Purpose This research aims to discuss the success of digital transformation focusing on the role of IT and management commitment in digitalization together with sectorial relevance as influencing factors. According to the literature, these dimensions are key elements of digitalization, and there is no consensus on their decisiveness. The authors measure the success of digital transformation with the digital innovation. The research is part of ongoing work, in which the IT-related practice of Hungarian organizations has been explored on an annual basis since 2009. Design/methodology/approach The research methodology is a combined one; both qualitative and quantitative methods were applied including surveying digital transformation literature, interviews with key representatives of Hungarian organizations, developing a survey to collect quantitative data, data collection and processing with PLS-SEM. Findings The results revealed that the digital innovations are strongly determined by business, management commitment and, to a far lesser extent, by strategy. In the case of digital transformation, the role of IT departments and the services they provide are less relevant. Research limitations/implications The most important limitation of the research is the size and composition of the sample. Results do not present the situation of a specific industrial sector. Originality/value Digital technologies influence and disrupt practically every industry; the development of information and communication technology has changed economies all over the world. Decisive factors of digital transformations are widely researched, but there is no consensus about them. This research contributes to understanding the role of IT department and their services in this process together with leadership, sectorial relevance as influencing factors.

Identifying investment patterns as part of customer segmentation is one of the most important tasks in retail banking. Clustering customers effectively is an important element of improving marketing policy and strategic planning. There are several methods for identifying similar groups of customers and describing their characteristics to offer them appropriate products. However, using machine learning methods is rare, and the application is limited for certain types of data. The aim of this study is to investigate the benefits of using a two-stage clustering method using neural-network-based Kohonen self-organizing maps followed by hierarchical clustering for identifying the investment patterns of potential retail banking customers. The unique benefit of this method is the ability to use both categorical and numerical variables at the same time. This research examined 1,542 responses received for an online investment survey, focusing on the questions that are related to the respondents’ investment preferences and their current financial assets. The research utilizes descriptive statistics and multiple correspondence analysis (MCA) to understand the variables and Kohonen self-organizing maps (SOMs), in combination with hierarchical clustering, to identify customer groups and describe the characteristics of these clusters. The analysis was able to identify clusters of potential customers with similar preferences and gained insights into their investment patterns related to their investment portfolio and investment behavior, including their savings profile, attitude to risk-taking, and preferences for investment advice. These findings were supported by additional insights through the application of multiple correspondence analysis (MCA) describing patterns of financial instruments and portfolios. The main contribution of the research is the combined application of the machine learning methods Kohonen SOM, hierarchical clustering, and MCA for investment pattern analysis in the retail banking business.

Developing a big data signal processing method is to monitor the behavior of a common component: a pneumatic actuator. The method is aimed at supporting condition-based maintenance activities: monitoring signals over an extended period, and identifying, classifying different machine states that may indicate abnormal behavior. Furthermore, preparing a balanced data set for training supervised machine learning models that represent the component's all identified conditions. Peak detection, garbage removal and down-sampling by interpolation were applied for signal preprocessing. Undersampling the over-represented signals, Ward's hierarchical clustering with multivariate Euclidean distance calculation and Kohonen selforganizing map (KSOM) methods were used for identifying and grouping similar signal patterns. The study demonstrated that the behavior of equipment displaying complex signals could be monitored with the method described. Both hierarchical clustering and KSOM are suitable methods for identifying and clustering signals of different machine states that may be overlooked if screened by humans. Using the proposed methods, signals could be screened thoroughly and over a long period of time that is critical when failures or abnormal behavior is rare. Visual display of the identified clusters over time could help analyzing the deterioration of machine conditions. The clustered signals could be used to create a balanced set of training data for developing supervised machine learning models to automatically identify previously recognized machine conditions that indicate abnormal behavior.

No abstract

Számos Ipar 4.0-hoz kapcsolódó kutatás zajlik napjainkban. A terület szakirodalma jelentős, több Ipar 4.0 érettségi modell ismert a vállalatok ipari digitalizációs felkészültségének azonosítására. E modellek elméleti háttere, fókusza és dimenzió szerkezete azonban eltérő, kritikájuk többek között a túlságosan technológiai szempontú megközelítés és a testreszabhatóság hiánya. Ez a cikk egy olyan Ipar 4.0 érettségi modellt (Company Compass 2.0 (CCMS 2.0)) tárgyal, amely a fenti kihívásokra válaszol. A definiált nyolc dimenzióhoz tartozó érettségi szintek feltérképezése előre megadott súlyozott kérdések segítségével történik. A dimenziókhoz beavatkozási pontokat azonosítottak a szerzők. A beavatkozási pontokhoz tartozó kérdésekre adott válaszokból képezik az egyes dimenziók Ipar 4.0 érettségi értékét. A CCMS 2.0 érettségi modell újszerűségét a fenti keretrendszer adja; azon túl, hogy holisztikusan vizsgálják a vállalatok Ipar 4.0 érettségét, a további fejlődéshez szükséges beavatkozási pontok azonosítása is megtörténik. A cikkben bemutatják a modell alkalmazása során gyűjtött tapasztalataikat és a javasolt bevezetési folyamatot is egy nagy vállalat és egy KKV példáján.