Pharmaceutical parallel trade is a legal trade in European countries, where traders can buy medicinal products in one country and sell them in other countries to make a profit. In the pharmaceutical parallel trade market, players such as manufacturers, wholesalers, parallel traders, pharmacies, and hospitals are involved. Studying and analyzing this market is of significant interest to economists and players involved. Agent-based modeling offers a robust algorithmic framework to analyze macroeconomic phenomena through micro-founded models. As an initial step in using agent-based modeling for the parallel trade of pharmaceuticals, we consider a simplified pharmaceutical trading market inspired by available game theory models. In this paper, we developed and elaborated the implementation of an agent-based model for the pharmaceutical trade market and employed it to run multiple scenarios that are impossible to analyze through game-theoretic models. Subsequently, we demonstrated how an agent-based model could be utilized to analyze the market from an economic perspective and how players in this market can recruit this model in their business decisions.

Digital twins enjoy increasing interest in a diverse array of industrial sectors, such as manufacturing, healthcare, urban planning, etc. Their usefulness depends on the robustness of the corresponding digital twin models; however, validation of the model, as a mean to ensure models' robustness, is a difficult problem. Moreover, traditional validation approaches need to undergo significant transformation to be made applicable to digital twins. To the best of our knowledge, there has not been a systematic treatment of validating digital twin models. This paper identifies several challenges facing the model validation within digital twins. Furthermore, we propose an initial framework to define basic rules of digital twin model validation and introduce a systematic approach to validation that seamlessly combines expert knowledge and data gathered from available Internet of Things (IoT) devices.

Accurate reliability modeling and assessment of manufacturing systems leads to lower maintenance costs and higher profits. However, the complexity of modern Smart Manufacturing Systems poses a challenge to traditional expert-driven reliability modeling techniques. The growing research field of data-driven reliability modeling seeks to harness the abundance of data from such systems to improve and automate the reliability modeling processes. In this paper, we propose the use of Process Mining techniques to support the extraction of reliability models from event data generated in Smart Manufacturing Systems. More specifically, we extract a stochastic Petri net which can be used to analyze the overall system reliability as well as to test new system configurations. We demonstrate our approach with an illustrative case study of a flow shop manufacturing system with parallel operations. The results indicate, that using Process Mining techniques to extract accurate reliability models is feasible.

Fault tree analysis is a probability-based technique for estimating the risk of an undesired top event, typically a system failure. Traditionally, building a fault tree requires involvement of knowledgeable experts from different fields, relevant for the system under study. Nowadays’ systems, however, integrate numerous Internet of Things (IoT) devices and are able to generate large amounts of data that can be utilized to extract fault trees that reflect the true fault-related behavior of the corresponding systems. This is especially relevant as systems typically change their behaviors during their lifetimes, rendering initial fault trees obsolete. For this reason, we are interested in extracting fault trees from data that is generated from systems during their lifetimes. We present DDFTAnb algorithm for learning fault trees of systems using time series data from observed faults, enhanced with Naïve Bayes classifiers for estimating the future fault-related behavior of the system for unobserved combinations of basic events, where the state of the top event is unknown. Our proposed algorithm extracts repairable fault trees from multinomial time series data, classifies the top event for the unseen combinations of basic events, and then uses proxel-based simulation to estimate the system’s reliability. We, furthermore, assess the sensitivity of our algorithm to different percentages of data availabilities. Results indicate DDFTAnb’s high performance for low levels of data availability, however, when there are sufficient or high amounts of data, there is no need for classifying the top event.

Pharmaceutical parallel trade emerged due to the European Union's single market for medicines. While many players, such as manufacturers, wholesalers, parallel traders, pharmacies, regulatory authorities, and hospitals, are involved in this market, having a model that accurately reflects the parallel trade market could be a considerable advantage for players in this market. One way to model the parallel trade market is by employing game theory, which is frequently used to model and explain business interactions. However, game theory imposes limitations on models. Agent-based modeling is a promising framework for studying the parallel trade market, which allows us to investigate macroscopic outcomes that emerge from microscopic rules, decisions, and interactions. Moreover, agent-based modeling allows for high expressiveness and complexity in agents, improving agents' efficiency in autonomy and reactivity compared to current game theoretic models. In this paper, we aim to build an agent-based model for the pharmaceutical parallel trading market based on the available game-theoretic model of the market.