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In this paper, we describe the concept and the implementation of an agent-based supply chain simulation
model designed as a teaching aid to support introductory classes in supply chain management. The
model helps students to understand the key cost and service level trade-offs in inventory policy design
and supplier selection. The model is implemented using Anylogistix software.
This work analyses the firm failure process stages using the Bayesian network as a modelling tool because it allows us to identify causal relationships in the firm profile. We use publicly available data on French, Italian and Russian firms containing five samples corresponding to periods from one to five years before observation. Our results confirm that there is a difference between the stages of the failure process. For firms at the beginning of a lengthy process (3–5 years before observation), cumulative profitability is the key that determines liquidity. Then, as the process develops, leverage comes to the fore in the medium term (1–2 years before observation) for economies with more uncertainty. This factor limits the opportunities for making a profit, leading to further development of the failure. There are also national specifics that are caused, firstly, by the level of economic development and, secondly, economic policy uncertainty.
This practical study is aimed at finding the value of synergy between the process mining and machine learning concepts using python programming. The paper introduces an analysis of an event log data with annual performance results for the purchase process. The purpose was to understand the whole process derived from data, indicate deviations from the standard sequence of events and visualize the process in Petri nets. For this purpose, the input data such as event log is transformed so that the use of process mining open source library is possible. For in-depth analysis the machine learning algorithms such as CatBoost were applied to find out how this sort of data can be used and how the machine learning problem such as regression problem can be solved.
The field of information security and privacy is currently attracting a lot of research interest. Simultaneously, different computing paradigms from Cloud computing to Edge computing are already forming a unique ecosystem with different architectures, storage, and processing capabilities. The heterogeneity of this ecosystem comes with certain limitations, particularly security and privacy challenges. This systematic literature review aims to identify similarities, differences, main attacks, and countermeasures in the various paradigms mentioned. The main determining outcome points out the essential security and privacy threats. The presented results also outline important similarities and differences in Cloud, Edge, and Fog computing paradigms. Finally, the work identified that the heterogeneity of such an ecosystem does have issues and poses a great setback in the deployment of security and privacy mechanisms to counter security attacks and privacy leakages. Different deployment techniques were found in the review studies as ways to mitigate and enhance security and privacy shortcomings.
The article deals with the general principles of construction and architecture of modern cognitive centres to support strategic decision-making on socio-economic development and its basic components based on a generalised simulation model of the socio-economic system, complemented by modern knowledge-oriented cognitive technologies, as well as data analysis and scenario analysis techniques.
Approaches to the construction of multi-paradigm simulation models based on the principles of a composite combination of system dynamics and agent-based simulation modeling are proposed, which allow describing the processes of development and self-organization in socio-economic systems.
The promising directions for the development of systems modeling technology and the construction of multi-model complexes in strategic development projects based on the principles of stratification, ontological modeling, scenario planning are outlined.
Provides a multidisciplinary perspective on the role of digitalization for society, economics, management, and education
Presents current research methods for digital technologies in management and organization
Highlights the most relevant topics in the field of digital technologies
This book gathers the papers on digitalization of society, economics and management in post-pandemic period. It shares the latest insights into various aspects of the digitalization of the economy and the consequences of transformation in public administration, business and public life. Integrating a broad range of analytical perspectives, including economic, social and, technological, this interdisciplinary book is particularly relevant for scientists, digital technology users, companies and public institutions.
Distributed Ledger Technology (DLT) is making the first steps toward becoming a solution for the growing number of various decentralized systems world-wide. Unlike pure Blockchain, DLT finds many uses across different industries, including eHealth, finance, supply chain monitoring, and the Internet of Things (IoT). One of the vital DLT features is the ability to provide an immutable and commonly verifiable ledger for larger-scale and highly complex systems. Today’s centralized systems can no longer guarantee the required level of availability and reliability due to the growing number of the involved nodes, complicated heterogeneous architectures, and task load, while the publicly available distributed systems are still in their infancy. This paper aims to provide an exhaustive topical review of the state-of-the-art of Distributed Ledger Technology applicability in various sectors. It outlines the importance of the practical integration of technology-related challenges, as well as potential solutions.
The sandpile cellular automata, despite the simplicity of their basic rules, are adequate mathematical models of real-world systems, primarily open nonlinear systems capable to self-organize into the critical state. Such systems surround us everywhere. Starting from processes at microscopic distances in the human brain and ending with large-scale water flows in the oceans. The detection of critical transitions precursors in sandpile cellular automata will allow progress significantly in the search for effective early warning signals for critical transitions in complex real systems. The presented paper is devoted to the detection and investigation of such signals based on multifractal analysis of the time series of falls of the cellular automaton cells. We examined cellular automata in square lattice and random graphs using standard and facilitated rules. It has been established that log wavelet leaders cumulant are effective early warning measures of the critical transitions. Common features and differences in the behavior of the log cumulants when cellular automata transit into the self-organized critical state and the self-organized bistability state are also established.
The paper aims to highlight the way how competencies are changing, following technological progress and new tasks for sustainable development. New social challenges demand a new generation of competencies to apply technologies to produce positive effects on the wellbeing of society. There are known approaches to describing the competencies based on the assumption that they are applicable to any industry, but they are not. We suggest considering the quantity and quality of competencies in the context of industry, identifying the special abilities for its improvement with help of digital technologies. The industrial transformation promotes sustainability if it is based on a deep understanding of technology and industry. The result of this research is the designed concept of competencies for digital transformation based on a variety of industries. This is a preliminary step for advancing the existing concepts of digital competencies to transform the industry to support sustainable development.
The issue of how IT impact the performance of an organization is still not fully explained. Many researchers believe that this effect is based on the automation of business processes and the replacement of unskilled routine labor. However, this does not explain the expected impact of digital transformation, since it offers completely new models. Relying on the achievements of organization theory, we suggest that the impact of IT on performance is realized through the quality of decision-making. We analyze the role of information processing in decision-making, identify the sources of inefficiency, which can be associated with incorrect assessment, lack or excess of information. Next, we revise the organization’s design strategies and classify the information systems according to their information processing capabilities. The proposed approach can explain the way how IT impact is created and manifested both for traditional enterprise information systems and for new digital technologies.
The Deffuant model is a model of opinion dynamics based on the factor of the degree of doubt of agents, called uncertainty. Despite its simplicity, the Deffuant model turned out to be technically extremely difficult to analyze, and its basic convergence properties, which are easy to observe numerically, are only empirical results. In the presented work, the agent-based Deffuant model is implemented within the FLAME GPU framework, designed to parallelize simulations of agent-based models based on GPUs. The identity of the results with the original single-thread model is demonstrated. This approach allows us to study various characteristics of the model, its development, modification of the configuration of the ensemble of agents, to conduct various analyses, in particular, cluster analysis.
The concept of Industry 4.0 has sparked many advances in academia and practice, ten years after it was announced at the Hanover Fair as part of the German High-Tech Strategy. Today, it has gained worldwide recognition as such or via comparable terms around the world. While many technological solutions have been found, small and medium-sized enterprises (SMEs) still lag behind in several regards, as they cannot grasp the potentials of Industry 4.0 as large enterprises do and face some distinct barriers to Industry 4.0. However, for Industry 4.0 to unfold successfully, SMEs need to be integrated within supply chains. Hence, also for large enterprises and entire supply chains, the implementation of Industry 4.0 in SMEs is vital. To better understand this phenomenon, this book presents insights from 14 countries around the world, in addition to Germany.
This book includes experiences of Industry 4.0 technology adoption among companies located in 14 countries around the world, including Brazil, China, Finland, France, Hungary, India, Iran, Italy, Lithuania, Poland, Russia, Serbia, the US, and the UK. Each chapter briefly describes the context and the digitalization policy adopted in the country, and highlights the barriers, drivers, and opportunities that companies identified in relation to Industry 4.0. The aim of this chapter is to summarize commonalities and differences of factors influencing Industry 4.0 in SMEs nationally and attempt to aggregate the Industry 4.0 opportunities, leading to further digitalization chances for SMEs.
The field of small and medium-sized enterprises (SMEs) digitalization is becoming more mature and stands to significantly contribute to the full development of the agenda of Industry 4.0. Although national digitalization programs have their own goals, the common focus is on the role of SMEs in global value chains. Since SMEs are known to have challenges around Industry 4.0 implementation, this book integrates experiences from 14 countries worldwide.
Industry 4.0 in SMEs across the Globe: Drivers, Barriers, and Opportunities provides an in-depth overview of Industry 4.0 in SMEs, covering various national, historical, and geographical settings in nine European countries: Finland, France, Hungary, Italy, Poland, Russia, Lithuania, Serbia, and the UK, complemented by five other countries from around the world: Brazil, China, India, Iran, and the U.S.
Each chapter describes the national digitalization program, along with barriers, drivers, and opportunities to implement Industry 4.0 in local SMEs. It subsumes the findings across these countries to identify common themes and clusters of drivers, barriers, and opportunities. The book concludes that there are common approaches of SMEs across the world to adopt Industry 4.0, which are to be understood to increase industrial competitiveness globally.
This book is a great resource for digitalization leaders and laggards, business consultants and researchers, as well as Ph.D. and master’s students from industrial engineering and manufacturing backgrounds. Policy makers can also use the contents to better understand the commonalities and differences of national digitalization programs and further support SMEs in their digitalization process.
This paper adopts a multi-tier perspective and aims to explore challenges of small and medium-sized enterprises (SMEs) in collaborative manufacturing amid the emergence of dedicated B2B platforms. Original equipment manufacturers welcome formation of demand-driven collaborations between SME suppliers to facilitate ramp-up of production capacity. While being potentially beneficial to suppliers, such collaborations face various barriers.
An exploratory study of 17 suppliers within the European Union’s aerospace industry was undertaken. The study comprised two stages. In the first stage, suppliers’ answers to self-administered interviews were analysed using thematic analysis. In the second stage, interactions between the barriers were determined through interviews with experienced SME collaboration facilitators. The authors apply system dynamics modelling to analyse the links between barriers and identify re-enforcing and balancing loops of other factors.
The authors establish five major groups of barriers to collaboration impeding: market transparency, access to orders, partner trust, contracting and (e) data sharing and coordination. The authors model application of four enablers that facilitate barrier removal for technology-enabled supply chains: digital platforms, supplier development, smart contracts and Industry 4.0.
The study is limited by the data collection from the aerospace industry; validation of the models in other low-volume high-variability manufacturing sectors is needed.
The reader will learn about the barriers which impede demand-driven SME collaboration within manufacturing supply chains, interrelationships between these barriers and suggestions about how to remove them. SME cluster managers will find managerial implications particularly interesting as they will help them to overcome collaboration concerns and better prepare cluster members for Industry 4.0.
The models developed within this study can be used to explore the effects of intervening at critical points in the model to create virtuous improvement cycles between key barriers and related variables in the model. This can help decision-making and policymaking in the area of supply chain integration.
There is currently a lack of studies about how the existing barriers amplify and de-amplify themselves and what the managerial approaches to tackle the barriers are. It is unclear how far companies will go in terms of information sharing, given the trust levels, power dynamics and governance structures evident in supply chains. This study contributes by explaining the reinforcing interaction between the barriers and showing ways to overcome these using enablers.
A growing body of literature has examined the potential of machine learning algorithms in constructing social indicators based on the automatic classification of digital traces. However, as long as the classification algorithms’ predictions are not completely error-free, the estimate of the relative occurrence of a particular class may be affected by misclassification bias, thereby affecting the value of the calculated social indicator. Although a significant amount of studies have investigated misclassification bias correction techniques, they commonly rely on a set of assumptions that are likely to be violated in practice, which calls into question the effectiveness of these methods. Thus, there is a knowledge gap with respect to the assessment of misclassification bias’s impact on a specific social indicator formula without strict reference to the number of classes. Moreover, given the erroneous nature of automatic classification algorithms, the quality of a predicted indicator can be assessed not only using regression quality metrics, as was done in existing literature, but also using correlation metrics. In this paper, we propose a simulation approach for assessing the impact of misclassification bias on the calculated social indicators in terms of regression and correlation metrics. The proposed approach focuses on indicators calculated based on the distribution of classes and can process any number of classes. The proposed approach allows selecting the most appropriate classification model for a particular social indicator, and vice versa. Moreover, it allows for assessment of the optimistic level of correlation between the indicator calculated based on the results of the classification algorithm and the true underlying indicator.
The goal of this research is to demonstrate model designs and approaches based on using modern paradigms and technological solutions in the field of simulation modeling of socio-economic processes and social forecasting that allow us to study complex dynamic occurrences in the development of socio-economic systems.
Strategic management of socio-economic system involves the analysis of structural changes and dynamic aspects of its development. The socio-economic system can be a specific dynamic behavior in terms of development. The search for an effective modeling constructs developing socio-economic systems due to several reasons, among which:
- necessity of choice and analysis of the trajectory of development in the conditions of formation of the strategy;
- structural changes and dynamic complexity of socio-economic systems;
- the need to consider behavioral aspects of individual social behavior and the activity of individual elements of a complex social system;
- the presence of self-organization in social systems where the dynamic behavior can occur spontaneously, depending on the internal structure and the influences from the external environment.
Observed in society and the socio-economic phenomena, the processes are similar to the processes studied in the area of systemological sciences, as synergetics. The article discusses the methodology and general technological approach to building simulation models describing such phenomena in socio-economic systems. Model design public system should link the micro level, where individuals (organization) decide and act and macro-level, describing the state, the basic structure and development of the system. All model variables are constantly changing for a long time under the influence of external factors and internal, in transforming the system of structures and properties of the socio-economic system. At the macro level model designs are produced by means of the aggregated system dynamics models describing the main elements and processes of development, the evolution of social systems: population, economy, production and social infrastructure, environment and other factors of social life. Through the description of microprocesses aggregated system dynamics model of socio-economic systems are complemented by agent-based models of individual social and economic behavior of decision makers, as well as describing the interaction of many social groups. The agent-based model allows to investigate the individual behaviour of different groups of agents, the specificity of their adaptation to the changing environment, and how the processes of self-organization influence the evolution and development of socio-economic system as a whole.
This approach in building a multi-model complexes based composite system dynamics and agent-based simulation models allows to investigate the dynamics and development of socio-economic processes through cyclical relationship of micro- and macro- levels in this socio-economic system.
As an integration base for creating a model set, the paper uses a suite of ontological models, whose framework is based on the conceptual approaches to the model set stratification proposed in the paper.
Research/ Practical/ Social/ Environment implications
A simulation model of the socio-economic system acts as the core of the procedure of strategic decision making in the information-analytical centers, along with the monitoring system, data analysis, methods of generating scenarios, the technology of the scenario studies and analyzing their results. Procedures expert of audit and expertise-cognitive analysis is used for stratification, ontological design simulated socio-economic systems, the formation of possible scenarios, playable on simulation models, and modelling "balance of interests".
Considered model construction by the socio-economic systems are considered and applied by the author of this article in the construction of dynamic models in the social sphere (health, housing, pension system), a regional system, the collaborative supply chain.
In the present article, we consider a new class of one-pad ciphers. We define its encryption algorithm as a partial function with two arguments. The first argument is a key, which is a random and equiprobable sequence of natural language letters. The length of the key is N. The second argument is a coherent (readable) text of length L, where L<N. The partiality feature of the encryption function does not allow us to apply C. Shannon’s mathematical model. For this class of ciphers, we present two operational modes. We chose positions (numbers) of letters in a given key with the encrypted coherent text in the first mode. The positions (numbers) of letters represent the encrypted text. If there is not enough key information to encrypt the whole text, we may repeat the encryption using the next chosen key. To illustrate cryptographic capabilities of this mode, we widely use a widely known perfect cipher secrecy concept of C. Shannon as a prototype to the concept of perfect partial secrecy. Newly presented concepts allow us to build a hypothesis stating that it is impossible to decrypt the presented cipher without finding the information about its key. The second operational mode of the cipher consists of multiple usage of the key to save the key information. We also calculate the complexity of finding the key.