The Productivity Paradox

Article 2 in our series Executives’ Perspectives on Digital Transformation
by Thomas Gessner, Business Development Manager MBSE Solutions, Zuken.

Investments in IT, including engineering IT tools and methods such as CAD and PLM, were initially driven by the aim of improving efficiency and productivity in engineering. Similarly, initiatives in other parts of the value chain, such as cost-reduction programs, were also aimed at improving efficiency and productivity. However, despite increasing investment levels, especially in IT and engineering, overall economic productivity appears to have plateaued.

This mismatch between rising investment and limited productivity gains is commonly described as the productivity paradox. As a recent study published by the European Central Bank indicates, further attempts to extract incremental productivity improvements from engineering alone may lead to diminishing returns. In practice, much of the productivity potential introduced by new engineering tools appears to be absorbed elsewhere. This points to structural constraints rather than a lack of technological capability.

The Efficiency Illusion in Modern Engineering

Our previous article, “Building Dynamic Capabilities for Product Success in a Changing World”, showed that modern engineering tools and methods can dramatically increase productivity. Yet, despite these advances, overall productivity in most Western economies has improved only marginally. This gap raises a key question: why?

One plausible explanation lies in the steady increase in product and process complexity. Greater complexity brings higher risk exposure, which in turn requires more extensive risk mitigation measures. These additional controls, reviews, and coordination efforts can slow decision-making and execution, ultimately offsetting many of the productivity gains achieved through improved tools and methods.

Speed and agility are often the first capabilities lost as complexity grows. At the same time, moving faster can increase the risk of poorly informed or short-sighted decisions. Faced with the choice between losing business due to a lack of speed or eroding profitability through wrong decisions, many companies find themselves caught between a rock and a hard place.

Industry 4.0: A Turning Point or a Missed Opportunity?

In this context, Industry 4.0 appears to have been introduced primarily as a political vision rather than a ready-to-implement concept, reflecting the growing pressures on manufacturing industries in the early 2010s. It brought together themes such as digital transformation, connectivity, IoT and data analytics, and autonomous systems, while building on traditional strengths of European manufacturing, including market flexibility, customized customer solutions, and service-oriented business models.

First, many organizations focused primarily on tangible, short-term initiatives such as improving process efficiency and price competitiveness, rather than pursuing longer-term and less tangible objectives, including fundamental changes in how the company operates, innovates, and creates value. Second, Industry 4.0 implicitly called for a shift in manufacturing business models, without providing sufficient guidance on how such a complex transformation could be implemented in practice.

With the benefit of hindsight, two insights have become clear:

• An exclusive focus on productivity and efficiency cannot be considered a viable strategy for future-proofing manufacturing industries.
• At the same time, many established methods used to support decisions and processes are ill-suited to dealing with uncertainty and complexity across the value chain.

What could be a possible solution, and why should IT be considered as part of it?

Over recent decades, the interaction between engineering disciplines has become increasingly important. At the same time, the growing role of software in products has driven the development of methods that support synchronized development processes across mechanical, electronic, and software engineering.

Model-Based Systems Engineering as the Key Enabler

At the core of this approach is the use of models that increasingly replace documents and document-like representations. In a model-centric environment, product information is captured in a structured, consistent form and linked across requirements, functions, architectures, and interfaces. This allows all product stakeholders—engineering, manufacturing, quality, and management—to work from a shared and up-to-date system view, rather than isolated documents.

As a result, dependencies become visible and decisions can be made with a clear understanding of their potential impact across the product and development process. In this way, model-based systems engineering (MBSE) provides a concrete foundation for digital transformation by enabling informed decision-making in complex product development environments.

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) provides a useful reference through its national approach to digital transformation (DX)..

In the next article, Yasuo Ueno, Senior Managing Executive Officer and Head of Business Strategy at Zuken, will examine how initiatives led by Japan’s Ministry of Economy, Trade and Industry (METI) are intended to strengthen dynamic capabilities by supporting manufacturers in managing complexity and, in some cases, turning it into a source of strategic advantage, and will consider whether this policy-driven approach offers relevant lessons for European industry.