Siravat Teerasoponpong
email: s.teerasoponpong@gmail.com

Apichat Sopadang
email: sopadang@gmail.com

Risk Probability Assessment Model Based on PLM's Perspective Using Modified Markov Process

Keywords: Product life cycle management, supply chain uncertainty, modified Markov process, stochastic process, risk assessment

The management of the supply chain in presence of uncertainty is a challenge task. This paper proposes a stochastic model for modeling both the structure and the operation of the supply chain. Existing approaches for this task are either deterministic or single level structure which might not be appropriate to capture the essences of the supply chain. The proposed method employs the Markov chain model as the foundation and incorporate the concept of multi-level. The levels are used to model both the internal events and the external events. In the proposed method, the product life cycle management is used as a guiding principle to identify each component of the supply chain.

Introduction

Dealing with such complex and uncertain environments, especially when you are the company within a large supply chain, it is a very difficult task and it is also hardly foreseeable for what would be coming onward every day. Today, supply chains or companies have to deal with several types of uncertainty and of course, several kinds of risk. Complex growth of supply networks, economic systems, globalization or natural disasters for instance, have brought up new types of uncertainty that could put supply chains or companies at risk. Several companies are put an effort to deal with the undesirable effects due to uncertainties by minimizing risks. The tool such as stochastic model has been widely used to maximize profit and minimize risk [START_REF] Goh | A stochastic model for risk management in global supply chain networks[END_REF]. For example, Markov process, which is the one of stochastic modeling that use to model and analyze supply chain, is used to develop discrete event dynamic systems (DEDS) to analyze the flow of physical entities or resource traveling within manufacturing line with changing attributes such as queues, destinations, inventory buffers, manufacturing speed, and customer demands. The discrete nature of event-based processes are captured. However, due to interlace occurrences of unforeseen disruptions in the real world (e.g. accidents, sick workers, natural disasters, machine breakdown) and changing of others parameter value, verisimilitude of using any optimization technique could be in questioned [START_REF] Riddalls | Modelling the dynamics of supply chains[END_REF].

Since Markov model is capable of model the systems consist of multiple state (like manufacturing systems, supply chain network), it's also used to study the dynamics of systems based on probability of discrete time or continuous time events [START_REF] Viswanadham | Performance analysis and design of supply chains: a Petri net approach[END_REF]. Two types of Markov process, which are Markov chain and hidden Markov chain, have been widely used in several analytics of dynamic systems including supply chain. However, the presences of Markov chain applications shows limitations of using both Markov chain and hidden Markov chain. Although Markov chain has visibility of the entire processes through state transitions, it cannot handle all possible observations. For hidden Markov chain, the output of each state might conditionally independence, but the processes ruled by hidden Markov chain are not entirely observe. The conditional independence is not always justified [START_REF] Berchtold | The double chain Markov model[END_REF]. For example, the observed variables in the real world have both direct and indirect interactions between each other and the states in these model could have interactions interlacedly which would lead to one output. This instance would require all possible observations and not a single process to justify the output. In order to evaluate the output of the stochastic process such as uncertainties in the more realistic way, this article has proposed the new method to evaluate the uncertainty in supply chain using modified Markov chain. The method shall cover the advantages of both Markov chain and hidden Markov chain. In this paper we focus merely on downside risks due to changing of environment.

Literature Review

This section divided into two parts, the first part has presented the importance of product life cycle management and its relationship to the supply chain management.

The second part is focus on applications of stochastic process in supply chain risk management.

Product Life Cycle Management and Supply Chain Management

Today, business environment and supply chain have grown complicatedly, the companies themselves are inevitably in such surroundings. Due to an increasing of environment's complexity in which the products were developed, the Product Lifecycle Management (PLM) concept emerged to cope with such complex environments by guiding the firms through proper management over each period of product's life. But to be able to create a more effective management system such PLM, it's necessary to understand how the environment would change overtime during the life cycle of the products. As the competitive success of the firms are rely on the success of their products, PLM aims to coordinate the right information in the context at the right time to the related processes through life cycle of the product and it's all about "knowledge management" [START_REF] Ameri | Product lifecycle management: closing the knowledge loops[END_REF]. Knowing how to manage supply chain in important, but knowing how to manage change is even more essential. Supply chain is rather dynamic than static, environmental changes also affect ordinary functions of supply chain and they have associated uncertainties and risks [START_REF] Stark | Product lifecycle management[END_REF]. PLM has opened new management dimensions in quite a dynamic way by managing products from the first idea until they disposed, this give the company to take control both products and supply chain processes involved. Several supply chain management model using PLM are based on information management. For example of a product information modeling framework [START_REF] Sudarsan | A product information modeling framework for product lifecycle management[END_REF], it's extended to support full range of information along product life cycle in order to gain full access product's description and design rationale. The other objective of this framework is to create a bridge for the future computer aided system, e.g. CAD, CAE, and CAM, and to capture product's development trajectory. According to the objectives, these could lead to further development of efficient supply and production planning, and collaborations of others supply chain activity involved throughout product's life cycle. However, involvement of complicated processes, systems and stakeholder, such as automotive assembly which requires dynamic participations of several discrete systems, might risk malfunctioning of product due to uncertainty and diversity of operations and environments [START_REF] Venkatasubramanian | Prognostic and diagnostic monitoring of complex systems for product lifecycle management: Challenges and opportunities[END_REF]. Thus, intelligent prognosis system is recommended in order to oversee the complex operations and systems throughout product life cycle and supply chain management. The prognosis system shall also include monitoring, analyzing, and diagnosis capability that would help provide critical information when abnormal events occur along life cycle of product or even at realtime supply chain operations. Product life cycle management and supply chain management are both clearly a complex task. They constitute of several complex participations of processes and involving systems which latent uncertainties and risks lie under. Thus, they need more than just static management but rather dynamic way to cope with such changing environments.

Stochastic Model and Supply Chain Risks Management

The modern supply chain management paradigm, such as PLM, has been enabling key technological and organizational approaches for effective managements [START_REF] Abramovici | Future trends in product lifecycle management (PLM). InThe future of product development[END_REF]. However, real industrial operations always involve changing of product environment, dynamic systems and processes. Risk factors affecting management of product throughout its life and management of supply chain can be found in both external sources and internal sources [START_REF] Stark | Product lifecycle management[END_REF]. Any risks associated with environmental changes along product lifecycle and supply chain process are considered to be a stochastic process which state of environmental or process changes are assumed to occupy only one state at present time to evolve to another state without effect of past history [START_REF] Stewart | Probability, Markov chains, queues, and simulation: the mathematical basis of performance modeling[END_REF]. The examples of stochastic process associated with supply chain risks such as, demand risk, exchange rate risk, natural disasters, supply risk, and etc. [START_REF] Goh | A stochastic model for risk management in global supply chain networks[END_REF], these risks capable of disrupting normal operations of supply chain which represents transition of state from normal condition to abnormal condition instantaneously. To represent such behavior of the system, stochastic model is widely used for describing transition of state in mathematical approach. Several applications of stochastic process have appeared in supply chain management, it is frequently uses in risk management and optimization issue. For instance of exchange rate risk hedging for multinational supply chain operations, a stochastic dynamic programming is used to determine optimal operation option for multi-product, and multi-stage supply chain. The results are operational flexibility that could reduce financial risk and demand risk due to market and location changes [START_REF] Huchzermeier | Global manufacturing strategy planning under exchange rate uncertainty[END_REF].

Another example of stochastic modeling found in application for risk minimizationprofit maximization problem for multi stage supply chain [START_REF] Goh | A stochastic model for risk management in global supply chain networks[END_REF] by providing general formulation of mathematical model for large scale supply chain network. The model incorporate several kinds of supply chain risks, such as supply risk, demand risk, exchange rate, and others disruption. In micro level application such as inventory control system [START_REF] Lee | Material management in decentralized supply chains[END_REF], stochastic model also used to determine material order policy based on required stock level for multi-product and multi-production site of Hewlett-Packard Company (HP). The model specifically attempted to achieve highest service level for a pull-type, periodic, order-up-to inventory system which the operation associated with uncertainties due to dynamic conditions, such as, change in market demand, change in supply network structure, changes in production capacity.

In conclusion, PLM has shown benefits to supply chain management by providing a more effective way. The PLM takes all of the factors of each process in the product life cycle into consideration. Despite the advantage of the PLM, there are some problems existed, risk and uncertainty. The stochastic process is a well-known tool that is widely used to handle the problem. As a consequence, the further development of stochastic modeling for uncertainty and risk assessment based on the PLM could enhance product management capability.

Proposed Method

In this section, we endeavor to present a framework of stochastic model of a global supply network using modified Markov Chain, consisting of as numerous uncertainties as possible. The goal is to illustrate the idea of new methodology in an evaluation of supply chain uncertainty, to predict the possible changing situations based on giving circumstances. The idea of the proposed method and also the inceptive model definition are presented.

Concept of the Proposed Method

The characteristics of dynamic and interlace environments in supply chains are often not restricted to deterministic system because they are quite uncertain and consist of several forms of risks [START_REF] Santoso | A stochastic programming approach for supply chain network design under uncertainty[END_REF]. Furthermore, the changing in actual business can be instantaneously, which is means that changing of the system from one state to another require zero time, and we've called it the "Transition of state". The system could evolve overtime and the future state depends on only its current state without its past history involved. Such a system can be represented by a "Markov Process" which can be found the same property in several actual systems such as the business, engineering, biological, physics, and social science [START_REF] Stewart | Probability, Markov chains, queues, and simulation: the mathematical basis of performance modeling[END_REF]. In this paper, the proposed method could be divided into three part. First, we use scope of PLM concept as the guideline for identifying uncertainties in each phase of product life cycle management. Each phase of product life cycle shall be clarified into sub-processes from manufacturer's perspectives; that would be easier to identify uncertainties and risks related to those sub-processes. Second, Uncertain environments shall be identified and defined as a set of uncertain environments regarding activities of product life cycle. Third, after the clarifications of uncertainties and risks are completed, Markov process is getting involve, the transition probability matrix of uncertainties and the transition probability matrix of all possible risks under each state of uncertainty are identified. This step requires the definition of relationship between uncertainties and risks which would leading to deployment of multilevel transition probability matrix. The 3 steps of proposed methodology is concluded in Fig. 1. and the conceptual framework of proposed method of risk's probability assessment model based on PLM using Markov process are shown in Fig. 2.

Breakdown the sub-processes of each phase of PLM and use them as a guideline to determine the uncertainties and the risks regarding each type of uncertainty.

Identify uncertainties and risks in order to create the set of uncertainties and sets of risks.

Deploy transition probability matrix and define the definition for set of uncertainties and sets of risks regarding each type of uncertainty.

Model Definition

Unlike traditional Markov process, this proposed adapted Markov process using in the research consists of multi-level Markov process, called Modified Markov Chain (MMC). This is a memory-less, multi-level, stochastic process. The MMC employs the downward causation to simulate the chain of events. In other word, the transition matrix of the chain won't be changed. Similar to the traditional model of Markov chain, the MMC has a number of finite states,

{ } 1 2
, , , n S s s s = K

Moreover, the states of the model is separated into levels

1 2 m L L L L = ∪ ∪ ∪ K (2)
where

1 2 , , , m L L L S ⊆ K (3)
Since the MMC has multi-level, the transition process of MMC has to be specified in induction style. As a consequence, there are 2 types of transition process.

The first type of the transition process is the same as traditional Markov chain model. This type is dedicated to the highest level of the model which change the states without the influence of the other state except the previous one. Therefore, the transition process is given by ij p where j denotes the state at time t , i denotes the state at time 1 twhere ( )

1 | - = = = ij t t p P X j X i (4)
The second type of the transition process is for the states that has a higher state which will be referred as super state. The lower state will be referred as sub state. In other word, this type of transition process is for the state resided in the level that is not the highest level. The transition of state of this type can be assigned by , k ij p where j denotes the state at time t , i denotes the state at time 1 tand k denotes the state of the super state (which current level is l ), ( )

,k 1 1
| ,

+ + = = = = ij t t l p P X j X i X k (5)
Normally, the states in the lowest level is the estimated event and the state in the higher level is the environment. For example, the manufacturing is a process in the production. Thus, it is the state in the lowest level. On the other hand, the reduction of currency value is the environmental factor. As a consequence, it should be modeled as a state in the level beside the lowest level.

Discussion and Conclusion

In this paper, the MMC has been proposed by extending conventional Markov modeling to support a more complex changing of state. Inspired by interlaced changing environment in the real world, MMC supports multi-level characteristics of state transitions found in supply chain, product life cycle or any other dynamic systems which incur downside risks capable of disrupting the systems in both direct and indirect ways. Each level of MMC can be easily described as the level of events, from the highest level which represents main causes of uncertainties (e.g. natural disasters, economic crisis, or political crisis) and the lower levels which are caused by higher state (e.g. destruction of facility, company bankruptcy or worker strike). These can be formulated into transition of states of each level and the transition probability matrix shall be able to develop regarding the relationship between each level of uncertainty and PLM sub-process. To illustrate the multi-level uncertainty, suppose that the laptop manufacturer has ordered parts form its supplier located within another continent. The parts will be delivered by sea freight across Pacific Ocean. Unfortunately, there is great hurricane formed in Pacific Ocean and the ship that responsible for parts delivery has to be delayed for at least 1 week. This instance has caused the aggregate production plan of the company delays for at least 3 weeks and product launching delays for a month. Given example has illustrate the interlacement of changing environments which aren't directly caused negative impacts on supply chain or production cycle but the impacts are caused by the collateral damages from those changing environments to the process of production within phase of product realize of product life cycle. For products with a short life cycle such as electronic devices, delaying in product launching, especially not a day but a month, could have made the company risk losing a huge amount of sale revenue, or even worse. MMC could add up both relevant and irrelevant issues regarding PLM context into the model along with their relationships based on probability of occurrence which would lead to manufacture of transition probability matrix. Future research will adopt this MMC into decision support system for predicting risks caused by uncertainties. Possible outcomes shall be able to estimate time until any risk occurs. Decision support system with MMC integrated shall provide information that helps the company to foresee possible uncertainties and risks before they occur.

Fig. 1 .

 1 Fig. 1. Proposed methodology for risk's probability assessment based on PLM.

Fig. 2 .

 2 Fig. 2. Conceptual framework of proposed method of risk's probability assessment model based on PLM using Markov process.

Acknowledgments. This research has been supported by the Excellence Center in Logistics and Supply Chain Management, Chiang Mai University, Thailand.


Alejandro Romero
email: romero-torres.alejandro@uqam.ca

Darli Rodrigues

How additive manufacturing improves product lifecycle management and supply chain management in the aviation sector?

Keywords: additive manufacturing, product lifecycle management, supply chain management, agile, 3D printing

Aviation is a high competitive industry where actors should be first adopters for leading market or following adopters to survive. Traditional manufacturing techniques pass into the background, manufacturing systems require using additive technologies for rapid adaptation to current demand and reduction of production cycle duration. Many large mechanic and aircraft engineering companies have already adopted additive manufacturing technologies in their future production strategy. The general concept of 3D printer on the basis of emanufacturing principles is aimed at integration of computer models of physical objects and processes. This change requires a deep transformation into enterprise business model, affecting either core or support activities. For instance, additive manufacturing could also change product lifecycle and supply chain management practices. This paper aims to identifying how these new technologies could improve support activities for aeronautical industry? Results show that additive manufacturing triggers a paradigm shift for aircraft lifecycle management and enable lean and agile practices for supply chain management.

Introduction

Aviation is a high competitive industry where actors should be first adopters for leading market or following adopters to survive. With a decreasing market size, aviation manufacturers are competing more than ever to gain new contracts. Customer, such as airlines, private and public organizations or civilian, are looking to spend less money and get the best products possible [START_REF] Franke | What comes next after recession?-Airline industry scenarios and potential end games[END_REF]. These factors create an enormous challenge for aviation actors to manufacture products with high performance, short production cycle time, low cost and fierce competition (Witick et al., 2012). Within technology evolution, major manufacturing firms invest hundreds of millions of dollars in introducing new innovations to improve their products or services to overtake their competitors.

Additive manufacturing AM seems to be the next hype technology driver for aviation industry to improve manufacturing operations (Smartech Markets, 2014). AM, commonly known as 3D printing, could be adopted to manage aircraft production, characterized by low manufacturing volumes, personalization, complex geometries and optimal balance between mechanical resistance of parts and weight [START_REF] Hopkinson | Rapid manufacturing. An industrial revolution for the digital age[END_REF]. McKinsey Global Institute (2013) estimates AM will generate up to 550,000 million of savings annually in 2025.

AM is considered as the real cornerstone of the industrial future for the most developed countries [START_REF] Gebhardt | Understanding Additive Manufacturing: Rapid Prototyping, Rapid Tooling, Rapid Manufacturing[END_REF]. AM is often presented as an industrial revolution, based on innovative technologies, challenging traditional manufacturing models and upsetting the relationship between actors [START_REF] Hopkinson | Rapid manufacturing. An industrial revolution for the digital age[END_REF]. However, this transformation cannot be reduced to the production activities, it could also require operational optimization for the entire enterprise business model, including support functions such as supply chain management or product lifecycle management. This paper aims to provide information about this transformation.

The objective of this paper is to assess how AM could transform product lifecycle management PLM and supply chain management SCM practices for aviation operations. In the following section, we first discuss the employment of AM and the trends in manufacturing. We then outline the basic features of aviation sector and its AM applications. In the next section, we describe key transformations for PLM and SCM practices. Finally, in the concluding section we state some implications of our study as well as directions for future research.

Additive Manufacturing

The third industrial revolution

In the last three decades, industries have experienced a transition to digital. This evolution can be illustrated by technological changes, such as: offices have moved from paper hand drawn design planes to parametric files, first in two dimensions (2D computer-aided-design software CAD) and then in three dimensions (3D CAD); communications have move from sending postal mail to the first appearance of the fax and then email. Manufacturers are not immune to this phenomenon. Traditional manufacturing techniques passes into the background, manufacturing systems require using of additive technologies for rapid adaptation to current demand and reduction of production cycle duration [START_REF] Fogliatto | Mass Customization: Engineering and Managing Global Operations[END_REF]. Indeed, several researchers consider this transition as the third industrial revolution [START_REF] Berman | 3-D printing: The new industrial revolution[END_REF][START_REF] Rifkin | The third industrial revolution: How the internet, green electricity, and 3-d printing are ushering in a sustainable era of distributed capitalism[END_REF].

Digital capabilities allow high speed processing of data overcoming unknown limits such as reliability and accuracy. For manufacturing industries, digital technologies could improve operation by introducing data and control technologies such as computer-aided-design (CAD), computer-aided-manufacturing (CAM) or computer-aided-engineering (CAE). However, manufacturing processes remain basically the same: first digital design, then piece production by material removal, cold or heat forming, casting or injection, and finally, surface finishing (Tiwary and Harding, 2011). The above processes face several limits such as high cost of tooling and machinery for complex geometries, long and complex supply chain to lower tooling costs (Berman, 2013), high "time to market" for new designs, loss of flexibility in decisionmaking due to tooling cost and development time [START_REF] Gebhardt | Understanding Additive Manufacturing: Rapid Prototyping, Rapid Tooling, Rapid Manufacturing[END_REF]; tooling collisions when complex geometries, curved cutting edges and drafting angle constraints, design and manufacturing tools designed to use Design for Manufacture and Assembly (DFMA) generating constraints for product design and, although not necessary for geometries, use of solid pieces [START_REF] Grimm | Users' guide to rapid prototyping[END_REF].

Furthermore, this manufacturing model is based on mass production [START_REF] Fogliatto | Mass Customization: Engineering and Managing Global Operations[END_REF]. Standardized parts and processes made economies of scale achievable, but limited design flexibility and personification. These limitations could block manufacturers' creativity and constitute a barrier for developing new products with high added value or new functionalities [START_REF] Fogliatto | Mass Customization: Engineering and Managing Global Operations[END_REF]). E-manufacturing or "smart production", the use of advanced and emerging information technologies to provide automated data-driven productivity optimization, takes advantage of all knowledge developed in the digital age to overcome the above traditional manufacturing limitations [START_REF] Nyanga | E-Manufacturing: A Framework For Increasing Manufacturing Resource Utilisation[END_REF]. At the heart of this new industrial revolution is additive manufacturing AM, which enables manufacturing complex geometries for several industries such as aviation, aerospace industry, power and healthcare.

Additive manufacturing definition

AM, more commonly known as 3D printing, is a process of creating a three dimensional object or 3D-model from a digital model. Using an AM machine, or printer, successive layers of material are very precisely laid down in arranged patterns and lines in accordance with the digital design. [START_REF] Wohlers | Additive manufacturing: going mainstream[END_REF] defined AM as the direct manufacturing of finished products with additives construction processes through a bottom-to-top approach by combining materials without any traditional tool or equipment. AM is used to produce models, prototypes, patterns, components, and parts using a variety of materials including plastic, metal, ceramics, glass, and composites [START_REF] Lyons | Additive manufacturing in aerospace: Examples and research outlook[END_REF].

In traditional manufacturing processes, a complex geometry requires more sophisticated manufacturing process, which results in an additional cost [START_REF] Gibson | Additive manufacturing technologies. Rapid prototyping to direct digital manufacturing[END_REF]. For AM, an elaborated geometry doesn't generate complexity for the manufacturing process; it enables material savings and time reductions. In addition, some complex items require joining several pieces to form the final product [START_REF] Campbell | Additive manufacturing in South Africa: building on the foundations[END_REF]. These elements are separately manufactured and they are integrating at the end of the manufacturing process. Fortunately, AM allows manufacturing these complex items in a single process enabling integration during design process [START_REF] Wohlers | Additive manufacturing: going mainstream[END_REF].

Additive manufacturing processes

Although the variety of different additive manufacturing techniques, they all follow the same pattern. As shown in the figure 1, AM processes could be divided into three fixed phases, namely digital phase, manufacturing phase and post-process phase. -Computer-aided design CAD: Object design is performed to get its digital design in 3D [START_REF] Grimm | Users' guide to rapid prototyping[END_REF]. CAD takes a series of digital images of a design or object and sends descriptions of them to an AM industrial machine. 3D design enables improving quality and reducing overall developmental time and costs by creating a model that is precise, easily replicated, and easily conceptualized [START_REF] Schindler | Product life cycle management: A collaborative tool for defense acquisitions (Master's thesis)[END_REF]. -Standard Tessellation Language STL: CAD software generates process files for AM ma-

Digital phase

M anufacturing phase

Post-process phase

Physical object Required object or idea chine in STL format. These files should be verified to avoid errors that may affect the total quality of the end product [START_REF] Grimm | Users' guide to rapid prototyping[END_REF]. Errors are identified and corrected by STL repair program or returning the file back to the design stage.

Manufacturing phase: This phase included two main activities: -Machine setup: STL files are transferred to the AM machine and raw materials for object production are loaded. -Production part: It is the process in which the AM machine uses the STL files to create the item by adding material layer-upon-layer. Layers, which are measured in microns, are added until a three-dimensional object emerges. AM machines could operate 24 hours a day without human intervention [START_REF] Campbell | Additive manufacturing in South Africa: building on the foundations[END_REF]. The only labour involved is the machine setup, build launch, and the removal of the prototypes or object upon completion. This phase can be directly last if the part does not require a withdrawal of supports or better than that offered by the machine surface finish [START_REF] Schindler | Product life cycle management: A collaborative tool for defense acquisitions (Master's thesis)[END_REF].

Post-process phase:

The part is then removed from the AM machine for post-processing such as removal of sacrificial supports for any overhanging edges. Cleaning and finishing of the object is the most manual, labour-intensive portion of the AM process [START_REF] Grimm | Users' guide to rapid prototyping[END_REF]. Sometimes, object should go though other manufacturing procedures such as thermal operations or copper empty for improving its properties.

Additive manufacturing applications

AM is industrially used for three main types of application: for developing prototypes known as rapid prototyping, for manufacturing tooling known as rapid tooling and for manufacturing functional mechanical parts known as direct manufacturing or rapid manufacturing.

-Rapid prototyping is the main application for AM [START_REF] Bibb | The Application of Advanced Design and Rapid Prototyping Techniques in[END_REF]. Prototypes require no dedicated tools and are produced in small series, generating high costs for industries. AM enables developing in short time prototypes with relatively low costs and using different type of materials. Furthermore, a rapid prototype can be used either for visual or functionality validation for final product reducing its development time.

-Rapid tooling provides a significant increase in speed and reduction in cost for complex tools [START_REF] Campbell | Additive manufacturing in South Africa: building on the foundations[END_REF]. This application allows replacing conventional steel tooling by soft material such as epoxy-based composites with aluminum particles, silicone rubber or low-melting-point alloys [START_REF] Noble | Rapid Tooling Injection Molded Prototypes: A Case Study in Artificial Photosynthesis Technology[END_REF]. -Direct manufacturing is the latest application developed using AM processes. It accounts a very small part of the market (Wohlers and Caffrey, 2012). It used for lowvolume products for aerospace, automotive or medical sector. However, direct manufacturing will also have implications for medium-to high-volume production as the AM technologies improve [START_REF] Hague | Implications on design of rapid manufacturing[END_REF].

Additive manufacturing benefits

Berman ( 2012) drew an analogy between AM effects and those observed during the emergence of digital printing 20 years ago. Digital printing has completely transformed the industry in a few years, since business change their model business to integrate digital competencies. Based on 2D printing effects, we can anticipate what will happen with AM in the world of manufacturing:

-Design benefits: AM technologies bring creativity and flexibility for product design. AM machines can produce parts with almost any shape or complexity and without geometric limitations as the conventional manufacturing processes. In traditional manufacturing processes, there is a direct connection between complexity and manufacturing costs (Wohlers and Caffrey, 2012). A relationship tying cost to complexity does not exist in AM. Furthermore AM technologies permits to manufacture objects with any shape or branching for circulation channels or with internal cavities [START_REF] Campbell | Additive manufacturing in South Africa: building on the foundations[END_REF]. -Manufacturing benefits: AM does not require any type of tooling as conventional manufacturing processes. From this feature, two advantages could be identified. First, investment in tooling for manufacturing parts is not necessary [START_REF] Lyons | Additive manufacturing in aerospace: Examples and research outlook[END_REF]. Second, there are no manufacturing geometric constraints arising from the use of tools, such as collision of pieces during machining or draft angles during part injection [START_REF] Lyons | Additive manufacturing in aerospace: Examples and research outlook[END_REF]. In addition, AM enables tools or pieces with 100% density (in the case of metal technology). These objects have no residual porosity generating excellent mechanical properties, unlike conventional powder metallurgical processes (Hopekinson, 2006). -Material benefits: AM permits maximum saving of material. The material is selectively added and not subtracted from a block. For some applications, wastes produced from raw material, especially in the metal sector, are reduced up to 40% when additive manufacturing technologies are used instead of subtractive technologies (machining). Thomas and co authors (2014) showed AM permits to reduce objects weight by 21%. In addition, between 95% and 98% of the material used can be totally recycled [START_REF] Reeves | Additive manufacturing for mass customization[END_REF]. -Time benefits: AM enables reducing the time required for placing on the market custom products (time-to-market). The introduction of new products is less risky than before, due to the elimination of costly production tooling and to the development of prototypes (Hopekinson, 2006). This has a strong impact on the post-processing of existing products.

Changes in the design can be published to the market even faster. Thomas and co authors (2014) studied AM impact on small innovative enterprises and they showed that organizations could save 24 days in production time.

AM has a low production speed, therefore, it's not used for large production volumes. Therefore, AM should be used considering where its application is an advantage and not for integral manufacturing [START_REF] Reeves | Additive manufacturing for mass customization[END_REF]. In the last case, AM may be complement with traditional manufacturing processes.

Additive manufacturing in the aviation sector

Many large mechanic and aircraft engineering companies have incorporated or are incorporating these technologies in their daily operations. AM is manly used for military and civil applications, accounting for approximately 12.1% of AM investment in U.S.A [START_REF] Ford | Additive Manufacturing Technology: Potential Implications for US Manufacturing Competitiveness[END_REF]. For instance, Boeing uses 3D printings to produce 200 pieces that are installed into 10 different aircrafts [START_REF] Harris | Development and Implementation of Metals Additive Manufacturing[END_REF]. A F-18 aircraft contains more than 90 3D-printedcomponents such as air ducts and light pieces [START_REF] Gibson | Additive manufacturing technologies. Rapid prototyping to direct digital manufacturing[END_REF]. As well, Boeing has included 32 different components for its 787 Dreamliner planes [START_REF] Freedman | Layer by layer[END_REF]. AM holds significant potential for driving down costs in the aviation sector by enabling manufacturing objects which are lightweight, strong, and geometrically complex and typically produced in small quantities [START_REF] Smelov | Lean Organization Of Additive Manufacturing Of Aircraft Purpose Products[END_REF]. Given that aviation products are mainly manufactured using expensive raw materials such as titanium, plastic, and other lightweight materials, AM could decrease production cost by keeping material amount used to a minimum. For instance, Airbus is assessing 90 separate cases where AM could be adopted to produce tools and pieces with less raw materials (10% less compared to the traditional manufacturing) [START_REF] Wood | Additive Layer manufacturing at Airbus-Reality check or view into the future?[END_REF]. In addition, Airbus is developing prototypes to manufacture most of its aircraft parts from ducts to turbine blades with AM technologies [START_REF] Gebhardt | Understanding Additive Manufacturing: Rapid Prototyping, Rapid Tooling, Rapid Manufacturing[END_REF].

Cost economies could also be observed for complex geometries. Manufacturing cost increases within pieces' complexity for conventional methods. In contrast for AM technologies, there is not related complexity cost, resulting in a low cost strategy with higher added value.

For instance, Turbomeca is employing AM technologies to manufacturing fuel injectors and combustion chamber turbines for helicopter engines, resulting in cost economies.

AM is also a driver to build a greener aircraft by reducing components' weight. A reduction of one kilogram in the weight of an aircraft could reduces carbon emissions and save $3,000 US in fuel per year [START_REF] Ford | Additive Manufacturing Technology: Potential Implications for US Manufacturing Competitiveness[END_REF]. For instance, GE is manufacturing 20% of its turbojet components for commercial aircraft using AM. These components are 25% lighter and as much as five times more durable than the existing model [START_REF] Zaleski | GE's bestselling jet engine makes 3-D printing a core component[END_REF]. GE has also announced a $50-million investment to implement AM infrastructure for its factory in Alabama [START_REF] Zaleski | GE's bestselling jet engine makes 3-D printing a core component[END_REF]. As GE, Pratt and Whitney has produced more than a dozen pieces of its PW1500G engine which is used in the new Bombardier C-Series aircraft.

Aviation manufacturers and service providers could get advantage from AM technologies to reduce their lead-time for either new or replacement parts. Smartech Markets (2014) stated that lead-time for a part could by reduced by 80%, compared with conventional manufacturing methods." For instance, Kelly Manufacturing Company, the world's largest manufacturer of general aviation instruments, has reduced their production time "for 500 housing components from three-to-four weeks to just three days using AM technologies" (Smartech Market 2014).

Additive manufacturing impacts for PLM and SCM

Many large manufacturers and system providers state that aircrafts configuration frequently changed, generating complexity for PLM and SCM. In particular, complexity could vary in function of customer's requirements or internal factors that leads to a delayed delivery or over budget. Furthermore, aviation organizations operate in a highly reactive environment not favourable to supporting strategic planning. Thus, these companies sometimes fail to improve product, its lifecycle and its supply chain.

PLM and AM

PLM, much more than a technology solution, is a strategy that contributes to share product data within the organization and throughout its value chain. PLM goal is to effectively and efficiently innovate, manage products and their related services from upstream to downstream of their lifecycle (see topside figure 2), and finally, optimize production processes. PLM also facilitates the continuous involvement and communication of internal and external stakeholders. Aviation industry, investing in PLM, seeks for mastering the full aircraft lifecycle, improving information and decision traceability, facilitating information communication among stakeholders and developing an optimal process flow.

AM could strengthen PLM competencies by enabling advances to improve aircraft performance, such as innovation capacity, frequency and time to market, quality assurance and development costs and materials control. As showed in the figure 2, AM could be used for rapid prototyping, rapid manufacturing and rapid tooling:

• Rapid prototyping enables to improve mainly two aircraft lifecycle phases, namely prefeasibility/feasibility and design. In this case, AM accelerates product development cycles from its design. In addition, 3D printers could be a wave for accelerating time to market as prototypes can be launched in short time to assess its performance or customer's satisfaction.

•

Rapid manufacturing could be adopted for three main phases: part and system production, assembly and maintenance, repairing and overhaul MRO. AM has the potential to reduce the costs of storing, moving, and distributing raw materials, mid-process parts, and end-usable parts. The ability to produce parts on demand without the need for tooling and setup could decrease production and MRO cycle times, as well as their related cost.

•

Rapid tooling could be implemented for three phases, namely aircraft assembly, MRO and final disposal AM enables building tools when they are required. This could bring several benefits for MRO and final disposal where service suppliers should manage several types of aircrafts requiring different type of tools for disassembling, repairing and assembling aircraft systems, parts or pieces.

Figure 2. Aircraft product lifecycle and AM applications AM changes deeply PLM paradigm since aviation organizations could focus on designing products with higher performance or functionality and without considering assembly or manufactory constraints. Design for Assembly and Design for Manufacturing methodologies aim to making products easier to manufacture and assembly based on the characteristics of current manufacturing methods, however these characteristics no longer apply when taking into account AM capabilities. Furthermore, piece design is improved with AM since aviation professionals can exchange their vision for the proposed solution into an easy common and visual language, accelerating analysis time and decision making. A design paradigm shift is needed in order to "bring designers and manufacturers to stop thinking in terms of limitations, but to think in terms of possibilities" (Rosenberg, 2008).

Finally, aircraft product cycles are characterized by long periods of time for technology maturity (between 10 to 20 years), for aircraft development (between 5 to 7 years), for aircraft production (between 15 to 20 months) and for MRO aircraft (between 2 days to 2 months). For commercial planes whose average life expectancy is almost 30 to 40 years, circumventing the need to maintain and replace old tooling is a notable inventory cost advantage for manufacturers. Airbus believes that AM holds the potential to keep the turnaround for test or replacement parts as low as two weeks. These parts can be rapidly shipped to and installed in a broken down plane to help get the plane back into the air and making money for the airline.

SCM and AM

Constructors and services providers execute aircraft production and its maintenance into extend supply chain frames, which result in increasing costs. Aviation industry must manage different providers (see figure 3), different aircraft pieces and deal with the variability of pieces and nomenclature. For example, an Airbus A380 requires more than 100,000 wires [START_REF] Romero | Traceability as a key competency for the aeronautical industry : an exploratory study[END_REF]. Finally, with the pressure to increase production pace, organizations should have the capacity to identify needs more quickly based on the establishment of a collaborative relationship with its suppliers (ranks 1 and 2) and its customers. AM could support SCM competencies by decreasing supply chain complexity. AM applications made supply chains more elastic, bringing manufacturing closer to the assembly, utilisation and MRO location. Following this approach, there will be fewer requirements for part and components transportation, which alter the production and MRO flow and will almost disappear logistics costs. AM enables industrial relocation with important issues such as production deregionalization for pieces, economies for import and export customs and environmental footprint reduction. Indeed, an aircraft integrator would not need to bring all the pieces from remote countries (such as Airbus which transports aircraft pieces from Spain, Germany, Portugal and England to France); they can be manufactured on-site (in Toulouse, France).

AM enables production and MRO without stock, which implies a supply chain more efficiently and less risky [START_REF] Khajavi | Additive manufacturing in the spare parts supply chain[END_REF]. For instance, MRO providers could have minimum inventory level for each piece since they could manufacture them if required, thus this enables reducing the need of maintaining safety inventory. In this case, organizations don't need to keep in stock high cost and long-lead parts such gear rotors because they can print them when needed. Therefore, management of spare parts inventory should require reorganization. Aircraft spare parts demand pattern follows a 20/80 Pareto curve: 80% of the parts are needed frequently; but they only account for 20% of the supply chain expenditure [START_REF] Liu | The impact of additive manufac-turing in the aircraft spare parts supply chain: supply chain operation reference (SCOR) model based analysis[END_REF]. In this case, two different approaches to integrate AM technologies could be adopted, namely centralized and distributed supply chain [START_REF] Holmstrom | Rapid manufacturing in the spare parts supply chain: alternative approaches to capacity deployment[END_REF]. The centralized is more suitable for parts with low average demand; relatively high demand fluctuation and longer manufacturing lead-time (20% of the parts needed). The distributed one is suitable for parts with high average demand or very stable demand and short manufacturing lead-time (80% of the parts demanded).

AM applications could also trigger lean and agile practices in the aviation supply chain. AM enables a greater production flexibility, which is achieved by reduction of time of new production launching and inventory, efficient capacity utilization, which is provided through labour costs reduction, delivery of materials calculation depending on the needs, special arrangement of production facilities.

Conclusions

Is AM a new industrial revolution? This paper has showed that aviation organizations are rethinking their business model by adopting AM technologies. AM changes PLM and SCM paradigm by eliminating manufacturing and assembling limits, by improving product design and by reducing lead-time for aircraft development, production and MRO. Therefore, aviation industry could become more agile and "lean". AM enables the elimination of waste in tooling, materials, labour and methods of production and reducing time to improve efficiency throughout the aviation supply chain and aircraft lifecycle.

Although the above improvements, there are limitations that make AM technologies have not yet been widely adopted in the aviation sector. The current drawbacks are slow print speed limiting AM use for mass production, technology costs, material quality problems and reliability and reproducibility limits. Furthermore, SCM and PLM aviation actors should integrate all together AM capacities to support 3D manufacturing flow. The AM process is well known, but now they should overcome some manufacturing constraints and explore new forms for 3D design. Such limitations are certainly surmountable, and constitute challenges for research, technological development and innovation.

New research issues emerge through our study. We have outlined how AM remodels PLM and SCM for aviation sector. Important issues rise when an innovation is adopted by an organization triggering important changes into core competencies. How should aviation organizations manage this transformation? Are environmental, technology or organization factors affecting AM adoption? Finally, from a marketing perspective, effective ways to demonstrate AM impacts should be studied, in order to gain maximum support from aviation actors.

Figure 1 .

 1 Figure 1. Additive manufacturing processes Digital phase: This phase included two main activities:-Computer-aided design CAD: Object design is performed to get its digital design in 3D[START_REF] Grimm | Users' guide to rapid prototyping[END_REF]. CAD takes a series of digital images of a design or object and sends descriptions of them to an AM industrial machine. 3D design enables improving quality and reducing overall developmental time and costs by creating a model that is precise, easily replicated, and easily conceptualized[START_REF] Schindler | Product life cycle management: A collaborative tool for defense acquisitions (Master's thesis)[END_REF]. -Standard Tessellation Language STL: CAD software generates process files for AM ma-

Figure 3 .

 3 Figure 3. Aircraft development and production actors (adapted from Autodesk, 2009)


Hannu Kärkkäinen
email: hannu.karkkainen@tut.fi

Anneli Silventoinen
email: anneli.silventoinen@lut.fi

Different Approaches of the PLM Maturity Concept and their Use Domains -Analysis of the State of the Art

Keywords: Product Lifecycle Management, Maturity approaches, Maturity models, State-of-the-Art, Comparison

Product lifecycle management (PLM) implementation and adoption involves extensive changes in both intra-and inter-organizational practices. Various maturity approaches, for instance based on CMM (Capability maturity modeling) principles, can be used to make the implementation of PLM a better approachable and a more carefully planned and coordinated process. However, there are a number of different types of current approaches which can be thought to fall under the concept of PLM maturity. The aim of this paper is to investigate, analyze and categorize the various existing PLM maturity approaches to get an organized picture of the models and their background presumptions, as well as their potential use domains, and to facilitate their proper use to better implement PLM in different industry contexts.

Introduction

The common aim of PLM adoption is to integrate people, processes, data, information and knowledge throughout the product's lifecycle, within a company and between companies. PLM includes very extensive changes in intra-and interorganizational practices, and requires new types of skills and capabilities, and moreover, large cultural and strategic changes.

Due to the magnitude of required transformations and coordination, a timely and well-coordinated PLM implementation can be very challenging, and companies often face difficulties when adopting it (e.g. [START_REF] Batenburg | PLM roadmap: stepwise PLM implementation based on the concepts of maturity and alignment[END_REF] [START_REF] Batenburg | PLM roadmap: stepwise PLM implementation based on the concepts of maturity and alignment[END_REF]). Failures and long implementation times are often a result of the technology driven approach: PLM implementations are often led by IT investments, and other important management areas (e.g. company strategies, business processes, or employee skills) are either lagging behind or are not properly aligned with IT.

One important rather recent solution for the above types of problems are various types of maturity -related approaches. There has been a growing attention to research to PLM implementation, adoption and maturity-related issues, as demonstrated by the increasing number of papers in maturity issues in the field of PLM. However, thinking of the broad definition of PLM maturity, as well as PLM adoption and implementation issues, there are a number of different types of current approaches which can be thought to fall under the concept of PLM maturity. The aim of this paper is to investigate the maturity paradigm with regard to PLM domain specificities and discuss appropriate approaches to tackle PLM maturity in a systematic manner. Several papers have studied PLM maturity and related approaches (e.g. [START_REF] Batenburg | PLM roadmap: stepwise PLM implementation based on the concepts of maturity and alignment[END_REF] [START_REF] Batenburg | PLM roadmap: stepwise PLM implementation based on the concepts of maturity and alignment[END_REF]; Pels et al., 2008 [2]; Silventoinen et al., 2010 [3]; Kärkkäinen et al., 2014 [4]; Vezzetti et al., 2014 [5]) but very few have tried to systematically interrelate and compare conceptually the different existing PLM maturity-related approaches, except for [START_REF] Vezzetti | A benchmarking framework for product lifecycle management (PLM) maturity models[END_REF] [5] and [START_REF] Stenzel | Comparison Framework for PLM Maturity Models[END_REF] [6]. Both of the articles focus on the benchmarking of existing models on a high level of abstraction, for instance to allow for the selection of an appropriate model from the existing PLMrelated models at large, as well as creating a generic benchmarking framework for PLM maturity approaches. They do not, however, go into a more detailed comparison and the in-depth analyses of the basic foundations and the presumptions of the models, for instance their basic presumptions of what PLM and PLM maturity really are. The current studies have not, in a broader sense, identified, compared and categorized the various different types of existing approaches of PLM maturity, and have not been able yet to analyze in more detail their most suitable use domains (e.g. industry types, or other use domains). PLM maturity research is currently fragmented, and should be made comparable from the larger PLM maturity perspective.

In maturity model building, defining the scope and the focus (specific/generic), as well as the more detailed intended domains of use of maturity approaches is important (Mettler, 2011 [7]; Pöppelbuss et al., 2011 [8]). PLM maturity approaches' domain specificity and the related presumptions have not been researched in detail. These have not always been brought clearly up-front in studies, but these may have a large influence on the applicability and usefulness of maturity models. In recent studies of [START_REF] Vezzetti | A benchmarking framework for product lifecycle management (PLM) maturity models[END_REF] [5] and [START_REF] Stenzel | Comparison Framework for PLM Maturity Models[END_REF] [6], this need has been noted, however. We will therefore aim to focus on the analyses of these factors, and if necessary, try to interpret the models' implicit aims and presumptions.

In this paper, therefore, not only CMM/CMMI -based maturity approaches will be considered, but also other types of approaches addressing PLM maturity assessment and development problems.

Then, the main research questions addressing the current research gap are: What do we know about the PLM maturity concept and approaches and their underlying foundational assumptions? What important differences are there in the existing maturity approaches, and how do they impact the potential domains of use of the models?

2 Defining PLM Maturity

Defining maturity and PLM maturity

In general, "maturity" can be defined as "the state of being complete, perfect or ready" [START_REF] Simpson | The Oxford English Dictionary[END_REF] [9]. Maturity thus implies an evolutionary progress in the demonstration of a specific ability or in the accomplishment of a target from an initial to a desired or normally occurring end stage [START_REF] Mettler | Maturity assessment models: a design science research approach[END_REF] [7]. Very broadly and briefly, "PLM maturity" can be seen to refer to the concept of how far an organization is in its implementation of PLM, and how much it still has to go to its targets in PLM implementation or "full PLM". PLM maturity can refer for instance to the maturity of PLM processes, objects (such as ICT systems, documents, data structures), and people (e.g. skills, roles, responsibilities) (e.g. Batenburg et al., 2006 [1]; Vezzetti et al., 2014 [5]). PLM Maturity can be defined more specifically for instance as "the ability to manage the knowledge and capabilities of an organization to respond effectively to specific customer needs, at any point in time." (Kärkkäinen et al. 2012 [14]).

In order to meet the new requirements of innovative products, the PLM systems should be improved to satisfy the new demands including faster and more convenient information interaction, better information sharing, capability to detect the successful features of new products, etc. More and more current PLM IT systems cannot fulfill the needs of different companies, and yet, no perfect approaches have been proposed to appropriately detect the drawbacks and the benefits of PLM in companies. In order to help the company to select the optimum PLM and understand the AS-IS and TO-BE situations, the PLM maturity approaches should be continuously studied to consider the strengths, weaknesses and limitations of PLM maturity and maturity approaches, and improve them accordingly. In addition, the range and the conception of PLM maturity should be changed with the increasing of smart products. In practice, new PLM maturity approaches should be, for instance, easier-to use, reduce the evaluation efforts, and capable to reflect the correct maturity levels [START_REF] Zhang | PLM Maturity Evaluation and Prediction Based on a Maturity Assessment and Fuzzy Sets Theory[END_REF] [START_REF] Zhang | PLM Maturity Evaluation and Prediction Based on a Maturity Assessment and Fuzzy Sets Theory[END_REF].

Instead of viewing PLM maturity merely from the perspective of IT and business alignment perspective (e.g. [START_REF] Batenburg | PLM roadmap: stepwise PLM implementation based on the concepts of maturity and alignment[END_REF] [START_REF] Batenburg | PLM roadmap: stepwise PLM implementation based on the concepts of maturity and alignment[END_REF] or the technology adoption and staged capability improvement perspective, PLM maturity assessment can be seen more from a measurement perspective. When aiming to measure something, such as PLM maturity, and aiming to build a consistent maturity model for PLM maturity assessment, several things should be defined and aligned according to the maturity measurement objectives (see Fig. 1 below). According to Mettler (2009) [11], it is very important to have a good understanding of what is meant by "maturity" when designing (or using) a maturity model approaches: for instance, having a processfocused understanding of maturity implies to focusing on activities and work practices for designing more effective procedures, while again, when the concept of maturity is focused to people's skills and proficiency, the emphasis of the model lies more on the softer capabilities (e.g. people's behaviour). By the clarification of the maturity concept, the goal function of the model (i.e. the way how maturity is facilitated) is influenced, and thus, the goal function being clear, the nature of the design process has to be determined in order to derive the maturity levels, the metrics, and the corresponding maturity improvement recommendations.

PLM and its objectives should be defined explicitly enough, preferably in a measurable way, in a manner that they help also the useful definition of PLM maturity and its measurable objectives. Finally, the whole structure of the model, including PLM maturity dimensions and individual maturity questions, should ideally serve strictly and solely the purpose of well-defined measurable PLM objectives.

PLM from maturity perspective

First, PLM is often seen essentially as PLM software (either essentially a single PLM solution, or a large group of different types of solutions, such as PDM, CRM, ERP, excel sheets, various collaboration tools etc.), and thus, PLM maturity approaches may for instance attempt to align the implementation of IT/PLM software with company's business goals. Commonly, however, PLM is seen as a larger PLM concept which involves e.g. people, processes and technological solutions. It is thus important to define and understand which of these are emphasized in PLM maturity approaches. (e.g. Schuh et al., 2008 [12]; Batenburg et al., 2006 [1]) Second, PLM is quite seminally about data, information and knowledge, and about getting these properly to serve a company's business and product development (e.g. Schulte, 2008 [13]; Kärkkäinen et al., 2012 [14]). One important difference between these is that while data and information can be managed and shared in a rather straight-forward manner, using e.g. traditional PDM and CRM systems, the management and transfer of knowledge can be enabled and supported but not essentially easily managed. PLM knowledge is partly explicit knowledge, recorded for instance in documents. Other PLM knowledge relates to the concept of tacit knowledge, present in people and derived from their experiences, but often hard to explicate verbally or in writing. Current PDM techniques, for instance, are still most suitable for managing explicit knowledge. (Kärkkäinen, 2012 [14])

Third, PLM can be seen from functional or organizational viewpoints, but in many industries, product lifecycle management is essentially about the concept of extended enterprise and networks of companies, which have different roles in a product's lifecycle and the management of products' lifecycle information. These emphases have also important implication for PLM maturity approaches and their use.

Furthermore, depending on the overall business goals and strategy of a company, as well as the type of business (e.g. project-based customer-oriented investment products versus mass produced and mass-customized), PLM objectives can be very different, either emphasizing strongly the efficiency of processes or the high satisfaction and service-level of customers). This should be taken into consideration in PLM maturity approaches, as well.

Maturity Approaches in PLM

To identify the most common different types of PLM-related maturity approaches, we started off with the two recently published PLM maturity model benchmarking studies, Vezzetti et al., 2014 [5], and Stenzel et al., 2014 [6]. These are to our knowledge, and according to the carried out literature research, the only existing studies that have carried out an academic research to identify the existing maturity approaches for PLM, and to compare and benchmark them, as well as to create frameworks for the higher-level comparison of PLM maturity models. [START_REF] Zhang | PLM Maturity Evaluation and Prediction Based on a Maturity Assessment and Fuzzy Sets Theory[END_REF]; PLMIG, 2007 [START_REF] Plmig | PLM Maturity Reference Manual (Version 1.0)[END_REF]; Kulkarni et al., 2004 [20]; CMMI-DEV [START_REF]CMMI for Development, Version 1.3[END_REF]; CM 3 [START_REF] Niknam | [END_REF]). Three doubles were omitted. Due to the fact that we wanted to identify and compare, in essence, approaches that were directly PLM-related, we had to leave out Kulkarni et al., 2004 [20], which is a quite generic knowledge management capability maturity model, and CMMI-DEV [START_REF]CMMI for Development, Version 1.3[END_REF] (a generic capability maturity model) and CM 3 [START_REF] Niknam | [END_REF] (configuration management maturity model; see e.g. Niknam et al., 2013 [23]). In addition, since, we wanted to compare different types of models, we left out Zhang et al, 2014 [START_REF] Zhang | PLM Maturity Evaluation and Prediction Based on a Maturity Assessment and Fuzzy Sets Theory[END_REF], which was by its basis rather similar to Savino et al., 2012 [18] model. Furthermore, due to accessibility and language reasons, Frigerio et al., 2012 [START_REF] Frigerio | Sviluppo Nuovo Prodotto-Benchmarking dei processi di Sviluppo Prodotto[END_REF], unfortunately had to be left out, as well. This produced 8 different types of PLM maturity-related approaches.

After making an additional literature search to verify potential missing models, we were able to identify two additional PLM-related models, Sharma (2005) [START_REF] Sharma | Collaborative product innovation: integrating elements of CPI via PLM framework[END_REF] and [START_REF] Stark | 21st Century Paradigm for Product Realization[END_REF] [START_REF] Stark | 21st Century Paradigm for Product Realization[END_REF]. [START_REF] Stark | 21st Century Paradigm for Product Realization[END_REF] [START_REF] Stark | 21st Century Paradigm for Product Realization[END_REF] model was essentially rather similar to [START_REF] Stark | Product Lifecycle Management -21 st Century Paradigm for Product Realisation[END_REF] [16], but the newer model was more oriented to PLM instead of PDM, and was also otherwise updated. Thus, the older was omitted, this leaving us with one added model. Taking into consideration that PLMIG proposes two different approaches for maturity evaluation, the other one being more process-oriented and the other activityoriented, this leaves us with altogether 9 models for our comparison and analyses:

Analysis and Comparison of Current PLM Approaches

Two experienced PLM maturity researchers with background also in the development and testing of current PLM maturity approaches analyzed separately the different identified maturity approaches. In case of differences in evaluations, the differences were discussed, and on the basis of the consensus derived from the discussions, the final evaluations were described.

On the basis of more generic comparison of identified different types of PLM approaches (see Table 2 in Appendix), the papers reporting the approaches did not clearly identify any specific domains or limitations of use for the approaches. This would let us presume that the approaches are quite generic and can be used widely in different companies implementing PLM for their business. The approaches were in general designed, not very surprisingly, for mainly companies in the manufacturing industry. However, through a more close analysis, we were able to recognize some important differences with the approaches and their most suitable use domains. The explicated purposes of the maturity approach use were also described at a rather large level of abstraction. Mostly, the papers broadly stated that the models were to identify the as-is and to-be situations of PLM, even if some studies, such as [START_REF] Batenburg | PLM roadmap: stepwise PLM implementation based on the concepts of maturity and alignment[END_REF] [START_REF] Batenburg | PLM roadmap: stepwise PLM implementation based on the concepts of maturity and alignment[END_REF], for instance, mentioned the purpose to be also to the alignment of business and PLM (IT) systems, Savino et al (2012) [START_REF] Savino | PLM Maturity Model: A Multi-Criteria Assessment in Southern Italy Companies[END_REF] to identify right PLM system and tools for companies, or Sharma (2005) [START_REF] Sharma | Collaborative product innovation: integrating elements of CPI via PLM framework[END_REF] to enable the adoption of collaborative product innovation in PLM.

Explicated PLM objectives were in many cases explicated at least somewhat in measurable terms, but in most cases, the PLM objectives were not directly and explicitly linked to the development of the model or approach content, so we cannot be sure whether the objectives were really used in the model design. Furthermore, the explicated PLM maturity objectives were, somewhat surprisingly, expresses in rather abstract terms, but we also tried to interpret the objectives from e.g. the higher level maturity descriptions. Many of the models were dealing with PLM maturity mostly from functional and/or organizational levels, but surprisingly few analysed PLM maturity taking the extended enterprise concept into consideration. We will later analyse this organizational focus in more detail. The more detailed objectives included e.g. PLM process standardization and optimization, or faster and better response to customer needs.

Finally, we analyzed the approaches and the related papers for their special viewpoints to PLM maturity, finding a variety of different types of approaches to PLM maturity, despite the approaches mostly having not identified any specific use domains or use purposes. For instance, some maturity approaches considered the process automation and optimization to be the goal of the highest maturity levels (e.g. approaches 1 and 6), while others emphasized on high maturity levels significantly the ability of companies to connect to customers and partners and their processes on need-basis and in an ad-hoc manner, making use of web-based approaches (e.g. model 4). These reflect very probably different presumptions behind the ultimate goals of PLM, or the different types of business logics behind the maturity model design. We will analyse in more detail the above types of different emphases in the identified PLM maturity approaches.

On the basis of our analyses of identified and evaluated PLM maturity approaches, we found several significant differences between the studied maturity models. The most significant foundational differences were related especially to the organizational development foci of PLM maturity approaches (Fig. 2), the focus on data, information and knowledge (Fig. 3), and the different types of emphases on process automation vs. ad-hoc and need-based process integration (Fig. 4). These emphases are analysed in more detail in the next figures. On the basis of our analyses (see Fig. 2), all the studied approaches, quite naturally, put most of their emphases on the organizational level of PLM development, and the maturity approaches seemed clearly to be mostly intended for the intra-enterprise development of PLM maturity. Most of the approaches also took into consideration and aimed to develop, at least on the lower levels of PLM maturity, the various company functions related to PLM. However, somewhat surprisingly, even if PLM is commonly understood to include the inter-organizational and extended enterprise aspect in the development of PLM, at least on the more mature levels of PLM, only few of the approaches took this important PLM aspect into a more in-depth consideration even on the higher levels of PLM maturity. Of the approaches that took the extended enterprise explicitly into consideration included, first, Batenburg's approach (1), which evaluated and measured in all its five maturity dimensions the PLM maturity by the extent of PLM developmental issues from ad-hoc to functional, organizational and inter-organizational levels. On the basis of the maturity model structure and individual maturity questions, it seemed to emphasize more the supplierside of inter-organizational integration.

Second, Sharma's model ( 4) emphasized on higher levels of maturity clearly the inter-organizational collaboration (customers and partners), and the typical features of such collaboration that would be expected from high-maturity companies. Third, Karkkainen's approach (5) emphasized the knowledge-oriented integration of the customer direction, which is important for instance for engineer-to-order companies, which are often based on the proper understanding of customers' needs and the business, and the proper transfer of customer knowledge to product development. In the PLMIG's approaches, the inter-organizational aspects seemed somewhat to be considered, but this was not very explicitly emphasized in the described models. Considering the viewpoint of data, information and knowledge (see Fig. 3), we found that most of the maturity approaches emphasize strongly the management of data and information by more traditional types of PDM and other information systems. Several models were found to have data and information driven approach for PLM implementation with an aim to prepare the organisation, people and processes to adapt to the PLM Information Technology investment. Mature organizations were seen as such to be capable of automating the data and formation flows, throughout the organisation and even with partner organizations and customers. This approach also assumes that PLM software handles standard workflows and procedures as well as structured and mostly explicit information. Models 1, 2, 3 and 9 were most strongly focused on this IT aspect of PLM. This is understandable, because quite often the management of structured data and information is at the core of PDM systems, and a large part of manufacturing companies find problems in the lifecycle management of structured data.

However, for instance customer focused and project-based engineer-to-order companies find serious problems in their product lifecycle management in making use of unstructured and tacit forms of knowledge from both customers and their partners, in which traditional data systems are not at their best. The models with knowledgeorientation had a people driven approach, making emphasis on experience and knowledge of people, organisational learning, collaboration and integration within inter-organisational networks. The role of activity-based tacit knowledge, interaction and experience was seen as very important for the high PLM maturity of companies, and therefore PLM software architecture can be considered as a tool for interaction, knowledge processes (creation, sharing, retrieval, storage and reuse) and should be flexible and adaptive to new situations and products. The approaches 5, 6 and 7 are most strongly oriented into the knowledge management aspect -6 and 7 have specifically differentiated the data and knowledge aspects in PLM by dedicating a separate management dimension for data and knowledge, and can be seen to consider both aspects relatively well. Approach 5 emphasises e.g. co-creation of knowledge and co-experimenting between customers and partners on higher levels from this perspective. As seen from Fig 4, a larger part of the maturity approaches emphasise higher levels of maturity to involve high levels of process standardization, automation and optimization, many of them (e.g. 3 and 6) basing their approach to the principles of CMM/CMMI, and e.g. 1 (basing additionally on COBIT approach principles) aiming rather strongly to the implementation of PLM software as central part of PLM maturity. Some of the others, such as 4, high levels of PLM maturity emphasize strongly the adaptive and flexible nature of the larger PLM concept (not only PLM IT) to integrate new partners, customers and their processes on need-basis to allow fast reaction to changes in markets and customers' needs. In this respect, the models describe mostly what characteristics of PLM are broadly to be implemented, but they do not address in more detail how to do this, thus emphasizing the descriptive instead of normative PLM maturity viewpoint.

Discussion and Conclusions

On the basis of the PLM maturity approach analyses, it can be stated that in most of the cases, the seminal presumptions behind the PLM maturity approaches and their design have not been defined in detail, for instance: how they define the very central concept "PLM" and its objectives, or how, in accordance with the previous definitions, the concept of PLM maturity is defined, and what types of measurable benefits should be expected from increased levels of maturity -efficiency of processes, flexibility of market changes, or better responsiveness and even prediction of customers' needs, and how the maturity investments are presumed to be paying off in a more quantitative manner. Most of the current maturity studies in the PLM domain contend to state that the purpose of their maturity approaches is to make the complex process of PLM adoption a more coordinated and stage-like.

The above differences have implications for instance to the selection of the most suitable maturity approaches for companies with different business logics and companies coming from very different types of industries. For instance, engineer-toorder-types of organizations might make use of models that are relatively knowledgeoriented and enable the fast, flexible and need-based integration of the customers and their knowledge to their own development processes, while companies that are massproduction-or mass-customization oriented, might benefit from the process standardization and automation-focused approaches. Companies should also, depending partly on their business and current maturity stage, take into consideration the extended enterprise viewpoint to PLM, and select suitable approaches accordingly. Companies that have merged with other companies might benefit from approaches, such as the Batenburg approach, which enable the more in-depth measurement and the benchmarking of the maturity levels, instead of approaches merely describing the typical maturity characteristics on each maturity level, to align the PLM maturity and related competences of their different business units.

This study carries some limitations and restrictions, because the authors have had to make some interpretations concerning e.g. the objectives, focal emphases and the limitations of the studied maturity approaches. The evaluations between the different researchers were generally, however, quite unanimous. It is also possible that some of the models have been further defined and refined in the case of e.g. the description of their foundational presumptions and use domains. However, such studies were not identified. However, broadly speaking, we can quite confidently draw the conclusion that the different maturity approaches carried significant differences in their background presumptions and goals towards PLM facilitation and focal domains of their use, even if the related studies did not generally bring these implicit or explicit presumptions forth.

Finally, the results of this study can be used by companies that want to facilitate their PLM implementation in a coordinated and systematic ways supported by the use of PLM maturity approaches. Also academically, this study provides avenues for further research of PLM maturity, and for the more systematic development of existing and new PLM maturity approaches, as well as for the testing of the benefits of current maturity approaches in line with their implicit maturity objectives.

Explicated

Provides a framework to build company-specific maturity model

Lower levels (0-2) of maturity emphasize strongly PDM maturity, PLM emphasized explicitly on higher levels of maturity [START_REF] Silventoinen | PLM Maturity Assessment as a Tool for PLM Implementation Process[END_REF](4)(5) ** = Explicitly and clearly defined (or measurable) in source; * = Somewhat defined (generic/not explicitly linked to approach/non-measurable/etc.) ; X = Not defined

Fig. 1 :

 1 Fig. 1: Aligning the PLM objectives and PLM maturity objectives

Fig. 2 :

 2 Fig. 2: Organizational development foci of studied PLM approach (1: Batenburg; 2: Schuh; 3: Saaksvuori; 4: Sharma; 5: Karkkainen; 6: PLMIG Structurebased; 7: PLMIG Activitybased; 8: Savino 9: Stark)

Fig. 3 :

 3 Fig. 3: Data and information vs. knowledge foci of studied PLM approaches (1: Batenburg; 2: Schuh; 3: Saaksvuori; 4: Sharma; 5: Karkkainen; 6: PLMIG Structurebased; 7: PLMIG Activitybased; 8: Savino 9: Stark)

Fig. 4 :

 4 Fig. 4: Process automation vs. ad-hoc process integration foci of studied PLM approaches (1: Batenburg; 2: Schuh; 3: Saaksvuori; 4: Sharma; 5: Karkkainen; 6: PLMIG Structurebased; 7: PLMIG Activitybased; 8: Savino 9: Stark)

  [START_REF] Vezzetti | A benchmarking framework for product lifecycle management (PLM) maturity models[END_REF] [5] identified and compared six approaches(Batenburg et al., 2006 [1]; Schuh et al, 2008[START_REF] Schuh | Process oriented framework to support PLM implementation[END_REF];Saaksvuori et al., 2008 [15];Stark, 2011 [16];Kärkkäinen et al., 2012 [14];Frigerio et al., 2012 [17]), while[START_REF] Stenzel | Comparison Framework for PLM Maturity Models[END_REF] [6] recognized and evaluated ten approaches(Batenburg et al., 2006 [1];Kärkkäinen et al., 2012 [14];Saaksvuori et al., 2008 [15];Savino et al., 2012 [18],[START_REF] Zhang | PLM Maturity Evaluation and Prediction Based on a Maturity Assessment and Fuzzy Sets Theory[END_REF]

Table 1 :

 1 PLM Maturity Approaches Analyzed in the Study

	Model	Reference

APPENDIX