Stefan Kehl
email: stefan.kehl@tu-clausthal.de

Carsten Hesselmann
email: carsten.hesselmann@tu-clausthal.de

Patrick D Stiefel
email: patrick.stiefel@volkswagen.de

Jörg P Müller
email: joerg.mueller@tu-clausthal.de

Static Product Structures: An Industrial Standard on the Wane

This paper aims at closing the gap between early phases (e.g. design) and later phases (e.g. procurement or production) of the Product Development Process (PDP) by proposing a Virtual Product Model (VPM) as a collection of individual components (VPMCs) without the need for a static structure. Based on an analysis of the requirements on product development in the automotive industry, the main problems we observe are limited transparency, limited continuity, and limited reusability throughout different phases of the PDP. Virtual Product Model Components (VPMCs) can be used in different products and allow the reflection of changes throughout the PDP as well as the derivation of domain-specific views on the overall product at runtime. We illustrate these concepts by use case scenarios derived from an analysis of automotive product development practices.

Introduction

Over the past years, the complexity of industrial products has been increasing due to rising expectations from customers and the resulting higher variety. E.g. in the automotive industry the number of possible variants is skyrocketing and has reached a level at which "the number of theoretical possible Vehicle Variants is higher than the number of sold Cars" [START_REF] Dr | Compositional variant management and its application in embedded software development[END_REF]. This trend is often referred to as mass customization. According to Tseng and Jiao [START_REF] Tseng | Mass customization: 25[END_REF], it is about "producing goods and services to meet individual customer's needs with near mass production efficiency". With this "explosion of potential offering variety" [START_REF] Chatras | High variety impacts on bill of materials structure: Carmakers case study[END_REF] many issues arise such as (i) the traceability of parts (and their geometric representation) used in multiple products, (ii) a comprehensive way to manage changes on components, and (iii) an effective structuring of product models which supports reusability, continuity and transparency [START_REF] Kehl | Changes on changes: Towards an agent-based approach for managing complexity in decentralized product development[END_REF] and need to be addressed in order to preserve success on the market and meet both new technological and customer requirements. Some practitioners propose concepts, like the modularization of complex products [START_REF] Clarkson | Predicting change propagation in complex design[END_REF][START_REF] Jarratt | Engineering change: an overview and perspective on the literature[END_REF], to share common parts in multiple products and product lines regarding their individual components [START_REF] Benavides | Automated analysis of feature models 20 years later: A literature review[END_REF].

In practice static product structures, such as Bill of Materials (BOMs) (see Sect. 2), are still heavily used as the data backbone for Product Lifecycle Management (PLM)-systems [START_REF] Adolphy | Method for automated structuring of product data and its applications[END_REF]. E.g. in [START_REF] Tekin | A method for traceability and "as-built product structure" in aerospace industry[END_REF], Tekin et al. described an approach to use multiple static product structures for different stages of the PDP. Structures which are used later during the PDP (so called As Built Bill of Materials (ABOMs)) rely on previous ones like Manufacturing Bill of Materials (MBOMs) and are reconciled with Engineering Bill of Materials (EBOMs). Chatras et al. argue in [START_REF] Chatras | High variety impacts on bill of materials structure: Carmakers case study[END_REF] that managing complex product models with BOMs is no longer an adequate solution because of the current scale of diversity.

Besides considering the aspect of how to structure a virtual product model much work focuses on the engineering perspective of the product development process [START_REF] Clarkson | Predicting change propagation in complex design[END_REF][START_REF] Alle | The ontology of gero's fbs model of designing[END_REF][START_REF] Jarratt | Engineering change: an overview and perspective on the literature[END_REF]. Within the MOKA product model [START_REF] Oldham | Moka-a methodology and tools oriented to knowledge-based engineering[END_REF] constraints which represent design restrictions are described. Among other things, those constraints can be used to model product choices and to define the order in which design decisions are made. Others like [START_REF] Roucoules | Ippop: an open source collaborative design platform to link product, design process and industrial organisation information[END_REF] take mechatronic aspects of the engineering processes into account. Furthermore, much attention has been paid to the field of engineering change management or the impact of geometrical changes of components on each other and the overall product [START_REF] Hamraz | A holistic categorization framework for literature on engineering change management[END_REF]. In [START_REF] Adolphy | Method for automated structuring of product data and its applications[END_REF] a method for an automated structuring of geometric product data in a two step procedure is presented which can be used on 3D Computer Aided Design (CAD) models.

We propose a new way of modeling a product without employing a static product structure as the backbone of the Product Data Management (PDM) system. We achieve this by introducing a Virtual Product Model (VPM) that allows the derivation of specialized views at runtime (e.g. a BOM) and that is based on VPMCs. A VPMC is a collection of data elements from different departments (e.g. design or procurement) that describe a single component. By assigning a VPMC to a product, context-specific instances of this VPMC and of the data elements are instantiated in the following denoted as VPMC-U.

The structure of this paper is as follows: In Sect. 2, we describe the current practice in managing BOM data and design data in the automotive industry. In Sect. 3, we describe the concept of the VPM, how VPMCs can be used to provide design data (based on industrial use cases), and how views on the VPM can be derived. In Sect. 4, we illustrate, that the VPM contains (at least) the same information as the EBOM presented in Sect. 2 by providing an algorithm to transform a set of VPMCs and their context specific instances (VPMC-U) into an EBOM. Finally, we conclude with a summary and outline avenues of future work.

State-of-Practice in Providing Design Data

In practice, static product structures, such as BOMs, are used to provide a data backbone for PLM systems [START_REF] Adolphy | Method for automated structuring of product data and its applications[END_REF]. E.g. for automobile Original Equipment Manufacturers (OEMs) multiple types of product structures are important. Two of the most relevant ones are: (i) An EBOM to organize a product's design data and (ii) a MBOM that focuses on manufacturing part sequences. In the following we focus on EBOMs.

The main goal of an EBOM is to integrate design data into a BOM. For releasing design data (appending the corresponding data elements to an EBOM), a BOM-based approach is no longer an adequate solution [START_REF] Chatras | High variety impacts on bill of materials structure: Carmakers case study[END_REF], because of the static and inflexible nature of an EBOM and the current scale of diversity. Such an EBOM has to meet requirements of all domain experts (from different departments like virtual prototyping, tool manufacturing, procurement) participating in the PDP. Therefore, the size of the structure easily gets unmanageable. Thus, PLM roll-out projects may fail, when designers are expected to use such an overloaded EBOM. Furthermore, transformations of static structures are computational complex and therefore very time-consuming.

The most important contribution of designers within the PDP is the so called partgeometry alignment that connects a part (logistical, BOM-oriented element that carries organizational information such as suppliers and procurement channels) and a revision of a geometry (design-oriented collection of 3DCAD-Files and several 2D-Drawings that carry information about the geometric characteristics of a part). In this paper we assume, that the relations between geometry revisions and parts are provided by the designer. Nevertheless, in previous work [START_REF] Bender | A comparison of agent-based coordination architecture variants for automotive product change management[END_REF] we proposed an agent-based approach to (partly) automate such alignments. This part-geometry alignment can be used in one or multiple contexts (generic term for a collaborative developed product or derivative of a product).

A typical product structure that represents a context (see Fig. 1(a)) consists of nodes (to structure the virtual product) and BOM items (parts), that specify a component of the virtual product in the context of the EBOM. In order to model different derivatives of the same product (e.g. sedan, roadster, station wagon), the so called technical validity of those parts (part variance ) is attached to the structure. E.g. the variance rule (BODY = SW) ∧ (HD = LEFT) describes the validity for a station wagon with left-hand drive.

Furthermore, the validity of a part according to the context-specific PDP can be defined by assigning milestones to the part-node, where a milestone marks the attainment of a specific stage of the PDP. For each milestone the components have to meet predefined characteristics and quality requirements. For Digital MockUp (DMU) a geometry-and context-specific position needs to be defined (e.g. the four rims of a car are positioned differently and POS = FrontLeft describes the left front wheel). In order to be able to differentiate between these alternate positions (e.g. to use only one positioned geometry for collision detection), position variance needs to be applied to the structure.

Taking the requirements above into account, such an EBOM easily gets unmanageable, due to its size and number of levels. They are the result of integrating multiple domains into a single structure. In contrast, the VPM provides the possibility to derive domain specific views based on the consolidated product data.

Providing Design Data using VPMCs

In this section we discuss the concept of the VPM based on VPMCs and their contextspecific usages. In Sect. 3.2 we introduce the basic concept, and the differences between VPMCs and their context-specific usages. Sect. 3.3 shows how use cases derived from the automotive industry, can be performed based on VPMCs. These use cases cover the evolution of a component over time and its usage in different contexts (derivatives or even products) with temporally disjoint PDPs (see Fig. 1(b)). Finally, Sect. . At milestone concept decision of the context I (sedan) in t = 1 only the first geometry revision of this rim is available. At this point there is no need for aligning a part, because organizational information (such as suppliers) are needed not until the production planning starts at t = 2.

In t = 3 (milestone start of the production of context I) the latest part geometry alignment consists of the second geometry revision G1.2 and part P2 (e.g. silver rim). Therefore, this alignment can be assigned to the sedan (context I) for milestone S. In t = 4 a new geometry revision has been development, but no part is assigned, because milestone S (start of production) of context I has past, and neither milestone F (facelift) of context I nor a crucial milestone of another context has been reached (see Fig. 1(b)).

The development of the roadster (second context II) starts at t = 5. At this point there are four different revisions of the geometry. The newest part is P3 (black rim). Therefore, the fourth geometry and the part P3 are assigned to the context roadster for milestone C. As mentioned in Sect. 2, parts , geometries , the relation(s) between them, and contextspecific information appended to these relations are the basis for components of the VPM. The set of these information regarding a single component is defined as a VPMC . Due to geometry being the most characterizing element (in contrast parts carry much organizational information and may differ according to market conditions), each VPMC is geometrically unique.

Fig.2. Concept of VPMCs, VPMC-Us, and dynamic views

A VPMC evolves over time, because product development is not a linear process, but a continuous sequence of changes [START_REF] Bucciarelli | Designing Engineers. Inside technology[END_REF][START_REF] Mcmahon | Observations on modes of incremental change in design[END_REF][START_REF] Cross | Engineering design methods: Strategies for product design[END_REF][START_REF] Cheng | A network-based assessment approach for change impacts on complex product[END_REF]. Taking into account the efforts of most practitioners to increase the commonality of different products [START_REF] Benavides | Automated analysis of feature models 20 years later: A literature review[END_REF], a component is most likely used in multiple contexts . In the following we refer to this context-specific objects as VPMC-Us . Accordingly, a VPMC is related to multiple VPMC-Us. Because VPMCs contain parts and geometries, these elements are related to multiple part usages P /geometry usages as well. These usages are related again to other context-specific elements such as milestones , the positions of the geometry in that context, and variance expressions regarding the geometrical (on the geometry usage) or technical validity (on the part usage). A VPMC itself has no relation to any context, but to several VPMC-Us; each VPMC-U serves as a collector for the context specific elements. The number of VPMC-Us depends on (i) the number of contexts the VPMC is used in, (ii) the number of occurrences of that VPMC in each context (e.g. a car typically has four tires), and (iii) the number of updates inside a VPMC-U (traceability -see Sect. Furthermore, a VPMC-U is a context-specific instance of a VPMC. It contains the following data elements: (i) A geometry usage with a relation to a geometry revision; (ii) the position of this geometry revision in the context; (iii) variance rules, that describe the validity of the geometry; (iv) several alternate geometry usages because of different positions; (v) a part usage that represents the part in that context; (vi) variance rules (on the part usage) that describe the technical validity of the part; and (vii) a milestone that defines the stage of the PDP the VPMC-U is released for.

Reflecting Changes of Components using VPMCs

In this section we discuss the data model of VPMCs and their context-specific usages (VPMC-Us) capable of managing the evolution of a component (especially their containing part-geometry alignments) in relation to context-specific and disjoint milestones (Fig. 1(b)).

At first the designer creates a new geometry G1 representing the rim of the previous example. Thus a new VPMC is instantiated. Furthermore, the designer provides a class α, that specifies the "type" of the VPMC (e.g. rim). V .

sedan (context I). Because of the existing relation between the geometry revision and the part, the corresponding usages are related to each other. Furthermore, both usages share the same validity for milestone C. Secondly, a variance expression that describes the validity of the part in relation to different configurations of context I. E.g. this specific silver rim might only be valid for cars with a costly equipment package. This variance expression is related to the part usage directly. At t = 3 (see Fig. 3(b)) the geometry of the silver rim is updated (G1.2), a new part P2 (black rim) is provided, and both are aligned to each other. These usages (and there the collector 𝑈 𝑉1.2

𝐼

) are valid for the sedan's milestone S. The first position of the geometry revision, the validities on the geometry usage 𝑈 𝐺1.2 1 and/or the part usage 𝑈 𝑃2 , can be taken from the previous VPMC-U or, on behalf of the designer, replaced by new information. Furthermore, there might be an alternate position of the geometry revision (e.g. to model the rim turned right or the front left rim). The data elements created are: (i) a geometry revision G1.2, a part P2, and an alignment between these elements, (ii) a VPMC-U valid for milestone S with a relation to the VPMC, (iii) a new part usage 𝑈 𝑃2 for the part P2. The variance expression of this part usage is taken from the part usage of the previous VPMC-U 𝑈 𝑉1. In summary this approach leads (for one VPMC) to a set of context-specific VPMC-Us with different validities. Each VPMC-U describes the current state of the related VPMC in a specific context at a particular point of the context's D . The separation of components into context-free (VPMC) and context specific (VPMC-U) elements, enables the development of a component to be independent of its actual usages. This solves the problem of using the same component (VPMC) in different contexts with disjoint PDPs mentioned at the beginning of Sect. 3.

Domain-Specific Views on a VPM

One of the main advantages of our VPM is the possibility to create views at runtime (see Sect. 3.2) by assigning VPMC-Us to leaf nodes of arbitrary structures based on their classification. Therefore, the computational complex transformation (see Sect. 2) mentioned in Sect. 2 are obsolete.

Furthermore, these views can either be context-specific (see Fig. 4(a)) or a collection of VPMC-Us of different contexts by adding context-specific nodes subordinated to the classified nodes of the arbitrary structures (see Fig. 4(b)).

The relation between the context-specific nodes and the VPMC-Us can be derived, because every VPMC-U knows the context it is assigned to. Therefore, this approach can be used to calculate structures at runtime, that describe the share of common parts in different contexts. Moreover, by selecting only the data elements of the VPMC-Us relevant for a specific domain (e.g. parts for purchasers or geometries for designers) these views become domain-specific (see. the right side of Fig. 1(a)).

Evaluation

To evaluate the information content of a product model based on VPMCs, in the following we describe an algorithm to transform a VPM into an EBOM illustrated in Sect. 2. It is unlikely, that this algorithm is used in practice, because of the ability of the VPM to create dynamic views at runtime, but it shows that the VPM contains (at least) the same information as an EBOM. See Fig. 5 for an illustration of the transformation and Alg. 1 for a description in pseudo code. for all GeometryUsage altGeoUsage: geoUsage.getAlternatives() do ᐅ add position node and geometry revision from geometry-usage 11:

Node altPosNode = partNode.addPositionedGeometry(altGeoUsage) 12:

altPosNode.setVariance(altGeoUsage.getPositionVariance())

Conclusion

In this paper, we described a data model for managing the development of complex products, where several domain experts take part in the development of new products and each domain has its own and specialized view on an overall product model. We proposed a novel Virtual Product Model (VPM) that supports reusability, transparency, and continuity. Each component of our VPM is modeled as a combination of context-free parts, geometries (Virtual Product Model Component (VPMC)), and their context-specific counterparts, part usages, geometry usages extended with milestones and variance-expressions and combined in a VPMC-U. As we pointed out in [START_REF] Kehl | Changes on changes: Towards an agent-based approach for managing complexity in decentralized product development[END_REF], the VPMCs are extended by domain experts within their specialized views throughout the Product Development Process (PDP). Therefore, the approach presented in this paper allows the derivation of specialized views (BOM or design oriented) on a single product or on a collection of multiple contexts. This reduces the amount of information each domain expert is required to manage.

To summarize, the main contributions of this paper are: (i) an analysis of an industrial use case to determine the minimal set elements needed in a Product Data Management (PDM)-System to support the development process, (ii) a conceptual definition (based on the use case analysis) of VPMCs and their elements to support the release process of design information, and (iv) the derivation of specialized and user-related views on the VPM.

Future work will focus on (i) an agent based model to handle the dependencies between elements of VPMC [START_REF] Bender | A comparison of agent-based coordination architecture variants for automotive product change management[END_REF], and (ii) methods for Feature Model Analysis to increase the transparency regarding the reuse of individual components in different products.

Fig. 1 . 3 . 1

 131 Fig.1.Current practice in providing design data using a static product structure3.1 Use Case: Aligning Design and Logistic DataChanges on a component usually result in changes on the geometric representation. In practice not every change on the geometry (new revision) is reflected on the part-geometry alignment, because (i) parts do not depend on the geometric level exclusively, but on changes regarding the procurement (or other organizational matters) as well; and (ii) not every geometry revision fulfills the quality requirements for post-design phases of the PDP. Taking into account the parallel and temporal independent development of multiple contexts (derivatives or products) different revisions of the component (and therefore of the geometry) might be valid for different context-specific milestones. Example:The component rim evolves over time t (see Fig.1(b)). At milestone concept decision of the context I (sedan) in t = 1 only the first geometry revision of this rim is available. At this point there is no need for aligning a part, because organizational information (such as suppliers) are needed not until the production planning starts at t = 2.In t = 3 (milestone start of the production of context I) the latest part geometry alignment consists of the second geometry revision G1.2 and part P2 (e.g. silver rim). Therefore, this alignment can be assigned to the sedan (context I) for milestone S. In t = 4 a new geometry revision has been development, but no part is assigned, because milestone S (start of production) of context I has past, and neither milestone F (facelift) of context I nor a crucial milestone of another context has been reached (see Fig.1(b)).The development of the roadster (second context II) starts at t = 5. At this point there are four different revisions of the geometry. The newest part is P3 (black rim). Therefore, the fourth geometry and the part P3 are assigned to the context roadster for milestone C.

2

 2 Virtual Product Model Component and VPMC-U

  3.3). Yet other elements of the VPM are predefined, structured, and tree-like views. The leaves of such a tree are classified . This classification is related to the class of the VPMCs. E.g. one leaf contains all components of the class rim. Therefore, all VPMCs with a relation to this class can be assigned to the rim node. Applying this concept to VPMC-Us provides a contextspecific and dynamic view based on VPMCs and classified views (see Sect.

  3.4). Fig.2illustrates the differences between VPMCs and VPMC-Us.In summary a VPMC is a reusable component with a unique geometry. It contains the following data elements: (i) A single geometry with several revisions (to trace changes on P the geometry); (ii) multiple parts that are realized by the geometry revisions; (iii) relations between geometry revisions and their parts (part-geometry alignment); and (iv) a reference to a class (classification of VPMCs).

Fig. 3 . 1 1 1 .

 311 Fig.3. Providing design data using VPMCs

1 𝐼, 1 and 𝑈 𝐺1. 2 2

 112 and (iv) two new geometry usages 𝑈 𝐺1.2 for the geometry revision G1.2. The position of the first geometry usage is taken from the geometry usage 𝑈 𝐺1.1 1 of the previous VPMC-U 𝑈 𝑉1.1 𝐼 the alternative position and the variance rule are provided.

Fig. 4 .

 4 Fig.4. Deriving views from a VPM

Fig. 5 .Algorithm 1

 51 Fig.5. Static Product Structure vs. Domain-Specific and Dynamic Views on VPMCs


Kumari Moothedath Chandran
email: kumari@cpdm.iisc.ernet.in

Amaresh Chakrabarti

Monto Mani

A spatio-temporal product lifecycle network representation

Keywords: Spatio-temporal network, Product lifecycle mapping, Global manufacturing

Product lifecycle is a complex network with large supply chains from multiple organisations. Lifecycle assessment of products of organisations with globally dispersed manufacturing supply chains and international market involves various spatial and temporal constraints. Even though organisations have data of their global supply chains, data is typically stored textually as spreadsheets, or visually as process flow charts. Visual representation of this large data using flow charts makes it complex and difficult to read and interpret. A decluttered, simplified product lifecycle data representation method is presented in this paper, which is developed for use alongside an LCA tool.

Globally Dispersed Product Lifecycle

Geographically dispersed manufacturing makes the lifecycle of a product from an organisation a large network with global supply chains. Companies build or acquire manufacturing facilities in different countries based on strategic business reasons. For example, General Electric Aviation headquartered in Ohio, USA, opened a component manufacturing, service, support and sales centre in 2010 in Singapore to cater to the Asia-Pacific region as well as the international market [START_REF]GE Aviation Produces New Components at its Singapore Facility[END_REF]. Original Equipment Manufacturers (OEM) source around 80% of the value of the final manufactured product from outside their companies [START_REF] Adam | Manufacturing in the 21st century: an holistic future[END_REF]. Apple iPhone manufacturing involves nine different companies from the USA, Germany, China, Korea, Japan, and Germany [START_REF] Xing | How the iPhone widens the United States trade deficit with the people's Republic of China[END_REF]. Hence, understanding the lifecycle of a company's product with globally dispersed manufacturing supply chain, market and service networks, involves spatial and temporal data. Decision to select manufacturing locations by companies are based on specification satisfaction, quality, cost of manufacturing, shipping costs and time, and process scheduling. Individuals-employees of companies with global manufacturing networks, component manufacturers, and material suppliers-do not have an understanding of where they stand, and how crucial the role they play is with respect to the product lifecycle network. This is not due to non-availability of information; but rather due to the complexity of information in the way it is currently stored. The following quote from Lewis E Platt, former CEO of HP illustrates this difficulty. "If HP knew what HP knows, we would be three times as profitable" [START_REF] Preis | The Role Of Knowledge Management In Aircraft Product Development[END_REF].

Even though organisations have record of their global supply chains, these are most often stored textually in the form of spreadsheets, or visually as process flow charts. Commercially available PLM tools (e.g. Enovia, Teamcentre) store these data in the form of textual records attributed to product geometry. LCA software (e.g. SimaPro) do not capture company data, but prompts the tool user to select equivalent processes from commercially available LCA databases (e.g, Eco-invent) and displays the environmental impact values of the product. Neither PLM nor LCA tools provide a complete understanding of the product lifecycle network. A product's lifecycle with global manufacturing, distribution, use, re-use and disposal network involves geographically dispersed processes across time zones and lifecycle phases. Hence it involves spatial and temporal information of these processes. A spatio-temporal product lifecycle representation method is presented in this paper, which is developed for use alongside an LCA tool.

Information Visualisation in Product Lifecycle

Visualisation is the process or activity by which non-visual information is converted to a visual form [START_REF] Meyer | The acceptance of visual information in management[END_REF][6][START_REF] Krrmker | Visualisierungssysteme[END_REF][START_REF] Charwat | Lexikon der Mensch-Maschine-Kommunikation[END_REF]. Companies use Gantt charts and flow charts to understand process sequences and identify critical processes for project scheduling and planning purposes. These diagrams capture the temporal and sequential dependencies of processes involved. The spatial aspects are not reflected in Gantt or flow chart diagrams. For example, Gantt charts would look the same whether two sequential processes were to take place in the same location or in two different continents, as long as the duration of these processes remained the same. Flow charts do not represent actual process durations involved unlike in Gantt charts, and do not show spatial information. A process flow chart would hence appear the same, irrespective of the variations in process durations, and whether individual processes took place across the globe or in the same plant. It merely shows the sequence or processes. Such oversimplification in visualisation leaves out essential information needed for decision making by an individual. One is unable to evaluate the real world reasons for supply chain disruptions or delays -for example, a natural calamity in the location where components are manufactured; a political scenario which led to closure of a factory; an increase in cost due to local economic conditions; an environmental law which prevented the functioning of a manufacturing plant due to its emissions, etc. Such aspects involving legislations, and demand for sustainable activities are increasing the complexity of product design [START_REF] Gmelin | Achieving sustainable new product development by integrating product life-cycle management capabilities[END_REF][START_REF] Bevilacqua | Development of a sustainable product lifecycle in manufacturing firms: a case study[END_REF][START_REF] Hu | Modeling sustainable product lifecycle decision support systems[END_REF]. Companies in high technology industries face challenges due to product complexity and globally dispersed product design activities [START_REF] Gmelin | Achieving sustainable new product development by integrating product life-cycle management capabilities[END_REF][START_REF] Grieves | Product Lifecycle Management: Driving the Next Generation of Lean Thinking[END_REF].

Geographic Information System (GIS) tools in logistics (E.g. ArcGIS) map supply chain information on a two-dimensional map. An example of how such a mapping would look for the Boeing 747-8's manufacturing network is shown in Figure 1. More information layers can be added on GIS tools over the 2D base map which would be displayed in the form of text or tables. GIS tools do not capture temporal dimensions of individual processes and their sequences like in Gantt and flow charts. However, they capture time indirectly through real time tracking for shipments. The Material Flow Analysis (MFA) method, which is based on material flows and mass balance of a system boundary, uses flow charts with stock quantities alongside for visual representation of data. An example of Titanium fan blade manufacturing process for Rolls-Royce Trent 1000 engines used in Boeing 787 Dreamliner is shown in Figure 2. This diagram captures the process sequence along with stock quantities of inputs and outputs. The MFA flowchart does not provide information on locations at which the processes took place, locations from where the inputs were sourced, locations where the waste generated were disposed, and locations where the recycled stocks were used. MFA flowcharts do not provide temporal information of processes as well. Sankey diagrams and bar charts are used by LCA tools (for e.g. SimaPro) to show the environmental impact and emission values. An example of Rolls-Royce Trent 1000 engine manufacturing process is depicted in a Sankey diagram in Figure 3. The Sankey diagram shows process-wise environmental impacts; the thicker the line, the higher the impacts are, and vice versa. This depiction is end-product focussed and does not show the spatial or temporal dependencies of the processes involved.

Other visual representations used in manufacturing and production planning and other spatio-temporal representations are reviewed elsewhere [START_REF] Chandran | A Spatio-Temporal Network Representation for Manufacturing[END_REF]. Most of these representations become complex and cluttered while representing large datasets of product lifecycle information. GIS based tools when used for product lifecycle data, display many criss-crossing lines connecting locations in maps where the user cannot comprehend the entire network. MFA flowcharts if drawn for entire product lifecycle, will have many processes with arrows in different directions and stock quantities. The flowchart size will be large and a user can get confused while following the arrows going back and forth. Sankey diagram for LCA results of a product lifecycle will have large number of processes with criss-crossing lines of varying thicknesses and colours. A decluttered and simplified product lifecycle data representation method with spatial and temporal information is necessary for better understanding and decision making involved with global supply chain networks. One such representation is proposed in this paper, which is developed for use in an LCA tool.

Proposed Space-Time Network Method

The need for a new visual representation arose when the data of geographically dispersed manufacturing, lifecycle processes, inventories, and environmental impacts associated with them had to be analysed to aid decision making, while using an LCA tool under development for a sponsored project from industry. Potential first tier end users of the tool include design engineers, department heads, project managers, R&D personnel and top-management. Second tier end users of the LCA tool would be component manufacturers, suppliers, partnering companies for sub-assembly manufacturing. The LCA results would be used for discussions with end users in order to optimize a product's global supply chain network for reducing environmental impacts. These results would also be useful in discussions with third tier users, e.g. factory floor employees. Since geographically dispersed supply chain involves working with people from different countries, the challenges involved with this are: a) the data and results must be easy to comprehend for people from varying educational backgrounds; b) it should be readable irrespective of linguistic barriers across regions; c) compatibility of the data representation method for use in print form at locations or situations where there are no computers or devices to display the data, such as smallscale manufacturing units in developing countries. The information layers needed for decision-making were arrived at based on the information the three tiers of users would need and how the challenges described above can be overcome. The information needed are as follows: a) Lifecycle processes and their sequence: A lifecycle process sequence is defined here as a set of all discrete processes which should take place in a desired sequence in order to manufacture, use, service, or dispose the product. It is a superset of processes involved in all lifecycle phases-material, manufacturing, assembly, transport, use, re-use/disposal. b) Spatial and temporal information of lifecycle processes: Spatial information includes locations of lifecycle processes-city and country names, or geographic coordinates. Temporal information includes durations of each process. c) Inputs and Outputs for each lifecycle process: Inputs and outputs include material, energy and information, e.g. material resources, products, electricity, fuels and lubricants. d) Equipment and People: Equipment are machinery involved in order to carry out a process. For e.g., large machines, computing, sensing and testing devices, and transport devices such as trolleys, cranes, and automobiles. e) LCA results: LCA results can be in the form of single point values or ranges for each process involved.

A new representation named Space-Time Network (STN) Method was designed based on the objectives and the information to be represented. Figure 4 describes the basic elements of STN method. The STN method uses an inverted graph to plot space along the x-axis, and time along the inverted y-axis. The unit of x-axis are the geographic co-ordinates of the locations involved. Every unique geographic coordinate value is allotted one unit position in the x-axis. The order of placing the locations is based on the West to East -North to South Rule (WENS Rule) explained below:

1. Successive positions in the x-axis from left to right in the STN graph will be allotted in the order of appearance of the longitude values from West to East in a world map.

2. If two or more geographic co-ordinates have the same longitude value, the successive positions in the x-axis from left to right will be allotted in the order of appearance of latitude values from North to South in a world map.

This rule makes it easier for anyone to mark the x-axis positions by just looking at a commonly used world map. A world map is used along with STN graph to map the locations from the 2D maps to the x-axis unit positions of STN graph. The example in Figure 6 illustrates this. The WENS Rule eliminates the clutter of criss-crossing lines as observed in GIS based tools (see Figure 1). The unit of time in STN graph can vary depending on the unit of process durations for each product lifecycle. The unit can be in seconds for a product with a shorter production time and lifecycle; it can be in days or months for products with longer production time and lifecycle. Absolute time can be used as well to represent the durations.

Each process is represented in an STN graph as a straight line with its x-axis representing the location of the process (Figure 4). The y-axis or the length of the line signifies the duration of the process. The diagonal lines in Figure 4 are connectors.

Global connectors (Global In and Global Out) and local connectors (Local In and

Local Out) are used to link process chains and material flows, between two locations and the same location respectively. The connectors are drawn as notations, and it is represented using a diagonal line of uniform length and angle with two nodes (start and end). The orientation of the connectors are different depending on the type of connector. There are four connector types: 1. Global In, Gin: a connector to the left drawn at the start node of a process. Gin denotes, the process to which it is connected receives input material sourced from another location. 2. Global Out, Gout: a connector to the left drawn at the end node of a process. Gout denotes, the process to which it is connected has an output to be shipped to another location.

Local

In, Lin: a connector to the right drawn at the start node of a process. Lin denotes, the process to which it is connected receives an input sourced from the same location. 4. Local Out, Lout: a connector to the right drawn at the end node of a process. Lout denotes, the process to which it is connected has an output which is either locally discarded, or goes for recycling.

Different combinations of input-output scenarios exist in a product lifecycle and supply chain. Every possible scenario can be depicted using these four connectors as shown in Figure 5. What each of these scenarios signify is explained in Table 1. The connectors eliminate the issue of clutter due to criss-crossing of lines in GIS based or any map based representations. The connectors enable the network to expand as more information becomes available, without having to redraw the rest of the graph. The equipment and people involved are shown, using graphic symbols of gear and human respectively, next to each process. The count of the symbols denote the equipment and people count in each process. Transportation involved in Gin and Gout is shown using a truck symbol next to these connectors.

Fig. 4. Basic elements of a Space-Time Network Graph

The logic of plotting an STN graph is described below: Here, S denotes space; t, time; P, process; Out, output; Inp, input.

1. Si (t) is a location point in the space-time co-ordinate at a given point of time. The co-ordinate values of the point Si (t) will be (Si, ti), where i = 1, 2, 3… 2. A process, Pij is defined by a pair of 2 points (Si, ti) and (Sj, tj) where tj > ti; i= 1, 2, 3…; j= 1, 2, 3… 3. A Gout or Gin is a process, Pij, defined by a set of 2 points, (Si, ti) and (Sj, tj); where tj > ti; Si≠ Sj; i= 1, 2, 3…; j= 1, 2, 3…

4. An Lin or Lout is a process, Pij defined by a set of Input-Output values of Pij and its succeeding process Pjk such that{Out Pij} ≠ {Inp Pjk}; where Sij=Sjk ; tk > tj > ti; i= 1, 2, 3…; j= 1, 2, 3…; k=1,2,3,… The basic elements of the STN graph have been described in this section. The next section explains how the STN graph depicting LCA results can be plotted, using an example of product supply chain and lifecycle network.

Example of Boeing 747-8 using STN Method

An example of Boeing 747-8's supply chain network, involving processes from the life cycle phases of material, manufacturing, assembly and transport, is demonstrated using the STN method. Data for this was accumulated by transcribing documentary videos of Boeing 747-8 and GE engine manufacturing [15,16], and from information available in company websites [17]. The data displayed is not an accurate representation of The Boeing Company's actual supply chain network. Assumptions have been made for demonstration purposes of the STN method. The Boeing 747-8 has 6 million parts, most of which arrive just-in time [15]. The major subassemblies for the Boeing 747-8 are the GE Engines GE nx-2B, wing elements, landing gear and structural elements. All of these are sourced from different plants and locations. The major components for GE nx-2B engines are turbine blades, fan cases and fan blades, which are sourced from different companies or manufactured at plants at different locations. The aircraft assembly, engine manufacturing, turbine blade, fan blade and fan case manufacturing for engine assembly are demonstrated in this example using the STN graph (see Figure 6). The range of impact values from LCA results are categorised into a colour spectrum (see Figure 6, right side). The LCA value for each process is checked against this colour spectrum and the corresponding colours are used for the process lines. The overall impact of the lifecycle network is shown on the right side (see Figure 6, below colour spectrum). The overall impact is shown in a circle, with the impact value depicted using the corresponding colour from the spectrum. The lifecycle phase-wise aggregated impact values are shown on the right side in a colour coded circle depicting its value, with a red line depicting the end of each phase. Each lifecycle phase is demarcated with respect to the product for which the lifecycle is evaluated, which in this example is the Boeing 747-8 aircraft. Space-wise aggregation of LCA results are shown using colour coded circles (as per the spectrum) at the end of each x co-ordinate position, which denotes the corresponding locations. An output is recycled and is reused by receiving it as an input to another process Output in the form of raw materials/products/finished goods are shipped to another location where it is an input to a process Locally sourced material is used as an input to a process and its output is locally disposed or recycled Locally sourced material is used as an input to a process and its output is shipped to another location

Advantages and limitations of STN method

A manufacturing plant has machines running continuously and simultaneously, producing parts and products in batches. At every instance of time, different batches of products are at varying degrees of completion. The STN method, by focussing on one manufacturing lot, produces a clear picture of a product's lifecycle network and its dependencies across different locations and processes. From Figure 6, one could distinguish 5 different chains showing processes taking place in 5 locations. The STN method also carries the good aspects of the commonly used graphical representations which are: the sequence from material flow analysis, geographic tagging from GIS, use of colours in Sankey diagrams for LCA results, and temporal information and process dependencies from Gantt charts. The material flow is captured, without having to show the quantities, by use of Gin, Gout, Lin and Lout. The colour coding of processes, and space-wise and lifecycle phase-wise aggregation of LCA results enable users to locate the individual process, or location, or the life-cycle phase, which contribute to higher environmental impacts. With this knowledge, decision makers have better chance to optimise the network effectively for reducing the environmental impacts. Alternative lifecycle networks can be compared easily using this method. Two networks can be compared, or the same network can be optimised for reducing environmental impacts, till the desired values are achieved.

The STN graph can be used for an entire product lifecycle or for individual lifecycle phases. It can also be drawn for an individual factory, where each zone is represented by different points in the x-axis. STN graph captures the real scenario when it comes to recycle-reuse-disposal phase. In most representations in PLM and LCA, re-cycle and re-use phases are depicted as a loop connecting the manufacturing process. In reality and in STN method, the Lout (discarded outputs) join as Lin (recycled inputs in this case) at the next point in space and time where those processes that take them as input actually occurs.

The STN method supports addition of more supply chain and life cycle data into the network as more information becomes available. The whole product lifecycle can be built in a modular manner, and each supplier, or component manufacturer can contribute its data to add to the network. The network can be easily drawn by hand for quick discussions and modification. The STN method is compatible to print media.

There is no loss of data when the STN graph is printed in colour. Black and white prints show the geographically dispersed lifecycle network.

The STN method is not a replacement for existing supply chain and logistics tools that handle large amounts of logistics related information including part numbers, real time tracking etc. This method is useful for the intended users as explained earlier for decision making purposes related to the product supply chain and lifecycle. The STN method have been presented to industry representatives, who acknowledged the potential usefulness of the method. User surveys of end users are planned to be carried out to estimate the level of understanding and ease of use of STN method compared to other graphical representations.

Conclusions

According to Winston [START_REF] Winston | Artificial intelligence, 2nd edn[END_REF], finding appropriate representation is a major part of a problem solving effort. A good representation 'makes important things explicit' and exposes 'the natural constraints inherent in the problem' [START_REF] Winston | Artificial intelligence, 2nd edn[END_REF][START_REF] Yuan | Extensions to geographic representations[END_REF]. The STN method proposes an information visualisation approach to graphical problem solving in the product lifecycle related domain. Supply chain infographics convey data in pictorial manner, but the ability to create good infographics depends on the skill of the designer. The STN graph is a generic and replicable design, from which the relationships between processes, locations, and network dependencies can be interpreted and extrapolated. An implementation of the STN method is currently being tested for its efficacy.

Fig. 1 .

 1 Fig. 1. Manufacturing network of Boeing 747-8 1 .

Fig. 2 .

 2 Fig. 2. Material flow of Titanium blade manufacturing process for Rolls-Royce Trent 1000 engine 1

Fig. 3 .

 3 Fig. 3. Sankey diagram showing LCA results of manufacturing processes for Rolls-Royce Trent 1000 engine 1

Fig. 5 .

 5 Fig. 5. Possible combinations of input-output scenarios represented using connectors in STN graphTable1. Meaning of connector combinations with respect to input-output scenarios

Fig. 6 .

 6 Fig. 6. Space-Time Network representation of Boeing 747-8. (Impact values are only for indicative purposes).Figure 7 shows an enlarged image of a portion of this diagram.

Figure 7

 7 Fig. 6. Space-Time Network representation of Boeing 747-8. (Impact values are only for indicative purposes).Figure 7 shows an enlarged image of a portion of this diagram.

Fig. 7 .

 7 Fig. 7. Enlarged portion of the STN representation of Boeing 747-8 example in Figure 6.

Data sourced from a video documentary, and online sources[START_REF]How To Build 787 Dreamliner Jet Engine[END_REF]. Assumptions of locations and values are made for representation purposes.

Acknowledgments. The authors acknowledge the contribution of The Boeing Company, USA for providing financial support under contract PC36018 at SID, IISc.


Karan Menon
email: karan.menon@tut.fi

Hannu Kärkkäinen
email: hannu.karkkainen@tut.fi

Jayesh Prakash Gupta
email: jayesh.gupta@tut.fi

Role of Industrial Internet platforms in the management of product lifecycle related information and knowledge

Keywords: Industrial internet, Industry4.0, Platform, PLM, PLM Challenges

This paper addresses the data, information and knowledge management challenges in product lifecycle management (PLM) using novel industrial internet/industry 4.0/cyber-physical system platforms. PLM is quite seminally related to data, information and knowledge, and about getting these properly to serve a company's business and product development as well as to create value for the customer. We analyze Industry platforms, that are enabled by industrial internet based technologies as well as certain collaboration and social media platforms that help solving certain PLM challenges. This analysis will allow companies to make informed decisions while selecting platforms to solve their PLM challenges.

Introduction

Product lifecycle management (PLM) can be defined as a systematic and controlled concept for managing product related information and products throughout the whole product lifecycle [START_REF] Kadiri | Ontologies in the context of product lifecycle management: state of the art literature review[END_REF]. 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. [START_REF] Schuh | Process oriented framework to support PLM implementation[END_REF], [START_REF] Kärkkäinen | Defining the Customer Dimension of PLM Maturity[END_REF], [START_REF] Kärkkäinen | Maturity assessment for implementing and using product lifecycle management in project-oriented engineering companies[END_REF]), as well as to create value for the customer [START_REF] Schuh | Process oriented framework to support PLM implementation[END_REF].

However, there are various challenges related to accessing and managing all relevant data, information and knowledge related to products' lifecycle, which is often due to the fact that such relevant information may be dispersed among a number of various actors, who also have their personal conception of the product and its performance [START_REF] Terzi | Product lifecycle management -from its history to its new role[END_REF]. Relatively recently, it has been understood that various technologies and approaches related to Internet of Things and Industrial Internet, such as sensors, machine-tomachine communication and various types of platforms can, however, offer quite important and novel solutions to the management of product lifecycle information, such as providing access and real-time insights to the data of many PLM-related actors.

For instance, the flow of real time data from the various sensors across the value chain will enable for the first time the chance to observe the entire value chain instantly. This will enable the optimization of the entire value chain rather than some part of it [START_REF] Buda | Data supply chain in Industrial Internet[END_REF]. Hence, industrial internet will go way beyond the traditional factory automation and will reduce the transaction costs for every transaction in the value chain. Yet, partly due to the recent maturation of many industrial internet related technologies and concepts, there is relatively little research that study the possibilities of industrial internet to PLM field. One of such topic is the role of various types of industrial internet-related platforms to enhancing the management of relevant data, information and knowledge related to products' lifecycle and different lifecycle phases.

The significance of platforms has grown increasingly (e.g. [START_REF] Gawer | Industry Platforms and Ecosystem Innovation[END_REF]). Importantly, platforms and platform-like digital services can provide new ways to access and accelerate the capturing of data and converting it into insightful information and knowledge. The role of various platforms, industrial internet -related platforms in particular, in the context of PLM and in facilitating the management of product lifecycle information has not been studied previously, according to our survey of current PLM and industrial internet literature ( [START_REF] Schuh | Process oriented framework to support PLM implementation[END_REF], [START_REF] Buda | Data supply chain in Industrial Internet[END_REF], [START_REF] Jun | Research issues on closed-loop PLM[END_REF], [START_REF] Agarwal | Strategic business transformation through technology convergence: implications from General Electric's industrial internet initiative[END_REF]).

Thus, we have come up with the following research questions which will be addressed in this study:

• Which kinds of PLM related data, information and knowledge management challenges are addressed by novel platforms in industrial internet? • How do the industrial internet platforms address the information and knowledge management issues in product lifecycle management? To address the above research questions, this study will try to discover and analyse novel types of platforms from academic literature as well as other relevant sources. Selection of different types of platforms for further analyses, and evaluation of their capabilities to address the various challenges related to the management of data, information and knowledge in PLM context will be done.

PLM and Product Lifecycle Management Challenges

Product lifecycle management (PLM) can be defined as a systematic and controlled concept for managing product related information and products throughout the whole product lifecycle [START_REF] Saaksvuori | Understanding the product lifecycle[END_REF]. However, the focus of our study lies essentially in the management of data, information and knowledge related to products and product lifecycle, not e.g. product management as such. PLM aims to provide a shared platform for effectively capturing, representing, organizing, retrieving and reusing productrelated lifecycle information across companies, and to support the integration of the existing software systems. 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.). Commonly, however, PLM can be seen as a larger PLM concept which involves e.g. people, processes and technological solutions. (e.g. [START_REF] Schuh | Process oriented framework to support PLM implementation[END_REF], [START_REF] Batenburg | PLM roadmap: stepwise PLM implementation based on the concepts of maturity and alignment[END_REF]). In our study, we focus on the latter, more extensive concept of PLM.

There are a number of different types of challenges related to the management of product lifecycle information. We will review some of the major challenges here, and analyse in later sections how such challenges can be dealt with through the novel possibilities of Industrial internet and specifically the identified various Industrial internet platforms. Here, we focus on the important overall challenges of PLM that can most probably be addressed through means of Industrial internet and Industrial internet platforms, not e.g. topics related to standards.

There are various PLM-related literature reviews and outlook or survey-type of articles (e.g. [START_REF] Kadiri | Ontologies in the context of product lifecycle management: state of the art literature review[END_REF], [START_REF] Terzi | Product lifecycle management -from its history to its new role[END_REF], [START_REF] Jun | Research issues on closed-loop PLM[END_REF], [START_REF] Elheni-Daldoul | Enterprise Information Systems' Interoperability: Focus on PLM Challenges[END_REF], [START_REF] Ming | Technology Solutions for Collaborative Product Lifecycle Management -Status Review and Future Trend[END_REF]) and other found relevant generic articles on the broad topic of data, information and knowledge management in PLM context. The many challenges in the management of data, information and knowledge in PLM context are rooted in the long lifecycles of products (e.g. [START_REF] Ball | An approach to accessing product data across system and software revisions[END_REF]); the transfer of information between product lifecycle phases (beginning, middle and end of life) and so-called "closed-loop" PLM (e.g. [START_REF] Kadiri | Ontologies in the context of product lifecycle management: state of the art literature review[END_REF]); problems related to the extended enterprise and the collaboration and communication of companies, customers and other relevant actors with relevant expertise and knowledge during the lifecycle (e.g. [START_REF] Ameri | Product Lifecycle Management: Closing the Knowledge Loops[END_REF]); the real-time accessing, transfer, management, aggregation and analytics of all different types of data, information and knowledge needed in PLM, including structured, not structured and even tacit knowledge of employees (e.g. [START_REF] Ameri | Product Lifecycle Management: Closing the Knowledge Loops[END_REF]) as well as making sense of the data and connecting it to the decision making of various PLM-related processes. The goals and challenges of PLM might be very different in different types of companies, e.g. project-based or one-of-the-kind organizations vs. mass-customized or many-of-thekind organizations (E.g. [START_REF] Kärkkäinen | Maturity assessment for implementing and using product lifecycle management in project-oriented engineering companies[END_REF], [START_REF] Terzi | Product lifecycle management -from its history to its new role[END_REF]). The interoperability of Information Systems throughout the product's life cycle is primordial for a successful Product Lifecycle Management approach. The ability of two (or more) systems to communicate, cooperate and exchange services and data, thus despite the differences in languages, implementations, executive environments and abstraction models [START_REF] Elheni-Daldoul | Enterprise Information Systems' Interoperability: Focus on PLM Challenges[END_REF].

Industrial Internet Platforms

Types of Platforms

Platforms on a very broad level can be divided into "internal" or firm level platforms and "external" or ecosystem level (industry wide) platforms. This broad classification allows us to place external or industry platforms as key enablers for enhancement in the management of data, information and knowledge during the lifecycle of a product.

We take the definition of Industry Platform by [START_REF] Gawer | Industry Platforms and Ecosystem Innovation[END_REF]. According to them, "industry platforms are defined as products, services, or technologies developed by one or more firms, and which serve as foundations upon which a larger number of firms can build further complementary innovations and potentially generate network effects." External or Industry platforms are probably most relevant forms of platforms in the context of PLM, because they can enhance the management of data, information and knowledge not only internally, but also amongst the various organizational actors throughout the lifecycle phases (BOL, MOL, EOL).

In case of Industry or external platforms, there are differences in the degree of platforms' openness meaning how 'open' the platform is in order to let third party developers and companies to make applications over the platform using the data and information from the platform ( [START_REF] Gawer | Industry Platforms and Ecosystem Innovation[END_REF], [START_REF] Eisenmann | Managing Proprietary and Shared Platforms[END_REF]). In an external platform the degree of openness can vary on a number of factors or dimensions [START_REF] Gawer | Industry Platforms and Ecosystem Innovation[END_REF]: the access to information in the platform to build applications can vary, the rules that allow the usage of platform can differ, and even the fee to get the access (license fee) can change. The more open the platform is in these three dimensions, the more easily it is for the different parties to access and share the relevant data through the platform.

Industrial Internet Platform Functionalities

Industrial internet, Industry 4.0 and CPS can be collectively defined as industrial systems that integrate computational and physical capabilities of machines in order to provide advanced analytics and interact with humans ( [START_REF] Agarwal | Strategic business transformation through technology convergence: implications from General Electric's industrial internet initiative[END_REF], [START_REF] Hermann | Design Principles for Industrie 4.0 Scenarios[END_REF], [START_REF] Lee | A Cyber-Physical Systems architecture for Industry 4.0-based manufacturing systems[END_REF], [START_REF] Evans | Industrial internet: Pushing the boundaries of minds and machines[END_REF], [START_REF] Lakhani | Digital Ubiquity: How Connections, Sensors, and Data Are Revolutionizing Business[END_REF]). In this study, we define industrial internet platforms as platforms which adhere to the general definition of industry platform (as in section 3.1) and the industrial internet definition mentioned above.

In the context of product lifecycle management (PLM), there has been a marked shift in its vision, which would ideally mean the ability to access, manage and control product related information across various phases of lifecycle [START_REF] Terzi | Product lifecycle management -from its history to its new role[END_REF]. In case of PLM, industrial internet platforms can provide the real time management of data and information flows and help in the data-information-knowledge (D-I-K) transformations along all the phases of product lifecycle.

The industrial internet platforms can access data from different sensors, actuators, enterprise systems, social media and other novel data sources ( [START_REF] Porter | How Smart, Connected Products Are Transforming Companies[END_REF], [START_REF] Porter | How Smart, Connected Products Are Transforming Competition[END_REF]). The industrial internet platform is able to aggregate data into a single database which can be stored, either in dedicated in-house servers or with other third party cloud storage providers ( [START_REF] Evans | Industrial internet: Pushing the boundaries of minds and machines[END_REF], [START_REF] Lee | The Internet of Things (IoT): Applications, investments, and challenges for enterprises[END_REF]). This organised data can be used, for example, by technicians to remotely monitor the condition of machine without physically being present [START_REF] Lesjak | A secure hardware module and system concept for local and remote industrial embedded system identification[END_REF], the data can also be run through machine learning algorithms to predict the health condition of a machine and notify the concerned technician to make an informed decision about the need to have the machine maintenance [START_REF] Lee | Service Innovation and Smart Analytics for Industry 4.0 and Big Data Environment[END_REF]. The data, via the platform, can provide different analytics like descriptive, predictive and prescriptive to create proper infographics which facilitate experienced knowledge workers [START_REF] Lee | A Cyber-Physical Systems architecture for Industry 4.0-based manufacturing systems[END_REF]. Consider the example of a new industrial internet platform based risk assessment solution in Oil & Gas sector, which allows real time visual representation of risks to oil pipeline, based on internal and external environment factors. These infographics provide the experienced pipeline operators a new way to check pipeline integrity. [START_REF] Winnig | GE's Big Bet on Data and Analytics[END_REF] In many cases the industrial internet platforms enable development of applications ("apps") on top of the platform. These applications help in sharing the relevant information between the different actors and can also help in sense-making [START_REF] Namvar | Using Business Intelligence to Support the Process of Organizational Sensemaking[END_REF].

Today, there is a plethora of platforms available. We selected a subset of platforms that enable efficient and real time management of data, information and knowledge over various lifecycle phases. These platforms were searched from mainly academic articles (example [START_REF] Hermann | Design Principles for Industrie 4.0 Scenarios[END_REF], [START_REF] Derhamy | A survey of commercial frameworks for the Internet of Things[END_REF]- [START_REF] Weinberger | Platforms for the Internet of Things : An Analysis of Existing Solutions[END_REF]) and other relevant sources which reviewed characteristics of platforms, functionalities of platforms and data and information management perspective of platforms. Some platforms, such as Exosite and IndustryHack, were added into this pool because they were discovered to be interesting in some of their characteristics. From this subset a large pool was selected based on the following inclusion criteria:

1. Platforms that are relevant to industrial internet and cater to manufacturing and industrial companies 2. Platforms that are international. This allows various actors involved in the lifecycle of the product to use them from different geographical locations. 3. Platforms that satisfy the definition of External/industry platforms [START_REF] Gawer | Industry Platforms and Ecosystem Innovation[END_REF] that allow the inter-organizational collaboration to manage data, information and knowledge Table 1. Large pool of industry platforms by their domain [START_REF] Hermann | Design Principles for Industrie 4.0 Scenarios[END_REF]), Internet of Things (IoT) ( [START_REF] Derhamy | A survey of commercial frameworks for the Internet of Things[END_REF], [START_REF] Weinberger | Platforms for the Internet of Things : An Analysis of Existing Solutions[END_REF]), social media platforms in manufacturing industrial companies [START_REF] Jussila | Social media utilization in businessto-business relationships of technology industry firms[END_REF], crowdsourcing and collaboration platforms [START_REF] Howe | Crowdsourcing: Why the Power of the Crowd is Driving the Future of Business[END_REF]. These examples are not an exclusive list of platforms but they are representatives of the domains.

Analysis of industrial internet platforms from PLM perspective

In order to do an in depth analysis of industrial internet platforms from the perspective of PLM, we further selected 10 platforms (see Table 2), that represent different platform domains and have unique features as platforms, considering especially their capabilities to address various challenges (drawn from section 2, and represented as the major evaluation criteria in Table 2). We further selected 5 platforms (see Table 3), that provide analysis to highlight the effects on management of data, information and knowledge across various PLM phases using industrial internet as a technology enabler.

In Table 2, IndustryHack is a unique actor as it uses the concept of hackathon [START_REF] Raatikainen | Industrial Experiences of Organizing a Hackathon to Assess a Device-centric Cloud Ecosystem[END_REF] in industrial setting to bring together outside experts who can help in rapid prototyping [START_REF] Gunasekaran | Agile manufacturing: enablers and an implementation framework[END_REF] and present a proof of concept for the given industrial problem. In terms of data access, Predix, MyJohnDeere and Bosch IoT Suite have the unique advantage of Table 2 Platform analysis based on industrial internet based on data, information and knowledge providing their own sensors which can work in different environments ( [START_REF] Agarwal | Strategic business transformation through technology convergence: implications from General Electric's industrial internet initiative[END_REF], [START_REF] Porter | How Smart, Connected Products Are Transforming Companies[END_REF], [START_REF] Winnig | GE's Big Bet on Data and Analytics[END_REF]). These platforms have capability to directly access a new source of data which is not possible for platforms which don't provide their own hardware (example sensors, actuators). On the other hand, IndustryHack collects data in terms of a pool of experts which can collaborate with industries in the hackathons. Hana, Azure, Predix, Thingworx also allow the real time data integration with data from novel data sources like social media ([28]- [START_REF] Familiar | IoT and Microservices[END_REF]). This kind of combination of data can lead to creation of new information. Platform like Cyberville provides feature like multilayered 3D view of complex network in real time. Thingworx lowers technological complexities for users through codeless mashup capabilities. This enables easy creation of variety of visual infographics. [START_REF] Heo | A Lightweight Platform Implementation for Internet of Things[END_REF], [START_REF] Weinberger | Platforms for the Internet of Things : An Analysis of Existing Solutions[END_REF] In business context, sense making needs experts who can help in making quality decisions after an informed sense making process (2015, Namvar et. al). While sense making is enabled by most of the platforms in the above list. Platforms like Yammer (microblog) and IndustryHack directly support sensemaking by bringing together relevant experts to make sense of provided information. Table 3 provides detailed analysis of 5 platforms that address the challenges of PLM in the context of data information and knowledge management. Platforms have different degrees of openness [START_REF] Gawer | Industry Platforms and Ecosystem Innovation[END_REF]: Access to data and information, Rules governing the platform and cost of access to data and information. Cyberville is open from the viewpoint of DIK in a way that it follows the open source standards of the internet technology. Access to data information and knowledge across the life cycle phases and within the different phases (in case of Closed Loop PLM) is the key to the value creation from product related lifecycle data, information and knowledge [START_REF] Jun | Research issues on closed-loop PLM[END_REF]. MyJohnDeere is a kind of a platform that enables the product manufacturer to tap into all the data and information throughout the lifecycle phases and also access within different lifecycle phases. Interoperability [START_REF] Elheni-Daldoul | Enterprise Information Systems' Interoperability: Focus on PLM Challenges[END_REF] which is one of the key issues in all industry softwares ( for example PLM, PDM, CRM, ERP) is solved by the use of industrial internet platforms by the use of plugins. In order to create value of data and information it is important to get this data and information in real time and provide analytics based on this data in real time as well. GE Predix and Microsoft Azure kinds of industrial internet platform have incorporated Big Data technologies in the platform architecture to enable real time monitoring as well as analytics ( [START_REF] Agarwal | Strategic business transformation through technology convergence: implications from General Electric's industrial internet initiative[END_REF], [START_REF] Winnig | GE's Big Bet on Data and Analytics[END_REF], [START_REF] Derhamy | A survey of commercial frameworks for the Internet of Things[END_REF]). One of the key differentiator between traditional industry softwares and platforms like PTC Thingworx, Cyberville, GE Predix is the availability of On-demand tailored solutions ( [START_REF] Heo | A Lightweight Platform Implementation for Internet of Things[END_REF], [START_REF] Weinberger | Platforms for the Internet of Things : An Analysis of Existing Solutions[END_REF]) or "apps" which result into a marketplace. These "apps" or applications create value of data, information and knowledge for the platform users. The popularity and acceptance of this marketplace generates network effects for the platform [START_REF] Gawer | Industry Platforms and Ecosystem Innovation[END_REF].

Discussion and Conclusion

The challenges with the management of data, information and knowledge in case of PLM is a very important area of research. Problems such as real-time data access, transfer, management and analytics are some of the challenges that need to be solved efficiently. Industrial internet platforms address to the challenges of data information and knowledge during and within the PLM phases. Most of the platforms are efficient and similar in the storing the data and sharing information. Platforms like IndustryHack that allow tapping into outside experts while Yammer is centered on supporting collaboration between experts enable the sense-making of the data and information. Advanced analytics like the multi-layered real-time 3D visualization and mashup kind of applications (for analytics) is enabled by industrial internet platforms. The Ondemand tailored solutions visualize and manage data and information using portable devices (mobile phones, smart watches) which the large PLM and PDM systems do in a restricted manner.

Our purpose was to analyse and understand the potential role of II platforms in the management of product lifecycle related information and knowledge. From our analyses, first, we can conclude that there exist quite different types of external industry platforms with different emphases in their capability of addressing various types of PLM challenges. In addition to IoT platforms, industry 4.0 platforms and CPS platforms, these include also crowdsourcing and social media platforms that can address some of the relevant PLM challenges that others are not well-positioned to do, such as tapping into external knowledge resources and helping to make sense of various existing data. The analysis, in overall, demonstrates that many of the platforms seem to cater for the PLM challenges in rather different and unique manner compared to traditional PLM software, even if various PLM software have recently adopted platforms features and even developed their own app store solutions. This uniqueness is related to for instance enhanced interoperability and the ability to make use of novel data sources in a flexible and real-time manner. Second, we claim that industrial internet platforms can and should, in many cases, be adopted to allow more extensive and useful management of all relevant data from the lifecycle, and combined to existing PLM software through the platforms' capabilities to connect to external software and hardware. We believe that such platforms, when selected in an informed manner, can have even a significant role in enhancing the management of product lifecycle related information and knowledge. Simultaneously, companies should be careful when selecting platforms (as well as any other software solutions), because switching to other software can be difficult later on due to the path-dependency of the decisions. A small or medium sized company might want to select a platform based on open source standards and open interfaces, such as CyberVille-Cyberlighting, while a large established company might prefer a more mature and developed platform like Predix or ThingWorx. Companies should also take care to make a decision which suits the core objectives of PLM in the longer term, defining these objectives carefully before the decision.

Table 3

 3 Detailed analysis of Industrial Internet Platforms in the context of PLM