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, Managing 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.