This overview describes our project still in development phase.
SemGo – Extraction of semantic relationships between business process models to support model integration in the context of the consolidation of heterogeneous operational information systems
Nowadays, business process models significantly influence the organizational structure and process organization of companies and constitute an important competitive factor. Due to the broad range of tasks covered by business process management, which considers processes from an evolutionary point of view, within a life cycle, variants and versions of process models increasingly emerge within companies. This emergence’s reason lies in the continuous optimization of processes and the corresponding process models. Owing to the considerable number of process models within companies, the development of new processes holds an extensive potential of reuse, analogously to the reference process models.
Therefore, the goal of the SemGo project plan it to implement an efficient reuse of processes and software modules involving an extensive process model repository. Thereby, an identification of similar process models and also process model variants shall be realized by means of tool support and semantic search.
SemGo is finaced by the Federal Ministry of Education and Research (ger. BMBF).
SiCoCheC – Secure Compliance Checker from the Cloud
Many production companies have to use hazardous substances during their manufacturing process. In order to prevent environmental damages and health risks the European Union established legal upper limits for the usage of critical substances. To ensure their compliance, enterprises have to document all hazardous ingredients and need to exchange this information with partners within their production chain and final consumers.
SiCoCheC is a project, which aims to reduce additional work and costs by simplifying the data exchange and documentation effort of product’s ingredients. Therefore, SiCoCheC uses cloud computing techniques and a database that will store and validate all important information about the composition of (intermediate) products. However, to prevent product imitation sensitive data is only stored temporarily. This makes it possible for partners and final consumers to access information about the product composition by using e.g. barcodes and serial numbers.
Ecological Workflow Patterns: Optimization of Business Processes by means of Ecological Design Patterns (GreenFlow)
A classical issue of Business Process Management (BPM) concerns the optimization of business processes on the base of economic criteria like lead time or use of resources. In the last years the BPM viewing angle extended, in addition to economic factors, also to ecological ones. The idea of optimization of business processes with regard to ecological aspects like energy consumption is summarised under the expression “Green Business Process Management” (Green BPM). One BPM method, which is traditionally used to optimize economic and ecological factors, is the business process simulation. Through simulation it is possible to analyse the impact of changes to a business process on dependent variables such as the energy consumption before the rollout of an optimized process. This has the advantage of minimizing the risk of occurrence of unexpected negative effects after the rollout of the revised process. In this way a new revision would not occur, which is a huge organizational burden for larger companies. The application of simulation studies is often not possible for small and medium-sized enterprises (SMEs) since the necessary technical and methodological competence is missing. Nevertheless, SMEs are also interested in optimizing their processes towards sustainability. The solution to this dilemma lies in the provision of business process management tools that allow SMBs to examine their business processes without profound expertise concerning potential for improvement from the environmental point of view.
Although a number of approaches to optimize business processes in terms of environmental objectives have been proposed in research, many of the techniques suffer from the poor availability of necessary data or skilled personnel that are familiar with the method. With GreenFlow we aim at developing an approach to optimize business processes with regard to the consumption of energy or natural resources. The approach will be easy-to-apply and draw from the principles of the design patterns originally proposed by Erich Gamma. The so-called Ecological Workflow Patterns (EWPs) are intended to provide information on good and bad practice in terms of ecological process design that can easily be applied to existing processes to find ecological shortcomings that should be revised to foster eco-friendly processes. To provide a proper tool support, the patterns will be implemented in state-of-the-art BPM tools such as the ARIS platform which not only allows to evaluate existing processes, but to include the Ecological Workflow Patterns in the initial stage of process design.
GreenFlow is financed by the Federal Ministry for Education and Research and is expected to be completed within September 30, 2017.
Project partner: Software AG
Various types of charts have become established in business process management in order to visually illustrate the processes of a company. Particularly in the early design phase of process models, these diagrams are often sketched in workshops. The simplicity of sketching and the intuitive handling contribute to the popularity of this method. Compared to digital modeling with dedicated software, the sketching of diagrams has a major disadvantage: sketches can now be photographed and thus stored digitally, but they are not recorded in their semantics in order to be further defined in existing digital modeling tools. The project INDIGO closes this gap. Based on a photo of a hand-sketched process diagram, a digital machine-readable process diagram is created using DeepLearning. These diagrams can be invited, processed and integrated into the existing process landscape by means of adapted interfaces in the modeling tools of the industrial partner Software AG.
The project, financed by Software Campus, was launched on April 2016 and is expected to be complete at the end of September 2017.
Due to the continuously increasing number of production orders with high number of units as well as the simultaneous demand for the production of orders with small numbers of units, the manufacturing industry is particularly affected by dynamic changes in the industry 4.0. Through the introduction of concepts such as the continuous networking of production machines to cyber-physical systems, the basic infrastructure necessary to implement this dynamic was created. Various sensors within production machines make enormous amounts of data accessible to a variety of analysis purposes, depending on the size of the production lines.
In order to implement the necessary flexibility also within the coarse and detailed planning of the production, complex dependencies between different information on order situation, personnel and machine availability must be used. So far, production planning in real-time is not possible or can not be adapted, but is carried out with a time offset from hours to days before the actual production.
In the project ProPlanE, an analysis platform is designed and implemented to integrate data from different systems and to support a production planning in real time with the aid of process mining. Continuous monitoring of expiring processes with respect to the dimensions of time, resources and costs allows a continuous adjustment of the production planning as well as a flexible handling of short-term changes in the production process.
Tha project was launched in January 2017 and is expected to be completed at the end of July 2018.
PRODIGY – Business Process Management using Big Data Driven Predictive Analytics
A thorough analysis of the scientific contributions to SCM planning and practical applications in industry shows that the decision makers are confronted with complex phenomena when modeling uncertainties in different production process parameters. A complete ignorance of such uncertainties or modeling based on false subjective assumptions would lead to undesirable organizational and economic consequences. Big-Data-assisted Predictive Analytics enables the integration of the methodological estimation of ambiguity in production planning. It is also very important to systematically capture the entire process chain for robust production planning under uncertainty in order to ensure the integrity and reliability of the entire information. The research project PRODIGY will examine the feasibility of the integration of the components from Business Process Management and Predictive Analytics, both theoretically and empirically, in order to be able to deal with the resulting uncertainty in production planning more efficiently. The current state of research and technology does not provide a sufficient solution. PRODIGY aimes at the development of a methodological framework as well as an executable prototype for the recording of the required production planning processes. Furthermore, the prediction of the necessary variables, the estimation of the relative uncertainties in these process parameters and the optimization of the defined objectives are to be established in the context of the summarized information on the entire process chain.
The project, carried out in cooperation with the industrial partner Software AG, was launched on April 1st, 2016 and it is expected to be completed in September 2017.
iPRODICT – Intelligent Process Prediction based on Big Data Analytics
In an interdisciplinary team of researchers and industry experts (Blue Yonder GmbH, Fraunhofer-Insitut für Intelligente Analyse- und Informationssysteme, Pattern Recognition Company GmbH, Sofware AG, Saarstahl AG), iPRODICT explores an intelligent approach to the semi-automated adjustment and improvement of business processes. In addition to the analysis of collected process data as well as the real-time evaluation of current context information from sensor networks, the optimal process sequence is anticipated in an innovative manner using forecast calculations (iPRODICT). In this way, processes can be individually adapted to the respective context situation by means of big-data analysis methods in real-time. The developed iPRODICT approach is implemented, tested and validated in the form of an integrated prototype within an ambitious application scenario from the process production at the application partner Saar-stahl AG. On the one hand, this should underline the feasibility of the approach and, on the other hand, make the utility potentials, such as the early anticipation of process problems, based on the analysis of large amounts of data measurable.
The project, financed from the Federal Ministry for Education and Research, was launched in September 2014 and is expected to be completed in August 2017.
PREFLOW – Textile-based assistance system for the analysis of physically demanding work processes
Demographic change is accompanied by an aging population. Expecially physically challenging occupations can only be exercised into advanced age if they are adequately healthy. Thus, systems which enable an ergonomic and preventive workplace design are extremely necessary.
This is where the project PREFLOW starts. A textile-based assistance system in the form of intelligent clothing should be developed in order to analyze work processes in physically demanding professions. Taking into account data protection, individual work steps are examined during the execution in order to determine their risk potential with regard to non-ergonomic movements.In order to support the health of the employees during the work steps, the intelligent clothing’s proposals for risk-minimizing behaviour are communicated via direct feedback (audio-visual, tactile). On the basis of the ergonomic data obtained, improvements of the work processes and the connected workflow management systems, an optimization of the work sequences as well as risk assessments of workplaces are to be carried out.
The connection between the recording of ergonomic data during the work process by means of intelligent clothing with its immediate evaluation and the immediate feedback of a possible risk and possibly risk-reducing behaviors is a unique innovation for keeping healthy during working hours.
The project coordinator is the Informations-Technologie für Menschen GmbH (ITfM). PREFLOW is carried out in cooperation with the ITP GmbH – Society for intelligent texile products and the University of Kassel. Associated partners are: Volkswagen AG – Vertrieb Originalteile (Ersatzteillogistik), Unabhängiges Datenschutzzentrum Saarland, Bayern Innovativ – Gesellschaft für Innovation und Wissenstransfer mbH, DB Fahrzeuginstandhaltung GmbH – Werk Kassel.
PREFLOW, financed by the Federal Ministry for Education and Research, has been launched in April 2015 and will be brought to a successful conclusion within July 2017.