Archive for June, 2016

June 16th 2016

Wise Practitioner – Manufacturing Predictive Analytics Interview Series: Chris Labbe at Seagate Technology

By: Bala Deshpande, Conference Co-Chair, Predictive Analytics World for Manufacturing 2016

In anticipation of his upcoming Predictive Analytics World for Manufacturing conference presentation, Building a Predictive Analytics Organization, we interviewed Chris Labbe, Chris Labbe HeadshotManaging Technologist at Seagate Technology. View the Q-and-A below for a glimpse of what’s in store at the PAW Manufacturing conference.

Q: What are the challenges in translating the lessons of predictive analytics from other verticals into manufacturing?

A: ​Manufacturing at a company like Seagate means volume.  Volume of parts produced is 10’s of millions per quarter and each of these devices generates many MB of data.  Daily we produce several TB of data from the drives themselves.  Then we have vertically integrated components generating a couple more TB per day and a massive number of machines that will benefit from advanced sensors.  This magnitude and velocity of data is well beyond the typical retail / marketing analytics challenge and stresses our IT systems to the limits.

Q: In your work with predictive analytics, what behavior do your models predict?

A: Although we built this team with a desire to help fix engineering problems, we found much stronger partners in the business side of the company.  Partially this is because engineering is pretty structured in data management and resistant to “advanced analytics” since they often feel they are using advanced techniques already.  Meanwhile the business teams know that they need help.  As such, not only are we working on better methods for quality management of the production system, but also ideas like customer ordering predictability, supply chain management, inventory reduction and build-ahead risk.  In a way, these are all manufacturing challenges since bad decisions in the business front lead to inefficiencies in production.

Q: How does predictive analytics deliver value at your organization? What is one specific way in which it actively drives decisions?

A: Our business is pretty mature, which means we have a lot of people that have been using data effectively yet inefficiently.  Some of the most important projects we are engaging in are attempts to pull the company into machine learned multi-variate methods instead of human biased univariate system for quality control.​

Q: Is your company supportive of the work your team is doing?  And are they well prepared to execute on the models and systems you develop?

A: We are very fortunate to have the attention of the President of Engineering, Manufacturing & Sales.  This means a lot in terms of stability for the team while we develop some of the projects as it can take a while to demonstrate effectivity.  Meanwhile, we came into this effort fairly unprepared for how big the gaps are in Seagate’s data management pipeline.  To move Seagate to a World Class Data Science company is going to take a lot of time and a lot of money.​

Q: What surprising discovery have you unearthed in your data?

A: There are several projects that we have been pulled in to help with visualizations and data automation.  What we see behind the scenes is often pretty scary, though.  From manual data processes that push sensitive data through email, to data manipulation between source and decision and even weak statistical methods being applied to the data.  Turns out we can help the company in many more ways than just machine learning.

Q: Sneak preview: Please tell us a take-away that you will provide during your talk at Predictive Analytics World for Manufacturing.

A: In building this new division at Seagate, there have been many lessons to learn.  Probably the most important of them all is how to invest efforts up front on data engineering and BI visualization tools.  This has given us the “keys to the castle” by allowing the team to fully understand the underlying math in a tool.  Once the target group is excited about the improvements in the tool, then we can begin a discussion about improving the model behind the scenes.​  When we have started a project with “we can make your model better,” the progress is slow at best.​

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Don't miss Chris’ conference presentation, Building a Predictive Analytics Organization, at PAW Manufacturing, on Tuesday, June 21, 2016, from 4:45 to 5:30 pm. Click here to register for attendance. 

By: Bala Deshpande, Founder, Simafore and Conference Co-Chair of Predictive Analytics World for Manufacturing.

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June 13th 2016

Wise Practitioner – Manufacturing Predictive Analytics Interview Series: Peter Frankwicz at Elmet Technologies

In anticipation of his upcoming Predictive Analytics World for Manufacturing conference presentation, Improved Statistical Process Control of Mature Manufacturing Processes Using Multiple Available Data Streams, we interviewed Peter Frankwicz, Senior Process Engineer at Elmet Peter Frankwicz imageTechnologies. View the Q-and-A below for a glimpse of what’s in store at the PAW Manufacturing conference.

Q: What are the challenges in translating the lessons of predictive analytics from other verticals into manufacturing?

A: Most small business manufacturing companies are focused on relatively simple statistical process control (SPC) or end-of-line quality control.  The next step to predictive statistical process control is a major undertaking in both collection of relevant manufacturing process data and product & yield metrics.  The data-based “return on investment” in higher yield has to overcome management angst of higher risk of product scrap at the end of the manufacturing line.

Q: In your work with predictive analytics, what behavior do your models predict?

A: Predictive statistical process control analytics are in use to optimize powder metallurgical properties, such as tap density, and sintered ingot properties for thermomechanical processing to sheet and rod products.

Q: How does predictive analytics deliver value at your organization? What is one specific way in which it actively drives decisions?

A: Many small business manufacturing companies have a “we have always made it this way” mentality.  Predictive analytics and statistical process control methods allows process engineering to deliver data-based and statistically significant understanding of manufacturing processes to management.  Predictive analytics drives several specialized [Elmet Technologies refractory metal product – manufacturing process] combinations to optimize yield and quality.

Q: Can you describe a successful result, such as the predictive lift (or accuracy) of your model or the ROI of an analytics initiative?

A: Predictive models were developed to understand refractory metal powder properties.  Use of these models to direct manufacturing production resulted in an over $20,000 monthly reduction in scrap product in the “downstream” sheet rolling department.

Q: What surprising discovery have you unearthed in your data?

A: Data mining and statistical analysis of available process data revealed surprising manufacturing process sensitives.  Many of these process sensitives were only known at the level of tribal knowledge on the manufacturing floor.

Q: Sneak preview: Please tell us a take-away that you will provide during your talk at Predictive Analytics World for Manufacturing.

A: There is a high need in small business manufacturing for simple and robust predictive analytical methods.  Even starting the path with data mining and statistical analysis of available data streams can discover surprising and valuable manufacturing process insights and yield optimization strategies.

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Don’t miss Peter’s conference presentation, Improved Statistical Process Control of Mature Manufacturing Processes Using Multiple Available Data Streams , at PAW Manufacturing, on Tuesday, June 21, 2016, from 2:40 to 3:25 pm. Click here to register for attendance.

By: Bala Deshpande, Founder, Simafore and Conference Co-Chair of Predictive Analytics World for Manufacturing.

 

 

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June 10th 2016

Wise Practitioner – Text Analytics Interview Series: Dirk Van Hyfte at InterSystems Corporation

By: Steven Ramirez, Conference Co-Chair of Text Analytics World Chicago

In anticipation of his upcoming conference co-presentation, Personalized Medicine and Text Analytics at Text Analytics World Chicago, June 21-22, 2016, we asked Dirk Van Dirk Van Hyfte imageHyfte, Senior Advisor for Biomedical Informatics at InterSystems Corporation, a few questions about his work in text analytics.

Q: In your work with text analytics, what behavior or outcome do your models predict?

A: To support the shift from reactive to pro-active medicine we look for patients who are at risk to develop Sepsis, Hepatitis C and Delirium. In the area of Behavioral Health we support harm reduction projects.

Q: How does text analytics deliver value at your organization – what is one specific way in which it actively drives decisions or operations?

A: The ability to effectively harness the mountains of unstructured data in healthcare is a key strategic asset for any successful organization.

Q: Can you describe a quantitative result, such as the predictive lift of your model or the ROI of an analytics initiative?

A: One California IDN has demonstrated great success in reducing sepsis mortality, bringing it down from 55% at the start of their interventions to a current level of 35%.  In an effort to bring sepsis mortality down even further to 25%, this IDN is now piloting the use of Text Analytic Tools to add unstructured data analysis to its armamentarium.

Q: What surprising discovery or insight have you unearthed in your data?

A: There are huge gaps in structured data fields. At a Cancer Registry we identified a data gap of 22% where HER-2 testing had been ordered but no definite outcome was recorded. The organization was able to identify a systematic shortfall in the availability of results and was able to investigate this.

Q: Sneak preview: Please tell us a take-away that you will provide during your talk at Text Analytics World.

A: Numerous case studies, a few of which have been discussed above, have demonstrated the shortcomings of structured data.  Important information, such as disease risk factors, might not exist within structured data at all, and even when the appropriate structured data field exists within a data model, the data can be missing or unavailable.  Unstructured data, which comprises perhaps 80% of all healthcare data, has great potential to replace that missing structured data and/or complement what’s already there. 

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Don't miss Dirk’s conference co-presentation, Personalized Medicine and Text Analytics on Wednesday, June 22, 2016 from 3:30 to 4:00 pm at Text Analytics World Chicago. Click here to register to attend.

By: Steven Ramirez, CEO at Beyond the Arc, and Co-Chair of Text Analytics World

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June 6th 2016

Wise Practitioner – Text Analytics Interview Series: Michael Dessauer and Justin Kauhl at The Dow Chemical Company

By: Steven Ramirez, Conference Co-Chair, Text Analytics World Chicago

In anticipation of their upcoming conference co-presentation, Understanding our Customers' Customers' Customers' Needs – Text Analytics for B-to-B Businesses at Text Michael DessauerAnalytics World Chicago, June 21-22, 2016, we asked Michael Dessauer, Data Scientist at The Dow Chemical Company and Justin Kauhl, Computational Linguistics Expert at The Dow Chemical Company, a few questions about their work in text analytics.

Q: In your work with text analytics, what behavior or outcome do your models predict?

A:  Our team, Dow’s Advanced Analytics Group, service company-wide needs from any Justin Kauhlof our businesses or functions so our project outcomes are very diverse. In general, we get many requests for market insight which boils down to consumer needs, so we try to categorize product features and sentiment around those features.

Q: How does text analytics deliver value at your organization – what is one specific way in which it actively drives decisions or operations?

A: The delivered value statements have come from businesses using our market listening insights to secure value pricing on new product introductions. It’s easier to sell when we can point to overwhelming consumer needs that our analysis uncovers.

Q: Can you describe a quantitative result, such as the predictive lift of your model or the ROI of an analytics initiative?

A: In one case Dow was able to value price a product by 10X and our insights were given credit for contributing to that increase.

Q: What surprising discovery or insight have you unearthed in your data?

A: We have had success using external unstructured data to improve short-term forecasting. It was a bit unexpected but very helpful for some of our existing modeling efforts.

Q: Sneak preview: Please tell us a take-away that you will provide during your talk at Text Analytics World.

A: We intend to share our experiences, both successful and unsuccessful, in developing effective text analytics projects with internal clients. We will share our process, technologies employed, and our best practices learned over the past several years working on text analytics projects.

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Don't miss Michael and Justin’s conference co-presentation, Understanding our Customers' Customers' Customers' Needs – Text Analytics for B-to-B Businesses on Tuesday, June 21, 2016, from 1:30 to 2:15 pm, at Text Analytics World Chicago. Click here to register to attend.

By: Steven Ramirez, CEO at Beyond the Arc, and Co-Chair of Text Analytics World

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June 3rd 2016

Wise Practitioner – Manufacturing Predictive Analytics Interview Series: Edward Crowley at The Photizo Group, Inc.

By: Bala Deshpande, Conference Co-Chair, Predictive Analytics World for Manufacturing 2016

In anticipation of his upcoming Predictive Analytics World for Manufacturing conference Edward Crowley imagepresentation, Predictive Analytics – What is 2% Worth, we interviewed Edward Crowley, CEO at The Photizo Group, Inc. View the Q-and-A below for a glimpse of what’s in store at the PAW Manufacturing conference.

Q: What are the challenges in translating the lessons of predictive analytics from other verticals into manufacturing?

A: The biggest challenge in any predictive analytics deployment is to have the understanding of the business processes, business models, and workflows required to build predictive analytics models that have a meaningful impact on the business. Clearly, these processes and models can be unique to each vertical. The bigger issue is, in my mind, that much of predictive analytics has been focused on marketing and customer facing applications; however, there are tremendous opportunities for business process and operational related predictive analytics solutions which bring immediate savings to your organization or your customer’s organization and which are often overlooked when the focus is on customer facing solutions.

Q: In your work with predictive analytics, what behavior do your models predict?

A: Today our models predict device failures for either A) very expensive capital equipment items which have significant costs associated with lost production capacity due to unexpected failures, or B) mass fleets (as high as several million units) of devices which benefit from predicting failure in order to reduce service costs, improve productivity, and early identification of mass failures.

Q: How does predictive analytics deliver value at your organization? What is one specific way in which it actively drives decisions?

A: By understanding when a printer or copier will actually be out of toner (versus when it is ‘low’ on toner), the amount of toner left in the cartridge when it is replaced can be reduced from up to 35% of total capacity to less than 10% of capacity. In this example, our model accurately predicts when toners will be empty, shipping the toner ‘just in time’ before the printer runs out versus shipping the toner when the printer initiates a toner low alert.

Q: Can you describe a successful result, such as the predictive lift (or accuracy) of your model or the ROI of an analytics initiative?

A: Our first model, a toner delivery optimization model, reduces ‘lost toner’ and excess shipping costs by over 50% per device. This translates into over $100 per year, per device savings – or from $5M to $50M for each of our OEM customers. The ROI for the model is immediate since we deliver this in a ‘as a Service’ model where the client just has to sign up; we have already developed the solution.

Q: What surprising discovery have you unearthed in your data?

A: Initially, we thought that the wasted toner from throwing away toner cartridges early was around 15% of the total cartridge capacity – but a validation phase of our project where we actually measured the amount of toner in a large volume of returned cartridges identified that the average waste is closer to 32% per cartridge.

Q: Sneak preview: Please tell us a take-away that you will provide during your talk at Predictive Analytics World for Manufacturing.

A: There are, in my view, three key building blocks to PA – industry knowledge, PA technology, and a knowledgeable team which can turn technology into a solution. It’s not just about the software – it’s about the industry knowledge!

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Don't miss Edward’s conference presentation, Predictive Analytics – What is 2% Worth, at PAW Manufacturing, on Wednesday, June 22, 2016 from 3:30 to 4:15 pm. Click here to register for attendance. 

By: Bala Deshpande, Founder, Simafore and Conference Co-Chair of Predictive Analytics World for Manufacturing.

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