Have you heard of the Hype Cycle, or exaggeration in IT innovation? This subject is under discussion in the innovation ecosystem. The Hype Cycle explains why some new technologies usually create more than high expectations, even though the expected productivity gains do not always come with these tools. Have you heard of “do for the sake of it” and not for a reason? That is what I am talking about. We are inside a market that more and more demands agile processes and investments in innovation. It is becoming more and more common for companies to invest in products and processes that do not solve any pain point.

However, how can you develop technologies that generate results and not only expectations? Come, get a little closer. I will talk about some pillars for building future-ready companies and how to get away from digital solutions that will not get anyone anywhere. Welcome to Industry 4.0.

Explaining Hype

The Hype Cycle explains why the emergence of some technologies creates high expectations and growth promises. It is made up of five different stages, and these stages are distributed in a quadrant that analyzes “expectations x time”:

Source: Data Geeks

• (1) Innovation Trigger happens with a new technology discovered and its first feature concept proofs execution. Even though this technology is still unusable, publicity related to it is usually stronger this time.

• (2) During the Peak of Inflated Expectations, after publicity and high expectations generation, some companies invest in the new technology because they are trying to become pioneers in its development. Some attempts result in success, but most of them do not.

• (3) Trough of Disillusionment happens when the development of these new technologies cannot be delivered in a commercial form, as they are technologically immature or not financially viable. Only a few companies focused on the solution commercialization stand at this point.

• (4) From those companies come more examples of how to offer these new technologies in the market, and then more companies start willing to finance 2nd or 3rd generation pilots. This stage is known as the Slope of Enlightenment.

• (5) At last, we understand these new technologies’ best usability in the market and where these technologies’ adoption happens on a larger scale at the Plateau of Productivity.

Hitting the Mark

I have used hype as one of the many whys that explain some investments in Digital Transformation and new technologies that do “not work” and do not meet their initial purposes. Such a scenario is common to several digital products in the development stages. For your investment to be assertive, there must be a lot of clarity of your main objective.

Here on ateliware’s blog, we have already discussed that new technologies usage in Digital Transformation is not the end goal, but it is a means to achieve innovation. From this point, we can improve productivity and enhance results.

Avoiding The Hype, And Building A Solid Basis for Quick Decision Making: Industry 4.0 Learnings

I will use the description of the Industrie 4.0 Maturity Index (a study developed at the University of Aachen, Germany) to try to explain in a better way how we can pursue new technologies and develop better digital products.

The term 4.0 emerged with its potential for revolutionary impact as an allusion to the first three revolutions in the industry sector. It is related to the spread of integration, communication, and information in industrial production. One of the challenges faced when building a future-ready company concerns the time of search and getting answers for problem resolution, therefore, the time and resources consumed for decision making.

Consider this: mainly within companies, the final product of investment in innovation and digital solutions focus on reducing the use of resources so that the “remaining” subsidies are better used, that is, for quick decision making, in a logical way, and lined up with the objectives of the company.

Industrie 4.0 Maturity Index defines the term as the “real-time, high data volume, multilateral communication, and interconnectedness between cyber-physical systems and people.” This connectivity enables a better understanding of how things relate, allowing faster decision-making processes. According to the researchers, this system provides companies the capacity to respond faster to the complex demands of their customers. Besides, it helps them be faster and more accurate in developing products that meet the needs of their markets, also granting scalability and faster offerings of their solutions. This agility will allow companies to adapt more dynamically to changes.

• You might be interested in: Tools, IT professionals, and Digital Transformation

6 Stages In Maturity Index

Each stage in the maturity index represents a stage or goal that your company can pursue in product development. It is also worth remembering that the further away from the matrix’s starting point, the greater the value it can generate for your company. And I tell you how.

Computerization is at the first stage of the development path. At this point, a company’s sectors use different technologies, isolated from each other, mainly focused on making repetitive tasks more efficient. In this phase, there are still machines without digital interfaces manually operated.

When it comes to the second stage, we have connectivity. Here, the company’s previously isolated information technology is now connected. Despite not being fully integrated, the applications and parts of the company’s operating systems are interoperable. At this stage, the digitization phase ends.

The “Industry 4.0” process starts at the 3rd level, visibility, where sensors, for example, are used to collect large numbers of data and information. Technologies like this allow data capture in real-time far beyond just the areas of operation and maintenance. At this stage, we have a digital, physical, and practically “live” model of company processes and events at all times, something that can be a great challenge for companies whose data can often be decentralized and not shared across the entire business. In some cases, companies do not account for some processes and procedures across the enterprise. In their turn, these processes and practices may retain valuable resources.

The next stage is transparency, which is designed for the company to understand why something happens and use this information to produce knowledge about the sources of problems and inefficiencies. At this stage, “digital shadows” reviews are carried out, and these studies provide content for quick decision-making. New technologies that make it possible to analyze large volumes of data can be of great help at this stage.

In the fifth stage, predictive capacity, the company can use the data and interpretations generated in the previous stage (transparency) to create predictive and future models that will demonstrate a variety of scenarios and how likely each one is to happen. An example of a more robust operation is reducing the number of unexpected events caused by disruptions.

The sixth and final stage in the development of Industry 4.0 is adaptability. Predictive capacity is an essential requirement for automatic and quick decisions. However, continual monitoring and adaptation allow the company to delegate some decisions to IT systems so that the company can adapt itself as soon as possible to the business environment and unexpected events.

• You might be interested in: Does Digital Transformation depend on digital products?

Conscious And Structured Investments

Few companies in the sector are implementing strategies within the four phases of Industry 4.0 (visibility, transparency, predictive capacity, and adaptability) because the construction is cumulative. In other words, one cannot move to the next phase without completing the previous one.

It is worth remembering that there is no obligation for the entire company to go through all the stages. You can start this process with a digital product, an efficient new approach. We are sharing this flow to help you and your company choose the best investment for the phase and project you are in today. Machine Learning (ML) usage is an example of what I mean.

Machine Learning is an application of artificial intelligence (AI) that gives systems the ability to automatically experiment, learn and improve their methods, even without being explicitly programmed for a task. There are several applications for Machine Learning, such as fraud detection, predictive maintenance, and the most common one, used in autonomous vehicles. But, if we stop to think about it, it would be useless to invest in ML if we did not have collected and organized data, much less if we did not have digitized data. Investment in new technologies needs to be more conscious and structured, less hype.

References:

• Digitizar, digitalizar e automatizar processos
• Redes distribuídas e novas formas de organização sócio-corporativas
• Acatech Study - Industrie 4.0 Maturity Index
• Technology Readiness Levels | The European Space Agency

Originally published on 03/04/2021. Translated by Reinaldo Zaruvni.