It’s been a number of months since we blogged about the progress of APM 4.0. Since then, we have published our new eBook, APM 4.0 Shifts Into Second Gear, which details the trends and LNS Research insights into asset performance management. But it is a fair question to ask, what has been the effect of the COVID-19-induced recession and oil price war on APM? Has interest wained, investment slowed down, and adoption stalled? Or are things still moving full speed ahead? Who’s doing what, and why?

LNS recently had the opportunity to participate in Siemen’s Media and Analyst Conference (MAC) held virtually this year because of the pandemic. While this year’s event held fewer presentations, the company still demonstrated that the pace of change at Siemens Digital Industries is accelerating.

Revisiting the Black Box

One of the questions that LNS Research often gets is how to explain Artificial Intelligence (AI) and Machine Learning (ML) not only to plant personnel, but also to operating management, in such a way so that they can understand them sufficiently to place a reasonable degree of trust in them. In other words, enough to allow their adoption for predictive maintenance and other data science-based applications.

In the early days of the COVID-19 crisis, the public health response was focused on containment, and the advice for industry was supply chain resiliency and risk management. Ensure that sole sourced critical components had been eliminated or weren’t coming from affected regions. In large part, North American and European manufacturing passed the test, due to several factors, including work already done after the Fujiyama nuclear disaster and supplier shifts forced by the several year-old trade-war.

In December 2019, 60k attendees descended upon Las Vegas for AWS re:Invent. Just like Amazon Web Services (AWS) generally, re:Invent is fast-growing and overwhelms with its sheer breadth of content. With dozens of announcements and multiple keynotes from senior executives, including an analyst-only session with AWS CEO Andy Jassy, re:Invent has quickly become a can’t miss tech event. Jassy clearly took pleasure in the success of AWS, as he should. Started in 2006, after just 13 years, the division has grown to $36B in revenue, is still growing at 35% compound annual growth rate (CAGR), and is Amazon’s most profitable.

A recent LNS Research article highlighted the need for Industrial Transformation (IX) exemplars, identified three major reasons why IX programs stall, and four major areas that companies need to focus on for ensuring a robust and scalable IX program.

On Wednesday, December 11, 2019, LNS Research hosted a webcast on “Industrial Transformation (IX) in Aerospace & Defense". The presentation covered the scope, scale, strategy, budget, organization and technology choices of Industrial Transformation (IX) Programs globally and in the Aerospace & Defense (A&D) industry specifically. We also looked at another regulated industry, Life Sciences, to validate our conclusions as to why A&D companies are leading in IX. The session concluded with detailing the 5 Best Practices for driving IX. Here we will look at the 6 most common questions that came out of that session.

Industrial Transformation, or IX, is new. There is no play book. There is no proven methodology. There is no exemplar. The improvement programs of the last generation are clear – Toyota and Lean, GE and Six Sigma, Dupont and Operational Excellence, among many others. But why were these companies successful in these endeavors – and why did they share them with the world?

IIoT has become a primary talking point about not only Industry 4.0 (I4.0), but all of the other digitalization topics around I4.0 such as analytics, artificial intelligence, augmented reality/virtual reality and digital twins. The ability to get information from the factory floor and move it anywhere and to anyone cheaply and in real time has driven IIoT into virtually every manufacturer and asset intensive industry client and vendor community discussion lately. With so much focus, both from management and leadership and technical and engineering staff, why then, have so few industrial internet of things (IIoT) projects moved out of pilot purgatory and into full scale production. Some will argue the business case isn’t there yet, others will argue that the technology is not ready or scalable yet while others say something else is lacking like leadership support or adequate deployment and support skills. There is possibly an even more fundamental cause projects are stalling; cultural wars inside the organization are inhibiting progress.

In the last several weeks there have been two big stories about major manufacturers abandoning robots, reverting to manual labor instead. Boeing struggled with fuselage assembly robots that were supposed to drill and rivet 777 aircraft fuselage sections and will revert to using human machinists instead. Adidas announced it was closing its “Speedfactory” plants in Germany and is moving production back to Asia where it has relied on humans to assemble footwear. Adidas has said that it does plan to introduce some of the Speedfactory technology into its Asian plants in the future. With two large multinational manufacturers seemingly reducing automation, in both low volume (Boeing) and high volume (Adidas) environments it may lead other manufacturers to question whether automation investments are wise at this time. The best way to answer that question is to understand the limits of technology, the organizational readiness to accept technology and assessing how technology will support the business strategy.