure, and other customer-facing initiatives. But an equally strong value opportunity exists for organizations that focus on leveraging big data for internal purposes. Attend as Craig highlights how companies are using machine data to improve product development, customer segmentation, warranty design and other areas to reduce costs and increase top-line growth.

Speaker: Craig Brabec, Enterprise Champion, Information Analytics, Caterpillar Inc.

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10:00-10:30am • Room: Foyer

Exhibits & Morning Coffee Break

10:30-11:15am • Room: S102A

Visualization & Dashboards
Case Study: Archer Daniel Midlands Company
Analytics Improves Productivity in Corn Processing Plant

Abstract to come...

Speaker: Jim Foster, Director of Industrial Process Research, Archer Daniels Midland Company

11:20am-12:00pm • Room: S102A

Tool Wear & Failure Prediction
Next-Gen Predictive Analytics Models for Parts Failure Using Telematics Data

Existing predictive models of reliability are based on design, field, quality and warranty data. With wider adoption of telematics, organizations are leveraging real-time performance data streams along with geospatial information. These streams of sensory data capture crucial ambient and operating conditions. A wide array of possibilities exists for building models that can predict with greater confidence the possible failure points and their correlations with these conditions. Big Data management techniques through collection, ingestion and persistence help to uncover and pinpoint the failure patterns and build causal relations over a large population of vehicles leading to unit-level failure predictions.

Speaker: Prasad Satyavolu, Global Head - Innovation, Manufacturing, Cognizant
Vivek Diwanji, Chief Architect Engineering & Manufacturing Solutions, Cognizant
Joseph Disantis, Computational GeoScientist Worldwide Drilling, Marathon Oil

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12:00-1:15pm • Room: Foyer

Lunch in the Exhibit Hall

1:15-2:00pm • Room: S102A

Enabling Smart Cars Using In-Vehicle Electronics Data Analytics

Abstract to come...

Speaker: Thomas Klier, Senior Economist, Economic Research Department, Federal Reserve of Chicago

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2:00-2:45pm • Room: S102A

The Internet of Things: Filling the Predictive Analytics Data Void

Predictive Analytics has dramatically improved corporate forecasting, but there's still a problem: you can't analyze data that you can't harvest. Now the "Internet of Things" promises to fill this data void, because -- for the first time -- it will be possible to receive and share, in real time, data from products and processes on how they actual operate and are used.

That will enable using P.A. tools to their full potential, allowing predictive maintenance, improving targeting of marketing (including in-store behaviors), allowing iterative product design based on actual performance, etc.

Speaker: W. David Stephenson, Author and Principal, Stephenson Strategies

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2:45-3:15pm • Room: Foyer

Exhibits & Afternoon Break

3:15-3:50pm • Room: S102A

Visualization & Dashboards
Case Study: Inteva Products
Vivid Visualization for Quality Improvement

Translating manufacturing results into vivid visuals allows the cross-functional team to focus and take action on the issues that matter most.

A collection of dashboard visuals representing multiple aspects of the quality equation are presented and explained. Before and after examples are shown to demonstrate the power of going visual. Tips for introducing new visualization methods to a diverse workgroup are shared.

Speaker: Catherine Lubchenko, Product Line Quality Manager, Inteva Products

3:55-4:30pm • Room: S102A

Measuring Business Impact of Manufacturing Analytics
Improving Supply Chain Transparency through Analytics

In today's world, IT systems are creating unprecedented amounts of data and supply chain operations is no different. So even though visibility or transparency are not novel ideas, it is not easy to get a pulse on how your operations are doing from an end to end perspective. Using analytics for analyzing, reacting and predicting events from the data we already have can help with supply chain transparency. IBM is using analytics as a way to identify exceptions and correlate several data points to highlight issues and risks. These issues and risks then become key events that are being use to drive visualization in several ways like heat maps, geo spatial maps and process maps and drive work flow to resolve the issues. We are also using mobile platform to notify key users of these events based on severity and subscription models. IBM's vision is to use analytics as an information and collaboration tool to help improve client experience and optimize supply chain operations.

Speaker: Anish Verma, Global Transformation Executive, IBM

4:35-5:10pm • Room: S102A

Digital Manufacturing
Case Study: GE Global Research
Predicting Variation in Product Performance by Integrating Geometric Modeling and Process Data

Analyzing the effect of variation in manufacturing on product performance is useful for specifying appropriate tolerances, design margins and robust manufacturing processes. This typically involves creating a transfer function from process variation to performance variation. In this talk, using case studies from our experiences at GE, we will demonstrate that the accuracy of estimation can be improved by introducing an "as-built" geometric model in the pipeline coupled with simulation. We will also discuss the various algorithms with which process variation can be geometrically modeled. In addition to more accurate estimation, this enables physics based interpretation of observed behavior.

Speaker: Arvind Rangarajan, Senior Engineer, GE Global Research

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