Software-Tool To Determine Functional Flexibility Based On Employee Specific Risks

During training of manual assembly operations, all employees experience continuous improvement caused by learning. This improvement is known as learning behaviour and describes individual improvement of competence and skill. In an assembly system, employees are required to learn various tasks to ensure overall productivity. Job rotation supports the constant change of tasks to enable an environment where employees maintain their skills by changing tasks in defined time-frames. Functional flexibility describes how many employee-workstation-combinations are possible and needs to be determined based on internal and external factors. Especially employee specific risks are predominant in terms of affecting the outcome and can be encountered by considering these risks when determining the level of functional flexibility. This paper provides an approach to assess employee specific risks with the goal to deduct an expected impact. An overall approach describes the process in order to implement a software-tool to determine the level of functional flexibility. The result is considered a tool to support the decision-making process of leaders and executives in production systems regarding determining a necessary competence matrix

Applicability Of Gamification In Industrial Work Processes To Influence Target Variables

Today more than ever, companies are forced to regularly initiate projects in their factories in order to ensure competitiveness through constant adaptation and change. Such projects are controlled and managed individually. Implementing many projects frequently leads to situations however, in which different projects overlap regarding their planning and control. Deviation in the projects’ duration and scope further intensifies this effect. To manage environments consisting of projects with different scopes and timelines, companies make use of models for multi-project management (MPM). Due to their aim for general validity, existing models for MPM generally lack a specific focus on the targets and tasks of the factory environment. A new process model is therefore needed to effectively and efficiently plan and control a multi-project environment in the factory. Therefore, the project context and the interdependencies of the model’s tasks shall also be taken into account. In order to build a process model for multi-project management, according to the requirements of the factory, insights from MPM as well as the production environment are needed. In this article an overview of the approach is given and first findings are presented. Based on analogies between models of MPM and production planning and control (PPC), an exemplary excerpt of a combined know-how catalogue is shown, laying the foundation for the further development of a holistic process model

Deriving of Sequencing Strategies for Multi-Stage Productions Supported by Logistic Models and Software Tools

Sequencing as a core task of production control has a significant influence on the logistical performance and efficiency of a single work system. Particularly in the presence of sequence dependent setup times, systematic sequencing can increase the productivity of a work system by saving them. This, however, leads to a decreasing schedule reliability of the work system, which creates an area of conflict. In recent years, mathematical models have been developed at the Institute of Production Systems and Logistics (IFA) that describe the influence of different sequencing rules on the schedule reliability and productivity of a work system. In a further step, these single so-called partial models can be linked with each other. This allows a calculation of the lateness behaviour of a multi-stage production in dependency of the sequencing rules assigned to the individual work system and thus of the overall sequencing strategy. This paper presents the possibilities of linking different logistic models in order to quantify the influence of sequencing on logistic target values as well as two software tools by which the impact and combination of various sequencing rules can be examined based on production feedback data or by means of a generic supply chain. As a result, it is possible to assess different sequence configurations of a multi-stage production and thus strategically align the production in the area of conflict between productivity and schedule reliability

Data-based identification of throughput time potentials in production departments

Logistics performance becomes an ever more important strategic factor for manufacturing companies to obtain a competitive advantage. Yet, numerous companies fail to meet their own corporate goals or customer requirements. One of the most important objectives in logistics is speed in terms of short delivery times which are mainly determined by the production throughput times. Derivation of effective improvement measures requires a profound understanding of logistic cause-effect relationships. At a time of increasing digitalization, an increasing amount of feedback data is available that offers great potentials to discover novel insights. Yet, the vast amount of data can also be overwhelming and result in unsystematic and ineffective analysis of less meaningful data. Therefore, in this paper a systematic procedure is presented that allows data-based identification of throughput time potentials in production departments. The quantitative analysis framework is based on a generic driver tree structuring the influencing factors on throughput time. The approach will boost the understanding about logistics relations and will particularly help SMEs to focus on the most relevant influencing factors and data. Furthermore, it provides a basis for future more advanced information systems that will help companies to continuously improve their logistics performance and adapt their supply chains to ever-changing conditions

Lean Changeability – Evaluation and Design of Lean and Transformable Factories

In today-s turbulent environment, companies are faced with two principal challenges. On the one hand, it is necessary to produce ever more cost-effectively to remain competitive. On the other hand, factories need to be transformable in order to manage unpredictable changes in the corporate environment. To deal with these different challenges, companies use the philosophy of lean production in the first case, in the second case the philosophy of transformability. To a certain extent these two approaches follow different directions. This can cause conflicts when designing factories. Therefore, the Institute of Production Systems and Logistics (IFA) of the Leibniz University of Hanover has developed a procedure to allow companies to evaluate and design their factories with respect to the requirements of both philosophies

Changeability of Business Organizations

Nowadays companies are facing an increasing turbulent environment. It is more and more important to react fast on changes to stay competitive. But not only the technology has to be adaptable; also the frame conditions for the production have to adapt as fast as the other elements of a manufacturing company. Therefore, the Institute of Production Systems and Logistics of the Leibniz University of Hanover has implemented a research project to describe and develop changeable organizational structures. The results of the analysis, which design principles can be used to evolve an organizational structure of a factory regarding their changeability will be presented in this paper

Multi-Objective Optimization of an Aerodynamic Feeding System Using Genetic Algorithm

Considering the challenges of short product life cycles and growing variant diversity, cost minimization and manufacturing flexibility increasingly gain importance to maintain a competitive edge in today’s global and dynamic markets. In this context, an aerodynamic part feeding system for high-speed industrial assembly applications has been developed at the Institute of Production Systems and Logistics (IFA), Leibniz Universitaet Hannover. The aerodynamic part feeding system outperforms conventional systems with respect to its process safety, reliability, and operating speed. In this paper, a multi-objective optimisation of the aerodynamic feeding system regarding the orientation rate, the feeding velocity, and the required nozzle pressure is presented

Cyber-physical production systems combined with logistic models - a learning factory concept for an improved production planning and control

Nowadays, production enterprises are faced with an array of challenges including increasing pressure regarding costs, demands for individualized products as well as the growing significance of logistic performance and costs, to name just a few. These in turn give rise to special requirements that the production planning, control and monitoring, among others, need to meet with suitable methods and techniques. Within this context, developments such as Industry 4.0 and cyber-physical production systems on the technology side, and approaches such as innovative learning factories for training employees hold great potential. This paper clarifies the advantages of cyber-physical systems in view of production planning, controlling and monitoring. Based on that, using the concept of IFA's Learning Factory, it describes how these can be specifically utilized in applying logistic models to improve order processing

Exploring the Potential of Digital Twins for Production Control & Monitoring

The achievement of a high level of logistical performance is a primary goal of manufacturing companies. In order to remain competitive, companies must constantly improve order processing to ensure short delivery times and high delivery reliability. Production planning and control is a core function of manufacturing companies and is responsible for routing production orders through the stations involved in order processing such as procurement, production and dispatch. Yet managing production efficiently and achieving a high logistical performance remains a genuine issue, even with increasingly digitalized and automated processes. The concept of the digital twin promises an improved database to enable companies to reach more informed decisions. As yet, the potential for utilizing this database has not been thoroughly explored in the context of a constant measurement of the backlog and output. In addition, there are various divergent definitions and approaches to the application of digital twins. This paper discusses the potentials of the different tasks of production control and monitoring in relation to the acquisition of dynamic data in real-time. A method is provided that continuously calculates the backlog and output. Furthermore, an example of application is presented that shows how this information can be used for production control. Our results indicate that by exploiting this information, logistic performance can be improved