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Europeans tend to have mixed feelings about revolutions. It is with some degree of irony that the roots of the fourth Industrial Revolution can be found in the conservative manufacturing heartland of Germany.  Beyond Germany, the impact of Industry 4.0 is global and the adjustment to change may be hardest on the European continent.  This article examines the outlook for Industry 4.0 for the European manufacturing sector.

Europe’s Current Manufacturing Base: Recent Winners and Loser

Before we examine the future of Europe’s industrial sector, let’s review the current state.

Germany position as Europe’s manufacturing powerhouse has solidified in recent years.   Starting with the so-called Hartz reform in 2003, the federal government restructured unemployment benefits, subsidized job training and created hiring incentives for German companies.  As its neighbors slipped into recession, Germany’s wages grew at a lower rate than productivity and manufacturing exports were boosted by the low Euro.

In parallel, economies in Central and Eastern Europe (CEE), experienced significant growth in their manufacturing sectors.  After joining the European Union, the relatively low labor costs attracted capital investment.   In recent years, CEE has become a car production hub. For instance, since the entry of Poland into the EU in 2004 until 2011, industry growth has averaged 8.7% (not counting the negative growth in 2008 caused by the punctual recession), although there has been a slowdown in recent years to an industrial growth of only 2 to 3%.

At the same time, the so-called Southern periphery has “become largely disconnected industrially from the core since the Eurozone debt crisis forced Greece, Spain, and Portugal to seek bailouts.”  There has been an industrial stagnation of Southern periphery countries relative to Germany. Because of the common use of the Euro, these countries cannot use currency depreciation to boost local production.  Furthermore, while Germany was successful in reforming its labor market, these countries maintained the welfare state model.

In summary, over the last decade, the shifts in manufacturing across the European continent can be traced to the 2008 financial crises, the Euro relative to global currencies, structural changes in the labor force and the labor cost arbitrage opportunity that resulted from absorption of CEE into the EU.

Industry 4.0:   A New Paradigm

There is almost a universal consensus that Industry 4.0 and industrial analytics for predictive maintenance will be disruptive and fundamentally change the nature of Europe’s industrial landscape.  Germany’s Federal Minister for Economic Affairs and Energy recently published a report that estimated that €153 billion in economic growth by 2020 that can be attributed to Industry 4.0.    Despite France’s lackluster growth in recent years, Emmanuel Macron stressed the opportunity of digitalization during the recent presidential elections.  By selecting entrepreneur Mounir Majhoubi as the minister to lead digitization, Macron demonstrated his commitment to Industry 4.0.

How does Industry 4.0 differ from the changes that have occurred in the last 10 years? The drivers of change in the last several years were factors unrelated to operations or technology.  The performance of the European manufacturing sector was due to traditional macro-economic policies or one-time geopolitical changes such as the fall of Communism.  Although there has been a convergence between Operational Technology (OT) and Information Technology (IT), this had a limited impact on the production mix, output or geographic locations of industrial plants.

The Disconnect Between Strategy and Results

We recently attended the annual Hannover Messe trade show where Chancellor Angela Merkel lamented that very few German companies have released products or services related to Industry 4.0.

Policy makers in Brussels have few tools to steer European manufacturers towards Industry 4.0.   Their policies are limited to standardization of systems or architecture for data transfer, labelling and certification of IT interfaces (hardware, data formats, web services), programming platforms and control software, and security procedures.

These are the major policy initiatives within the EU:

The Horizon 2020 research program:  The EU will provide € 80 billion for research and innovation and fund transformation of research into prototypes and products. With the EU, Horizon 2000 is viewed as the primary entry point into Industry 4.0.

The Task Force on Advanced Manufacturing for Clean Production (2013) and the Strategic Policy Forum on Digital Entrepreneurship (2014):  These initiatives were designed to improve ICT standards to accelerate the digitalization of the EU’s industrial base.  Specific digitalization targets were set for each EU state.

At an EU-wide level, there have been few concrete actions taken.

Enablers of Industry 4.0

Industry 4.0 requires industrial plant owners to re-architect their manufacturing capabilities.   At its core, Industry 4.0 and IIoT Predictive Maintenance is based on the application of Big Data and Machine Learning to the industrial sector.   Until recently, these concepts were studied at a theoretical level in academia.

According to PwC, an Industry 4.0 enabled factory is comprised of the following elements:

  • IoT Platform / PaaS
  • 3D Printing
  • Advanced Human Machine Interfaces
  • Cloud Computing
  • Authentication & Fraud Detection
  • Location Detection Technologies
  • Smart Sensors
  • Big Data Analytics
  • Augmented Reality
  • Mobile Devices
  • Multi-Level Customer Interaction and Profiling

These interconnected capabilities are not cheap or easy to deploy and factories run a risk of over-committing to nascent technologies without a clear ROI.

An Incremental Approach to Industry 4.0

Not all revolutions happen overnight and Industry 4.0 is not different in this regard.   European manufacturers have a responsibility to their shareholders and employees to maintain and grow their existing revenue base while investing wisely for the future.   There is a hesitancy on the part of many traditional manufacturers about over-investing in an IoT platform infrastructure, given the number of unknowns.

At Presenso we work with some of the leading industrial manufacturers in Europe.  Some have been successful with Industry 4.0 initiatives, but many are staying on the sidelines.  An incremental approach means focusing on a limited number of Industry 4.0 projects while addressing the underlying infrastructure requirement for data connectivity, storage and transmission.

Industrial Analytics for Predictive Maintenance:  The Low Hanging Fruit

For many industrial plants, IIoT Predictive Maintenance is the highest priority Industry 4.0 initiative.  If executed successfully, Predictive Maintenance increases uptime (and revenue).  Operation and Maintenance (O&M) budgets can be re-allocated to other activities when asset degradation or failure is predicted with sufficient advanced notice.

There are several approaches to IIoT Predictive Maintenance. Perhaps the most high-profile solution is the Digital Twin that is based on a virtual simulation of a physical asset.  The virtual clone provides a real-time analysis of the performance of the underlying equipment and it can predictive failure before it occurs.

The limited penetration of the Digital Twin is due to a couple of factors.  First, because of the steep cost associated with the Digital Twin, facility owners often struggle to justify an investment based on positive ROI.   Of equal significance is that the Digital Twin sits on top of platform infrastructures such as GE Predix or SAP Hana.  The decision about selecting a cloud platform is complex which can delay the ultimate decision.

Another approach to IIoT for Predictive Maintenance is to apply Unsupervised Machine Learning solutions to the existing sensor data that resides in a manufacturing facility’s Historian database.  With Unsupervised Machine Learning, all the sensor data from an industrial plant is extracted to the cloud.   Advanced algorithms based on Artificial Intelligence detect abnormal machine behavior and correlated patterns of abnormal behavior.   Based on this, factory asset degradation and machine failure are predicted before it occurs.

The advantage of implementing IIoT Predictive Maintenance is that it is not dependent on new revenue lines or customer-centric process.   Factories are suffering from labor shortages and savings from lower maintenance costs make their way to the bottom line.

Summary and Conclusion

All revolutions are marked with upheaval and the creation of new centers of power. Although it is too early to predict the ultimate winners in this Industrial Revolution, new manufacturing models will emerge.  Not all industries have the bandwidth or financial resources to migrate to Industry 4.0 and these entities should consider incremental approaches such as Unsupervised Machine Learning for Predictive Asset Maintenance.

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Samuel Attal

Samuel Attal

Samuel-Georges Attal is a marketing intern at Presenso and a third-year student the London School of Economics and Political Science (LSE).

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