What data is worth collecting during chemical industry production, and how can these be managed so that they can later be analyzed and support business decisions? We examine why having the right foundational data is crucial, how much data is needed for meaningful analysis, and what questions should be asked already in the planning phase of data collection — all illustrated through practical use cases.
The event is organized by the Hungarian Chemical Society – Division of Chemical Digitalization, in collaboration with its partners EUROAPI, Stylers Group and Protechtor.
Program:
12:30 – 13:00 Arrival and registration
13:00 – 13:05 Opening Remarks – László Réthelyi, Director of the CEO Office and Project Management Offices @ Egis
13:05 – 13:20 Pharma 4.0: How to Build a Value-Creating Digitalized Manufacturing Database? – József Vida, Head of Project Portfolio Management Group @ Egis
A digitalized, Pharma 4.0–based manufacturing database integrates production, laboratory, and release data, commercial demand, etc., enabling deeper process understanding, traditional or AI-based optimization, and more accurate root-cause analysis.
13:20 – 13:35 Process Optimization Through Sensor Analysis of Industrial Batches – Barbara Honti @ EUROAPI
Through an industrial case study, we show how Statistical Process Analysis (SPA) supports better understanding of manufacturing processes, how process parameters are analyzed and modeled from sensor data, and what methods can be used to identify differences between high-yield and low-yield batches. Practical results and development opportunities in digital process improvement.
13:35 – 13:50 Data-Driven Solution: Batch Tracker – Ákos Ruska, Digital Industry Manager @ Ceva Santé Animale
How can existing data be used to transparently track the release of production batches? A BI tool demonstration that creates business value by connecting systems and processes. The complexity of the tool is not determined by its technical implementation, but by understanding the different processes and the systems controlling them, and then organizing them into a common structure.
13:50 – 14:05 How can MDA ring formation be predicted based on process parameters? – Regina Kecskés, Junior Process Engineer @ BorsodChem Zrt.
Sharing best practices and experiences regarding the challenges and potential directions of data processing. The goal of the workshop is to help organizations involved in digitalization navigate the complexities of data processing.
14:05 – 14:45 Break
14:45 – 15:00 Workshop Introduction: Challenges of Data Processing – Levente Havas, Partner I Enterprise Analytics Cluster and Tamás Csaba, Principal expert I Enterprise Analytics Cluster @ IFUA Horváth
The purpose of the introductory presentation is to provide an overview of the key elements of digital data management. To support understanding, a data flow diagram will also be presented.
The introduction is based on several years of theoretical and practical experience, including both successful solutions and instructive challenges.
14:45 – 16:00 Workshop: Challenges of Data Processing – coordinated by IFUA Horváth’s professionals: Levente Havas – Partner I Enterprise Analytics Cluster, Tamás Csaba – Principal expert I Enterprise Analytics Cluster, Gábor Vida – Partner I CLS, Balázs Szórádi – Principal I CLS
Building on the introductory presentation, this interactive, small-group workshop supports participants in assessing their organization’s level of maturity in digital data management, as well as in identifying key challenges and potential development directions. During the joint work, participants will have the opportunity to share and discuss best practices and concrete, real-life experiences — including both successful and instructive cases.
16:00 – 16:30 Closing, networking
Additional information:
- Date: January 27, 2026 (Tuesday) | 13:00–16:30 (arrival from 12:30)
Registration deadline: January 22, 2026 (Thursday)
Venue: Egis Pharmaceutical Factory Clubhouse (1106 Budapest, Orbán István Street 6.) - Parking: Egis external parking lot (entrance at the intersection of Keresztúri Road and Orbán István Street)
- Public transport access: From Örs vezér tere: by HÉV, bus 44 or 45 (stop: Rákosfalva), or bus 67 (stop: Egis Gyógyszergyár).
Experts:
Tamás Csaba
Senior EA/BI expert at IFUA Horváth with decades of experience in building data warehouse and BI systems. He has delivered systems across multiple industries and business functions. In recent years, he has focused primarily on designing AI-ready data environments using Microsoft and Databricks technologies.
Levente Havas
Partner at IFUA Horváth responsible for the EA/BI practice. His expertise lies in translating data-driven business problems across different levels of architecture. He is proficient in numerous technologies. His key strength is finding answers to business, data-driven questions within complex, multi-layered technology architectures.
Barbara Honti
I graduated as a chemical engineer, but alongside process engineering, I have always been fascinated by modelling. I am currently a PhD student at the BME Department of Organic Chemistry and Technology, where my research focuses on chemical process modelling—ranging from the mechanistic modelling of crystallization to industrial Big Data analysis using Machine Learning (ML) methods.
I strongly believe in the synergy between industry and academia. Parallel to my doctoral studies, I work at Euroapi Hungary Ltd. within the digitalization department, where I have spent the past few years contributing to statistical process analysis projects.
Regina Kecskés
I completed my studies at the University of Pannonia, Faculty of Engineering, earning both my Bachelor’s and Master’s degrees in Chemical Engineering. I began my career as a process engineer at the Process Design Office of BorsodChem Zrt. In my daily work, I focus on industrial technology modeling and process design, while also utilizing various software tools and data-driven methods to support technological decision-making. In my presentation, I will demonstrate a practical example of constructing a black-box model.
Ákos Ruska
I am a mechanical engineer by training, but for over 10 years, I have specialized in IT systems within manufacturing environments—initially in the automotive industry and currently in the pharmaceutical sector. While the key focus of manufacturing data management in the automotive world is MES (Manufacturing Execution Systems), in pharma, it is the batch record. I have had the privilege of leading numerous projects in both areas.
I am a firm believer in continuous learning, which in my field primarily translates to staying current with IT-related technical knowledge. It creates an interesting dynamic that while my daily work often involves paper-based documentation, I am simultaneously following professional forums about the breakthrough potential of MCP (Model Context Protocol) in agent-to-agent communication. We must all stay prepared: digital transformation will eventually reach every field.
Balázs Szórádi
Head of the Chemicals and Pharmaceuticals Competence Center at IFUA Horváth. He coordinates IFUA’s data-related projects within the industry.
Gábor Vida
Partner at IFUA Horváth responsible for the chemicals and pharmaceuticals practice. He continuously encounters and resolves data-related challenges within the industry.
József Vida
I am currently leading the LCM Project Portfolio Management group at Egis Pharmaceuticals Plc. I completed my studies at the Budapest University of Technology and Economics, where I obtained a degree in Chemical Engineering.
I began my career at the Servier Research Institute of Medicinal Chemistry, and in 2010 I joined Egis as a Development Analytical Scientist. From 2015, I worked as a Drug Product Development Project Manager, and since 2020 I have continued my career as an LCM Project Portfolio Manager. Since 2022, I have been leading the LCM Project Portfolio Management group, where one of my key responsibilities is coordinating the Pharma 4.0 / Process Analytical Technology (PAT) implementation program within the drug product manufacturing development area.
I am passionate about pharmaceutical development and innovation, particularly in the fields of advanced manufacturing technologies and analytical methods. My goal is to contribute to the modernization, digitalization, and efficiency improvement of pharmaceutical manufacturing.
