How to combine industry 4.0 technologies to improve performance

But smart business solutions should involve more than just adopting disconnected technologies, as a group of researchers point out in a recent article. Instead, the authors suggest ‘bundling’ technologies together. This helps achieve synergies, leading to major business improvements. Yet the complementarities between different I4.0 technologies are not well understood, despite the vast emerging literature on I4.0. This is why Daniele Battaglia, Francesco Galati, Margherita Molinaro and Elena Pessot, set out to “understand how combinations of I4.0 technologies – and their functionalities – can be realized in multiple ways, aiming to better unleash performance outcomes beyond single technology adoption.”

The various combinations of I4.0 enabling technologies

By systematizing the I4.0 technologies considered in the literature, the authors thus identify them as: Visualization technologies (e.g. smart glasses), Computing technologies (e.g. modelling solutions), Network and sharing technologies, Digital production process technologies, Data processing technologies (AI, Big Data Analytics). 

While previous work has studied 'bundles' of technologies, with the 'bundles' identified based on their application areas (smart manufacturing, products, working and supply chain) or production targets (such as flexibility or process quality), there still lacked a detailed overview of the potential patterns of such I4.0 complementarity. So, adopting the holistic perspective of systems theory, the researchers did just that.

To examine the complementarity of I4.0 technologies and their performance impact, they studied the large and vibrant I4.0 ecosystem being developed in Italy via interviews with 13 experts in the field and more than 150 use cases. “Overall, our results show that I4.0 technologies can impact a firm’s performance when adopted both as stand-alone technologies and as bundles,” they write.

Both scattered adoption and bundled technologies can boost performance

The adoption of most classes of technologies, even if not combined with others, may still positively influence performance. Only Computing technologies never emerged as an effective stand-alone adoption. 

For example, the experts surveyed cited several times the IoT to create smart products, which can enhance customer service and improve the perceived product quality. In the Data processing group, two technologies were discussed as stand-alone effective, namely Big Data and Analytics, and Artificial Intelligence. The former can increase the overall equipment efficiency in the manufacturing process, and, as a smart working application, reduce demand planning efforts, whereas the latter can improve production processes, for example quality control, with AI algorithms analyzing photographic images of the products to quickly identify possible defects.

The second result shows that I4.0 technologies may be combined in ten different bundles to increase performance. Such bundles are underpinned by two many groups of technologies, which seem to play a pivotal role in all the other combinations: Network and sharing technologies, and Data processing technologies – which the authors describe as “fundamental building blocks (…) operating as platforms for the other I4.0 technologies.” Combined together or with the other technologies, they affect 33 performance dimensions; the most frequently cited outcomes are error reduction, product quality increase and increased worker safety.

The authors detail one case of the adoption of Network and sharing + data processing technologies (termed “B2” bundle): “B2 can support the management and control of a production process, thanks to the collection, storage, and advanced analysis of manufacturing data, thus improving almost all the performance categories, including energy consumption. (...) Competence Center A recently supported a firm operating in the automotive sector to diagnose energy inefficiencies in its industrial process by leveraging IoT and big data and analytics. The solution has been installed into 4 plants of the automotive company, leading to a 5% energy saving (quantified as 40 million euros).”

A framework of I4.0 technology bundles for managers

Drawing from their results and considerations about “platform” technologies, the researchers identified three kinds of complementarities:

• Platform complementarity is obtained by combining platform technologies, for example Cloud computing and AI, which follows a process of knowledge recombination which supports companies' decision-making, thus increasing their competitive advantage.
• Hybrid complementarity derives from the combination of platform technologies with either Visualization or Digital production process technologies. For example, for an assembly task, AI may enhance the use of cobots (collaborative robotics, already an effective stand-alone technology if it provides workers with ergonomic benefits), reducing the cycle time rate and improving operational efficiency.
• Full complementarity involves technologies (i.e. Computing technologies) which may not impact performance if adopted in isolation, like simulation technologies, which aren't beneficial unless data are collected from the operational systems (i.e. through IoT).

Battaglia et a. draw two conclusions. From a theoretical viewpoint, the “whole could differ from the sum of the impacts reached with the single technologies, enabling benefits not reachable by scattered adoption.” From a practical viewpoint, the framework informs managers which technologies provide a specific outcome, for them “to avoid technological adoption based on guesses, gut feeling, or experience, which, often, may result in a weak decision-making approach.” In particular, platform technology bundles represent the solution with the highest versatility.