Automotive Industry

Abstract

This paper presents a new quality- and lifecycle-oriented approach of integrated production engineering in automotive industry. In a first step, current production engineering projects are analyzed and present methodical, information-technical and organizational challenges regarding the project phase of concept planning are depicted. Based on this, existing industrial- and research-oriented solution approaches are illustrated and critically evaluated. Considering the weaknesses of these solutions, this paper introduces the new developed quality- and lifecycle-oriented pro- duction engineering approach. As one key issue of this new planning approach, the idea of using a model- and rule-based configuration system is presented.

Introduction

In order to gain important market shares, car manufacturers (OEMs) are currently engaged in an innovation race characte- rized by the following market-driven key demands:

• Soaring number of product variants with many product derivates
• Increasing product complexity due to increasing quality demands and increasing mechatronic components (e.g. powerful and reliable driver assistance systems)
• Increasing time pressure due to decreasing innovation and model cycles
• Reduction of internal costs (e.g. development costs)

These global trends inevitably have an effect on all phases within the overall product creation process – especially on the project phase of production engineering. On the one hand, the processes within production engineering become increasingly more complex and, in consequence, more error-prone. On the other hand, the time for production engineering has to be cut to the bone. In addition, these trends cause extensive changes of the production systems: Production facilities become more and more flexible (as base for producing several product types into one production line), their lifecycles extend and the number of worldwide production ramp-ups will be continually rising – especially after integration processes during running production.

As portrayed in Fig. 1, this paper focuses on the project phase of concept planning in the field of automated assembly systems (e.g. car marriage). Dependent on the special car pro- ject, the concept planning phase starts about 1.5 years before SOP (start of production) and has a duration of about half a year. The tasks within concept planning are very different and can be divided into technical, economic and organizational tasks. Examples of these tasks are the development of the con- cept of the production line (e.g. the degree of flexibility), the process sequences, the layout of the production line including the line-specific bill of material as well as the accomplishment of economic calculations or the definition of the project plan for the phases of detailed planning, realization and ramp-up. All these results lead into specification documents. These spe- cifications forms the basis for the later placing of the respec- tive production facilities.

In general, production engineering takes place within two different application fields: the first planning of a new produc- tion line (green field) and the re-planning of an existing pro- duction line (brown field). Due to economic issues, the num- ber of brown field projects will be more and more rising in the future. More information regarding these integration projects can be found in [1]. As depicted in Fig. 1, in both scenarios production engineering forms the linkage between product de- velopment and production.

Due to the described global trends and the key position of production engineering within product creation process, there are various challenges and goals regarding the project phase of concept planning such as:

• Reduction of engineering times and lifecycle costs
• Highest engineering quality as base both for shorter and more robust production ramp-ups and highest production quality
• Managing of rising product, process and resource complexities and risks
• Managing of rising heterogeneous data (goal: seamless digital CAx process chain)
• Seamless change management between product development, production engineering and production
• Unified and standardized specifications as base for short allocation phases
• Seamless and standardized communication and data exchanges between OEM and line builder

Current solution approaches

In order to cope with these various challenges in the field of concept planning, different industrial- and research-orien- ted solution approaches have already been developed. The following sections illustrate both the characteristics and the critical evaluation of the most essential solutions with the special focus on the planning process of automated assembly systems.

Methods of quality management

 Advanced quality planning is a crucial element of an operating management system. Its target lies on a logical and structured planning process ensuring best possible quality for the lowest costs. Within automotive industry, the method of Advanced Product Quality Planning (APQP) is an established standard. APQP is a framework, which includes quality-orien- ted procedures and tools within the product creation process. APQP postulates to establish in

• establishing interdisciplinary teams working efficient on procedures and tools
• use of different methods of quality management (e.g. QFD: Quality Function Deployment, FMEA: Failure Mode and Effects Analysis)
• documentation of results

Within the product creation process, APQP mainly covers the phases of product development, realization and product launch. As illustrated in Fig. 2, APQP includes five major ac- tivities, which can be divided into five phases: Phase 1 stands for the planning and definition of the program. Product design and development verification (second phase) uses preventive quality methods such as FMEA or FTA (Fault tree analysis). Within the third phase, process design and development veri- fication, the main features of the production system are evalu- ated. Used methods in this phase are for example the process FMEA, process charts or standard operation procedures. Pro- duct and process validation as next phase assesses the produc- tion system and the containing processes by using e.g. pilot production and approvals. The last phase includes launch of production, assessment and corrective action [2].

Concept planning of an assembly system covers the third phase of APQP. Within this phase, the method of FMEA is used. In general, FMEA is described as highly structured and systematic method for failure analysis based on experiences e.g. with similar or former products and processes. As method for preventive system and risk analysis, FMEA already starts in an early state of the product creation process. By applying FMEA, weak points of the considered system are analyzed, measurements are initiated and risks are assessed. In this way, FMEA focuses on the prevention of potential product and process failures within product creation process [2].

APQP is mainly used for product development. For the project phase of production engineering it is not exhaustively applied due to following reasons:

• Project time is limited
• Main focus lies on other priorities
• Sustainable FMEA needs resources and knowlegde

Big OEMs has detected the problem and qualify their engi- neers in advanced quality planning methods. At present, there is a large gap between theory and practical application.

Risk Management

 Risk is not only described as negative effects facing a com- pany but also the conditions and environment within or out- side the company, which could cause negative effects [4]. In literature, the term of risk is defined in several ways [2, 5, 6]. Managing business risks in a systematic way is the main goal of risk management, including all activities of a company co- ordinating extensively and coherently their risks for managing and controlling [5]. The new approach regarding risk manage- ment is Enterprise Risk Management (ERM). ERM describes an integrated management of all risks an organization faces and conciliates risk management with corporate governance and strategy. Manifold definitions are listed in [7]. Risk ma- nagement is described as process [5]:

• Identify risk aims to carve out potential threats [2]
• Analyze and rate risks based on identified risks; requires comprehensive analyzing methods e. FMEA [3]
• Evaluate identified and analyzed risks; choose criteria
• Full risk assessment with two dimensions: probability and severity; followed up by an illustration e.g. in a risk port- folio diagram [7]
• Depict risks strategies execute for the risks’ treatment [2]
• Implement and follow up measurements for treatment

Different approaches for risk classification as context-ori- ented, cause- and effect-related or related to project-manage- ment. There are a non-exhaustive enumeration of risk classifi- cations [3]: Technical, organizational, contractual as well as financial, internal and external, political and sociocultural.

Early prevention of production planning failures and failures within product development is one of the most fre- quently mentioned reasons for risk analysis according to [8] in a manufacturing company. All classifications of risk occur in production engineering projects. Risk management is a vital method, but following aspects reduce its power:

• Risk management as process is not installed as mandatory within the concept phase
• Risk awareness according to multiplicity of decisions in the concept phase is often lacking
• Systematic and structured risk prevention is not used
• Changeable Manufacturing – (Re-)configuration

 ElMaraghy defines changeability as “an umbrella frame- work that encompasses many paradigms, such as agility, adaptability, flexibility and re-configurability” [9]. Change- ability can be accomplished by a system and its components possess certain properties. According to Wiendahl only these certain system-properties or features allows the system to transform. Defined as change enablers there are universality, mobility, scalability, modularity and   compatibility [10]. Due to modularity acts as a booster for all change enablers, it is re- cognized as an elementary key-demand of a changeable sys- tem [11]. Changeability represents the characteristic of a pro- duction, which allows an adaption of the production system. For this purpose both technical requirements and conditions like a consequent modulized, standardized and mobile pro- duction equipment and organizational requirements and con- ditions have to be defined. Existing approaches and concepts for a changeable production system are listed in [12].

Modularity represents the idea of “plug and produce”. This means standardized and pre-tested units and elements, which are separated of each other and regards technical facilities of the factory as well as structures of the organization [11]. A module embodies all resources needed to ensure its functiona- lity and autonomy and presents the smallest element of a system that can be multiplied, displaced or eliminated as a hole [13]. According to Pahl and Beitz, modularity effects more benefits as there are re-use and re-combination of mo- dules and upgradeability [14]. Modularity is the basic require- ment for reconfiguration. Mittal and Frayman define a confi- guration task as found in [15]. According to this definition, a configuration task requires components, which are specified, able to connect and with inherent properties.

Configuration includes two aspects: modularization and integration [12]. Only the connection of modularity and the change-enabler compatibility creates a valid functional unit [10]. Reconfiguration means the ability to adapt a manufactu- ring system on a fast, target-oriented and economic way in re- lation to functionality, capacity and technology. As an answer to changing specifications components, machines, cells etc. are able to adapt (adding, removing, interchanging or adjus- ting). Integrating modules rapidly and precisely will be provi- ded by standardized mechanical, informational and control interfaces for an automated assembly system [16].

The approach of integrated product and assembly configu- ration presented by Landherr premises a consequent function- oriented modularization. Within the concept-phase there are some limitations such as:

• Configuration is not comprehensively used
• Reconfiguration-enablers require inherent properties
• Re-use of parts of existing assembly systems limited due to missing connectivity
• Configuration-tool for concept planning is missing
• Life Cycle Costing

Life cycle Costing (LCC) is a specific and holistic way of accounting used for terotechnology [17]. LCC summarizes all costs which are generated during planning, initial acquisition, implementation, operations, maintenance, reconfiguration, shutdown and recycling [10]. Derived from the factory life cycle and the reasons for planning there exists six planning types: re-planning, extension, re-newal of structure, reduction, transfer and outsourcing. Including a methodology of strategic planning of reconfiguration within the initial planning process requirements for reconfiguration could be integrated and de- picted within LCC. Hence, the planning methodology des- cribed by Karl, have to be executed during the purchasing or designing phase and the re-configuration have to be planned before implementation [18].

Methods of LCC are effectively for comparison all costs evokes during life cycle in case of evaluation of planning alternatives methods of a configured production object. Allowing an efficient comparison between different planning alternatives, LCC provides a holistic life cycle-oriented fun- dament for a decision.

Usually, in the planning process LCC is not applicable for following reasons:

• Capital expenditure for production equipment etc. kept down due to low manufacturing costs and low project costs, including cost-cutting
• Benefits of changeability due to modularity just estimated and uncertain [10, 18].
• Requirements for flexibility, changeability and specifi- cations for re-use not mandatory and target within the

• Data Management

 Data management contains all organizational and technical tasks regarding the process by which the required data is acquired, validated, stored, protected, and processed, and by which its accessibility, reliability, and timeliness is ensured [19]. Managing product data (Product Data Management) and extended over the whole product life cycle (Product Lifecycle Management) the complexity and variety rise up. An ideal PLM system is able to manage diverse information: technical data, knowledge of constraints and requirements, capabilities as well as marketing specifications. Several approaches could be found in [20, 21]. Including all data and information of an organization for all business processes there are several data management systems such as PLM and ERP [22].

According to [23], used software tools within the phases of product development and production engineering are classi- fied into:

• CAx- and office-tools
• PDM-, ERP and PLM-tools as integration platform or as data backbone
• Visualization tools

In connection with production engineering and digital factory the requirements for data management could be found in [23]. Linking methods, models and tools for modelling product, process and resource data require a holistic and integrated data and IT management. The postulated comple- tely integrated and seamless data management is still not exis- ting. Furthermore, the controlling of increasing complexity of data management is rudimentary. An integrated data manage- ment is essential, but there are following restrictions:

• Changes demand actualization in all models and affected uses; often no automatic updates
• Data management overall business processes is not completely integrated
• Interfaces between tools and models, standalone solutions hinder the collaboration and change management
• Digital validation methods (as part of digital factory)

 Within the development process of automated assembly systems, the use of digital validation methods is well estab- lished in automotive industry. As portrayed in [24], digital va- lidation methods are defined as methods to check and evaluate specific product-, process- or production-related quality cha- racteristics using digital data models. Examples of such used digital validations solutions are the powerful methods of vir- tual engineering and virtual commissioning. The development of further production-oriented digital validation methods such as physics-based analysis, tolerance analysis or mixed reality are currently in progress.

As a part of digital factory production-oriented digital vali- dation methods have apart from many powerful benefits some limitations such as:

• Methods are not widespread used; application criteria are not described in a standardized way
• Focus of application: Project phase of detailed planning; validation of pure technical quality characteristics (e.g. collision checks, cycle times)
• Methods are not included in an integral planning process (stand-alone character); efforts for developing the needed data models are quite high
• Methods are not part of a seamless CAx process chain; no seamless IT infrastructure

Quality- and lifecycle-oriented production engineering 

A holistic quality and lifecycle-oriented production engineering approach requires a concept containing technical, economic and organizational domains and tasks. Within pro- duction engineering, there are manifold aspects regarding quality, lifecycle, risk, digital factory and LCC. As discussed above, there are several approaches and methods in the context of production engineering. However, regarding the mentioned issues and goals within concept planning, the explained solution approaches don’t achieve a holistic and sustainable approach. Considering these quality- and lifecycle aspects, the new concept integrates and links the explained methods within the production engineering process as illustra- ted in Fig. 3.

Advance quality planning offers diverse quality methods for an integrated planning. Implement FMEA by applying its continuous updated data within the product and production system lifecycle improve permanently outcome. By using the method FMEA the planner is supported in focusing on main risks and in capturing structural and preventive measurements for risks’ treatment. As described above, the method is sui- table to prevent risk in the early phase of the production engi- neering process.

Considering risk management as a process it has to be completely integrated into production engineering process. Output of risk management have to be deep-seated in the project milestones. Measurements out of risk analysis have followed up structurally and sustainably. Risk management should implemented in an early stage of planning ensuring a reduced risk in the entire production life cycle. Manufacturing knowledge and experience out of previous projects and daily business should be integrated into new projects in a structural way to speed up time to production and efficient planning outputs. Postulating a seamless planning process an integrated communication system, a consistent knowledge and infor- mation data management is a cornerstone for high-quality planning results. A coherent, agile data management and a seamless digital CAx process chain is essentially for pro- cessing changes within the planning phase.

Questions regarding economic efficiency in a quality- and lifecycle-oriented production engineering concept requires an integrated approach, too. LCC offers a calculation formula. But guidelines and objectives for a financial project con- trolling over the life cycle needs a corporate-wide standar- dized target given by board of management. Integrating the economic and financial aspects LCC needs to be adjusted for production engineering for a quality- and lifecycle-oriented focus. Project-KPIs needs to be defined covering the new as- pects.

Specification documents are specified as a crucial result of the planning process. Using the idea of a changeable manu- facturing by planning an assembly line composed of modules with inherent properties (considering change enablers) offers the planner new possibilities for changes in the lifecycle of the automated assembly system.

Production engineering takes a key positon within the product creation process and within the lifecycle of an automated assembly system as well. Hence, requirements on the planning process and its influence on the output is highly rated in order to reach the goals regarding the project phase, e.g. to reduce engineering time. As evaluated, a holistic pro- duction engineering approach containing and processing ques- tions and requirements regarding quality, risk, LCC, change- ability and flexibility in an integrated data management is missing. But a fast and efficient way of concept planning is one of the most important goals. That requires an approach for processing these versatile and partially interdependent data and information within the process. A model- and rule-based configuration system could be a basic approach for the design of such a complex system like an assembly system in order to reach standardization and speed within the concept phase.

As depicted in Fig. 4, a model- and rule-based configu- ration system processes as input data, information and para- meters of all domains. Following items stands for an exem- plary input list:

• Project characteristic numbers (e.g. yearly production output)
• Product data (e. g. CAD data, bill of material)
• Target production costs
• Financial budgets (e.g. for assembly system)
• Project specific framework (e.g. re-use of assembly system modules)
• Assembly specific targets (e.g. cycle time, scrap rate)

These inputs will be processing within a rule-based confi- guration system. Knowledge bases, which contain experiences and best practice from former projects, can be included to profit from this knowledge. Rules for configuration process could be for example:

• Structure of an assembly system
• Specifications for modules (e.g. preselection due to single supplier for modules)
• Process specific rules (e.g. sequence planning)

Calculation formula (e.g. for production costs, amortization)

The configuration system needs to process models as well as it configure models. After the configuration process, for all domains (e.g. process planning, material flow, quality, costs) an output should be available. Typical outputs of the model- and rule-based configuration system are for example:

• Specification of assembly system (as base of allocation phase)
• Lifecycle costs
• Quality aspects (e.g. risks)
• Layout and resources
• Manufacturing bill of material

Conclusion and Outlook

This contribution present a new sustainable and holistic de- veloped quality- and lifecycle-oriented production enginee- ring approach. Integrating the descripted methods like APQP and risk management into the planning process empowers to reach the above-mentioned goals within the concept planning. As an integrated configuration tool, the introduced model- and rule-based configuration system affords further research. This powerful system needs to be designed and evaluated. In order to integrate all domains within the planning process to speed up the process and receive higher quality, standardized and holistic outputs of the concept planning are necessary.