Building business cases is often the first of many challenges today’s industrial companies have to face as part of their Industrial Transformation (IX) programs. The ideal business case should help companies identify and prioritize the correct problems and provide a clear roadmap for the solution.

Quality 4.0 is a concept LNS Research first introduced in 2017, and it has been gaining momentum ever since. However, the concept has also picked up some misconceptions along the way. To clear up any confusion, we will redefine Quality 4.0 here and answer several key questions about its scope, value, and role in Industrial Transformation (IX).

Industrie 4.0 is often referred to as the Renaissance of manufacturing. But many of us don’t realize how many parallels there really are between today’s Industrial Transformation (IX) programs and the Italian Renaissance period – a time famous for transitioning from the Middle Ages to modernity. In this blog post, we will describe three such parallels and explore how today’s manufacturing leaders can apply them in their IX programs.  

Recently, I noticed an interesting data point from our Analytics That Matter research survey: 58% of surveyed companies claimed to be implementing or piloting an analytics program, but only 11% are seeing dramatic business impacts. The reason? I believe that while these programs are often led by the right vision, most of them aren’t necessarily supported by the required execution.

Quality management has been one of the earliest areas of research for us here at LNS Research, and we have been closely monitoring the space for several years now. Our recent research on Quality 4.0 Readiness shows that Quality 4.0 programs have been gaining steady momentum and have become an important piece of the Industrial Transformation puzzle today’s manufacturing companies are trying to solve; so much so that the Quality 4.0 adoption rate has jumped from 16% to 27% in just two years.

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.