Industry 4.0 and Digital Transformation – Implication on Work Level

On 8-12 October 2018, the INDEED research group organized a 5-days training with the topic “Industry 4.0 and Digital Transformation – Implication on Work Level”. The participants of the training are directors, managers, and engineers from various departments, such as R&D, operations, production, marketing, etc., of Weichai, a large state-own Chinese company. The training is a part of a long-term cooperation between Weichai and Jacobs University Bremen.

The training comprised series of lectures, group works, workshops, and quizzes/games led by Prof. Dr.-Ing. Hendro Wicaksono with the following goals
1. To assess how ready Weichai for industry 4.0.
2. To discuss the requirements for work of the future and the role of new technologies, such as Internet of Things, Big Data, Virtual and Augmented Reality, and 3D printing technologies.
3. To develop the steps to introduce those technologies to improve business processes, products, and services of Weichai.
4. To apply technology and innovation management methods to transform new ideas using those technologies to a marketable product/service and to formulate the suitable business model.
5. To prepare the capabilities and willingness of Weichai’s employees for digital transformation and industry 4.0.

Weichai is state-owned Chinese enterprise with more than 64,000 employees that designs and manufactures diesel engines, vehicles, luxury yachts, and automotive parts for worldwide market. It has more than 80 subsidiaries throughout China and overseas. It has been awarded several times for its innovations and quality.

Research collaboration result published in IEEE Transactions on Cybernetic

The result of a research collaboration between INDEED research group of Jacobs University Bremen, Cardiff University (UK), University of Exeter (UK), Trinity College Dublin (Ireland), and Karlsruhe Institute of Technology (Germany) has been published in the IEEE Transactions on Cybernetics. It is one of the top IEEE journals in computer science and has an impact factor of 8.803 (2018).

The journal paper presents a novel cloud-based building energy management system, which comprises the whole information lifecycle, from data collection using sensors and software systems, data integration through semantic middleware, advanced analytics with machine learning, and visualization through a responsive web-based application. The solution integrates sensor-metering and simulation data to solve the discrepancy between theoretical simulation models and real metering data. The solution has been validated in five real pilot buildings in northern and southern Europe and has shown energy savings up to 25%.
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The full paper can be accessed through the following link:
https://ieeexplore.ieee.org/document/8412214/