Data driven approaches:

• Semantic data management and interoperability 
• Causal AI 
• Explainable AI
• Applied machine learning
• Digital product passport and digital twins

Application fields:

• Supply chain management 
• Sustainable industrial systems including adoption of zero carbon technologies
• Smart cities and transportation systems

• Dr.-Ing. in Information Management for Engineering, Karlsruhe Institute of Technology, Germany
• M.Sc. in Information and Communication Engineering, Karlsruhe Institute of Technology, Germany
• B.Sc. in Informatics, Bandung Institute of Technology, Indonesia

• Visiting Professor, University of Exeter, UK
• Diaspora Expert, Deutsche Gesellschaft für Internationale Zusammenarbeit
• Adjunct Professor, Sebelas Maret University, Indonesia
• Adjunct Professor, Airlangga University, Indonesia
• Academic Leader, Bandung Institute of Technology, Indonesia

Talenta

BMDV, 01/2023 – 06/2025

In the transportation sector, the implementation of digital twins is part of the digitization measure to improve resource efficiency in infrastructure management. However, the use of digital twins is still limited due to challenges such as lack of common understanding of digital twin models, difficult model integration, security issues, lack of access to important data, and high costs due to inefficient business models.

The aim of the project is the development of an asset management platform suitable for SMEs for the cross-company, secure and intuitive collaborative management of assets of the digital twins. This can be achieved by developing a standardized graph-based semantic model of the asset, explainable machine learning (XAI) and a scenario-based intelligent search and discovery mechanism of the asset.

As part of the project, a uniform semantic description of digital assets using ontology is being developed in order to promote a common understanding of data and models between companies/organizations. Data sources with different formats are linked to the ontology by AI approaches and led to a knowledge graph. The project is developing an XAI toolset/model library to improve the transparency of the information obtained from the digital twins.

Delfine (Digitalization of Energy Transition)

BMWK, 08/2020 – 05/2024

Delfine aims to accelerate the adoption of demand response systems. It is an interdisciplinary project and develops a solution for the participation of industrial end customers in both price and incentive-based DR programs. The aim is to determine the influence of such programs on the network as such and on the development of electricity costs in the manufacturing industry. With the support of Stadtwerke Trier as a network operator and an interdisciplinary consortium with complementary competencies, this project strives for a technical solution that can be used in various areas. A continuous data network is developed using semantic middleware, from the automated creation of generation and demand forecasts to the dynamic design of electricity prices and the energy-efficient and intelligent use of production resources. The holistic consideration of the addressed issues by the interdisciplinary project consortium enables business models to be developed for the use of the project results by electricity providers, aggregators, and the manufacturing industry.

Delivery Assurance & Operational Supply Chain: Enhancing delivery performance with decision targeted analytics using causal machine learning

Industry PhD fellowship, 11/2021 – 10/2024

Supply Chains (SC) are complex, data driven systems which deal with the flow of information, goods, services, and money. In such fast-paced environments, the lack of data has been replaced by concerns of abundance of data, which has created difficulties in properly synthesizing the data and deriving meaningful conclusions from it. This makes it challenging to establish the relationships between different components of the SC. Nowadays, ‘optimized’, ‘resilient’ and ‘fail-safe’ are key words for a successful SC, however, conventional Machine Learning (ML) approaches have proven to lack the capability to provide the necessary tools to support SCs in fully achieving their goals. ML has found its way in many use cases in SCM such as predictive demand forecasting, intelligent partner selection, and assistance systems for resource management. However, the heavily statistical mode of most ML systems entails several limits on their power and performance. In a world filled with uncertainties (political, social, environmental etc.) it can be difficult to establish a resilient SC, especially without fully understanding the cause effect relationships between its different external and internal KPIs. Establishing and understanding such relationships would aid in a more effective Risk Management system for SCs, by minimizing disruptions when there are challenges in any stage of the SC.

Current ML approaches are not fit to optimize the movement of people or goods around the globe, as they are insufficient for robust predictions and reliable decision-making based on their correlational pattern recognition nature (causaLens). That is because current ML models are based on past patterns that may not hold in the future. They also produce hard-to-trust black box predictions without fully understanding the business context, therefore, making predictions rather than recommendations. Businesses ultimately want to do more than just predict the future; they also want to actively shape it by fully understanding and utilizing their data. Causal Machine Learning (CML), an emerging field in AI, could prove to be a very helpful tool in adding the missing links to SC, since identifying causal effects is an integral part of understanding and learning from behaviors, as well as shaping them. The proposed doctoral research project aims to address these challenges. The research project utilizes multi criteria decision making methods for interviewing experts to first identify and quantify the external risk factors, internal supplier performance KPI’s and find causal relationships. All the knowledge of experts will be then used in Causal Machine Learning Model to help have a better-informed decision and business recommendations.

The research defines three milestones to be achieved. Each objective has research questions to be answered by the research. Those research objectives are Quantification and utilizing of Experts Experience, to make the Operational Supply Chain more proactive by early prediction of escalated suppliers and their KPI’s and to know the Effect of External Risk Factors and other KPI’s on the suppliers and to give business recommendations for the next steps that helps reduce human biases and increase the proactiveness.

Almais, A.T.W., Susilo, A., Naba, A., Sarosa, M., Crysdian, C., Tazi, I., Hariyadi, M.A., Muslim, M.A., Basid, P.M.N.S.A., Arif, Y.M., Wicaksono, H., 2023. Principal Component Analysis-Based Data Clustering for Labeling of Level Damage Sector in Post-Natural Disasters. IEEE Access.

Beibit, R., Fatahi Valilai, O. and Wicaksono, H., 2023, January. Estimating the COVID-19 Impact on the Semiconductor Shortage in the European Automotive Industry using Supervised Machine Learning. In Proceedings of the 2023 10th International Conference on Industrial Engineering and Applications (pp. 302-308).

Krstevski, S., Fatahi Valilai, O. and Wicaksono, H., 2023, January. Integrating Real-Time Dynamic Electricity Price Forecast into Job Shop Production Scheduling Model with Multiple Machine Environments. In Proceedings of the 2023 10th International Conference on Industrial Engineering and Applications (pp. 98-106).