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The global economy increasingly responds to industrial digitization trends and develops its digitalization strategies, i.e., through application of Industry 4.0 concepts. Thereby, initiatives and developments are often carried out on a company-, industry-or country level. Hungary is one of the most industrialized countries in Europe since many leading car companies, together with their suppliers and related industries have production sites in this area. Witnessing the unprecedented scale and speed of industry transformation due to digitization in general and the emergence of autonomous solutions in all aspects of production and logistics in particular, it is well justified to deal with the question of what Autonomous Systems (ASys) will look like. How influential will it be when the changes' hard challenges and disruptive effects will be overcome, and what are the chances to meet the critical requirements of collaboration and cooperation? What are the technological and management tools to avoid being stuck in the so-called pilot purgatory? To analyse the ASys development status on a high level in the main part of this study, we introduce a conceptual model, including pillars and dimensions aiming to understand the Hungarian ecosystem in terms of ASys. The suggested framework follows a holistic approach in assessment by integrating the following pillars: ecosystem smart factory, human, product. In the second part a questionnaire's first results are presented: there is yet a lot to do to facilitate a definite turn in the mindset of the actors and to make them realize the relevance of simultaneous and continuous cultural and technological development. It's a newly developed, but already tested model to evaluate ASys ecosystems, which can be used in other countries, as well. [ABSTRACT FROM AUTHOR]

Nowadays, manufacturing companies face more difficulties than ever. Unrest in global supply chains triggered by fluctuating customer demand, raw material shortages and crises (Covid pandemic, global warming, wars) complicate the utilization of production resources necessary for economic success. Also, the rapidly changing environment causes existing production plans to be adapted, which results in order changes, causing additional costs for manufacturers. One solution to cope with these problems is cooperation and sharing resources: requesting capacity from partners when having shortages and offering them temporarily in case of excess capacity. In this paper, a platform-based resource sharing mechanism is investigated from the economic perspective. In the mechanism, requests and offers are matched by a central platform applying a complex matching logic. The platform provides valid alternatives based on the incoming orders that the requesting company can choose from. Companies are rating each other’s performance after each interaction based on delivery accuracy; choosing between resource offers is made based on the cumulated rating about the offeror and the price of the offer. Within this paper, the aim is to investigate the resource sharing mechanism from the economic point of view based on an approach to the responsiveness of a supply chain structure to turbulence, to support decision-makers trying to cope with unexpected changes. For this purpose, here the mechanism is briefly introduced, and basic concepts about turbulences in supply chains are also presented. Cost types related to resource sharing manufacturing companies are distinguished, and the model is validated with agent-based simulation. A simulation experiment is performed to investigate the use-case of outsourced jobs having different price levels. Based on the experiment, it can be concluded that there is a price level limit in such a resource sharing federation, under which it is worth it to collaborate with partners by outsourcing certain jobs to them. [ABSTRACT FROM AUTHOR]

Today, a significant number of different models and methods are available to assess the readiness of industrial companies concerning Industry 4.0 implementation. In this respect, the SMEs are in the most vulnerable position. Thus, there is a need for a critical review and analysis of the existing models' fit while recognizing the specific requirements of this type of enterprise. The results of a study that has become a point of reference (Mittal 2018) show that only a limited number of the smart manufacturing and Industry 4.0 roadmaps, maturity models, frameworks, and readiness assessments available today reflect at least partly the specific requirements and challenges of SMEs. In this paper, an attempt is made to introduce a solution to mitigate this gap, that considers cultural and location-specific, ecosystem-related restrictions or influences (macro-level view) and particular company-level aspects (micro-level view), as well. The CCMS (Company CoMpaSs) model developed by EPIC InnoLabs is based on an online survey and considers both business and technological views. The answers given by the respondent company are weighted, and intervention points are selected from a predefined set of eight elements per dimension, on the basis of analyzing the responses (coming from different areas). The output of the model (besides the statistical evaluation of the results like comparisons to averages derived from metadata) is a prioritized list of intervention points that shows the most important fields of action for the respondent on an interactive dashboard. The applied weightings are determined by the model creators and result from the inherent structure of the model. The questions and the weights can also be changed as the used technologies evolve. CCMS provides a quick (meaning that the respondent gets prompt results), low-risk, online maturity assessment for manufacturing SMEs, which helps decision-makers to determine target maturity levels in different fields. [ABSTRACT FROM AUTHOR]