The Productivity Paradox

Investments in IT, including Engineering IT tools and methods such as CAD and PLM, were initially driven to increase efficiency and productivity in engineering. Similarly, initiatives in other areas of the value chain such as cost-down initiatives were aimed at the same target. However, it can be observed that overall economic productivity seems to have reached a plateau, despite increasing investments, especially in IT and engineering.

The productivity paradox is still very much alive, as a recent study of the European Central Bank shows: It is rather pointless to try and squeeze more productivity out of engineering. Somewhere, somehow, most of the productivity potential that comes with new engineering tools seems to be consumed.

The Efficiency Illusion in Modern Engineering

Our previous article showed that modern engineering tools and methods can dramatically increase productivity. Yet, overall productivity in most Western economies has hardly improved. This raises a key question: why?

It may be a valid assumption to blame increasing product and process complexity as the cause. With increased complexity comes increased risks and with this comes the need to address them with involved risk mitigation processes, ultimately slowing the organization down.

Speed and agility are key capabilities lost in complexity. But moving fast may come at the expense of wrong or short-sighted decisions: If the alternative is losing business because of a lack of speed or losing profitability because of wrong decisions, many companies seem to be stuck between a rock and a hard place.

Industry 4.0: A Turning Point or a Missed Opportunity?

One could argue that in light of this, Industry 4.0 – although at its inception more a political term rather than a ready-to-implement concept – came at the right time. It combines Digital Transformation, Networking, IoT and Big Data, Autonomous Systems with more traditional strengths of European manufacturing like market flexibility, individual customer solutions and service-oriented business models.

However, to many companies this call for a “fourth industrial revolution” raised more questions than it provided answers. Why? Two possible reasons come to mind:

• Companies might have been focused more on tangible, short-term initiatives like increasing their process efficiency and price competitiveness instead of focusing on long-term, less tangible goals like transforming how the company operates, innovates, or creates value in the future.
• Industry 4.0 asked for a shift in the business model of manufacturing companies without providing guidance on how such a complex change could be accomplished.

Two things we understand today:

1. The focus on increasing productivity and efficiency cannot be considered a strategy to future-proof the manufacturing industries.
2. Existing methods that support decisions and processes are not suited to handling uncertainties and complexities across the value chain.

What could be a possible solution and why should we look at IT for it?

Over the last decades, the interplay of the engineering disciplines has become more important and the increasing role of software in the product has led to the development of methods that enable synchronized development processes across mechanical, electronic and software engineering.

Model-Based Systems Engineering as the Enabler

The key is the use of models that increasingly replace documents or document-like information. A model-centric approach enables all product stakeholders to understand dependencies and to make decisions in the context of their possible impact. At the heart of Digital Transformation, MBSE might be the key enabler.

Outlook: Learning from Japan’s Digital Transformation

The call for a model-centric, digitally integrated approach is clear – but how can it be put into practice? Japan’s Ministry of Economy, Trade and Industry (METI) offers an intriguing example through its national Digital Transformation (DX) strategy.

In the next article, Yasuo Ueno explores how METI’s initiative aims to build dynamic capabilities—helping manufacturers not only manage complexity but turn it into a strategic advantage.

Can this politically driven approach offer valuable lessons for European industry?