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7TRIZ IN TECHNOLOGY AND IT SECTION, TRIZ IN BUSINESS SECTION OCTOBER 15-16, 2021
DOI: 10.24412/cl-37100-2023-12-6-9
S. Boika, G. Martsinovskii
Digital transformation in terms of TRIZ laws of system evolution INTRODUCTION
As a result of the pandemic, many companies consider digital transformation as the most important strategic direction of their development, which will help them maintain and develop their business in new conditions. At the same time, according to McKinsey, only 30% of companies successfully accomplish digital transformation of their businesses [1].
The complexity of digital transformation results from the fact that it affects almost all aspects of the company: the business model, product lines and services, customer segmentation, customer experience, operational processes, and technological solutions. In addition, there are many intangible factors, such as speed of adaptation, agility, innovation culture and leadership, which must not be neglected to succeed in digital transformation.
During digital transformation, companies face multiple complex interrelated problems that need to be identified and addressed. Using TRIZ as one of problem-solving frameworks for digital transformation projects have been already discussed [2-4]. The papers usually consider the use of individual TRIZ tools for solving particular problems that arise during digital transformation.
In this article we would like to investigate digital transformation from the point of view of the system evolution laws of TRIZ. Such analysis will allow us to develop a consistent view of digital transformation process as a stage of the system evolution, prioritize and reframe the arising problems in terms of evolution laws, provide recommendations on application of TRIZ tools. The ultimate goal of this study is to maximize the effect of using TRIZ framework in digital transformation process. We will also validate our conclusions with real cases from our practice.
DIGITAL TRANSFORMATION IN TERMS OF S-CURVE ANALYSIS
S-curve evolution is a central TRIZ concept of system development. Usually the S-curve describes evolution of a technical system utilizing a certain operation principle. As the system approaches the development limit of the operation principle it transits to a new operation principle with a higher development limit.
The same consideration applies to companies as business systems. From this point of view a company can be represented as a combination of a business model that delivers a certain value and a set of technologies supporting the above business model (see Fig.1). Such technologies include design and development of products and services, marketing, distribution, logistics, customers management, finance management etc.
Successful digital transformation affects all aspects of the business. The use of digital technologies themselves does not automatically shift the whole business system to a new S-curve unless the business model is redesigned to deliver a more competitive value and enable higher scalability of this value. We will consider cases from our practice that illustrate this point later in the article.
Figure 1. Different levels of digital transformation
When searching for a new business model companies often take the easiest way and copy the models of the most successful competitors or use digital business models that have already become a "standard" in the domain. To some extent, this reduces the company's risks and makes the transformation process more predictable, however, in the end, the company does not achieve the main goal - i.e., it does not get a significant competitive advantage, especially taking into account the market positions of such major players as MAANG. To start a new S-curve with respect to the business performance the company is to find an individual solution that gives a significantly better customer value and experience basing on understanding the customers and emphasizing the uniqueness of the company itself. This explains why there is no single recipe of successful digital transformation and why there are not so many companies whose digital transformation has really disrupted the market [5]. In turn, TRIZ offers a systematic approach to this challenge. TRIZ claims that when switching to a new operating principle of the system, a certain contradiction or several contradictions that limited the development of the previous system, i.e., the previous business model, are always resolved. Identification and resolving of such contradictions seem to be the main task for the use of TRIZ in relation to the processes of digital transformation.
TRENDS RELATED TO DIGITAL TRANSFORMATION
In most cases the development limit of the initial s-curve ultimately results from lack of information availability in the system. Digital transition allows the company to overcome the development limits of the legacy business model and technologies. Clear understanding of the nature and mechanisms of the development limit usually gives an insight regarding direction of a possible disruptive solution.
TRIZ suggests one of evolution trends as increasing in utilization of information flow [6-7]. Even though we do see such a trend in digital transformation, we cannot reduce digitalization just to increasing existing useful information flow conductivity and usability and reducing those for harmful or waste information flows. The effect of information flow is so significant that it results in complete reconfiguration of the functional architecture of the system. If we compare this to a technical system, it would be an equivalent of transition to a new operation principle. We observe such drastic change because in business systems the information is in the core of interaction between the system components. A jump in the amount of information available within the system and between the system and supersystem in combination with capabilities of deep and fast processing of this information using AI results in fundamental change in the customer experience, business models, business operation processes.
In [8], the authors describe the transformation of the system due to the information flows arising in it. According to this trend, initially information flows in a technical system are used in its control loop. As the technical system develops, information flows grow. At a certain stage, an information model of the activity of the source system is formed; and then such an information model is transferred to the level of the supersystem, where it begins to manage not only a separate system, but also all instances of the source system. Today one can see manifestations of this trend in the form of "digital twins", IoT platforms and development of complex swarm-like systems.
In [9], the authors explain the relationship between digitalization and exponential growth of the company providing practical recommendations on the directions of digital transformation of the company to create conditions for exponential growth.
TYPICAL SCENARIO FOR TRIZ ENGAGEMENT AND CASE STUDY
Our extensive experience in digital transformation shows that most of our clients usually tend to primacy of the technology: the better technology they find the better solution they receive. They start with looking for a technology provider, who can offer the most advanced technology and can prove it applies well to their industry. When the technology is implemented, it turns out that even though the technology works the overall solution does not provide the anticipated business effect. The desirable disruptive shift has not happened, and the client needs to return to the starting point with wasted money and time. This means that the underlying conflict or contradiction has not been identified and resolved to form a new business model.
Figure 2. Typical scenario of TRIZ engagement for digital transformation
Let us consider a couple of examples from our practice (see fig. 1). In the first case we had an LPG company that fills gas in cylinders and sells them to consumers via a network of retailers. The company wanted to implement a solution for tracking gas cylinders. Each cylinder was equipped with an ID tag, which was scanned at key points of the distribution process. Tracking was expected to increase cylinder rotation rates, reduce cylinder losses in the supply chain, and reduce manual labor for inventory. Such a tracking solution provides a desirable effect only if the cylinders are tracked through the entire chain including retailers and consumers. The filling company faced an issue with engaging the retailers and their customers in a new tracking process. They did not see much value for themselves in doing extra work of tag scanning for the filling company. Analysis of retailer operation and consumer journey showed that there is another data, which is more critical for them: remaining amount of gas in the cylinder. Monitoring gas consumption would allow retailers to plan cylinder replacement process and provide a better service for consumers. It would bring consumers peace of mind as they do not have to worry about suddenly running out of gas. Knowing consumption data is important also for the filling company to predict demand and identify new opportunities for sales growth. Thus, the final solution combined gas cylinder tracking and consumption monitoring to deliver value to each stakeholder and motivate them to adopt a digital solution.
The second case is related to a producer of medical devices that are used for surgical operations. The company delivers its products to hospitals, where the devices are stored until the hospital uses them for operations. The hospital pays for a device only when the device is used. The hospital periodically informs the company about which devices have been used and the producing company issues an invoice. In the present process the producing company suffers loss through delays of the information from the hospitals. As a part of digital transformation, the company needed to develop a solution for automated notifying about product consumption. The technical solution was based on using a sensor, which was embedded into the product package to periodically send information about package integrity to the cloud. To reduce the sensor cost it was preferable to use an edge device that communicated to multiple sensors in the storage room and then sent aggregated data to the cloud. Such a solution required installation of those edge devices in the hospital. However, the hospitals usually refuse to install any additional infrastructure or do not allow using their own infrastructures unless it gives an essential value to them. As the initial issue of the information delays concerned only the producing company, one had to figure out how to make the hospital a part of the new value chain. One of the possible solutions proposed to use the same infrastructure to let the hospital track their medical equipment inside the building. Thus, a new value was created for the hospital to justify the efforts for installation of an extra infrastructure.
The above examples illustrate typical conflicts between the stakeholders during implementation of digital transformation. In both cases improving only technical parameters of the initial solution does not resolve the conflict. We need to look for a solution in the supersystem, i.e., to redesign our value producing system to include other stakeholders, who were a part of the supersystem of the initial solution.
CONCLUSIONS
In terms of TRIZ evolution laws digital transformation is a transition to a new s-curve for the company performance. This transition occurs due to resolving a certain contradiction that limits scalability of the previous business model. The new model is implemented with a set of digital technologies.
Application of TRIZ in digital transformation projects should primarily be aimed at identifying and resolving contradictions that limit existing business models thus providing fundamentally new value and new opportunities for scaling this value. Transition to supersystem seems to be the
most effective approach to resolution of the above contradictions because they are usually associated with the large number of restrictions and limitations.
In most cases companies come to understanding the need to rethink the business model after trying to achieve significant improvements in performance by digitizing and automation of existing processes. Our study confirms that a more efficient approach starts with defining new value proposition followed by selection of the appropriate digital technologies.
ACKNOWLEDGEMENT
We thank Andrei Kuryan for very useful discussion and comments that greatly improved the paper.
REFERENCES
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