Identifying target oriented Industrie 4.0 potentials in lean automotive electronics value streams

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Procedia CIRP 00 (2017) 000–000

www.elsevier.com/locate/procedia

Peer-review under responsibility of the scientific committee of the 28th CIRP Design Conference 2018.

28th CIRP Design Conference, May 2018, Nantes, France

A new methodology to analyze the functional and physical architecture of

existing products for an assembly oriented product family identification

Paul Stief *, Jean-Yves Dantan, Alain Etienne, Ali Siadat

École Nationale Supérieure d’Arts et Métiers, Arts et Métiers ParisTech, LCFC EA 4495, 4 Rue Augustin Fresnel, Metz 57078, France

* Corresponding author. Tel.: +33 3 87 37 54 30; E-mail address: paul.stief@ensam.eu

Abstract

In today’s business environment, the trend towards more product variety and customization is unbroken. Due to this development, the need of agile and reconfigurable production systems emerged to cope with various products and product families. To design and optimize production systems as well as to choose the optimal product matches, product analysis methods are needed. Indeed, most of the known methods aim to analyze a product or one product family on the physical level. Different product families, however, may differ largely in terms of the number and nature of components. This fact impedes an efficient comparison and choice of appropriate product family combinations for the production system. A new methodology is proposed to analyze existing products in view of their functional and physical architecture. The aim is to cluster these products in new assembly oriented product families for the optimization of existing assembly lines and the creation of future reconfigurable assembly systems. Based on Datum Flow Chain, the physical structure of the products is analyzed. Functional subassemblies are identified, and a functional analysis is performed. Moreover, a hybrid functional and physical architecture graph (HyFPAG) is the output which depicts the similarity between product families by providing design support to both, production system planners and product designers. An illustrative example of a nail-clipper is used to explain the proposed methodology. An industrial case study on two product families of steering columns of thyssenkrupp Presta France is then carried out to give a first industrial evaluation of the proposed approach.

Peer-review under responsibility of the scientific committee of the 28th CIRP Design Conference 2018.

Keywords: Assembly; Design method; Family identification

1. Introduction

Due to the fast development in the domain of communication and an ongoing trend of digitization and digitalization, manufacturing enterprises are facing important challenges in today’s market environments: a continuing tendency towards reduction of product development times and shortened product lifecycles. In addition, there is an increasing demand of customization, being at the same time in a global competition with competitors all over the world. This trend, which is inducing the development from macro to micro markets, results in diminished lot sizes due to augmenting product varieties (high-volume to low-volume production) [1]. To cope with this augmenting variety as well as to be able to identify possible optimization potentials in the existing production system, it is important to have a precise knowledge

of the product range and characteristics manufactured and/or assembled in this system. In this context, the main challenge in modelling and analysis is now not only to cope with single products, a limited product range or existing product families, but also to be able to analyze and to compare products to define new product families. It can be observed that classical existing product families are regrouped in function of clients or features. However, assembly oriented product families are hardly to find.

On the product family level, products differ mainly in two main characteristics: (i) the number of components and (ii) the type of components (e.g. mechanical, electrical, electronical).

Classical methodologies considering mainly single products or solitary, already existing product families analyze the product structure on a physical level (components level) which causes difficulties regarding an efficient definition and comparison of different product families. Addressing this

Procedia CIRP 72 (2018) 1003–1008

Peer-review under responsibility of the scientific committee of the 51st CIRP Conference on Manufacturing Systems. 10.1016/j.procir.2018.03.003

ScienceDirect

Procedia CIRP 00 (2018) 000–000

www.elsevier.com/locate/procedia

Peer-review under responsibility of the scientific committee of the 51st CIRP Conference on Manufacturing Systems.

51st CIRP Conference on Manufacturing Systems

Identifying target oriented Industrie 4.0 potentials in lean

automotive electronics value streams

Tobias Wagner

a

_{*, Christoph Herrmann}

a

_{, Sebastian Thiede}

a

a_{Chair of Sustainable Manufacturing & Life Cycle Engineering, Institute of Machine Tools and Production Technology (IWF),}

Technische Universität Braunschweig, Langer Kamp 19b, 38106 Braunschweig

* Tobias Wagner. Tel.: +49 5341 28-6731. E-mail address: t-a.wagner@tu-braunschweig.de

Abstract

The digital transformation changes and disrupts principles of production. For an implementation of Industrie 4.0 technologies into existing production systems adjusted methods are needed. Value stream mapping is a well-known method from the lean production toolset and can be extended with an Industrie 4.0 perspective. The benefit of this method is a simplifying overview about the whole value stream combined with a quick analysis of improvement potentials. Identified potentials can be raised with solutions from Industrie 4.0 by making use of the qualitative correlation between Industrie 4.0 technologies and production targets. The target oriented integration of Industrie 4.0 technologies into value stream design is a further important step to realize the Industrie 4.0 vision in existing lean production systems, demonstrated in a use case in automotive electronics production.

Peer-review under responsibility of the scientific committee of the 51st CIRP Conference on Manufacturing Systems.

Keywords: digital transformation; smart factory; Industrie 4.0; value stream mapping; design thinking; cyber physical production system; connected industry; cyber physical system; lean production; automotive electronics production

1. Introduction

1.1. Trends in global automotive industry

Over a time of decades the traditional automotive industry was a stable, oligopolistic one dominated by a few big players. This environment is currently affected by several disrupting influences. The vision of shared, digital, and autonomous driven electric vehicle starts a new era with new players out of the field of digital business and high-tech startups [1].

The future market for vehicles with electrified drive systems represents another major change for car manufacturers and the products of their suppliers. E-mobility has a main impact on the product portfolio of supplier companies. The variance increases from conventional combustion parts to a mix of products for conventional, hybrid and full electric vehicles. The growing share of electronics, connectivity and intelligence driving assistance systems in vehicles is also generating potential competitors from the consumer electronics sector [2].

This increasing complexity calls into question existing concepts, tools and methods of production especially for electronic producers in the automotive business sector.

One dominating discussion in research and society is the integration of information technologies and communication technologies into industrial production to handle the increasing complexity. This approach of digital transformation is generally known as Industrie 4.0 vision or smart factory in an environment of internet of things and services [3].

1.2. State of research and gap of integrating Industrie 4.0 technologies into lean production value streams

Industrie 4.0 and lean production meet each other in industrial companies. According to Siepmann and Graef 2016 the development of the smart factory began in the 1970s with the approach of computer integrated manufacturing (CIM). The authors describe lean production as a step in between CIM and the smart factory of Industrie 4.0 and demand a further

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continuous development of the lean approach towards Industrie 4.0 [4].

A first published concept with the title “value stream mapping 4.0” by Meudt et al. 2017 enhances the existing value stream mapping method with an added analysis on waste in data flow and information logistic process. This allows a new perspective on the process efficiency in data generation and transfer, data processing and storage as well as data usage [5].

With the aim of an applicable framework an Industrie 4.0 reference architecture was developed by Müller et al. 2017. This general framework describes the four perspectives manufacturing process, devices, engineering and software to support the fusion of real and virtual systems [6].

In the current state of research many studies investigate the development of specific Industrie 4.0 solutions. However the correlation of this technologies to existing lean production systems is broadly not specified. A framework which combines the principles, methods and tools of existing lean production systems and the upcoming IT-technology driven potentials of Industrie 4.0 in an applicable framework is missing.

To join the approaches of lean production and Industrie 4.0 an ongoing investigation of the first successfully implemented Industrie 4.0 projects was started in cooperation with the global technology group Robert Bosch GmbH. The target of the project is to work out a decision supporting framework to identify potential Industrie 4.0 solutions in the environment of lean production systems [7].

This paper shows an overview of improvement potentials from Industrie 4.0 technologies. Based on theory lean production is presented as a standard approach for industrial production systems in which value stream mapping is a well-known method for identifying improvement potentials. Following the design thinking approach is presented to enable the creative process to identify innovative improvements while considering Industrie 4.0 technologies. The presented approach for a target oriented integration of Industrie 4.0 in lean production systems integrates design thinking elements into the value stream mapping method and supports the technology decision making by a qualitative correlation between production targets and Industrie 4.0 technologies. Finally the approach is evaluated in a use case in the value stream of an automotive electronics producer.

2. Improvement potentials from Industrie 4.0 technologies in lean production value streams

Industrie 4.0 should initially be understood as a management vision, initiated by the German federal government in 2011. This vision describes previously outlined implementation of smart factories with necessary adjustments of management strategies, investigations into new business models and platforms for new service processes [3], [8].

The integration of Industrie 4.0 technologies in lean production value streams offers various improvement potentials for manufacturing processes. Industrie 4.0 basically refers to the technical integration of cyber physical systems (CPS) into the field of production and logistics and its application in an internet of things and services for industrial processes [3]. The network of industrial applied CPS in a so called cyber physical

production system (CPPS) requires an interaction of different approaches and technologies.

Based on the elements data acquisition, cyber world, feedback and control as well as physical world a high potential of applications in the field of industrial production is available. For that the physical world and the cyber world have to be connected and the cyber world has to rely on data acquisition from the physical part and close feedbacks in between. The development and implementation of cyber physical solutions will enable decentralized autonomous controlling of complex production systems and can bring an increase in production productivity, efficiency and product quality, but also social and ecological benefits [9].

The main characteristics of CPPS are intelligence, connectedness and responsiveness of elements like machines, processes, material, products and systems. For this the technological basis has to be implemented to enable this elements in order to acquire information from their surroundings and process them into an intelligent controlling of physical processes [10].

To realize this demand CPPS elements should be embedded with microelectronic, sensors, communication and processing modules. As result they get a kind of basic intelligence to react on internal and external changes. The Internet of Things and Services creates the possibility to connect such smart objects with the global internet [4]. Inside of a CPPS a common virtualization platform is needed to integrate all cyber physical elements in a standard environment. This will create the link to existing manufacturing executive systems (MES) and will be the source of data for analytics applications [11].

3. State of the art for identifying improvement potential in industrial automotive electronics production

3.1. Lean as a standard approach for industrial production systems

Lean production was developed by Toyota Motor Corporation in the 1970s as Toyota Production System. This approach is an integrated management philosophy including a set of methods and tools to focus on the customer oriented key performance indicators (KPI) for quality, delivery time and costs [12], [13]. The basic idea is a continuous improvement of production by eliminating seven kinds of waste (Muda) and to produce profit through cost reduction [14]. Lean production defines everything that does not create customer oriented value as waste, especially: overproduction, waiting for work, conveyance, extra or wrong work, inventory, motion and correction of mistakes [15].

Complementary operational management principles like 5S, kaizen, just-in-time (JIT), jidoka, heijunka, standardization, tact time, pull flow, man-machine separation, people and teamwork and the named waste reduction are integrated elements of lean production [16].

Starting from this technical view on methods, tools and principles western companies began to develop their own production system and several implementation approaches [17], [18], [19]. Only after the holistic approach of human, technology and organization the expected success was

achieved. To reach excellence the lean production philosophy has to be integrated into the business culture by leadership and coaching to improve processes every day [20], [21], [22].

The rate of successful lean production implementation varied depending on the company size. Nowadays most industrial company groups are following lean principles. Whereas in comparison to that the implementation rate is low in small and medium sized companies [23], [24], [25].

The focused automotive electronic production is widely designed according to lean production standards in a general high maturity level. Many companies in this sector are reaching a point of optimization where improvements broadly being made. The current activities being pursued mostly relate to the transfer of lean production principles to indirect areas such as research and development such as administration [26], [27].

3.2. Value stream mapping as an improvement method in lean production systems

The term value stream originates from the Toyota production system and points to an effective lean production toolset to gain a holistic overview of the conditions of the production and its organization. It describes the operative processes that are necessary for creating a product in a perspective from supplier to customer. A value stream includes its flow of material and information as well as all control and steering activities. It consists of all value adding, non-value adding and supporting activities [28].

Later adaptions by Erlach 2013 based on Rother and Shook 2003 and add an additional focus on the analysis of customer demands. Following this the author discusses the target of production and references to the trinity of costs, quality and time. He comes to the finding that the coverage of product variety is an important demand in customer markets. This results in new demands on the flexibility and adaptability for producing companies. Adaptability has to be added as the fourth target of production [29]; [28].

The value stream mapping procedure is described in the four steps deduction of product families, analysis of customer demands, value stream mapping and potentials for improvement. After a clarification of the strategic target of production, the complex processes of a production system should be reduced to a representative product family. The deduction of product families can be challenging in production systems for high customized product mix in a variant production on high flexible production equipment. In this case the definition of the value stream can be supported by an initial resource-oriented segmentation, market-oriented segmentation, demand-orientated segmentation or even the product family-oriented segmentation to find a representative product family. In the next step the analysis of customer demands for this product family delivers further information about the performance requirements on the value stream described in form of the customer tact and the demand fluctuations. The third step describes the actual method of value stream mapping by drawing a current state of the focused value stream process by process including all relevant data and indicators. For the visualization a standardized symbolic is part of the method. Based on the current state discrepancies between

processes, waste and improvement potentials can be identified [29].

4. Design thinking method for creating disruptive innovations

Design thinking is an approach aiming at rethinking problems fundamentally and enabling disruptive innovations by using methods from different disciplines. It encourages people to an open mindset and creative collaboration in interdisciplinary teams to create meaningful, needs-oriented inventions [30]. The core of this approach is to combine methods from scientific problem solving and such from design problem solving and benefit from the advantages of both.

Scientific problem solving originated from epistemology based on theories, concepts, taxonomies, or models with a strong focus on analytical thinking. Problem solving in this context reduces the complexity of an unsolved problem until it is finally un-wicked and at least describable. In contrast designers do not have the possibility to reduce the complexity of problems. Design problems are originated from exogenous perspectives of customers, clients or users to find out what novel solution fits best in a social or technical system [31], [32]. Most authors follow the design thinking process by Plattner 2011, shown in Fig. 1. This six steps are iterating with the freedom of adaption to handle unexpected findings during the design process [33].

Fig. 1. Design thinking process with problem and solution space [32], [34].

In these steps design thinking follows the phase exploration of problem space and exploration of solution space with an iterative alignment between them. The extent of creativity first diverges and finally converges in both spaces. Inside of the problem space an intuitive understanding should be established by observing of use cases or scenarios. Based on this diverging problem space general hypotheses or theories regarding the problem should be found to synthesize this knowledge to points of view on the root problem. Inside of the solution space a diverging possibility of alternative and parallel ideas should be found. By elaborating, sketching and prototyping these ideas converge into tangible representatives of a final solution. During the process the design thinking team communicates these use cases, scenarios, ideas and representatives with its users and further stakeholders. This information will be used for refining and reversing the chosen development paths [32].

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