Smart Production & Organization

Ever-changing demands on companies and their production facilities, processes and organizational structures are posing ever-greater challenges. The evaluation of new products, business models and production processes as well as the modernization of existing facilities is the basis for future competitiveness. The targeted use of modern technologies and production simulations, the preparation and analysis of data and the optimization of value-added processes lead to a holistic view of production and organization.

 

Robotics & Production Technologies

Targeted use of industrial robotics, image processing and automation enables intelligent, versatile and efficient production cells and systems. Simulations can be used to evaluate and optimize their design and their components already in the planning phase in order to counteract potential sources of error before implementation.

The rapid further development of technologies, such as collaborative robotics, autonomous transport or machine-learning based image-processing systems, continuously changes the technical possibilities of production plants. Through conducting feasibility studies, concept development and functional prototype development, these can be evaluated in a company-specific manner and, if necessary, adapted and implemented.

Production Planning, Optimization & Simulation

Methods of operations research and simulation-based studies support system analysis and decision making for companies. An integrated data analysis forms the basis for a systematic investigation of scenarios and effects. The evaluation of production and logistics systems can also be used to develop optimization possibilities.

The collection, storage, processing and analysis of production data can be used for process improvement and evaluation of meaningful Industrial Internet of Things (IIoT) scenarios. Furthermore, monitoring of process stability can be established and reaction times can be shortened.

Production Optimization within the context of lean and six sigma aims at an increase in efficiency and quality while simultaneously reducing lead times. Analytical methods for process optimization can be applied and corresponding competencies can be transferred to the company through employee training.

Technological Innovation & Organizational Transformation

Holistic analysis and solution approaches consider the potential of new business models as well as associated value-added processes and (production) technologies and define a roadmap, which may be divided into agile sprints for targeted development and implementation.

A sustainable transformation is only possible if the individual employee and the entire organization also support this change. With the help of qualitative research methods, company-specific challenges are examined in detail, appropriate success factors are developed, and the findings are translated into implementation recommendations.

At the intersection of new technologies and human behavior, (production) assistance systems as well as different interaction strategies and their effects on business processes are examined. Resulting studies investigate the acceptance, usability and ergonomics of technology, as well as the optimal transfer of necessary competences – transferred into company-specific training concepts.

Software & Automation Systems in Production

Modern production is dependent on appropriate software systems. An analysis is required for each individual company in order to be able to take the necessary steps sensibly and effectively. Through the development of a holistic approach and an appropriate software and system design, short, medium and long-term expansion stages can be defined. This includes the conception and development of new methods and tailor-made algorithms as well as individual software modules - for example for machine and production data acquisition, for the integration of autonomous transport systems or for the use of machine learning algorithms. Through targeted modernization of existing systems, these can also be integrated into higher-level systems or the Industrial Internet of Things (IIOT).

Contact
 Benjamin Massow, B.Sc., M.Sc. | Lecturer Bachelor's program Mechatronics
Benjamin Massow, B.Sc., M.Sc. Lecturer +43 512 2070 - 3938

If you have any questions regarding this research area, please contact us: smartproduction@mci.edu


Research & Transfer Centers
 Benjamin Massow, B.Sc., M.Sc. | Lecturer Bachelor's program Mechatronics
Benjamin Massow, B.Sc., M.Sc. Lecturer +43 512 2070 - 3938
 Thomas Hausberger, BSc, MSc | Teaching & Research Assistant Bachelor's program Mechatronics
Thomas Hausberger, BSc, MSc Teaching & Research Assistant +43 512 2070 - 3954
Prof. Dr.-Ing. Sebastian Repetzki | Mechanical Engineering Bachelor's program Mechatronics
Prof. Dr.-Ing. Sebastian Repetzki Mechanical Engineering +43 512 2070 - 3932
Prof. Dr. rer. pol. Antje Bierwisch | Management, Strategy & Innovation Bachelors's program Business Administration / online
Prof. Dr. rer. pol. Antje Bierwisch Management, Strategy & Innovation +43 512 2070 - 4233
   |
Prof. Dr. Oliver Som | Innovation & Technology Management Master's program International Business & Management
Prof. Dr. Oliver Som Innovation & Technology Management +43 512 2070 - 3132
Dr. techn. Franz-Josef Falkner | Lecturer Bachelor's program Mechatronics
Dr. techn. Franz-Josef Falkner Lecturer +43 512 2070 - 3935
Mag. Anita Onay, PhD | Senior Lecturer Master's Program Industrial Engineering & Management
Mag. Anita Onay, PhD Senior Lecturer +43 512 2070 - 4131
DI Christina Stampfer | Teaching & Research Assistant Bachelor's program Industrial Engineering & Management
DI Christina Stampfer Teaching & Research Assistant +43 512 2070 - 4151
Assoz. FH-Prof. Dipl.-Ing. Dr. techn. Manuel Ferdik | Digitilization & Data Science Bachelor's program Industrial Engineering & Management
Assoz. FH-Prof. Dipl.-Ing. Dr. techn. Manuel Ferdik Digitilization & Data Science +43 512 2070 - 4128
Prof. Dr.-Ing. Martin Pillei, BSc MSc | Head of Department & Studies Bachelor's program Industrial Engineering & Management
Prof. Dr.-Ing. Martin Pillei, BSc MSc Head of Department & Studies +43 512 2070 - 4133

Industry 4.0 Readiness - Model for determining the Industry 4.0 maturity level for medium-sized companies
Duration:
2016 - 2017

Project Lead:
FH-Prof. Dr. Oliver Som
Mag. (FH) Mario Moser, MSc

Description:
The "Industry 4.0 Readiness" project aims to create a standard framework in the sense of a matrix of terms and methods and thus to offer small and medium-sized companies the opportunity to incorporate the currently widely used term "Industry 4.0" into their own entrepreneurial thinking and actions.

EMPATHIC
Duration:
2017 - 2021

Project Lead:
FH-Prof. Dr. Stephan Schlögl

Description:
The EMPATHIC (Empathic, Expressive, Advanced Virtual Coach to Improve Independent Healthy-Life-Years of the Elderly) project aims at exploring and validating new paradigms and platforms, laying the foundation for future generations of Personalized Virtual Coaches to assist elderly people living independently at and around their home. Innovative multimodal face analytics, adaptive spoken dialogue systems and natural language interfaces are part of what the project investigates, in order to help dependent aging persons and their carers. The project uses remote non-intrusive technologies to extract physiological markers of emotional states in real-time for online adaptive responses of the coach, and advance holistic modelling of behavioral, computational, physical and social aspects of a personalized expressive virtual coach. It further aims at developing causal models of coach-user interactional exchanges that engage elders in emotionally believable interactions keeping off loneliness, sustaining health status, enhancing quality of life and simplifying access to future telecare services.


Collaborative robotics for the production of individual stainless steel furniture
Duration:
2018 - 2019

Project Lead:
Benjamin Massow, B.Sc., M.Sc.

Team:
Matthias Decker, MSc

David Altmann, BSc

Description:
In the course of the feasibility study, the entire manufacturing process should be recorded and analyzed. Processes that show potential for automation are collected and initial concepts for solutions are developed. As a result, a rough concept for the implementation of the automation is created for each automatable process. A risk analysis, a required security concept and an associated workflow are created for each process. The risk analysis includes both technical difficulties of implementation and integration into the existing manufacturing process, as well as financial risks of implementation. The safety concept considers all risks of the human-machine interface, all possible contact areas and the necessary safety technologies. A created workflow describes the integration of the possible solution into the existing processes or the redesign of these.

Digital Innovation Hub West (DIH West)
Duration:
2019 - 2022

Project Lead:
FH-Prof. Dr. Oliver Som

Team:
Juliana Pattermann, BA MA

Benjamin Massow, B.Sc., M.Sc.

Description:
The DIH West aims to support SMEs in Western Austria in the process of digital transformation. Further, it aims to strengthen these companies' innovation potential by providing them with institutionalized access to the know-how of research institutions through various activities. In accordance with the needs of SMEs in Salzburg, Tyrol and Vorarlberg, the DIH West focuses on the following application areas: Industry 4.0 for manufacturing companies, and e-services for companies in the tourism, trade, and commerce industry. The activities of the DIH West concentrate on information, individual counselling, further education, thematic networking of SMEs and research institutions through working groups, and the transfer of research results into standardized services such as guidelines, modular systems, etc. These services will be accompanied by individual coaching and support measures as well as access to the relevant infrastructure of the research institutions. The DIH West consists of region-specific agencies, interest groups, and research institutions from Salzburg, Tyrol, and Vorarlberg, which contribute their respective expertise in order to support SMEs.

Project homepage:
https://dih-west.at

Operational Excellence in the Pharmaceutical Industry
Duration:
2015 - 2016

Project Lead:
FH-Prof. Dr.-Ing. Gerhard Hillmer, MSc

Team:
Dr. Peter Bekk

Mag. (FH) Mario Moser, MSc

Description:
This scientific study is used to take stock and analyze existing operational excellence approaches in the process industry. The analysis is carried out with the help of selected companies in the process industry. This is intended to identify critical success factors for operational excellence. The focus is on the analysis of the currently existing obstacles to implementation of operational excellence approaches as well as the development of potentials for an integrative, holistic production system.

Flexible and modular production automation
Duration:
2020 - 2022

Team:
Armin Lechner, BSc, MSc, MBA

Benjamin Massow, B.Sc., M.Sc.

Description:
The aim of this project is the development of a holistic strategy for long-term (i) flexibility and automation of production capacities as well as (ii) data integration and networking. This should enable smaller batch sizes in particular to be produced more efficiently and orders with high traceability requirements - e.g. from the aerospace sector - to be accepted. For the long-term flexibility and automation of production capacities, concepts for flexible machine equipment with the help of collaborative robot systems are to be developed and evaluated using functional prototypes. For data integration and networking, in the course of the project (i) a project planning of customers to be advised in the long term, (ii) derived requirements for the systems, (iii) an inventory of the current systems and (iv) a project planning of the systems to be created are carried out.

WTZ West - 2019
Duration:
2019 - 2021

Team:
Mag. Desiree Wieser, PhD

Armin Lechner, BSc, MSc, MBA

FH-Prof. Mag. Dr. Claudia Mössenlechner

Thomas Margreiter, BSc

Benjamin Massow, B.Sc., M.Sc.

Description:
The WTZ West (Knowledge Transfer Center West) has set itself the goal of intensifying and professionalizing the transfer of knowledge in the long term through joint measures, opening up potential for exploitation, deepening and expanding cooperation with business and society as a whole, pursuing transdisciplinary and innovative approaches and possible synergies to use to achieve these goals. Six universities and five technical colleges from Vorarlberg, Tyrol, Salzburg and Upper Austria form the consortium of the Knowledge Transfer Center West.

Development prospects of industrial medium-sized companies
Duration:
2016

Project Lead:
FH-Prof. Dr. Oliver Som

Description:
As an "innovation engine", German medium-sized companies are one of the central pillars of innovation and technological performance - and thus of the international competitiveness of German industry. Small and medium-sized enterprises (SMEs) play a prominent role in industrial innovation chains when it comes to bringing new technologies into industrial application. The project team, consisting of researchers from MCI, Fraunhofer ISI and Free University of Berlin, focuses on the extent and distribution of different innovation patterns of SMEs in the German manufacturing sector. Among other research goals, on the basis of the existing innovation patterns, the extent to which the different types are captured by existing instruments and programs of public funding policy in Germany and Europe are examined. It will also be evaluated, how the specific needs and challenges of the different innovation patterns can become more targeted and more efficiently in the future through tailored policy measures.

Use of neural networks for automated data analysis
Duration:
2018 - 2019

Project Lead:
Benjamin Massow, B.Sc., M.Sc.

Team:
Alexander Fritzsche, BSc

René Nußbaumer, BSc, MSc

Sebastian Stabinger, MSc

Description:
The aim of the project is to develop an automated evaluation of qualitative data using an algorithm. For the first time, qualitative data can be evaluated deterministically and in real time. For the first time, any number of qualitative data should be evaluated and analyzed in one click. As a result, a significant time saving by the researcher can be achieved and it qualitative data in representative sample sizes are analyzed. In addition, the qualitative data can be evaluated in real time. Companies can be informed about critical deviations of planned customer experiences in real time. This innovative approach represents a new product in a new and rapidly growing market.

Project partners:
More than Metrics
Unternehmenssektor Inland
Mohemian Services
Unternehmenssektor Inland

DeepQualityControl – Quality Assurance System Based on Machine Learning
Duration:
2019 - 2021

Project Lead:
Benjamin Massow, B.Sc., M.Sc.

Description:
In industry, due to increasing complexity, there is an increasing demand to integrate image processing systems into machines and systems. In most cases, standard image processing methods with object-recognizing properties are used. Basically, a system is created with sample images that can recognize the orientation or dimensions as well as edges and shapes in an image. The comparison with so-called target images is also a common method. The problem with such processes is the high susceptibility of the systems to small changes to the product. Likewise, complex structures can only be evaluated with difficulty or not at all. Another disadvantage is very high computing power in order to be able to guarantee the cycle times required for industrial plants. The objective of this project is the integration of machine learning algorithms into industrial quality control. Other areas of application of such neural networks suggest that the disadvantages of conventional image processing methods can be minimized in many applications and that there are numerous cases in which it is only possible to achieve a positive result using such a method.

Useful application possibilities for collaborative robots
Duration:
2019

Description:
The present project concept examines the potential of different collaborative robots in relation to different industrial applications such as packaging, checking, assembling etc. and deals with the development, elaboration and integration of possible applications in an industrial environment. The individual systems are examined with regard to their positioning accuracy, repeatability, handling capacity, safety and user ergonomics. Short- and medium-term potential use cases are collected as comprehensively as possible and grouped according to various criteria, such as area of application, potential benefits and necessary effort. Representative for each group, selected use cases are implemented with collaborative robots from different manufacturers in order to be able to assess the short and medium-term feasibility and necessary technical developments.

Digital Dentures
Duration:
2019 - 2021

Team:
Benjamin Massow, B.Sc., M.Sc.

Matthias Decker, MSc

Description:
Individualized products in the dental field, such as bridges and crowns, are increasingly replacing standardized dentures. This development goes hand in hand with a change in the supply chain or production chain: Classically, these are planned by the dentist using precision impressions and then produced by external dental technicians or prosthetists using steps such as pouring plastic, plaster and pressed ceramic processes. The technicians are increasingly turning to digitizing the impressions or converting them into digital production data and - mostly externally - to have them manufactured using e.g. 5-axis machining centers or additive manufacturing (AM) processes. As a consequence, the generation of the digital production data takes place in some cases directly at the dentist, who then assigns their production directly to the contract manufacturer. For each discrete manufacturing process, 5 - 50 products, e.g. crowns, are produced at the same time in one installation space and, due to their very similar appearance, can only be assigned to individual orders (or patients or dentists) with great difficulty. Previous labeling, e.g. by numbered flags or similar, has reached its limits. They mean additional work and in some cases cannot be used at all in modern manufacturing processes. The aim of this project is the conception and prototypical implementation of a complete system including (i) feed to image processing, (ii) implementation through image processing, (iii) image processing and connection to CAD database, (iv) return and reorientation of unrecognized parts, (v) kinemetics , (vi) various gripper modules, (vii) order-related intermediate storage and (viii) removal and picking of finished orders.


  • Ostheimer, P., Lins, A., Massow, B., Steger, B., Baumgarten, D., Augustin, M. (2022). Extraction of Eye Redness for Standardized Ocular Surface Photography. In: Antony, B., Fu, H., Lee, C.S., MacGillivray, T., Xu, Y., Zheng, Y. (eds) Ophthalmic Medical Image Analysis. OMIA 2022. Lecture Notes in Computer Science, vol 13576. Springer, Cham. https://doi.org/10.1007/978-3-031-16525-2_20
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