LNS Research first introduced the term Industrial Transformation (IX) in our research on Industrial Transformation Readiness in 2018. We chose the moniker to specifically distinguish from the existing Digital Transformation term as we see IX as an important but distinct subset of the broader Digital Transformation umbrella. Our definition of Industrial Transformation states that IX is a proactive and coordinated approach in leveraging digital technologies to create step-change improvement in industrial operations.

Manufacturing and IT leaders have been pursuing IT-OT convergence strategies for many years now. To reach that converged state, LNS Research sees establishing a common data model including both IT & OT data as an essential step. This common data model ensures that the data leaving the plant from multiple sources and of multiple types are accurate, trusted, and secure.

Introducing Survey Snippets

LNS Research has started a blog series called Survey Snippets that will include call-outs from our recent survey data and compelling insights for manufacturing executives. The first blog post of this series is about analytics’ role in a company’s Industrial Transformation (IX) program, and how leaders in this field are leveraging metrics to distinguish themselves from the rest.

The relationship between asset performance and operational performance are well known and pervasive. All too often, however, companies manage them in sub-optimized in silos. Recent discussions with clients clearly reinforced our insight that operating in silos is counter-productive to industrial transformation (IX). It doesn't matter if the company is an asset-intensive business and the most critical metrics are uptime and reliability, or a product-centric manufacturer where quality and efficiency are most critical. If the organization doesn't manage these things holistically, it will lose ground to competitors.

During a discussion on industrial analytics at a recent Smart Factory conference in Columbus, Ohio, an IT professional in the audience said that he planned to upload his machine data to online analytical processing (OLAP) cubes and run Business Intelligence (BI) applications on top of the data. The analytics professionals on stage appeared horrified for a moment and went to great lengths to explain the distinction between industrial analytics and BI.

The manufacturing industry has had a reputation for being late on technology adoption ever since the steam engines brought the first industrial revolution forward. Even today, while the consumer market has seen the likes of Uber, NeXT, and Spotify leverage latest technologies like Big Data, machine learning, and sentiment analysis to provide products with cutting-edge technology, the manufacturing industry is several steps behind. Even among the manufacturers who are ahead in the technology adoption curve, a good percentage of them have had trouble scaling pilot projects into financially successful projects across the organization.

Until recently, manufacturing operations management (MOM) software and Industrial Internet of Things (IIoT) platforms were two distinct solutions serving different purposes and providing separate capabilities. That’s no longer the case. MOM software vendors today embed several IIoT capabilities that enable the applications to run at any level in the operational structure. This new degree of connectivity, together with smart connected devices and inexpensive sensors have enabled manufacturers to replace older, monolithic MOM solutions with IIoT-based MOM applications in the plant.

The Smart Manufacturing Experience launched its first edition of manufacturing event on April 30, 2018 at the Boston Convention Center. The 3-day event, which focused mostly on Smart Manufacturing, the Industrial Internet of Things (IIoT), additive manufacturing, and augmented reality/virtual reality (AR/VR), witnessed several hundred presenters and attendees from an assortment of companies including IIoT Platform start-ups, industrial analytics providers, 3D printing companies, and big technology players like Microsoft, PTC, among others.

Quality leaders often view customer complaints as a reactive metric and the last line of defense in their arsenal. Though the term may seem pessimistic on a first glance, when collected, analyzed, and promptly handled, customer complaints have the potential to be a net positive for manufacturers. Not only from a metrics and operations standpoint but also in enabling closed-loop processes and driving a culture of quality throughout the organization.

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