Event

2019 Analytics Solutions Conference

June 18-20 | Minneapolis, US

Make connections, be inspired and discover how data-driven tools can transform your business from R&D to production.

Register today and take advantage of the early bird prices!

Stat-Ease and Camo Analytics are partnering together to host the premier conference on the practical applications of industrial analytics. Topics feature industrial analytics methods; in particular design of experiments (DOE), multivariate analysis (MVA) and process analytical technology (PAT).
The 2019 Analytics Solutions Conference is the perfect venue to discover how others are using these statistical tools to dramatically impact the bottom line. Industries include, but are not limited to: pharmaceutical, medical device, electronics, food science, oil and gas, chemical processes, and aerospace.

Conference highlights:

– Multi-Track Conference program with more than 20 presentations and keynotes
– Hear inspiring stories on topics like Design of Experiments (DOE), Multivariate Analysis (MVA), Process Analytical Technology (PAT) and data analytics
– Join the pre-conference workshops and elevate your analytical skills: Practical DOE – ‘Tricks of the Trade’ and Realizing Industry 4.0 through Industrial Analytics

General outline:

  • June 18: Workshops
  • June 19-20: Multi-Track Conference
We will be updating this page as we finalize the speakers and topics. If you would like to be a part of it, please head over to the Call for speakers page and send us an abstract.

Register today and take advantage of the early bird prices!

DisperSol
Angela Spangenberg:

Real-time Spectroscopic Process Control

Eastman
Howard Rausch:

Analyzing Experiments Involving an Amount Factor with a Zero Setting

Ecolab
Paul Prew:

Text Mining: Discovering Themes in Text Records

Camo
Geir Rune Flåten:

Introduction of Hyperspectral Image Analysis for Quality Control

Collins
Carol Parendo:

How to Increase Design of Experiments Success

Patheon
Eric Jayjock:

PAT in Contract Manufacturing

UColorado
Noah Johnson:

Developing an Assay for Screening Alzheimer’s Inhibitors using RSM

StatEase
Mark Anderson:

Know the SCOR for Multifactor Strategy of Experimentation

Quaker
Jason Pandolfo:

Investigate the Impact of Amines on Metalworking Fluid Lubricity

Arendt
William Arendt:

DOE: A Formulator’s Perspective

Merck
Chuck Miller:

Process Analytical Technology (PAT) Best Practices

Camo
Geir Rune Flåten:

Using Multi-Sensor Data Fusion for Process Analysis and Control

Camo
Geir Rune Flåten:

Using Multi-Sensor Data Fusion for Process Analysis and Control

ZF
Michal Majzel:

Improving Collapsibility Robustness of an EPS-CD by means of Simulation and Six Sigma

StatEase
Martin Bezener:

Practical Considerations in the Design of Experiments for Binary Data

Geoff
Keynote Geoff Vining:

Solving Complex Problems

Organizations face increasingly complex problems that are critical to their operation and, in some cases, for their survival. Such problems require the proper use of data and its interpretation. A major issue is how to develop appropriate solution strategies to develop good solutions efficiently and effectively.

Historically, data analysis focuses on tools: design of experiments (DOE), regression analysis, statistical process control, modeling. More recent tools form the foundations for analytics and data science. Tools are important for creating good solutions to complex problems. However, it is crucial to understand “the right tool, for the right job, at that right time, correctly applied.” Today, there are too many people who claim that their tool is the universal solution. The reality is more complex.

This talk outlines the new discipline that is devoted to the art and science for creating good solutions for complex problems using data. The paradigm for this new discipline is chemical engineering. Chemical engineering builds upon both chemistry and mechanical engineering to create new chemical processes and to improve existing chemical processes efficiently and effectively. Crucial to these solutions is the concept of “unit operations”. Chemical engineering theory focuses on understanding these core operations and how to develop proper strategies to deploy them. Our new discipline, statistical engineering, takes such an approach to the complex problems facing organizations today.

This talk introduces at a high level this new discipline. It then outlines the important roles that both DOE and analytics play in the solution of complex problems. In the process it emphasizes the importance of strategy and understanding exactly what the tools can and cannot do.

Dennis
Keynote Dennis Cook:

A Primer on Partial Least Squares Regression

Partial least squares regression, which has been around for about four decades, is a dimension-reduction algorithm for fitting linear regression models without requiring that the sample size be larger than the number of predictors. It was developed primarily by the Chemometrics community where it is now ingrained as a core method, and it is apparently used across the applied sciences.

And yet it seems fair to conclude that PLS regression has not been embraced by some, even as a serviceable method that might be useful occasionally. Nor does there seem to be a common understanding as to why this rather enigmatic method should not be used, although bumptious discussions of PLS failings can be found in some applied areas. Perhaps this is as it should be — perhaps not.

This talk is intended as a relatively informal overview of PLS regression from a statistical perspective, including historical context, personal encounters, methodology, relationship to envelopes and, near the end, a few recent asymptotic results for high-dimensional regressions.

Sharon
Keynote Sharon Flank:

Field Authentication and Adding Chemistry to Blockchain

In a time where digital manufacturing transforms the flow of goods into a flow of data and raw materials brand owners need effective ways to secure quality and protect their products against counterfeiting. New technologies increase speed and quality in distributed manufacturing, but add complexity to the supply chain … and possible security challenges. Intellectual property protection requires strategies to ensure that both the data and the goods are secure.

Non-destructive, speedy, and user-friendly field testing boosts both security and quality monitoring. New handheld instruments are cost-effective, especially when their capabilities are boosted with strong analytics. A blend of classic methods and new technologies using chemical tags, spectrometers and analytics provide protection for products threatened by counterfeiting, saving money and reputation.

This talk outlines the elements of the solution from the chemical “fingerprint” to field authentication using spectroscopy. This can be applied on pharmaceuticals, cosmetics, spare parts, electronics, wine and additive manufacturing, and the talk will include use cases and real-life examples. It will also touch on blockchain and when blockchain is helpful, and when it is just expensive hype.

Short course 1:

Practical DOE — ‘Tricks of the Trade’.

In this dynamic short-course, Stat-Ease consultants Pat Whitcomb and Martin Bezener reveal tricks of their trade, made possible via Design-Expert® software, that make the most from statistical design and analysis of experiments. Come and learn many secrets for design of experiment (DOE) success, such as:

  • How to build irregularly-shaped DOE spaces that cover your region of interest
  • Using logistic regression to get the most from binomial data such as a pass/fail response
  • Clever tweaks to numerical optimization
  • Cool tools for augmenting a completed experiment to capture a peak beyond its reach
  • Other valuable tips and tricks as time allows and interest expressed

Don’t miss this chance to sharpen up your DOE skills!

Short course 2:

Realizing Industry 4.0 through Industrial Analytics.

Industrial Analytics is a main component within Industry 4.0 as well as other industrial improvement and change programs.Across industries one of the main challenges in the manufacturing processes is to monitor and control variation. Successful variation control ensures predictable production and accompanying business benefits.

The Industrial Analytics toolbox includes Design of Experiments, Statistical Process Control, and multivariate methods, among others, and the choice of method depends of course on the type of data available and the purpose of the analytics.

Identifying and choosing the appropriate Industrial Analytics approach is the first challenge but to actually benefit from the solution it must be implemented in the production environment. The complete solution requires relevant data sources and connection to these, real time handling and use of data to provide timely and relevant statistics, and integration with production control systems.

In this course the process from data collection, to data analysis and finally implementation of the solution in the production environment will be discussed. Best practices and pointers for different types of challenges will be provided. All solutions and examples presented utilize Camo’s Industrial Analytics platform.

Show your expertise.

Do you have a story to tell? Share your industrial analytics success with us. We invite you to submit an abstract to speak at the 2019 Analytics Solutions Conference.

The program committee is looking for presentations that:

  • Describe real-world problems solved by implementing industrial analytics tools.
  • Contain innovative or visionary use of DOE, MVA, or PAT methods
  • Illustrate a successful DOE using Design-Expert® software, including what breakthroughs were made and lessons learned.
  • Showcase business results achieved using multivariate analysis (MVA) with Unscrambler® software.
  • Inspire others to use industrial analytics.

Submit your abstract today.

Abstract submission must include:

  • Author Name
  • Affiliation
  • Phone number
  • Mailing address
  • Email address
  • Abstract – limited to 1 page
  • Time Preference: 25 or 50 minute talk (Availability of times is limited and final selection is at the discretion of the organizers)

Submit abstracts to Shari Kraber: shari@statease.com

Travel information.

Airport
The local airport is Minneapolis-St. Paul International Airport (MSP). It is a Delta hub, but of course many other airlines fly in and out.

Hotel
The conference will be held at the Courtyard Minneapolis Downtown. Book early to guarantee your room rate of $144/night. Booking deadline is Monday, May 20. If you are driving to the conference, be advised that this hotel uses a public parking ramp, so parking will be an extra fee for everyone. Book your room.

G

Ground transportation
You can rent a car, use a taxi/ride-share service, or use Metro Transit (our public transportation system) to get around the Twin Cities. Public transit (plan your trip at www.metrotransit.org) goes from the airport to downtown Minneapolis, but is not great to get to the conference hotel (you will ride a train, walk a bit, then get on a bus). Renting a car is recommended if you want to venture out and do a little sight-seeing or have access to more dining/shopping establishments.

Register now and get early bird pricing.

The Early Bird prices of $495 per person ($275 if you are in academia) are in place.
The pre-conference Short Courses are $295 each.

Registration is done through our partner Stat-Ease and you will be automatically redirected to the registration form on their website.