- Email: [email protected]
Evolution of SMEs towards Industrie 4.0 through a scenario based learning factory training
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Procedia Manufacturing 00 (2017) 000–000 Available online Available onlineatatwww.sciencedirect.com www.sciencedirect.com Procedia Manufacturing 00 (2017) 000–000
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Procedia Manufacturing 23 (2018) 141–146 Procedia Manufacturing 00 (2017) 000–000 www.elsevier.com/locate/procedia
8th Conference on Learning Factories 2018 - Advanced Engineering Education & Training for 8th Conference on Learning Factories 2018 - Advanced Engineering Education & Training for Manufacturing Innovation Manufacturing Innovation
Evolution of SMEs towards Industrie 4.0 through a scenario based Evolution SMEs towards Industrie 4.0 through a scenario based Manufacturingof Engineering Society International Conference 2017, MESIC 2017, 28-30 June learning factory training 2017, Vigo (Pontevedra), Spain learning factory training Wienbruch, Thoma*; Leineweber, Stefana; Kreimeier, Dietera; Kuhlenkötter, Bernda
a a Costing models capacity Stefan optimization inDieter Industry 4.0: Trade-off Wienbruch, Thomafor *; Leineweber, ; Kreimeier, ; Kuhlenkötter, Bernda Ruhr-Universität Bochum, Chair of Production Systems, Universitätsstaße 150, 44801 Bochum, Germany between usedChair capacity and Universitätsstaße operational efficiency Ruhr-Universität Bochum, of Production Systems, 150, 44801 Bochum, Germany a a
Abstract Abstract a,* in the manufacturing The concept of Industrie 4.0 A. has Santana initiated ana,evolutionary process industry.b Especially small and medium P. Afonso , A. Zaninb, R. Wernke The of Industrie 4.0 has initiated an evolutionary processthis in the manufacturing industry. Especially smallconcept and medium sizedconcept companies lack the capacities and knowledge to accomplish process autonomously. The learning factory offers University of Guimarães, sized companies thecompanies capacities within and aknowledge toMinho, accomplish process Theenterprises learning factory offers the possibility to lack second this process. This4800-058 paperthis will presentautonomously. aPortugal concept of how can be concept trained within b Unochapecó, 89809-000 Chapecó, the possibility secondfactory companies this process. This paper will present aBrazil concept of howregarding enterprises be dimensions trained within realm of a to learning basedwithin on scenarios of different Industrie 4.0SC, evolutionary steps thecan three of the sociotechnical realm of a learning factory based on scenarios Industrie evolutionary steps regarding the three dimensions of approach. These scenarios focus of ondifferent demonstrating and4.0 assessing the dependencies of assorted characteristics the sociotechnical approach. scenarios supporting focus on demonstrating andprocess assessing dependencies of assorted characteristics of Industrie 4.0 category groups.These An important tool during this of the assessment is a holistic Industrie 4.0 maturity Industrie 4.0 category groups. An important supporting tool during this process assessment is a holistic Industrie 4.0 maturity model, which will assist the enterprises in recognizing the dependencies duringofdifferent evolutionary steps. The concept was Abstract model, which will the enterprises in recognizing the dependencies during different evolutionary steps. The concept was developed based onassist the results of the research project Adaption, which are outlined beforehand. developed based on the results of the research project Adaption, which are outlined beforehand. Under "Industry production processes will be pushed to be increasingly interconnected, © 2018 the The concept Authors.of Published by 4.0", Elsevier B.V. © 2018 The Authors. Published by Elsevier B.V. information based on a real time basis and, necessarily, much more In this context, capacity optimization © 2018 The Authors. Published by of Elsevier B.V. committee Peer review under responsibility the scientific of theefficient. 8th Conference on Learning Factories 2018 Peer-review under responsibilityaim of the scientific committee of the 8th Conferencealso on Factories 2018 - Advanced Engineering th Learning goes beyond the traditional of capacity maximization, contributing for organization’s profitability and 2018 value.Peer review under responsibility of the scientific committee of the 8 Conference on Learning Factories Advanced Engineering Education & Training for Manufacturing Innovation Education & Training for Manufacturing Innovation.
Indeed, lean management and continuous approaches suggest capacity optimization instead of Advanced Engineering Education & Training improvement for Manufacturing Innovation Keywords: learningThe factory; Industrie 4.0; maturity model; learning based maximization. study of capacity optimization andconcepts; costingscenario models is an important research topic that deserves Keywords: learning factory; 4.0; maturity learning concepts; scenario based contributions from bothIndustrie the practical and model; theoretical perspectives. This paper presents and discusses a mathematical model for capacity management based on different costing models (ABC and TDABC). A generic model has been 1. Introduction developed and it was used to analyze idle capacity and to design strategies towards the maximization of organization’s 1. Introduction value. The trade-off capacity maximization vs operational efficiency is highlighted and it is shown that capacity Even though Industrie 4.0 is a popular topic in the manufacturing industry, many enterprises especially small and optimization might hide operational inefficiency. Even though Industrie 4.0 is a popular topic in systematically the manufacturing industry, many4.0. enterprises especially small and medium sized ones do not know how to evolve towards Industrie Often the first advancement © 2017 The Authors. Published by Elsevier B.V. medium sized ones do not know how to evolve systematically towards Industrie 4.0. Often the first advancement attempts fail due to a lack of knowledge of the interdependencies between the three dimensions technology, Peer-review under responsibility of the scientific committee of the Manufacturing Engineering Society International Conference attempts fail and dueemployees. to a lack Hence, of knowledge of the interdependencies between thea three dimensions technology, organization these enterprises need to be trained to obtain fundamental understanding of 2017. organization and employees. Hence, these enterprises need to be trained to obtain a fundamental understanding of Keywords: Cost Models; ABC; TDABC; Capacity Management; Idle Capacity; Operational Efficiency * Corresponding author. Tel.: +49-234-32-27821; fax: +49-234-32-08713.
1. Introduction address:author. [email protected] * E-mail Corresponding Tel.: +49-234-32-27821; fax: +49-234-32-08713.
E-mail address: [email protected] The cost of idle is a fundamental for companies and their management of extreme importance 2351-9789 © 2018 Thecapacity Authors. Published by Elsevier information B.V. Peer review underThe responsibility ofInthe scientific of unused the 8th Conference Learning Factories 2351-9789 ©production 2018 Authors. Published by Elsevier B.V. in modern systems. general, it iscommittee defined as capacity oronproduction potential2018 and-can be measured
Advanced Training for committee Manufacturing Innovation Peer reviewEngineering under responsibility of&the scientific of the 8 Conference etc. on Learning Factories 2018 in several ways: tonsEducation of production, available hours of manufacturing, The management of -the idle capacity th
Advanced Engineering Education & Training for Manufacturing Innovation * Paulo Afonso. Tel.: +351 253 510 761; fax: +351 253 604 741 E-mail address: [email protected]
2351-9789 © 2017 The Authors. Published by Elsevier B.V. Peer-review under of the scientificbycommittee the Manufacturing Engineering Society International Conference 2017. 2351-9789 © 2018responsibility The Authors. Published Elsevier of B.V. Peer-review under responsibility of the scientific committee of the 8th Conference on Learning Factories 2018 - Advanced Engineering Education & Training for Manufacturing Innovation. 10.1016/j.promfg.2018.04.007
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Industrie 4.0 and related topics. Learning factories have been developed to provide students and participants of industry seminars in a real-world manufacturing environment with in-depth knowledge of concepts and methods of improvement processes [1,2]. The advantages of a real-world manufacturing environment can be used for both the academic education of students and the training of industry employees [3]. Learning factories can thus make a significant contribution to understanding Industrie 4.0 [4, 5]. So far, the focus has been primarily on the shop floor level. Due to the enterprise policy driven by Industrie 4.0 and digitization, employees have to deal with new and hitherto unknown technologies and systems (e.g. assistance systems) in the context of their activities [6]. The concept of learning factories has already been successfully implemented in this area [7, 8]. But also employees at management level must possess a distinct understanding of the topic of Industrie 4.0 and be able to identify the existing fields of tension in this environment in order to correctly assess the current enterprise situation with regard to Industrie 4.0 and, even more essentially, to pursue a target-oriented development of the enterprise [6]. So far, however, it has not been possible to cover this demand. This paper presents a support approach in the form of a learning factory module, which addresses current and prospective employees at decision level as well as students. 1.1. Enhancement of the LPS Learning Factory for Industrie 4.0 The first learning factories were mainly used in the context of lean management and process optimization. In the course of time, however, other topics found their way into the portfolios of learning factories. This also applies to the learning factory of the Chair of Production Systems at the Ruhr-University in Bochum. Over the process of time, it was extended to include the areas of resource efficiency as well as management and organization (company codetermination), which were integrated into the existing learning factory concept as part of a holistic approach [1, 9]. This approach is centred on the manufacturing of a real product. The advancing technological development in the context of Industrie 4.0 entails a comprehensive change in the role of the employees and also necessitates an adaptation of the organization in the enterprises. These changes and adjustments affect employees from all areas, from shop floor to management level [4]. Driven by these changes and necessary adaptations, the LPS provided a concept for an Industrie 4.0 learning factory that is based on the already existing learning factory concept [10]. As a result of the need to challenge Industrie 4.0 issues to maintain the company's competitiveness, decision-makers in management positions also face new challenges [11]. They must be aware of the elements of Industrie 4.0 in order to be able to identify the current status quo of their enterprise in this field. In addition, they must specify objectives for the development process as well as the necessary measures to achieve these and monitor their implementation. Within this migration process, existing links between various elements of the socio-technical dimensions must be taken into account [6]. In order to support current and future decision-makers and participants in the realization of this migration process, a scenario for the module Audit/Maturation provided for in the LPS-Industrie 4.0 learning factory concept is presented below. This scenario is based on the procedure model and the associated tools, which were developed as part of the research project Adaption [12]. 2. Theoretical Background 2.1. The underlying iterative procedure model The iterative procedure model developed in the research project Adaption, describes the necessary steps a company is to undertake during the evolution towards Industrie 4.0. The model covers the span from the determination of the company objectives on the strategic management level up to the implementation of specific measures. It supports the companies in every phase of the procedure with a maturity model and a collection of smaller methods and tools. The model consists of five main phases with three gates in between the first four phases. These gates represent the decision processes that require the participation of a decision maker of the management level. The procedure model conforms an iterative cycle. This approach and the particular phases have been derived from the Deming-Cycle, which is commonly used in the area of project and Lean management [13].
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The iterative structure is a concept commonly used in methodological approaches1 † and is one of the main implications of the continuous improvement philosophy implemented in lean management [13]. Alike the improvement processes in lean production the evolution towards Industrie 4.0 is an ongoing development rather than an eruptive revolution and hence needs to be undertaken in a continuous process. Therefore, the Adaption procedure model is based on the methodical approach of an iterative cycle to shape this process in an adequate way. The necessary steps are passed consecutively alongside with the real development process. The procedure model begins with the definition of the company’s long-term aims at the strategic management level. Subsequently the relevant aims concerning the development towards Industrie 4.0 will be identified. The evolution to Industrie 4.0 shall not serve as an end in itself, whereas it must support the long-term corporate goals. In the first phase of the cycle the preliminary aims are defined, and a first rough current-maturity level is raised with the help of a “Quick check” which is based on the maturity model. Depending on the structure of the company, different people from the affected areas are required for this process. All relevant decision makers have to be involved in the process right from the start. In the second phase, a detailed audit of the previously defined areas will be conducted. The result of this phase is the detailed maturity level with a concrete description of the problem. The subsequent definition of the objectives as well as the topic areas in which a development towards Industrie 4.0 is to be pursued, requires again a decision of authorized persons. Next, the target maturity levels, are defined in the maturity model. From these it is possible to derive a comprehensive target concept, which corresponds to a requirement document and completes this phase. Afterwards the responsible persons must make a decision again, this time regarding the concrete measures. In order to carry out the fourth phase of the implementation, a plan of measures, a schedule and a functional specification document will be elaborated. Subsequently, the implementation of the specified measures is conducted. The final phase is the evaluation of the implementation of the measures with suitable monitoring approaches and key figures that enable an assessment of the implementation progress and the effectiveness of the selected measures. The result of this phase is a progress report. In principle, all phases of the procedure model are always undertaken in each iterative cycle however, not all phases have to be performed again at the same level of detail. 2.2. The Adaption maturity model The most relevant tool used in the process model described above is a specially developed Industrie 4.0 maturity model. In this maturity model, the topic "Industrie 4.0 in the production environment" is described in 44 different criteria, which in turn can be divided in up to 8 characteristics. Prior to this, relevant definitions of Industrie 4.0 with regard to their content and existing Industrie 4.0 maturity models with reference to their structure and substance were analysed. Furthermore, an industry-related orientation of the model content is ensured by integrating the feedback of application enterprises of the research project Adaption. In order to maintain a transparent form and, more importantly, to take the socio-technical approach into account, these 44 criteria are each assigned to one of the three dimensions technology, organization and personnel. However, this dimension assignment does not mean that criteria can only be assigned to a certain dimension, but merely describes a main assignment. In the context of the application in the research project Adaption and thus in the industrial environment the maturity model presented here is used for a detailed determination of the enterprise's current state as well as for the determination of desired target states in a variety of areas. The model demonstrates to enterprises the extent to which the target status they are striving for will lead to further progress in other areas based on existing dependencies, both within one dimension and across dimensions. In the context of the learning factory module presented here, the model is therefore used to support the training participants in classifying various Industrie 4.0 evolution stages simulated in the learning factory of the LPS. This has the goal of sensitizing them to the topic Industrie 4.0 in general, as well as to train their estimation ability regarding subsequent dependencies with the achievement of a desired enterprise target state.
1
E.g. the compilation of an ecobalance [DIN EN ISO 14040]
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3. Didactic concept of the new learning factory module In the following, the learning factory module is presented, which is based on the procedure model described beforehand. It enables participants to manage an Industrie 4.0 migration process taking the example of the LPS learning and research factory as a representative of a typical small enterprise. Consequently, the process flow of the learning factory module largely reflects the approach within the procedure model, whereby theoretical contents are discussed in further steps. The entire process flow of the module is illustrated in the following figure 1. The phases 3-8 correspond to the phases of the Adaption procedure model described in section 2.2.
Fig. 1. Structure of the learning factory module.
In the first phase of the learning factory module "Introduction of Industrie 4.0”, the participants are provided with a basic understanding of Industrie 4.0. Since Industrie 4.0 is more than a collection of new technologies the high relevance of the three socio-technical dimensions technology, organisation and personnel is pointed out, and the required holistic, integrated view of all three dimensions and the interdependencies that exist here are conveyed. This will be demonstrated by means of various Industrie 4.0 use cases. Furthermore, it is pointed out at this phase that the development of an enterprise to Industrie 4.0 is not a revolutionary and eruptive process but a continuous one, which is individual for each enterprise. Accordingly, participants will be sensitized to extend their understanding of Industrie 4.0 beyond the contents of this learning factory module and to transfer and adapt the knowledge to their own enterprise. In the second phase "Overview procedure model", the participants are introduced to the Adaption procedure model described in chapter 2.2. and are given an overview of the tools and methods used. Following the explanation of the necessary basics, phase three "Determination of the objectives" introduces the iterative application of the procedure model to the LPS learning and research factory. Therefore, the participants will begin by recording the strategic goals of the LPS Learning Factory using a customized version of the method “Balanced Score Card” [14]. A training supervisor acts as a LPS management person (LPS_CEO) who sets out the general strategic objectives for the long-term development of the learning factory to the participants. Subsequently, the participants will identify the relevant goals for the further development of the learning factory towards Industrie 4.0 (Smart Learning Factory) with the help of the knowledge gained in the first two phases and will then coordinate them with the management person. After identifying the specific Industrie 4.0 objectives, the participants will assess the actual situation in the LPS learning and research factory in the course of the Industrie 4.0 audit, which is divided in three segments according to the socio-technical approach. Each of the three audit segments comprises specific methods which are suitable for auditing the current state of the respective dimension (Technology: Technology Checklist; Organization: BPMN 4.0; Personnel: Principled interviews). In order to clarify the importance of an integrated examination of the three socio-technical dimensions, the participants will be divided into three groups. The groups initially assess different areas in the learning factory and apply the corresponding audit methods of a dimension to this area. Subsequently, the groups change the area as well as the audit method. After three rounds, therefore, each group has assessed all three dimensions in a different one of the three areas respectively used the corresponding methods. In order to obtain a comprehensive overview for each area, the three groups must combine their results and evaluate them. The following figure (Fig. 2) illustrates the proceeding described above.
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Fig. 2. Allocation of the participants during the audit phase.
Phase five then includes the application of the Adaption maturity-model which is introduced with means of use cases to illustrate different maturity levels in selected criteria of the maturity model and their possible interactions. In this phase, the participants transfer the consolidated results of the detailed audit to determine the learning factories' current state in the 44 criteria of the maturity model and the corresponding characteristics. Based on the actual status in the maturity model and the strategic objectives of the learning factory identified in phase 1, the participants will jointly develop potential target states for the learning and research factory with renewed support of the maturity model. These potential target states are then presented to the management person (LPS_CEO) of the learning and research factory. In turn, the management person selects from the participants' suggestions relevant and thus to be monitored target states. This also forms the basis for the actions to be determined in the next phase 6. The aim of phase six "Deduction of measures" is to sensitise the participants to the interdependencies and interactions between the socio-technical dimensions, which occur when determining the target conditions and the subsequent derivation of concrete actions. When selecting an action, the interdependencies are particularly important for assessing the economic efficiency of actions. Individual actions cannot be derived in isolation, but often involve other necessary actions in the other dimensions which must be taken into account in a comprehensive assessment. The transfer of methodical basics for implementation planning is the final step of phase six. In the 7th phase, an evaluation of the actions previously taken is conducted in the group. Furthermore, methods are provided to the participants to monitor the progress of implementation and evaluate the impact of the actions. In the penultimate phase 8, the iterative approach of the learning factory module is explained. The participants then perform a second iteration in which the phases 3-7 are traversed again. This illustrates the importance of an iterative approach, which is essential for the successful evolution of an enterprise towards Industrie 4.0. However, not all phases are used in this second pass with the same level of detail as in the first iteration cycle. In this phase, the focus is primarily on teaching participants how to proceed in order to identify the effects of changed objectives on the derivation of new actions and how to quickly record and analyze the new actual situation on the basis of the audit. In the 9th and last step of the learning factory module, all contents are finally reflected again. On the basis of the documented results of earlier executions of the learning factory module, the history of the past development of the LPS learning and research factory towards a Smart Learning Factory is presented. 4. Conclusions This paper presents a concept for the new module Audit/Maturation of the LPS Learning and Research Factory that is based on the results of the research project Adaption and focuses on the evolution of companies towards Industrie 4.0. The research results show how important it is to consider the interdependencies within the sociotechnical dimensions in particular to assess economic efficiency of derived measures. In the majority of cases SMEs do not have the financial opportunities to examine and develop Industrie 4.0 approaches in depth by themselves, especially according to new technologies. Moreover, they prevalently do not dispose a complete understanding of the elements and the benefit of Industrie 4.0. The described approach using the model for Industrie 4.0 gives the SMEs the opportunity to learn about concrete solutions and the new socio-technical developments Industrie 4.0 offers. The learning factory concept supports the imparting of the holistic approach that is essential for the ability to identify the
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complex dependencies between the dimensions described in the model. In this way, the SMEs have the possibility to assess the benefit Industrie 4.0 can have for them individually and without the need of investments. This preserves them from misinvestments into expensive technologies. Besides the support for SMEs, the peculiarity of this new module lies within the concept arranged around the trainer who is acting as the responsible management person of the LPS Learning Factory. Due to the possibility of the LPS_CEO to influence the determination of the target states, it is possible to define the area of application for the further steps in such a way that certain, predefined priorities are focused. For these the interactions between the three dimensions can be analyzed beforehand and in detail be demonstrated to the participants. Furthermore, the implementation of the derived measures can be prepared and thus put into practice, by the participants. This usually requires more implementation time than is available during the execution of the learning factory module. The management person takes on the role of a game leader without the need to reveal this to the participants. In this way, it is possible to maintain the immersion of the participants within the realistic learning factory scenario. Although this learning factory module uses approaches of a business game, the instance of the management person still preserves the core idea of a learning factory - the independent application of what has been learned in a realistic working environment by the participants themselves. In addition, the content of the learning factory module can thus be continuously adapted to the current status of the LPS Learning and Research Factory and enables thereby a constantly up-to-date learning experience. Acknowledgements Parts of this article have been elaborated during the research and development project Adaption ”Reifegradbasierte Migration zum Cyber-physischen Produktionssystem” (reference number: 02P14B020) within the „Industrie 4.0 – Forschung auf den betrieblichen Hallenboden“ call, which is promoted by the German Federal Ministry for Education and Research (BMBF) and is supervised by the Project Management Agency Karlsruhe (PTKA) at the Karlsruher Institute of Technology (KIT). References [1] D. Kreimeier, F. Morlock, C. Prinz, B. Krückhans, D. Bakir, H. Meier: Holistic learning factories – A concept to Train lean management, resource efficiency as well as management and organization improvement skills. Variety Management in Manufacturing. Proceedings of the 47th CIRP Conference on Manufacturing Systems (2014), p. 184-188. [2] E. Abele, J. Metternich, M. Tisch, G. Chryssolouris, W. Sihn, H. ElMaraghy, V. Hummel, F. Ranz, "Learning Factories for Research, Education, and Training", (LF), Procedia CIRP, 5th Conference on Learning Factories, 7-8 July, Bochum, Germany, Volume 32, pp.1-6 (2015) [3] E. Abele, N. Eichhorn, F. Brungs: Mitarbeiterqualifikation in einer realen Produktionsumgebung - Langfristige Prozessverbesserungen durch praxisnahe Lernformen. ZWF 2007; 102(1-2):741–5. [4] C. Prinz, F. Morlock, S. Freith, N. Kreggenfeld, D. Kreimeier, B. Kuhlenkötter: Learning Factory modules for smart factories in Industrie 4.0: 6th CLF - 6th CIRP Conference on Learning Factories, (2016), p. 113-118. [5] C. Ullrich, M. Aust, R. Blach, D. Kahl, C. Prinz, N. Kreggenfeld: Assistance- and Knowledge-Services for Smart Production. In: S. Lindstaedt, T. Ley, H. Sack.: Proceedings of the 15th International Conference on Knowledge Technologies and Datadriven Business: International Conference on Knowledge Technologies and Data-driven Business (iknow). ACM, 2015. [6] H. Hirsch-Kreinsen: Wandel von Produktionsarbeit – ‚Industrie 4.0'. WSI-Mitteilungen (2014) [7] C. Prinz, D. Kreimeier, B. Kuhlenkötter: Implementation of a learning environment for an Industrie 4.0 assistance system to improve the overall equipment effectiveness. 7th Conference on Learning Factories, CLF (2017), p. 159-166 [8] M. Reuter, H. Oberc, M. Wannöffel, D. Kreimeier, J. Klippert, P. Pawlicki, B. Kuhlenkötter: Learning factories‘ trainings as an enabler of proactive workers’ participation regarding Industrie 4.0. 7th Conference on Learning Factories, CLF (2017), p. 354-360. [9] B. Krückhans, T. Wienbruch, S. Freith, H. Oberc, D. Kreimeier, B. Kuhlenkötter: From Learning factories and their enhancements - A comprehensive training concept to increase resource efficiency. The 5th Conference on Learning Factories (2015), p.47-52. [10] B. Krückhans, F. Morlock, C. Prinz, S. Freith, D. Kreimeier, B. Kuhlenkötter: Learning Factories qualify SMEs to operate a smart factory. In: COMA'16 Proceedings: International Conference on Competetive Manufacturing. 27-29. Republic of South Africa; (2016), p. 457–460. [11] T. Bauernhansel, J. Krüger, G. Reinhart, G. Schuh: WGP-Standpunkt Industrie 4.0. WGP e.V. Darmstadt, (2016). [12] Morlock, F.; Wienbruch, T.; Leineweber, S.; Kreimeier, D.; Kuhlenkötter, B.: Industrie 4.0-Transformation für produzierende Unternehmen. ZWF-Zeitschrift für wirtschaftlichen Fabrikbetrieb, 5.2016. S. 306. Carl Hanser Verlag (2016), München. [13] Brunner, Franz J.; Wagner: Japanische Erfolgskonzepte. Carl Hanser Verlag München, Wien (2011) [14] Friedag, Herwig R.: Balanced Scorecard - einfach konsequent. Haue-Lexware GmbH & Co. KG, Freiburg (2014)
ScienceDirect ScienceDirect
Procedia Manufacturing 00 (2017) 000–000 Available online Available onlineatatwww.sciencedirect.com www.sciencedirect.com Procedia Manufacturing 00 (2017) 000–000
ScienceDirect ScienceDirect
www.elsevier.com/locate/procedia www.elsevier.com/locate/procedia
Procedia Manufacturing 23 (2018) 141–146 Procedia Manufacturing 00 (2017) 000–000 www.elsevier.com/locate/procedia
8th Conference on Learning Factories 2018 - Advanced Engineering Education & Training for 8th Conference on Learning Factories 2018 - Advanced Engineering Education & Training for Manufacturing Innovation Manufacturing Innovation
Evolution of SMEs towards Industrie 4.0 through a scenario based Evolution SMEs towards Industrie 4.0 through a scenario based Manufacturingof Engineering Society International Conference 2017, MESIC 2017, 28-30 June learning factory training 2017, Vigo (Pontevedra), Spain learning factory training Wienbruch, Thoma*; Leineweber, Stefana; Kreimeier, Dietera; Kuhlenkötter, Bernda
a a Costing models capacity Stefan optimization inDieter Industry 4.0: Trade-off Wienbruch, Thomafor *; Leineweber, ; Kreimeier, ; Kuhlenkötter, Bernda Ruhr-Universität Bochum, Chair of Production Systems, Universitätsstaße 150, 44801 Bochum, Germany between usedChair capacity and Universitätsstaße operational efficiency Ruhr-Universität Bochum, of Production Systems, 150, 44801 Bochum, Germany a a
Abstract Abstract a,* in the manufacturing The concept of Industrie 4.0 A. has Santana initiated ana,evolutionary process industry.b Especially small and medium P. Afonso , A. Zaninb, R. Wernke The of Industrie 4.0 has initiated an evolutionary processthis in the manufacturing industry. Especially smallconcept and medium sizedconcept companies lack the capacities and knowledge to accomplish process autonomously. The learning factory offers University of Guimarães, sized companies thecompanies capacities within and aknowledge toMinho, accomplish process Theenterprises learning factory offers the possibility to lack second this process. This4800-058 paperthis will presentautonomously. aPortugal concept of how can be concept trained within b Unochapecó, 89809-000 Chapecó, the possibility secondfactory companies this process. This paper will present aBrazil concept of howregarding enterprises be dimensions trained within realm of a to learning basedwithin on scenarios of different Industrie 4.0SC, evolutionary steps thecan three of the sociotechnical realm of a learning factory based on scenarios Industrie evolutionary steps regarding the three dimensions of approach. These scenarios focus of ondifferent demonstrating and4.0 assessing the dependencies of assorted characteristics the sociotechnical approach. scenarios supporting focus on demonstrating andprocess assessing dependencies of assorted characteristics of Industrie 4.0 category groups.These An important tool during this of the assessment is a holistic Industrie 4.0 maturity Industrie 4.0 category groups. An important supporting tool during this process assessment is a holistic Industrie 4.0 maturity model, which will assist the enterprises in recognizing the dependencies duringofdifferent evolutionary steps. The concept was Abstract model, which will the enterprises in recognizing the dependencies during different evolutionary steps. The concept was developed based onassist the results of the research project Adaption, which are outlined beforehand. developed based on the results of the research project Adaption, which are outlined beforehand. Under "Industry production processes will be pushed to be increasingly interconnected, © 2018 the The concept Authors.of Published by 4.0", Elsevier B.V. © 2018 The Authors. Published by Elsevier B.V. information based on a real time basis and, necessarily, much more In this context, capacity optimization © 2018 The Authors. Published by of Elsevier B.V. committee Peer review under responsibility the scientific of theefficient. 8th Conference on Learning Factories 2018 Peer-review under responsibilityaim of the scientific committee of the 8th Conferencealso on Factories 2018 - Advanced Engineering th Learning goes beyond the traditional of capacity maximization, contributing for organization’s profitability and 2018 value.Peer review under responsibility of the scientific committee of the 8 Conference on Learning Factories Advanced Engineering Education & Training for Manufacturing Innovation Education & Training for Manufacturing Innovation.
Indeed, lean management and continuous approaches suggest capacity optimization instead of Advanced Engineering Education & Training improvement for Manufacturing Innovation Keywords: learningThe factory; Industrie 4.0; maturity model; learning based maximization. study of capacity optimization andconcepts; costingscenario models is an important research topic that deserves Keywords: learning factory; 4.0; maturity learning concepts; scenario based contributions from bothIndustrie the practical and model; theoretical perspectives. This paper presents and discusses a mathematical model for capacity management based on different costing models (ABC and TDABC). A generic model has been 1. Introduction developed and it was used to analyze idle capacity and to design strategies towards the maximization of organization’s 1. Introduction value. The trade-off capacity maximization vs operational efficiency is highlighted and it is shown that capacity Even though Industrie 4.0 is a popular topic in the manufacturing industry, many enterprises especially small and optimization might hide operational inefficiency. Even though Industrie 4.0 is a popular topic in systematically the manufacturing industry, many4.0. enterprises especially small and medium sized ones do not know how to evolve towards Industrie Often the first advancement © 2017 The Authors. Published by Elsevier B.V. medium sized ones do not know how to evolve systematically towards Industrie 4.0. Often the first advancement attempts fail due to a lack of knowledge of the interdependencies between the three dimensions technology, Peer-review under responsibility of the scientific committee of the Manufacturing Engineering Society International Conference attempts fail and dueemployees. to a lack Hence, of knowledge of the interdependencies between thea three dimensions technology, organization these enterprises need to be trained to obtain fundamental understanding of 2017. organization and employees. Hence, these enterprises need to be trained to obtain a fundamental understanding of Keywords: Cost Models; ABC; TDABC; Capacity Management; Idle Capacity; Operational Efficiency * Corresponding author. Tel.: +49-234-32-27821; fax: +49-234-32-08713.
1. Introduction address:author. [email protected] * E-mail Corresponding Tel.: +49-234-32-27821; fax: +49-234-32-08713.
E-mail address: [email protected] The cost of idle is a fundamental for companies and their management of extreme importance 2351-9789 © 2018 Thecapacity Authors. Published by Elsevier information B.V. Peer review underThe responsibility ofInthe scientific of unused the 8th Conference Learning Factories 2351-9789 ©production 2018 Authors. Published by Elsevier B.V. in modern systems. general, it iscommittee defined as capacity oronproduction potential2018 and-can be measured
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Industrie 4.0 and related topics. Learning factories have been developed to provide students and participants of industry seminars in a real-world manufacturing environment with in-depth knowledge of concepts and methods of improvement processes [1,2]. The advantages of a real-world manufacturing environment can be used for both the academic education of students and the training of industry employees [3]. Learning factories can thus make a significant contribution to understanding Industrie 4.0 [4, 5]. So far, the focus has been primarily on the shop floor level. Due to the enterprise policy driven by Industrie 4.0 and digitization, employees have to deal with new and hitherto unknown technologies and systems (e.g. assistance systems) in the context of their activities [6]. The concept of learning factories has already been successfully implemented in this area [7, 8]. But also employees at management level must possess a distinct understanding of the topic of Industrie 4.0 and be able to identify the existing fields of tension in this environment in order to correctly assess the current enterprise situation with regard to Industrie 4.0 and, even more essentially, to pursue a target-oriented development of the enterprise [6]. So far, however, it has not been possible to cover this demand. This paper presents a support approach in the form of a learning factory module, which addresses current and prospective employees at decision level as well as students. 1.1. Enhancement of the LPS Learning Factory for Industrie 4.0 The first learning factories were mainly used in the context of lean management and process optimization. In the course of time, however, other topics found their way into the portfolios of learning factories. This also applies to the learning factory of the Chair of Production Systems at the Ruhr-University in Bochum. Over the process of time, it was extended to include the areas of resource efficiency as well as management and organization (company codetermination), which were integrated into the existing learning factory concept as part of a holistic approach [1, 9]. This approach is centred on the manufacturing of a real product. The advancing technological development in the context of Industrie 4.0 entails a comprehensive change in the role of the employees and also necessitates an adaptation of the organization in the enterprises. These changes and adjustments affect employees from all areas, from shop floor to management level [4]. Driven by these changes and necessary adaptations, the LPS provided a concept for an Industrie 4.0 learning factory that is based on the already existing learning factory concept [10]. As a result of the need to challenge Industrie 4.0 issues to maintain the company's competitiveness, decision-makers in management positions also face new challenges [11]. They must be aware of the elements of Industrie 4.0 in order to be able to identify the current status quo of their enterprise in this field. In addition, they must specify objectives for the development process as well as the necessary measures to achieve these and monitor their implementation. Within this migration process, existing links between various elements of the socio-technical dimensions must be taken into account [6]. In order to support current and future decision-makers and participants in the realization of this migration process, a scenario for the module Audit/Maturation provided for in the LPS-Industrie 4.0 learning factory concept is presented below. This scenario is based on the procedure model and the associated tools, which were developed as part of the research project Adaption [12]. 2. Theoretical Background 2.1. The underlying iterative procedure model The iterative procedure model developed in the research project Adaption, describes the necessary steps a company is to undertake during the evolution towards Industrie 4.0. The model covers the span from the determination of the company objectives on the strategic management level up to the implementation of specific measures. It supports the companies in every phase of the procedure with a maturity model and a collection of smaller methods and tools. The model consists of five main phases with three gates in between the first four phases. These gates represent the decision processes that require the participation of a decision maker of the management level. The procedure model conforms an iterative cycle. This approach and the particular phases have been derived from the Deming-Cycle, which is commonly used in the area of project and Lean management [13].
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The iterative structure is a concept commonly used in methodological approaches1 † and is one of the main implications of the continuous improvement philosophy implemented in lean management [13]. Alike the improvement processes in lean production the evolution towards Industrie 4.0 is an ongoing development rather than an eruptive revolution and hence needs to be undertaken in a continuous process. Therefore, the Adaption procedure model is based on the methodical approach of an iterative cycle to shape this process in an adequate way. The necessary steps are passed consecutively alongside with the real development process. The procedure model begins with the definition of the company’s long-term aims at the strategic management level. Subsequently the relevant aims concerning the development towards Industrie 4.0 will be identified. The evolution to Industrie 4.0 shall not serve as an end in itself, whereas it must support the long-term corporate goals. In the first phase of the cycle the preliminary aims are defined, and a first rough current-maturity level is raised with the help of a “Quick check” which is based on the maturity model. Depending on the structure of the company, different people from the affected areas are required for this process. All relevant decision makers have to be involved in the process right from the start. In the second phase, a detailed audit of the previously defined areas will be conducted. The result of this phase is the detailed maturity level with a concrete description of the problem. The subsequent definition of the objectives as well as the topic areas in which a development towards Industrie 4.0 is to be pursued, requires again a decision of authorized persons. Next, the target maturity levels, are defined in the maturity model. From these it is possible to derive a comprehensive target concept, which corresponds to a requirement document and completes this phase. Afterwards the responsible persons must make a decision again, this time regarding the concrete measures. In order to carry out the fourth phase of the implementation, a plan of measures, a schedule and a functional specification document will be elaborated. Subsequently, the implementation of the specified measures is conducted. The final phase is the evaluation of the implementation of the measures with suitable monitoring approaches and key figures that enable an assessment of the implementation progress and the effectiveness of the selected measures. The result of this phase is a progress report. In principle, all phases of the procedure model are always undertaken in each iterative cycle however, not all phases have to be performed again at the same level of detail. 2.2. The Adaption maturity model The most relevant tool used in the process model described above is a specially developed Industrie 4.0 maturity model. In this maturity model, the topic "Industrie 4.0 in the production environment" is described in 44 different criteria, which in turn can be divided in up to 8 characteristics. Prior to this, relevant definitions of Industrie 4.0 with regard to their content and existing Industrie 4.0 maturity models with reference to their structure and substance were analysed. Furthermore, an industry-related orientation of the model content is ensured by integrating the feedback of application enterprises of the research project Adaption. In order to maintain a transparent form and, more importantly, to take the socio-technical approach into account, these 44 criteria are each assigned to one of the three dimensions technology, organization and personnel. However, this dimension assignment does not mean that criteria can only be assigned to a certain dimension, but merely describes a main assignment. In the context of the application in the research project Adaption and thus in the industrial environment the maturity model presented here is used for a detailed determination of the enterprise's current state as well as for the determination of desired target states in a variety of areas. The model demonstrates to enterprises the extent to which the target status they are striving for will lead to further progress in other areas based on existing dependencies, both within one dimension and across dimensions. In the context of the learning factory module presented here, the model is therefore used to support the training participants in classifying various Industrie 4.0 evolution stages simulated in the learning factory of the LPS. This has the goal of sensitizing them to the topic Industrie 4.0 in general, as well as to train their estimation ability regarding subsequent dependencies with the achievement of a desired enterprise target state.
1
E.g. the compilation of an ecobalance [DIN EN ISO 14040]
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3. Didactic concept of the new learning factory module In the following, the learning factory module is presented, which is based on the procedure model described beforehand. It enables participants to manage an Industrie 4.0 migration process taking the example of the LPS learning and research factory as a representative of a typical small enterprise. Consequently, the process flow of the learning factory module largely reflects the approach within the procedure model, whereby theoretical contents are discussed in further steps. The entire process flow of the module is illustrated in the following figure 1. The phases 3-8 correspond to the phases of the Adaption procedure model described in section 2.2.
Fig. 1. Structure of the learning factory module.
In the first phase of the learning factory module "Introduction of Industrie 4.0”, the participants are provided with a basic understanding of Industrie 4.0. Since Industrie 4.0 is more than a collection of new technologies the high relevance of the three socio-technical dimensions technology, organisation and personnel is pointed out, and the required holistic, integrated view of all three dimensions and the interdependencies that exist here are conveyed. This will be demonstrated by means of various Industrie 4.0 use cases. Furthermore, it is pointed out at this phase that the development of an enterprise to Industrie 4.0 is not a revolutionary and eruptive process but a continuous one, which is individual for each enterprise. Accordingly, participants will be sensitized to extend their understanding of Industrie 4.0 beyond the contents of this learning factory module and to transfer and adapt the knowledge to their own enterprise. In the second phase "Overview procedure model", the participants are introduced to the Adaption procedure model described in chapter 2.2. and are given an overview of the tools and methods used. Following the explanation of the necessary basics, phase three "Determination of the objectives" introduces the iterative application of the procedure model to the LPS learning and research factory. Therefore, the participants will begin by recording the strategic goals of the LPS Learning Factory using a customized version of the method “Balanced Score Card” [14]. A training supervisor acts as a LPS management person (LPS_CEO) who sets out the general strategic objectives for the long-term development of the learning factory to the participants. Subsequently, the participants will identify the relevant goals for the further development of the learning factory towards Industrie 4.0 (Smart Learning Factory) with the help of the knowledge gained in the first two phases and will then coordinate them with the management person. After identifying the specific Industrie 4.0 objectives, the participants will assess the actual situation in the LPS learning and research factory in the course of the Industrie 4.0 audit, which is divided in three segments according to the socio-technical approach. Each of the three audit segments comprises specific methods which are suitable for auditing the current state of the respective dimension (Technology: Technology Checklist; Organization: BPMN 4.0; Personnel: Principled interviews). In order to clarify the importance of an integrated examination of the three socio-technical dimensions, the participants will be divided into three groups. The groups initially assess different areas in the learning factory and apply the corresponding audit methods of a dimension to this area. Subsequently, the groups change the area as well as the audit method. After three rounds, therefore, each group has assessed all three dimensions in a different one of the three areas respectively used the corresponding methods. In order to obtain a comprehensive overview for each area, the three groups must combine their results and evaluate them. The following figure (Fig. 2) illustrates the proceeding described above.
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Fig. 2. Allocation of the participants during the audit phase.
Phase five then includes the application of the Adaption maturity-model which is introduced with means of use cases to illustrate different maturity levels in selected criteria of the maturity model and their possible interactions. In this phase, the participants transfer the consolidated results of the detailed audit to determine the learning factories' current state in the 44 criteria of the maturity model and the corresponding characteristics. Based on the actual status in the maturity model and the strategic objectives of the learning factory identified in phase 1, the participants will jointly develop potential target states for the learning and research factory with renewed support of the maturity model. These potential target states are then presented to the management person (LPS_CEO) of the learning and research factory. In turn, the management person selects from the participants' suggestions relevant and thus to be monitored target states. This also forms the basis for the actions to be determined in the next phase 6. The aim of phase six "Deduction of measures" is to sensitise the participants to the interdependencies and interactions between the socio-technical dimensions, which occur when determining the target conditions and the subsequent derivation of concrete actions. When selecting an action, the interdependencies are particularly important for assessing the economic efficiency of actions. Individual actions cannot be derived in isolation, but often involve other necessary actions in the other dimensions which must be taken into account in a comprehensive assessment. The transfer of methodical basics for implementation planning is the final step of phase six. In the 7th phase, an evaluation of the actions previously taken is conducted in the group. Furthermore, methods are provided to the participants to monitor the progress of implementation and evaluate the impact of the actions. In the penultimate phase 8, the iterative approach of the learning factory module is explained. The participants then perform a second iteration in which the phases 3-7 are traversed again. This illustrates the importance of an iterative approach, which is essential for the successful evolution of an enterprise towards Industrie 4.0. However, not all phases are used in this second pass with the same level of detail as in the first iteration cycle. In this phase, the focus is primarily on teaching participants how to proceed in order to identify the effects of changed objectives on the derivation of new actions and how to quickly record and analyze the new actual situation on the basis of the audit. In the 9th and last step of the learning factory module, all contents are finally reflected again. On the basis of the documented results of earlier executions of the learning factory module, the history of the past development of the LPS learning and research factory towards a Smart Learning Factory is presented. 4. Conclusions This paper presents a concept for the new module Audit/Maturation of the LPS Learning and Research Factory that is based on the results of the research project Adaption and focuses on the evolution of companies towards Industrie 4.0. The research results show how important it is to consider the interdependencies within the sociotechnical dimensions in particular to assess economic efficiency of derived measures. In the majority of cases SMEs do not have the financial opportunities to examine and develop Industrie 4.0 approaches in depth by themselves, especially according to new technologies. Moreover, they prevalently do not dispose a complete understanding of the elements and the benefit of Industrie 4.0. The described approach using the model for Industrie 4.0 gives the SMEs the opportunity to learn about concrete solutions and the new socio-technical developments Industrie 4.0 offers. The learning factory concept supports the imparting of the holistic approach that is essential for the ability to identify the
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complex dependencies between the dimensions described in the model. In this way, the SMEs have the possibility to assess the benefit Industrie 4.0 can have for them individually and without the need of investments. This preserves them from misinvestments into expensive technologies. Besides the support for SMEs, the peculiarity of this new module lies within the concept arranged around the trainer who is acting as the responsible management person of the LPS Learning Factory. Due to the possibility of the LPS_CEO to influence the determination of the target states, it is possible to define the area of application for the further steps in such a way that certain, predefined priorities are focused. For these the interactions between the three dimensions can be analyzed beforehand and in detail be demonstrated to the participants. Furthermore, the implementation of the derived measures can be prepared and thus put into practice, by the participants. This usually requires more implementation time than is available during the execution of the learning factory module. The management person takes on the role of a game leader without the need to reveal this to the participants. In this way, it is possible to maintain the immersion of the participants within the realistic learning factory scenario. Although this learning factory module uses approaches of a business game, the instance of the management person still preserves the core idea of a learning factory - the independent application of what has been learned in a realistic working environment by the participants themselves. In addition, the content of the learning factory module can thus be continuously adapted to the current status of the LPS Learning and Research Factory and enables thereby a constantly up-to-date learning experience. Acknowledgements Parts of this article have been elaborated during the research and development project Adaption ”Reifegradbasierte Migration zum Cyber-physischen Produktionssystem” (reference number: 02P14B020) within the „Industrie 4.0 – Forschung auf den betrieblichen Hallenboden“ call, which is promoted by the German Federal Ministry for Education and Research (BMBF) and is supervised by the Project Management Agency Karlsruhe (PTKA) at the Karlsruher Institute of Technology (KIT). References [1] D. Kreimeier, F. Morlock, C. Prinz, B. Krückhans, D. Bakir, H. Meier: Holistic learning factories – A concept to Train lean management, resource efficiency as well as management and organization improvement skills. Variety Management in Manufacturing. Proceedings of the 47th CIRP Conference on Manufacturing Systems (2014), p. 184-188. [2] E. Abele, J. Metternich, M. Tisch, G. Chryssolouris, W. Sihn, H. ElMaraghy, V. Hummel, F. 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