Jaswinder Walia

GE Aviation's Outcome-Based Data Strategy

Leveraging GE’s parent company success within the Industrial Internet space, GE Aviation has executed an aggressive strategy execution aligning their efforts not just with digital engineering practices but with an entire, fully digital organization.

In this interview, Jaswinder highlights the key factors for a successful translation of data strategies in digital business practices. Merging IT and engineering into one digital thread Jaswinder shares how to:

  •  Develop and define an outcome-based data strategy
  •  Manage big data across your products’ lifecycles
  • Identify and exploit the full potential of data
  • Bridge the gap between digital and physical assets

Andreas Schierenbeck

Lifting Data Analytics to the Next Level

In 2015 thyssenkrupp Elevator launched MAX, a game-changing predictive and pre-emptive service solution that extends remote monitoring capabilities to dramatically increase current availability levels of existing and new elevators. Utilizing the power of Microsoft Azure Internet of Things (IoT) technology, MAX makes it possible for an elevator to “tell” service technicians its real needs, including real-time identification of repairs, component replacements, and proactive system maintenance.

thyssenkrupp Elevator is now revolutionizing the industry and started something nobody else in their field has done before: To transform a century-old industry that has relied on established technology until now.

Topics covered in this interview include:

  • Learning from past data experiences
  • Commercial opportunities and commercial limits
  • The retro-fitting formula: how and when to upgrade existing units
  • Taking the ultimate step: moving from preventive to pre-emptive maintenance
  • Utilizing virtual and augmented reality to offer a new quality of remote services

Gahl Berkooz

How are big data and analytics changing the PLM landscape?

The PLM vendor landscape first started with a group of niche companies that offered a combined hardware/software solution. They persisted up until 'work stations' and improved functionality in the database space arose that killed these vendors off. This gave birth to today's generation of vendors that for the past 5-10 years have really held the PLM fort.

But today, Big Data is about taking the data-based technology underlying Google (Hadoop) and making this available to everyone. The fundamental changes that will follow in how data is collected, processed and ultimately used are pushing these PLM household names into a niche group of their own and it stands to reason that they too will become outdated dinosaurs and become extinct paving the way for the next generation of technology.

This session discusses:

  • 'A Brief History of PLM' - how is the vendor data management changing?
  • 'Dawn of the Big Data Giants' - how is big data transforming the way we do business?
  • Enabling superior product development through Big Data & Analytics
  • Improving the inclusion of the consumer's voice into product design
  • Enhanced Test Data Management through better mining practices and subsequent avoidance of failure
  • Evolving the Connected Vehicle
  • Wrapping up Big Data capabilities and exploring how best to link this with PLM and product development

Mark Halbish

Data-Driven Business Strategies at TI Automotive

Connecting all core BU systems to the BI platform at TI Automotive has been key to transform the traditional of TI from a service provider into a value driver. Leveraging analytics enabled the organization to bridge core data sets across quality, sales, legal, PLM, change management, warranty database, and HR to tie the information back into the product development decision-making process.

Topics covered in this interview include:

• Main characteristics of data-driven business strategies

• The interplay between IT and business strategies

• Recognizing and exploiting the full potential of data

• Data as additional value driver

Charlie Holmes

Effectively deploying Data Analytics

Most companies are looking to data analytics as a way to provide their executives the transparency they need to run an agile, flexible and profitable business. At Kohl's, they are taking this one step further.

As a multi-brand organisation, their focus is brand clarity, brand alignment and customer-centricity. And data analytics lends itself nicely to this; by exploring, mining and developing existing data as well as incorporating new data from both their customers and their competitive industry at large, the hope is that they will gain true visibility across the corporation and in doing so better support effective decision making in the product development and innovation space. This session covers:

  • Examining all existing datasets and their accessibility
  • Re-evaluating how changes in data infrastructure might make business better
  • Exploring existing brand and customer profiles, and putting a data mining process in place with clear parameters
  • Understanding the team-specific siloed nature of data collection and re-building this
  • Building a data analytics team and fostering the right culture
  • Supporting a 'less-time mining, more-time decision making' policy
  • Creating a centralized data organization that is visually presentable and circles back to innovation
  • Supporting competitive product development through analytics