Changeable Production Systems Research Articles

Order Management Perspective on Fluid Manufacturing Systems

Market requirements are constantly changing, for the manufacturing industry which results in a volatile environment that poses specific requirements to maintain successful future operations. An important concept to cope with these uncertain requirements is changeability. Over the recent years, specific production systems like the Fluid Manufacturing System (FLMS), were developed which promise to support the required changeability. Previous concepts mainly consider the technical aspects of the production system and not the order management processes. To enable the desired changeability, an order management system covering the production planning, control and monitoring of all activities along the end-to-end value chain from customer inquiry to shipment is needed. In addition to order management, planning and control of logistic processes are affected by the increase of flexibility through FLMS as well, which is also often neglected in changeable production systems such as order management. This paper investigates the impact of changeable production systems onto the order management and logistics. The FLMS forms a respective use case, to identify the required changes within the order management approach. This paper shows that the most significant changes compared to traditional production systems concern production planning and control. Production planning as a static process requires discrete-event production planning results from technological, capacity, economic, social and material flow perspectives. To exploit the degrees of freedom of FLMS, production control is based on a dynamic basis, based on real-time information through Asset Administration Shell (AAS). The main change in logistics is that in FLMS, logistics has the same status as manufacturing. Logistics serves as the interface between order management (digital world) and production (physical world).

Changeable Production Systems for Learning Factories and Research-based Learning

Changeable Production Systems for Learning Factories and Research-based Learning

Fluid Automation — A Definition and an Application in Remanufacturing Production Systems

Production systems must be able to quickly adapt to changing requirements. Especially in the field of remanufacturing, the uncertainty in the state of the incoming products is very high. Several adaptation mechanisms can be applied leading to agile and changeable production systems. Among these, adapting the degree of automation with respect to changeover times and high investment costs is one of the most challenging mechanisms. However, not only long-term changes, but also short-term adaptations can lead to enormous potentials, e.g. when night shifts can be supported by robots and thus higher labor costs and unfavorable working conditions at night can be avoided. These changes in the degree of automation on an operational level are referred to as fluid automation, which will be defined in this paper. The mechanisms of fluid automation are presented together with a case study showing its application on a disassembly station for electrical drives.

Classification coding of production systems for identification of platform candidates

Production platforms present an attractive solution to managing increasing product variety in production systems. Forming production systems platforms can potentially facilitate changeable manufacturing and co-development of products and production systems. One of the first steps in developing a production platform is the identification of the potential candidates. A scheme for classification coding of production systems is presented. Coded production systems can be compared across a manufacturer’s departments in order to identify commonalities between systems, and thus identify candidates for platform development. The production system classification code consists of four sub-codes with four to ten digits each describing physical or logical characteristics on production system or cell levels. The four sub-codes are structured into blocks of classification code, which digitally represent and describe complete production systems. They facilitate the comparison and identification of similarity and commonality patterns using the code digits and values. A production system case study is used for illustration and is represented as a block of code following the developed coding scheme. The production systems classification code can be used as a decision support tool in the early phases of production systems platform development, and for capturing information on the rationale, structure, processes and enablers of flexible and changeable production systems.

A novel I4.0-enabled engineering method and its evaluation

Recent trends show that products are becoming more complex and multi-variant. Therefore, future production systems need to become more advanced in terms of reconfigurability, flexibility, and transformability. To achieve these advancements, future systems must be highly changeable and support plug-and-produce approaches. The majority of today’s engineering methods focus on static workflows based on predefined assets and setups. As a consequence, changes in the production system come with high costs, especially during production process execution. Therefore, new engineering methods are required which are explicitly designed for highly changeable production systems. To contribute towards fully changeable production systems, an I4.0 framework is proposed that covers the entire engineering process. The focus is set on presenting a graphical I4.0-enabled engineering method that enables dynamic workflows with varying assets and setups. Moreover, in order to evaluate the method, a user study was conducted, in which participants were asked to solve multiple engineering tasks by utilizing the presented I4.0-enabled method as well as a conventional approach. The results indicated that the proposed I4.0-enabled engineering method significantly outperformed the conventional method in terms of required engineering times and subjective ratings.

Decentralized Control of Logistic Processes in Cyber-physical Production Systems at the Example of ESB Logistics Learning Factory

The increasing emergence of cyber-physical systems (CPS) and a global crosslinking of these CPS to cyber-physical production systems (CPPS) are leading to fundamental changes of future work and logistic systems requiring innovative methods to plan, control and monitor changeable production systems and new forms of human-machine-collaboration. Particularly logistic systems have to obey the versatility of CPPS and will be transferred to so-called cyber physical logistic systems, since the logistical networks will underlie the requirements of constant changes initiated by changeable production systems. This development is driven and enhanced by increasingly volatile and globalized market and manufacturing environments combined with a high demand for individualized products and services. Also nowadays mainly used centralized control systems are pushed to their limits regarding their abilities to deal with the arising complexity to plan, control and monitor changeable work and logistic systems. Decentralized control systems bear the potential to cope with these challenges by distributing the required operations on various nodes of the resulting decentralized control system.Learning factories, like the ESB Logistics Learning Factory at ESB Business School (Reutlingen University), provide a wide range of possibilities to develop new methods and innovative technical solutions in a risk-free and close-to-reality factory environment and to transfer knowledge as well as specific competences into the training of students and professionals. To intensify the research and training activities in the field of future work and logistics systems, ESB Business School is transferring its existing production system into a CPPS involving decentralized planning, control and monitoring methods and systems, human-machine-collaboration as well as technical assistance systems for changeable work and logistics systems.

Design of Changeable Production Units within the Automotive Sector with Axiomatic Design

Corporations within the automotive sector are faced with major challengescaused among othersby high volatile demand fluctuations. An answer to this problem is given by flexible and changeable production systems which allow reactingefficiently to these fluctuations in demand. To achieve this aim, so called enablers for changeability have to be considered already during the design process of single production units within a whole production system. Furthermore the complexity of production units has to be reduced in order to obtain changeable and reliable manufacturing systems.In the presented paper an approach is proposed with the aim to consider enablers of changeability in Axiomatic Design (AD). Moreover it is outlined which different interest groups within a single corporation have a direct or indirect influence on production unit design. Frequently complexity originates from many different attributes demanded by the single groups. On this basis, it is demonstrated how AD can help to enter into dialogue with the mentioned groups with the aim to lower the requirements of the production unit and to reduce complexity of the resulting design. This is done by using the design matrix which illustrates the dependencies between functional requirements (FRs) and design parameters (DPs) as a basis for discussion.

Developing products for changeable learning factories

Experiential teaching and research facilities such as learning factories provide a favorable environment and tools to develop, test and implement new products and manufacturing systems concepts and solutions. Learning factories that are geared towards the capabilities and requirements of changeable manufacturing systems (CMS) are used for validating new products, their variants and changeable production systems design, planning and control methods. Existing approaches of developing new products are reviewed, and a new approach to develop products for changeable learning factories is presented and validated by a case study. The new development approach is dedicated to changeable learning manufacturing systems by choosing a product line among many candidates to suite the learning system capabilities and desired learning outcomes. Designs for product variants that suite the learning factory and planned learning scenarios and training experience. This paper focuses on learning factories for changeable manufacturing systems, which are constructed to possess the necessary changeability enablers such as mobility, modularity, scalability, universality and compatibility. It is challenging to associate learning and research to a changeable manufacturing system.

Planning and Optimization of Changeable Production Systems by Applying an Integrated System Dynamic and Discrete Event Simulation Approach

Today, the planning and optimization of changeable production systems (CPS) has become a top priority item for the strategic management in most industries. Research has already provided a whole bunch of supposedly effective, mainly technical solutions that enhance the changeability of production systems. However, to actually select solutions for CPS, decision-makers do also need tools for their ex-ante evaluation. These are still missing – especially to assess the impact of organizational measures. Further, it appears that decision-makers have difficulties in coping with the dynamic complexity implied by CPS in planning activities. CPS are complex socio-technical systems – both in terms of their structure and their dynamic behavior – and so must be the underlying planning problems. Thus, any planning approach and in particular its related decision support tools must not only fit the dynamic complexity of the planning problem as a formal requirement, but must at the same time foster the decision-maker's understanding of the underlying managerial problem, i.e. it must foster managerial insight.Therefore, this paper aims at closing this gap and proposes an integrated planning approach based on a hybrid Discrete Event Simulation (DES) and System Dynamics (SD) framework. Both simulation methods are interlinked by an integrated planning approach sharing the same conceptual model and a common set of parameters and key performance indicators (KPI). The DES is applied (1) for an in-depth analysis of the effects of any measures to improve the changeability, based on KPIs relevant to the decision-maker, and (2) to verify the major parameters utilized in the SD model. However, the DES can only be applied for a given structural state of the system. Consequently, SD is utilized to investigate its dynamic behavior when it changes from one structural state to the other. The planning approach was successfully applied in an industry context, where it proved its ability to actually leverage the decision quality in current practice to manage CPS.

Control Theoretical Modeling of Transient Behavior of Production Planning and Control: A Review

To remain competitive, manufacturers need to adapt to increasingly dynamic and turbulent markets; therefore, production engineers and business managers need tools for mathematically modeling, analyzing and designing agile and changeable production systems that incorporate policies that are robust in the presence of disturbances and mitigate the negative impacts of turbulence in the production environment. The spectrum of potential contributions of control theory to understanding the dynamic behavior of production systems in the presence of turbulence is broad. In this paper, the focus is on classical control theoretical modeling of the transient behavior and fundamental dynamics of production planning and control, which generally is considered to include scheduling, sequencing, loading and controlling. Publications in this area in recent years are reviewed, with contributions reported in publications of the CIRP (International Academy for Production Research) receiving particular emphasis.

Changeable production systems by the use of a holistic modularization: considering of technology, organization and staff

The steadily alternating and unpredictable requirements for industrial companies implicate the challenge to enhance production systems. As a consequence, the need for action leads to a paradigm shift in production. A transformation from a flexible production to a changeable company occurs. In this context, changeability will be decisive for the desired success. Production systems are used for the purpose of manufacturing products and are attributed to socio-technical systems. A production system is a unit used to realize the production and can include a single machine as well as a complete production area or the whole factory. The staff is also part of production systems and does not only assume organizational responsibilities but also technical tasks. Many already known drivers of change, e.g. new innovations in technologies, have an impact on these socio-technical systems. One chance to react to changing requirements is to apply the principle of modularization. Conventionally, modularization is considered in a technical context. The consideration of staff and organization is disregarded. This article introduces a new approach to design changeable production systems on the basis of modularization, including the dimensions of technology, organization and staff, which will enable industrial companies to make these complex socio-technical systems manageable.

Changeability by a Modular Design of Production Systems – Consideration of Technology, Organization and Staff

The steadily alternating and unpredictable requirements for industrial companies implicate the challenge to enhance production systems. As a consequence, the need for action leads to a paradigm shift in production. A transformation from a flexible production to a changeable company occurs. In this context, changeability will be decisive for the desired success. Production systems are used for the purpose of manufacturing products and are attributed to socio-technical systems. A production system is a unit used to realize the production and can include a single machine as well as a complete production area or the whole factory. The staff is also part of production systems and does not only assume organizational responsibilities but also technical tasks. Many already known drivers of change, e.g. new innovations in technologies, have an impact on these socio-technical systems. One chance to react to changing requirements is to apply the principle of modularization. Conventionally, modularization is considered in a technical context. The consideration of staff and organization is disregarded. This article introduces a new approach to design changeable production systems on the basis of modularization, including the dimensions of technology, organization and staff, which will enable industrial companies to make these complex socio-technical systems manageable.

Valuation of changeable production systems

The introduction of changeable production systems increases the adaptability of factories in an uncertain environment. This paper analyzes the valuation of the economic efficiency of such systems using the real options theory. The developed approach proposes a structured valuation process for selecting options related to production engineering and factory planning. Based on a classification of possible modifications of a production system, options profiles are derived. Considering the existing uncertainty of a company’s environment these profiles may be utilized to identify real options which ought to be included when valuating a technical production system design.

Robotic assembly of wire harnesses: Economic and technical justification

Market requirements are constantly changing, for the manufacturing industry which results in a volatile environment that poses specific requirements to maintain successful future operations. An important concept to cope with these uncertain requirements is changeability. Over the recent years, specific production systems like the Fluid Manufacturing System (FLMS), were developed which promise to support the required changeability. Previous concepts mainly consider the technical aspects of the production system and not the order management processes. To enable the desired changeability, an order management system covering the production planning, control and monitoring of all activities along the end-to-end value chain from customer inquiry to shipment is needed. In addition to order management, planning and control of logistic processes are affected by the increase of flexibility through FLMS as well, which is also often neglected in changeable production systems such as order management. This paper investigates the impact of changeable production systems onto the order management and logistics. The FLMS forms a respective use case, to identify the required changes within the order management approach. This paper shows that the most significant changes compared to traditional production systems concern production planning and control. Production planning as a static process requires discrete-event production planning results from technological, capacity, economic, social and material flow perspectives. To exploit the degrees of freedom of FLMS, production control is based on a dynamic basis, based on real-time information through Asset Administration Shell (AAS). The main change in logistics is that in FLMS, logistics has the same status as manufacturing. Logistics serves as the interface between order management (digital world) and production (physical world).