Matteo Rucco
email: ruccomatteo@gmail.com

Katia Lupinetti
email: katia.lupinetti@ge.imati.cnr.it

Franca Giannini
email: giannini@ge.imati.cnr.it

Marina Monti
email: monti@ge.imati.cnr.it

Jean-Philippe Pernot
email: jean-philippe.pernot@ensam.eu

J.-P Pernot

CAD Assembly Retrieval and Browsing

Keywords: Assembly retrieval, shape matching, information visualization

Introduction

The large use of CAD (Computer Aided Design) and CAM (Computer Aided Manufacturing) systems in industries has generated a number of 3D databases making available a large amount of 3D digital models. The reuse of these models, either single parts or assemblies, and the exploitation of the knowledge associated with them are becoming an important way to facilitate new designs. To track and organize data related to a product and its lifecycle, modern CAD systems are integrated into PDM (Product Data Management) and PLM (Product Lifecycle Management) systems. Among others, the associated data usually involve the technical specifications of the product, provisions for its manufacturing and assembling, types of materials used for its production, costs and versioning. These systems efficiently manage a search based on textual metadata, which cannot be sufficient to effectively retrieving the searched data. Actually, standard parts, text-based annotation and naming convention are company-or operator-specific, thus difficult to generalize as search keys. To overcome these limitations, content-based algorithms for 3D model retrieval are being developed based on shape characteristics. A wide literature is available and some commer-cial systems provide shape-based model retrieval. [START_REF] Biasotti | Retrieval and clas-sification methods for textured 3D models: a comparative study[END_REF][START_REF] Cardone | A survey of shape similarity assessment algorithms for product design and manufacturing applications[END_REF][START_REF] Iyer | Three-dimensional shape searching: state-of-the-art review and future trends In[END_REF] provide an overview of the 3D shape descriptors most used in the CAD domain. However, these descriptors focus solely on the shape of a single component, which is not adapted for more complex products obtained as assemblies. An effective assembly search cannot be limited to simple shape comparison among components, but requires also information that is not always explicitly encoded in the CAD models, e.g. the relationships and the joint constraints between assembly components.

In this paper, we present methods for the retrieval of globally and/or partially similar assembly models according to different user-specified search criteria [START_REF] Lupinetti | CAD assembly descriptors for knowledge capitalization and model retrieval[END_REF] and for the inspection of the provided results. The proposed approach creates and exploits an assembly descriptor, called Enriched Assembly Model (EAM), organized in several layers that enable multi-level queries and described in section 4.1. The rest of the paper is organized as follows. Section 2 provides an overview of related works. Issues related to assembly retrieval are described in Section 3, while Section 4 presents the assembly descriptor and the comparison procedure. Section 5 reports some of the obtained results, focusing on the developed inspection capabilities. Section 6 concludes the paper discussing on current limits and future work.

Related works

Shape retrieval has been investigated far and wide in the recent years [START_REF] Biasotti | Retrieval and clas-sification methods for textured 3D models: a comparative study[END_REF][START_REF] Cardone | A survey of shape similarity assessment algorithms for product design and manufacturing applications[END_REF][START_REF] Iyer | Three-dimensional shape searching: state-of-the-art review and future trends In[END_REF][START_REF] Tangelder | A survey of content based 3D shape retrieval methods[END_REF]. However, most of the work present in literature deal with the shape of a single component and do not consider other relevant information of the assembly such as the relationships between the parts. One of the pioneer works dealing with assembly retrieval was presented by Deshmukh, et al. [START_REF] Deshmukh | Content-based assembly search: A step towards assembly reuse In[END_REF]. They investigate the possible usage scenarios for assembly retrieval and proposed a flexible retrieval system exploiting the explicit assembly data stored in a commercial CAD system. Hu et al. [START_REF] Hu | Relaxed lightweight assembly retrieval using vector space model In[END_REF] propose a tool to retrieve assemblies represented as vectors of watertight polygon meshes. Identical parts are merged and a weight based on the number of occurrences is attached to each part in the vector. Relative positions of parts and constraints are ignored, thus the method is weak in local matching. Miura and Kanai [START_REF] Miura | 3D Shape retrieval considering assembly structure In[END_REF] extend their assembly model by including structural information and other useful data, e.g. contact and interference stages and geometric constraints. However, it does not consider high-level information, such as kinematic pairs and some information must be made explicit by the user. A more complete system is proposed by Chen et al. [START_REF] Chen | A flexible assembly retrieval approach for model reuse In[END_REF]. It relays on the product structure and the relationships between the different parts of the assembly. The adopted assembly descriptor considers different information levels including the topological structure, the relationships between the components of the assembly, as well as the geometric information. Thus, the provided search is very flexible accepting rough and incomplete queries. Anyhow, most of the work require user support for the insertion of the required information and weakly support the analysis and browsing of the obtained results, which for large assemblies can be very critical. To overcome these limitations, in this paper, we present an assembly descriptor (i.e. the Enriched Assembly Model), which can support user requests based on different search criteria not restrained to the identification of assembly models with the same structure in terms of sub-assemblies, and tools for facilitating the inspection and browsing of the results of the retrieval process.

Assembly retrieval issues

Retrieving similar CAD assembly models can support various activities ranging from the re-use of the associated knowledge, such as production or assembly costs and operations, to part standardization and maintenance planning. For instance, knowing that a specific subassembly, which includes parts having a high consumption rate due to their part surrounding and usage, is present in various larger products may help in defining more appropriate maintenance actions and better planning of the warehouse stocks. Similarly, knowing that different products having problems share similar configurations can help in detecting critical configurations. Considering these scenarios, it is clear that simply looking for products (i.e. assemblies) that are completely similar to a given one is important but limited. It is therefore necessary to have the possibility to detect if an assembly is contained into another as well as local similarities among assemblies, i.e. assemblies that contain similar sub-assemblies. These relations can be described using the set theory. Being ≅ the symbol indicating the similarity according to given criteria, given two assemblies A and B, we say that:

A is globally similar to B iff for each component a Depending on the retrieval purpose, not only the criteria change but also the interest on the similarity among the parts or on their connections can have different priority. It is therefore important to provide flexible retrieval tools that can be adapted to the specific need and thus able to consider the various elements characterizing the assembly despite on how the assembly was described by the user (e.g. structural organization) or on the information available on the CAD model itself (e.g. explicit mating conditions).

i ∈ A, ∃ b h ∈ B s.t. a i ≅ b h , for each relation (a i, a j ) ∈ A, ∃ (b h, b k ) ∈ B s.t. (a i,
In addition, it might be difficult to assess the effective similarity when various elements contribute to it. It is crucial to provide tools for gathering results according to the various criteria and for their inspection. This is very important in the case of large assemblies, where detecting the parts considered similar to a given assembly might be particularly difficult.

4

The proposed approach

Based on the above considerations, we propose a method for the comparison of assembly models exploiting various levels of information of the assembly. Differently from most of the work presented in literature, our method can evaluate all the three types of similarity described above. It uses a multilayer information model, the socalled Enriched Assembly Model (see section 4.1), which stores the data describing the assembly according to three different layers, in turns specified at different level of details thus allowing a refinement of the similarity investigation. Depending on the type of requested similarity, an association graph is build putting in relation the elements of the EAM of two CAD models to be compared. The similar subset of these two models are then corresponding to the maximal clique of the association graph (see section 4.2). To analyze the retrieved results, a visualization tool has been developed; it highlights the correspondences of the parts and provides statistics on the matched elements (see section 4.3).

Enriched Assembly Model (EAM)

The EAM is an attributed graph, where nodes are the components and/or composing sub-assemblies while arcs represent their adjacency relations. It uses four information layers: structure, interface, shape and statistics [START_REF] Lupinetti | CAD assembly descriptors for knowledge capitalization and model retrieval[END_REF].

The structural layer encodes the hierarchical assembly structure as specified at the design stage. In this organization, the structure is represented as a tree where the root corresponds to the entire assembly model, the intermediate nodes are associated with the sub-assemblies and the leaves characterize the parts. Attributes to specify parts arrangement (regular patterns of repeated parts) are attached to the entire assembly and to its sub-assemblies [START_REF] Lupinetti | Use of regular patterns of repeated elements in CAD assembly models retrieval In[END_REF]. The organization in sub-assemblies is not always present and may vary according to the designer's objectives.

The interface layer specifies the relationships among the parts in the assembly. It is organized in two levels: contacts and joints. The first contains the faces involved in the contact between two parts and the degree of freedom between them. The joint level describes the potentially multiple motions resulting from several contacts between two parts [START_REF] Lupinetti | Automatic Extraction of Assembly Component Relationships for Assembly Model Retrieval[END_REF].

The shape layer describes the shape of the part assembly by several dedicated descriptors. Using several shape descriptors helps answering different assembly retrieval scenarios, which can consider different shape characteristics and at different level of details. They include information like shape volume, bounding surface area, bounding box and spherical harmonics [START_REF] Kazhdan | Rotation invariant spherical harmonic representation of 3 d shape descriptors[END_REF].

The statistics layer contains values that roughly characterize and discern assembly models. Statistics are associated as attributes to the various elements of the EAM. For the entire assembly and for each sub-assembly, they include: the numbers of subassemblies, of principal parts, of fasteners, of patterns of a specific type, of a specific joint type. To each node corresponding to a component, the statistics considered are: percentage of a specific type of surface (i.e. planar, cylindrical, conical spherical, toroidal, free form), number of maximal faces of a specific type of surface. Finally, for each arc corresponding to a joint between parts, the stored statistics include the number of elements in contact for a specific contact type.

The E.A.M. is created using ad hoc developed modules [START_REF] Lupinetti | CAD assembly descriptors for knowledge capitalization and model retrieval[END_REF][START_REF] Lupinetti | Use of regular patterns of repeated elements in CAD assembly models retrieval In[END_REF][START_REF] Lupinetti | Automatic Extraction of Assembly Component Relationships for Assembly Model Retrieval[END_REF], which analyze the content of the STEP (ISO 10303-203 and ISO 10303-214) representation of the assembly and extract the required information.

EAM comparison

Adopting this representation, if two models are similar, then their attribute graphs must have a common sub-graph. The similarity assessment between two EAMs can then be performed by matching their attribute graphs and finding their maximum common subgraph (MCS). The identification of the MCS is a well-known NP-hard problem and among the various techniques proposed for its solution [START_REF] Bunke | A comparison of algorithms for maximum common subgraph on randomly connected graphs[END_REF] we chose the detection of the maximal clique of the association graph, since it allows identifying also locally similarities.

The association graph is a support graph that reflects the adopted high-level similarity criteria. Each node in the association graph corresponds to a pair of compatible nodes in the two attributed graphs according to the specified criteria. Associated arcs connect nodes if they have equivalent relations expressed as arcs connecting the corresponding nodes in the attribute graphs.

A clique is a sub-graph in which for each couple of nodes a connecting arc exists. For the clique detection we applied the Eppstein-Strash algorithm [START_REF] Eppstein | Listing all maximal cliques in large sparse real-world graphs[END_REF]. This algorithm represents an improved version of the algorithm by Tomita [START_REF] Tomita | The worst-case time complexity for generating all maximal cliques and computational experiments[END_REF], which is in turn based on the Bron-Kerbosch algorithm for the detection of all maximal cliques in graphs [START_REF] Bron | Algorithm 457: finding all cliques of an undirected graph[END_REF]. As far as we know, Eppstein-Strash algorithm is up to now the best algorithm for listing all maximal cliques in undirected graphs, even in dense graphs. The performances of the algorithm are in general guaranteed by the degeneracy ordering.

The algorithm of Eppstein-Strash improves Tomita's algorithm by using the concept of degeneracy. The degeneracy of a graph G is the smallest number d such that every subgraph of G(V, E) contains a node of degree at most d. Moreover, every graph with degeneracy d has a degeneracy ordering: a linear ordering of the vertices such that each node has at most d neighbors after it in the ordering. Eppstein-Strash algorithm first computes the degeneracy ordering; then for each node v in the order, starting from the first, the algorithm of Tomita is used to compute all cliques containing v and v's later neighbors. Other improvements depend on the use of adjacency lists for data representation. For more details we refer to [START_REF] Eppstein | Listing all maximal cliques in large sparse real-world graphs[END_REF].

Among all the maximal cliques present in the associated graph, we consider as interesting candidates of the similar sub-graphs only those having: 1) the majority of arcs corresponding to real joints between the corresponding components, 2) a number of nodes bigger than a specified value. In this way, priority is given to sub-graphs which contain a significant number of joined similar components, thus possibly corresponding to sub-assemblies. Then, for each selected clique, a measure vector is computed. The first element of the vector indicates the degree of the clique, while the others report the similarity of the various assembly characteristics taken into consideration for the similarity assessment. Depending on the search objectives the set of characteristics to consider may change. The default characteristics are the shape of the components and the type of joint between them. The examples and results discussed in the next section consider the default characteristic selection.

Result visualisation

The proposed retrieval system has been implemented in a multi-module prototype system. The creation of the EAM description is developed by using Microsoft Visual C# 2013 and exploiting the Application Programming Interface (API) of the commercial CAD system SolidWorks. The matching and the similarity assessment module is developed by using Java and is invoked during the retrieval as a jar file. In the end, to analyze the obtained results, a browser view has been implemented. It is obtained by multiple dynamic web pages that are based on HTML5, jQuery, Ajax and PHP. Moreover, Mysql is used as database system, while X3D library is used for the STEP model visualization.

The system has been tested on assembly models obtained from on-line repositories [17, 18, 19] and from university students' tests. 2 shows an example of the developed user interface, where the design can choose an assembly model as query and set the required criteria of similarity. In this example, it is required to retrieve models similar for shape and joint. Some results of this query are shown in Fig. 3. The first model in the picture (top-left) coincides with the query model. The retrieved models are gathered together in the other views of Fig. 3. Each retrieved and displayed assemblies has a clique that has been detected in the association graph and satisfies the required conditions. The assemblies are visualized in X3D view that allows rotating, zooming and selecting the various 3D components of the retrieved assembly. Components are visualized in the transparency mode to make possible to see also the internal ones. Under each model, three bars are shown to quickly get an idea of how similar to the query the retrieved assemblies are. The first two indicate the percentage of coverage (i.e. percentage of matched elements) with respect to both the query and target model. Thank to these bars, the user can see the type of similarity (i.e. global, partial or local). If the green is not complete, it means that just a subset of the query model is matched, thus the similarity is locally. The global similarity is shown by the purple bar, if this bar is not complete, then the similarity is partial. The last bar shows the average shape similarity among the components associated with the displayed clique. Simply looking at the reported model and checking the purple bar, the user can notice that (except the first model which represents the query model) no models are globally similar to the query one according to the criteria he/she has specified. The first model in the second row is partially similar with the query one, since the query is completely included in it (see the green bar). Other models are locally similar with the query model, thus just a subset of the query model is included in them. If the user wants to further analyze the levels of similarity of the chosen characteristics or to visualize all the subsets of matched parts, he/she can select one of the retrieved assemblies and a new browser page is prompted. Once selected, a new page as in Fig. 4 is available, where the user can get the list of all the interesting clique, using the sliders at the top of window. With these sliders, the user can choose some thresholds that the proposed results have to satisfy. In particular, they refer to the dimension of the matched portion, the shape similarity measure and the joint similarity measure. After the setting of those parameters, the button "Clique finding" can be pressed to get the results displayed in a table as visible on the left of Fig. 5. The rows of the table gather together all the matching portions that satisfy the required criteria. In this example, four information are accessible for each matching portion: an identification number, the number of matched parts, the shape similarity and the joint similarity. Selecting one of them, the corresponding clique is visualized within the assembly. It highlights the component correspondence with the query model using same colors for corresponding components in the two objects, as shown on the right part of Fig. 4. To easy the comparison according to several available criteria (here reported just the default ones), a radar chart is used. It illustrates the shape and joint similarities among the overall assembly in relation to the clique degree. This type of visualization is very useful to compare multiple data and to have a global evaluation in just a look. Moreover, the radar charts are convenient to compare two or more models on various features expressed through numerical values. The larger is the covered area, the more the two assemblies are similar. In the reported case, the user can observe immediately that the two models are not completely matched, even if they look very similar. This is because the gears in the two models have a significant different shape, which avoids including those parts among the matched one, thus decreasing the global level of similarity. On the other side, the retrieved portion completely satisfies the requests, thus reporting an assemment of 1.

Conclusions

In this paper, methods for the identification and evaluation of similarities between CAD assemblies are presented. While almost all of the products are made of assembled parts, most of the works present in literature are addressing the problem of similarity among single parts. For assemblies, the shape of the components is not the only characteristic to be considered. Increasing the number of elements to consider, augments from the one hand the possibility of adapting the search to specific user needs, and on the other hand the difficulty to evaluate the results. The method here described can consider all or a subset of the various aspects of the assembly, namely the shape of the components, their arrangements (i.e. patterns), their mating contacts and joints. The evaluation of the retrieved results is supported by exploiting colour variations in the 3D visualisation of the components in correspondence between the compared assemblies. Measures and statistics quantifying the similarity of the overall assemblies and of the matched subparts are reported according to the various considered characteristics.

In future work, we plan to introduce graph databases, such as Neo4j, for speedingup the search of local similarity among big assembly models. We also intend to improve the clique-finding algorithm by allowing it to select automatically the dimension of the biggest clique. Moreover, we intend to involve the definition of a single measure for the overall ranking of the retrieved assemblies similar to a query one. This information will be displayed in an ad-hoc infographics, which will be developed for improving the user-experience.

Fig. 1 .

 1 Fig. 1. Example of different type of similarities

Fig. 2 .

 2 Fig. 2. An assembly model and the similarity criteria used for the matching

Figure

  Figure2shows an example of the developed user interface, where the design can choose an assembly model as query and set the required criteria of similarity. In this example, it is required to retrieve models similar for shape and joint. Some results of this query are shown in Fig.3. The first model in the picture (top-left) coincides with the query model. The retrieved models are gathered together in the other views of Fig.3. Each retrieved and displayed assemblies has a clique that has been detected in the association graph and satisfies the required conditions. The assemblies are visualized in X3D view that allows rotating, zooming and selecting the various 3D components of the retrieved assembly. Components are visualized in the transparency mode to make possible to see also the internal ones. Under each model, three bars are shown to quickly get an idea of how similar to the query the retrieved assemblies are. The first two indicate the percentage of coverage (i.e. percentage of matched elements) with respect to both the query and target model. Thank to these bars, the user can see the type of similarity (i.e. global, partial or local). If the green is not complete, it means that just a subset of the query model is matched, thus the similarity is locally. The global similarity is shown by the purple bar, if this bar is not complete, then the similarity is partial. The last bar shows the average shape similarity among the components associated with the displayed clique. Simply looking at the reported model and checking the purple bar, the user can notice that (except the first model which represents the query model) no models are globally similar to the query one according to the criteria he/she has specified. The first model in the second row is partially similar with the query one, since the query is completely included in it (see the green bar). Other models are locally similar with the query model, thus just a subset of the query model is included in them.

Fig. 3 .

 3 Fig. 3. A sample of the retrieved models for the proposed speed reducer query (top left)

Fig. 4 .

 4 Fig. 4. Initial page for investigating the model similarity

Fig. 5 .

 5 Fig. 5. Example of matching browsing


Widad Es-Soufi
email: widad.es-soufi@ensam.eu

Esma Yahia
email: esma.yahia@ensam.eu

Lionel Roucoules
email: lionel.roucoules@ensam.eu

A Process Mining Based Approach to Support Decision Making

Keywords: Process mining, Decision mining, Process patterns, Decisionmaking, Business process

Currently, organizations tend to reuse their past knowledge to make good decisions quickly and effectively and thus, to improve their business processes performance in terms of time, quality, efficiency, etc. Process mining techniques allow organizations to achieve this objective through process discovery. This paper develops a semi-automated approach that supports decision making by discovering decision rules from the past process executions. It identifies a ranking of the process patterns that satisfy the discovered decision rules and which are the most likely to be executed by a given user in a given context. The approach is applied on a supervision process of the gas network exploitation.

Introduction

Business process is defined as a set of activities that take one or more inputs and produce a valuable output that satisfies the customer [START_REF] Hammer | Reengineering the Corporation: A Manifesto for Business Revolution[END_REF]. In [START_REF] Weske | Business Process Management: Concepts, Languages, Architectures[END_REF], authors define it as a set of activities that are performed in coordination in an organizational and technical environment and provide an output that responds to a business goal. Based on these definitions, the authors of this paper describe the business process, as a set of linked activities that have zero or more inputs, one or more resources and create a high added value output (i.e. product or service) that satisfies the industrial and customers constraints. These linked activities represent the business process flow and are controlled by different process gateways (And, Or, Xor) [START_REF]Business Process Model and Notation (BPMN) Version 2[END_REF]Sec. 8.3.9] that give rise to several patterns (pattern 1 to 9 in Fig. 1) where each one is a linear end-to-end execution. The "And" gateway, also called parallel gateway, means that all the following activities are going to be executed in several possible orders. The "Or" gateway, also called inclusive gateway, means that one or all the following activities are going to be executed based on some attributes conditions. The "Xor" gateway, also called exclusive gateway, means that only one following activity among others, is going to be executed based on some attributes conditions.

The presence of gateways in business processes results in making several decisions based on some criteria like experience, preference, or industrial constraints [START_REF] Iino | Decision-Making in Engineering Design: Theory and Practice[END_REF].

Making the right decisions in business processes is tightly related to business success. Indeed, a research that involved more than a thousand companies, shows an evident correlation between decision effectiveness and business performance [START_REF] Blenko | Decision Insights: The five steps to better decisions[END_REF]. In [START_REF] Es-Soufi | On the use of Process Mining and Machine Learning to support decision making in systems design[END_REF], authors explain that the process of decision-making can be broken down into two sub processes: The global and the local decision making. In this research, authors focus on the global decision making and aim at developing a generic approach that assists engineers in managing the business process associated with the life of their products or services. The approach automatically proposes a predicted ranking of the business process patterns, that are the most likely to be executed by a given user in a given context. This comes down to exploring these patterns and the decisions that control them in a complex business process, i.e. where all gateways are present (Fig. 1). Authors assume that this objective can be achieved using process mining techniques.

This paper is organized as follows. In Section 2, a literature review on decision and trace variants mining are discussed. The proposed approach is presented in Section 3 and then illustrated in a case study in Section 4. Finally, the discussion of future work concludes the paper.

Literature Review on Decision and Trace Variants Mining

Process mining is a research field that supports process understanding and improvement, it helps to automatically extract the hidden useful knowledge from the recorded event logs generated by information systems. Three types of applications in process mining are distinguished: discovery, conformance, and enhancement [START_REF] Van Der Aalst | Process mining: overview and opportunities[END_REF]. In this paper, authors focus on the discovery application, namely, the decision mining and the trace variants mining. A brief summary is provided of each.

Decision mining is a data-aware form of the process discovery application since it enriches process models with meaningful data. It aims at capturing the decision rules that control how the business processes flow (e.g. conditions 1,2,3,4 in Fig. 1). In [START_REF] Rozinat | Decision mining in business processes[END_REF], authors define it as the process in which data dependencies, that affect the routing of each activity in the business process, are detected. It analyses the data flow to find the 1 http://www.bpmn.org/ Process patterns: 1-A1A2A3A4A5A6A7 2-A1A2A3A5A4A6A7 3-A1A2A3A5A6A4A7 4-A1A2A5A6A3A4A7 5-A1A2A5A3A6A4A7 6-A1A2A5A3A4A6A7 7-A1A8A9A11 8-A1A8A10A11 9-A1A8A9A10A11 10-A1A8A10A9A11 rules that explain the rationale behind selecting an activity among others when the process flow splits [START_REF] De Leoni | Data-aware Process Mining: Discovering Decisions in Processes Using Alignments[END_REF].

While executing a business process, one may adopt the same logic several times (e.g. always executing pattern 1 in Fig. 1, rather than patterns 2 to 6, if condition 1 is enabled). This results in the existence of similar traces in the recorded event log. Trace variants mining aims at identifying the trace variants and their duplicates (e.g. patterns 1 to 9 in Fig. 1). Each trace variant refers to a process pattern that is a linear end-to-end process execution where only the activities execution order is taken into account [START_REF] Es-Soufi | On the use of Process Mining and Machine Learning to support decision making in systems design[END_REF].

Decision Mining

The starting point of the most common decision mining techniques is a recorded event log (i.e. past executions traces) and its corresponding petri net2 model that describes the concurrency and synchronisation of the traces activities. To automatically generate a petri net model from an event log, different algorithms were proposed. The alpha algorithm, alpha++ algorithm, ILP miner, genetic miner, among others, are presented in [START_REF] Van Dongen | Process mining: Overview and outlook of petri net discovery algorithms[END_REF], and the inductive visual miner that was recently proposed in [START_REF] Leemans | Exploring processes and deviations[END_REF].

Many research works contribute to decision mining development. In [START_REF] Rozinat | Decision mining in business processes[END_REF], authors propose an algorithm, called Decision point analysis, which allows one to detect decision points that depict choice splits within a process. Then for each decision point, an exclusive decision rule (Xor rule) in the form "v op c", where "v" is a variable, "op" is a comparison operator and "c" is a constant, allowing one activity among others to be executed is detected. The decision point analysis is implemented as a plug-in for the ProM3 framework. In [START_REF] Leoni | Discovering branching conditions from business process execution logs[END_REF], authors propose a technique that improves the decision point analysis by allowing one to discover complex decision rules for the Xor gateway, based on invariants discovery, that takes into account more than one variable, i.e. in the form "v1 op c" or "v1 op v2", where v1 and v2 are variables. This technique is implemented as a tool named Branch Miner 4 . In [START_REF] Catalkaya | Enriching business process models with decision rules[END_REF], authors propose a technique that embeds decision rules into process models by transforming the Xor gateway into a rule-based Xor gateway that automatically determines the optimal alternative in terms of performance (cost, time) during runtime. This technique is still not yet implemented. In [START_REF] Bazhenova | Deriving decision models from process models by enhanced decision mining[END_REF], authors propose an approach to derive decision models from process models using enhanced decision mining. The decision rules are discovered using the decision point analysis algorithm [START_REF] Rozinat | Decision mining in business processes[END_REF], and then enhanced by taking into account the predictions of process performance measures (time, risk score) related to different decision outcomes. This approach is not yet implemented. In [START_REF] Dunkl | A method for analyzing time series data in process mining: application and extension of decision point analysis[END_REF], authors propose a method that extends the Decision point analysis [START_REF] Rozinat | Decision mining in business processes[END_REF] which allows only single values to be analysed. The proposed method takes into account time series data (i.e. sequence of data points listed in time order) and allows one to generate complex decision rules with more than one variable. The method is implemented but not publicly shared. In [START_REF] Ghattas | Improving business process decision making based on past experience[END_REF], authors propose a process mining based technique that allows one to identify the most performant process path by mining decision rules based on the relationships between the context (i.e. situation in which the past decisions have taken place), path decisions and process performance (i.e. time, cost, quality). The approach is not yet implemented.

In [START_REF] De Leoni | Data-aware Process Mining: Discovering Decisions in Processes Using Alignments[END_REF], authors introduce a technique that takes the process petri net model, the process past executions log and the alignment result (indicating whether the model and the log conform to each other) as inputs, and produces a petri net model with the discovered inclusive/exclusive decision rules. It is implemented as a data flow discovery plug-in for the ProM framework. Another variant of this plug-in that needs only the event log and the related petri net as inputs is implemented as well. In [START_REF] Mannhardt | Decision mining revisited-discovering overlapping rules[END_REF], authors propose a technique that aims at discovering inclusive/exclusive decision rules even if they overlap due to incomplete process execution data. This technique is implemented in the multi-perspective explorer plug-in [START_REF] Mannhardt | The Multi-perspective Process Explorer[END_REF] of the ProM framework. In [START_REF] Sarno | Decision mining for multi choice workflow patterns[END_REF], authors propose an approach to explore inclusive decision rules using the Decision point analysis [START_REF] Rozinat | Decision mining in business processes[END_REF]. The approach consists in manually modifying the petri net model by transforming the "Or" gateway into an "And" gateway followed by a "Xor" gateway in each of its outgoing arcs.

Trace Variants Mining

Different researches were interested in trace variants mining. In [START_REF] Song | Trace Clustering in Process Mining[END_REF], authors propose an approach based on trace clustering, that groups the similar traces into homogeneous subsets based on several perspectives. In [START_REF] Bose | Abstractions in process mining: A taxonomy of patterns[END_REF], authors propose a Pattern abstraction plug-in, developed in ProM, that allows one to explore the common low-level patterns of execution, in an event log. These low-level patterns can be merged to generate the process most frequent patterns which can be exported in one single CSV file. The Explore Event Log (Trace Variants/Searchable/Sortable) visualizer 5 , developed in ProM, sorts the different trace variants as well as the number and names of duplicate traces. These variants can be exported in separate CSV files, where each file contains the trace variant, i.e. process pattern, as well as the related duplicate traces.

Discussion

In this paper, authors attempt to discover the decision rules related to both exclusive (Xor) and inclusive (Or) gateways, as well as the different activities execution order. Regarding decision mining, the algorithm that generates the petri net model should be selected first. Authors reject the algorithms presented in [START_REF] Van Dongen | Process mining: Overview and outlook of petri net discovery algorithms[END_REF] and select the inductive visual miner [START_REF] Leemans | Exploring processes and deviations[END_REF] as the petri net model generator. Indeed, experience has shown that only the inductive visual miner allows the inclusive gateways to be identified by the decision mining algorithm. This latter should afterward be selected.

The research works presented in [START_REF] Rozinat | Decision mining in business processes[END_REF], [START_REF] Leoni | Discovering branching conditions from business process execution logs[END_REF]- [START_REF] Ghattas | Improving business process decision making based on past experience[END_REF] attempt to discover exclusive decision rules considering only the exclusive (Xor) gateway. The work presented in [START_REF] Sarno | Decision mining for multi choice workflow patterns[END_REF] considers the inclusive and exclusive decision rules discovery, but the technique needs a manual modification of the petri net model which is not practical when dealing with complex processes. Therefore, authors assume that these works are not relevant for the proposition and consider the works presented in [START_REF] De Leoni | Data-aware Process Mining: Discovering Decisions in Processes Using Alignments[END_REF] and [START_REF] Mannhardt | Decision mining revisited-discovering overlapping rules[END_REF] which allow the discovery of both inclusive and exclusive decision rules. Moreover, authors assume that the data flow discovery plug-in [START_REF] De Leoni | Data-aware Process Mining: Discovering Decisions in Processes Using Alignments[END_REF] is more relevant since the experience has shown that the other one [START_REF] Mannhardt | Decision mining revisited-discovering overlapping rules[END_REF] could not correctly explore the decision rule related to the variables whose values do not frequently change in the event log.

Regarding trace variants mining, authors do not consider the approach presented in [START_REF] Song | Trace Clustering in Process Mining[END_REF] as relevant for the proposition since the objective is to discover the patterns that are exactly similar, i.e. patterns with the same activities that are performed in the same order. The work presented in [START_REF] Bose | Abstractions in process mining: A taxonomy of patterns[END_REF] and the Explore Event Log visualizer are considered as relevant for the proposition. Since none of the proposed techniques allow one to export a CSV file that contains only the trace variants and their frequency, authors assume that exploring trace variants using the Explore Event Log visualizer is more relevant because the discovered patterns can be exported in separate CSV files, which facilitates the postprocessing that needs to be made.

Decision and Trace Variants Mining Based Approach

The approach presented in Fig. 2 is the global workflow of the proposal and enables the achievement of the current research objective through seven steps. The first step of the approach concerns the construction of the event log from the past process executions. These latter represent the process traces generated with respect to the trace metamodel depicted in [START_REF] Roucoules | Engineering design memory for design rationale and change management toward innovation[END_REF][START_REF] Es-Soufi | Collaborative Design and Supervision Processes Meta-Model for Rationale Capitalization[END_REF] and expressed in XMI (XML Metadata Interchange) format. These traces should be automatically merged into a single XES 7(eXtensible Event Stream) event log in order to be processed in ProM, the framework in which the selected decision mining technique is developed. This automatic merge is implemented using ATL 8 (Atlas Transformation Language).

The second step concerns the generation of the petri net model from the event log. To this end, the inductive visual miner is used. Having both the event log and its corresponding petri net model, the decision mining practically starts using the data flow discovery plug-in as discussed in Section 2.

The third step aims at deriving the decision rules related to all the variables in the event log and exporting them in a single PNML 9 (Petri Net Markup Language) file. PNML is a XML based standardized interchange format for Petri nets that allows the decision rules to be expressed as guards, this means that the transition from a place (i.e. activity) to another can fire only if the guard, and thus the rule, evaluates to true. For instance, condition 1 in Fig. 1 is the decision rule that enables the transition from A1 to A2. The experience has shown that when all the variables in the event log are considered in the decision mining, some decision rules related to some of these variables may not be derived as expected, the origin of this problem is not yet clear. Therefore, to avoid this situation and be sure to have a correct decision rule, authors propose to execute the data flow discovery plug-in for each variable, this results in as much decision rules as variables (step 3 in Fig. 2).

The PNML files, that are generated in step 3, should be automatically merged into one single PNML file that contains the complete decision rules, i.e. related to all the event log's variables (step 4 in Fig. 2). This automatic merge is implemented using the Java programming language.

In parallel with decision mining (finding the Or and XOR rules), the trace variants mining can be performed in order to find the end-to-end processes (e.g. patterns 1 to 9 in Fig. 1). The Explore Event Log visualizer, as discussed in Section 2, is used to explore patterns in an event log. The detected patterns are then exported in CSV files where each file contains one pattern and its duplicates (step 1' of Fig. 2). To fit our objective, the patterns files need to be automatically post processed. This consists in computing the occurrence frequency of each pattern and removing its duplicates and then creating a file that contains a ranking of the different, non-duplicate, patterns based on their occurrence frequency (step 2' in Fig. 2). This post processing is implemented using the Java programming language.

During a new process execution, the ranked patterns file is automatically filtered to fit both the discovered decision rules and the user's context (user's name, date, process type, etc.). In other words, the patterns that do not satisfy the decision rules and those that are, for example, performed by another user than the one that is currently performing the process are removed. As a result, a ranking of suggestions (i.e. patterns that are the most suitable for the current user's context) are proposed to the user (step 5 in Fig. 2). The selected pattern is, then, captured and stored in order to enrich the event log.

4

Case Study: Supervision of Gas Network Exploitation

Systems supervision is a decision-based activity carried out by a supervisor to survey the progress of an industrial process. It is a business process that produces an action, depending on both the supervision result and the set-point (i.e. target value for the supervised system), that resolves systems malfunction. The authors of this paper present a supervision case study where the supervisor of an industrial process should take, in the shortest time, the right decision in case an alarm is received. The challenge here is to provide this supervisor with a ranking of the process patterns that are the most likely to be executed in his context. The proposed approach is verified under a specific supervision process related to gas network exploitation.

The process starts by receiving the malfunction alarm. The Chief Operating Officer (COO) has, then, to choose the process that best resolves the problem in this context. This latter can be described by the field sensors values, season, supervisor's name, etc. The first step of the proposed approach is to transform the already captured sixty traces of this supervision process into a single XES event log (step 1 in Fig. 2) and then generate its corresponding petri net model (step 2 in Fig. 2). Then, from the event log and the petri net model, generate the decision rules for each variable and export them in PNML files (step 3 in Fig. 2). In this process, the decision variables are: Pressure, season, network status, flow rate, human resource (the decision rule related to the pressure variable is depicted in Fig. 3). These PNML files are then merged into one single PNML file that contains the complete decision rules related to all the decision variables (step 4 in Fig. 2).

Fig. 3. Discovered decision rules for the pressure variable

In this process, based on both pressure value and season, the COO decides whether to send an emergency or a maintenance technician. If the emergency technician is sent (i.e. the decision rule: ((pressure>22millibars)and(season≠fall)) evaluates to true), he has then to decide which action should be performed based on the measured flow rate. If the decision rule ((pressure≤22millibars)and(season=fall)) evaluates to true, then the maintenance technician is sent. Moreover, if the rule (pressure<19millibars) evaluates to true, then in addition to sending the maintenance technician, the supervisor should extend the time scale then share it and write the problem then share it. In this last case, the inclusive logic is transformed into a parallel logic, and thus the activities may be executed in different possible orders.

In parallel with the decision rules mining, the step 1' and 2' in Fig. 2 are performed; the patterns that are contained in the event log are discovered (Fig. 4.a) then exported in CSV files and finally post processed by removing each pattern's duplicates and computing their occurrence frequency. If we consider all the possible process patterns and the different rules, it is possible to construct the BPMN process depicted in Fig. 5. These patterns (Fig. 4.a) are, then, filtered based on the current context and the decision rules that are generated (step 4 in Fig. 2). For instance, if the alarm is received in the fall by John, and the pressure of the supervised network equals to 18 millibars which is less than both 22 and 19 millibars (Fig. 5), the approach proposes two possible patterns to solve the problem (Fig. 4.b), where the first one "P12" is the most frequently used in this context.

Conclusion and Future Work

The objective of this paper is to support engineers in their decision-making processes by proposing the most relevant process patterns to be executed given the context. Through the proposed approach, the past execution traces are first analysed and the decision rules that control the process are mined. Then, the patterns and their occurrence frequency are discovered, postprocessed and filtered based on the discovered decision rules and the user context parameters. A ranking of the most likely patterns to be executed are then proposed. This approach illustrates the feasibility of the assumption about using process mining techniques to support decision making in complex processes that are controlled by inclusive, exclusive and parallel gateways. Future work consists in fully automating the approach and integrating it in the process visualizer tool presented in [START_REF] Roucoules | Engineering design memory for design rationale and change management toward innovation[END_REF]. It also consists in evaluating this approach, using real-world design and supervision processes, with respect to some performance indicators such as execution time, quality of the proposed decisions, changes propagation, etc.

Fig. 1 .

 1 Fig. 1. Example of process patterns (expressed in BPMN 1 )

Fig. 2 .

 2 Fig. 2. Overview of the proposal (expressed in IDEF0 6 )

Fig. 4 .

 4 Fig. 4. (a) Discovered patterns, (b) proposed patterns

Fig. 5 .

 5 Fig. 5. Part of the resulting supervision process with the different rules (expressed in BPMN)

https://en.wikipedia.org/wiki/Petri_net

http://www.promtools.org/

http://sep.cs.ut.ee/Main/BranchMiner

https://fmannhardt.de/blog/process-mining-tools

https://en.wikipedia.org/wiki/IDEF0

http://www.xes-standard.org/

https://en.wikipedia.org/wiki/ATLAS_Transformation_Language

http://www.pnml.org/

Acknowledgments. This research takes part of a national collaborative project (Gontrand) that aims at supervising a smart gas grid. Authors would like to thank the companies REGAZ, GDS and GRDF for their collaboration.


Mourad Messaadia
email: mmessaadia@cesi.fr

Fatah Benatia
email: fatahbenatia@hotmail.com

David Baudry
email: dbaudry@cesi.fr

Anne Louis
email: alouis@cesi.fr

PLM Adoption Model for SMEs

Keywords: PLM, ICT Adoption, SMEs, Data analysis 1

de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

INTRODUCTION

The literature review has addressed the topic of PLM from different angles. However, the adoption aspect was only dealt by a few works such [START_REF] Bergsjö | PLM Adoption Through Statistical Analysis[END_REF] where author proposes statistical tools to improve the organizational adoption of new PLM systems and highlights on the importance of survey early in the PLM introduction process; [START_REF] Ristova | AHP methodology and selection of an advanced information technology due to PLM software adoption[END_REF] provides a review of the main developments in the AHP (Analytical Hierarchy Process) methodology as a tool for decision makers to be able to do more informed decisions regarding their investment in PLM; [START_REF] Rossi | Product Lifecycle Management Adoption versus Lifecycle Orientation: Evidences from Italian Companies[END_REF] on the adoption of PLM IT solutions and discussed the relationship between "PLM adopter" and "lifecycle-oriented" companies in order to achieve the adoption aspect we have considered PLM as an innovate ICT for SMEs. Thus we integrated works on ICT and innovation adoptions. ICT technology is one of the ways, at the disposal of a company to increase its productivity. ICT can reduce business costs, improve productivity and strengthen growth possibilities and the generation of competitive advantages [START_REF] Bergsjö | PLM Adoption Through Statistical Analysis[END_REF]. Despite the work done and large companies evolution in terms of PLM, SMEs still have difficulties to understand all the potential of such technologies [START_REF] Hollenstein | The decision to adopt information and communication technologies (ICT): firm-level evidence for Switzerland[END_REF]. Their adoption of ICT is slow and late, primarily because they find that ICT adoption is difficult [START_REF] Hashim | Information communication technology (ICT) adoption among SME owners in Malaysia[END_REF] and SMEs adoption is still lower than expected.

When implementing a PLM solution in a company, the implementation difficulties are directly dependent on the complexity of the organization, costs and the possible opacity of the real behaviours in the field. Indeed, the implementation of PLM solution seems to scare SMEs in terms of resource costs and deployment.

The integration of the PLM solutions and its adoption by the SMEs has succeeded the interest of several research works. Among these research works we distinguish those on adoption process improving through statistical tools [START_REF] Bergsjö | PLM Adoption Through Statistical Analysis[END_REF]. In the same way authors in [START_REF] Fabiani | ICT adoption in Italian manufacturing: firm-level evidence[END_REF] conducted an investigation around 1500 enterprises and analyse the process adoption. This investigation shows that size on enterprise, human capital of the workforce and the geographic proximity with large firms has an impact on ICT adoption. In another hand, we find investigation based on empirical analysis which highlights the role of management practices, especially the manager, and quality control on the ICT adoption.

Another investigation was conducted on a thousand firms in manufacturing in Brazil and India and examines the characteristics of firms adopting ICT and the consequences of adoption for performance [START_REF] Basant | ICT adoption and productivity in developing countries: new firm level evidence from Brazil and India[END_REF]. In addition to previous results, they show the impact of educational system and the positive association between ICT adoption and education. Several barriers to IT adoption have been identified, including: lack of knowledge about the potential of IT, a shortage of resources such financial and expertise and lack of skills [START_REF] Hashim | Information communication technology (ICT) adoption among SME owners in Malaysia[END_REF].

According to [START_REF] Forth | Information and Communication Technology (ICT) adoption and utilisation, skill constraints and firm level performance: evidence from UK benchmarking surveys[END_REF] the skill workers have an impact on ICT adoption. Workers with high (low) proportions of skill can have a comparative advantage (disadvantage) in minimizing the costs both of ICT adoption and of learning how to make best use of ICTs.

An investigation of works done on ICT adoption conclude on the importance to analyse the impact on ICT system implementation and adoption processes and how they do so, and how implementation and adoption processes could be supported on the organizational, group, and individual levels [START_REF] Korpelainen | Theories of ICT system implementation and adoption-A critical[END_REF]. Based on previous works, we will consider that PLM is an innovative ICT solution for SMEs.

Next paragraph will introduce the problem statement and context of study. Third paragraph is on the proposed the model of PLM adoption based on quantitative KPIs. The fourth paragraph highlights the obtained results and their discussion. Finally, we conclude and discuss future work on how to improve and deploy our model.

STUDY CONTEXT

The first initiative of this work was conducted during the INTERREG project called "BENEFITS" where different adoption KPI's was identified [START_REF] Messaadia | PLM adoption in SMEs context[END_REF].

On the basis of an analysis of the various studies carried out with several companies, it is possible to collect different indicators. These indicators have been classified according to 4 axes identified through PLM definitions analysis. The 4-axis structure (Strategy, Organisation, Process and Tools) seemed clear and gave a good visibility to the impact of the indicators on the different levels of enterprise [START_REF] Messaadia | PLM adoption in SMEs context[END_REF].

For our work, Survey conducted followed different steps from questionnaire designing until data analysis [START_REF]Survey methods and practices[END_REF]. One of problems faced during questionnaire design is the decision of what questions to ask, how to best word them and how to arrange the questions to yield the information required. For these questions were conducted on the basis of indicators, words were reviewed by experts and finally we reorganised questions according to new 4 axes: Human Factors, Organisational Factors, Technical Factors and Economic Factors. This new decomposition does not affect the indicators but brings a fluidity and easier understanding for the interviewees (SMEs).

Fig.1.PLM axis structuration

Also, the objective of the investigation is to understand the needs of SMEs according to the introduction of digital technology within the automotive sector and to anticipate the increase in competence needed to help these SMEs face the change by setting up the necessary services and training. The survey was conducted on a panel of 33 companies (14 with study activities and 19 with manufacturing activities) of which 50% are small structures as shown in Fig. 2.

Fig.2.Panel of SMEs interviewed

PLM ADOPTION INDICATORS

The concept of adoption may be defined as a process composed of a certain number of steps by which a potential adopter must pass before accepting the new product, new service or new idea [START_REF] Frambach | Organizational innovation adoption: A multilevel framework of determinants and opportunities for future research[END_REF]. Adoption can be seen as an individual adoption and organizational adoption. The individual one focuses on user behaviour according to new technology and have an impact on the investment in IT technology [START_REF] Magni | Intra-organizational relationships and technology acceptance[END_REF]. In the organisational adoption the organisation forms an opinion of the new technology and assesses it. Based on this, organisation makes the decision to purchase and use this new technology [START_REF] Magni | Intra-organizational relationships and technology acceptance[END_REF]. Based on work done in [START_REF] Messaadia | PLM adoption in SMEs context[END_REF] we developed the questionnaire according to adoption factors (Table 1.).

QUESTIONNAIRE ANALYSIS

The previous step was the construction of the questionnaire by methodological tool with a set of questions that follow in a structured way (Fig. 3). It is presented in electronic form and was administered directly through face to face and by phone. ε

For:

1, … , , the hypothesis related to the model (eq.1) is the distribution of the error ε is independent and the error is centred with constant ance ~ 0, ; In order to conclude that there is a significant relationship between PLM level and Adoption factors, the Regression (eq.1) is used during estimation and to improve the quality of the estimates. The first step is to calculate the adoption factors according to: Once the fourth factors calculated, the matrix form of our model becomes:

M ' ⋮ $ O = M '' '' '' '' ' ' ' ' ⋮ $' ⋮ $' ⋮ ⋮ $' $' O P Q + M ' ⋮ $ O ⟺ = S = TU + E (2)
For resolving our equation (Eq.2) we need to calculate the estimated matrix B. With estimated B called:

U W = T X T Y' T X S (3)
Through all these equations (observation) we can give the general regression equation of PLM.

= + + + + (4)
The methodology adopted started by determining (estimating) , , , parameters of the multiple-regression function. The result of estimation is defined by: Z, W , , \ . For this, we choose the method of "mean square error" calculated through Matlab. In the second step, we calculate the dependency between PLM level (result of multipleregression) and the adoption factors (H, O, T and E) by the regression coefficient (R), especially the Determination Coefficient (D).

Where:

] = ^ = ___ _H , aa^=

Numerical Results

After the investigation the PLM-Eval-Tool generates a data table (Fig. 4) of evaluated responses that will be used to build our adoption model. Once the data collected, we applied our approach for obtaining the estimated parameters Z, W , , \ through (Eq.3).

Fig.4. Brief view of collected data

M Z W ̂ \ O P 0.0697 0.6053 0.1958 0.1137 Q (5)
With ^ 0.9841 which is considered as a very good regression, and validate the proposed equation (Eq.1).

The numerical result equation is:

KmnopnX q$ = 0.0697 + 0.6053 + 0.1958 0.1137 (6)

Result discussion

Concerning the "Error" we will consider the highest one which is equal to

' $ ∑ s.t 'u vv 0.0128.
This means that all values of PLM_Evaluation will be considered with ± 0.0128. We can also determine confidence interval for the parameters a, b, c and d using the student law t x ,y , where α is the Confidence threshold, or the Tolerance error rate, the choice of the value α in our case is α 0.05 and { = 4 is the degree of freedom (the number of parameters) σ } ~ is the standard deviation (the square root of the variance). In our case / •,€ / s.s•,t 2.132 (Fig. 5). Using data from a sample, the probability that the observed values are the chance result of sampling, assuming the null hypothesis ( s ) is true, is calculated. If this probability turns out to be smaller than the significance level of the test, the null hypothesis is rejected. † s : = 0 ' ∶ … 0

For this we will calculate:

= |n Z| ‡ } ˆ
Then we will compare it to the value of / s,s•; t = 2,13 If T‰ / s,s•; t = 2,13, we accept s : = 0, the H parameter does not influence the realization of PLM and we will then recreate another equation of regression without H. The same analysis was done for b, c, d.

DISCUSSION

Once the model developed another aspect of the analysis was explored, that of the recommendations. Effectively, the PLM-Eval-Tool offer also a view (fig. 6.) the results according to such factors as change management, structured sharing, extended enterprise, evaluation capacity and willingness to integrate. These factors are seen as a numerical focus, and first returns on SMEs analysis are:

• 30% of companies consider themselves to be under-equipped regarding to information technology. • Companies recognize that information technology is very much involved in the development process, but for the majority of them organizational aspects and informal exchanges are decisive.

• They believe that they have the in-house skills to anticipate and evaluate techno- According to obtained results, here is a list of first actions that we propose to implement:

• To make the players in the sector aware of the evolution of this increasingly digital environment.

• Diagnose the existing digital chaining in companies to promote the benefits of the PLM approach. (Processes, tools, skills, etc.) • To propose levers of competitiveness by the identification of "Mutualized Services" and "Software as a Service" solutions.

• To propose devices to gain skills and accompany the change management of manufacturers, equipment manufacturers, to the SMEs in the region.

CONCLUSION

The statistical analysis allowed us to develop a mathematical model to evaluate the adoption of an SME in terms of PLM. Thus, SMEs will be able to carry out a first self-evaluation without calling on honest consultants. However, this model will have to improve with more SMEs results and taking into account the different activity sectors aspect.

As future work, we envisage to work on several cases studies (deployment on France) in order to improve the mathematical model. Also, another work will be carried out in order to generate recommendations automatically. The aim of this approach is to offer SMEs a tool for analysis and decision-making for the upstream stage in the introduction or adoption of PLM tools.

ACKNOWLEDGEMENT

Acknowledgement is made to PFA automotive which has initiated this study around the technical information, processes and skills management system, which provides data structuring for the extended company with the support of the DIRECCTE IdF, the RAVI for the identification of companies and the CETIM to conduct the interviews.

Fig. 3 .

 3 Fig. 3. PLM-Eval-Tool: questionnaire

  () * +, -(*./ + . *0 /* /+ /ℎ* * + +) 0 , /+

  a+)* .-( * ^*J *.. + ; aa = +/ 0 a+)* a-( *; aa = a+)* .-( * + . aa = aa^+ aa = If |^| → 1, We have a strong dependence and good regression

Fig. 5 .

 5 Fig.5. Student table

Fig. 6 .

 6 Fig.6. Radar showing the average of the results obtained by the companies that responded to the questionnaire. Scaling from 0: Very low to 5: Very good

Table 1 .

 1 Adoption factors according to the 4 th axes

	Axes	Questions according to adoption factors
	Human factor	Ability to assess technological opportunities (FH1)
		Resistance to change (FH2)
		The learning effects on previous use of ICT technology (FH3)
		Relative advantage (FH4)
		Risk aversion (FH5)
		Emphasis on quality (FH6)
	Organisational	Average size of effective of SME between 50 and 200 (FO1)
	factor	Age of SMEs (FO2)
		Competitive environment (FO3)
		Rank of SME (FO4)
		Geographical proximity (FO5)
		Number of adopters (FO6)
		Interdependencies Collaboration (FO7)
		Existing leading firms (OEM) in your economic environment
		(FO8)
		Informal communication mode (FO9)
		Existing Innovation process (FO10)
		Knowledge Management (FO11)
		Process synchronization (FO12)
		Existing R&D activities (FO13)
		Existing certified (QM) system (FO14)
	Technological	The position of SME related to ICT technologies (FT1)
	factor	Interoperability (FT2)
		Ergonomic (FT3)
		Compatibility with similar technology (FT4)
		Compatibility with needs and existing process (FT5)
		How is evaluated before adopting technology (FT6)
		Have you had the opportunity to test the technology before its
		adoption (FT7)
		Complexity (FT8)
		The frequency of new technology integration (FT9)
		Level of skill and knowledge (FT10)
		Existing software (PDM, CAD/CAM,ERP) (FT11)
	Economical	Indirect costs (FE1)


Joel Sauza Bedolla

Gianluca D'antonio
email: gianluca.dantonio@polito.it

Frédéric Segonds
email: frederic.segonds@ensam.eu

Paolo Chiabert
email: paolo.chiabert@polito.it

PLM in Engineering Education: a pilot study for insights on actual and future trends

Keywords: Product Lifecycle Management, Education, Survey

Universities around the world are teaching PLM following different strategies, at different degree levels and presenting this approach from different perspectives. This paper aims to provide preliminary results for a comprehensive review concerning the state of the art in PLM education. This contribution presents the design and analysis of a questionnaire that has been submitted to academics in Italy and France, and companies involved in a specific Master program on PLM. The main goal of the survey is to collect objective and quantitative data, as well as opinions and ideas gained from education expertise. The collected results enable to depict the state of the art of PLM education in Italian universities and to gain some insights concerning the French approach; the structure of the survey is validated for further worldwide submission.

Introduction

Product Lifecycle Management (PLM) is a key factor for innovation. The PLM approach to support complex goods manufacturing is now considered as one of the major technological and organizational challenges of this decade to cope with the shortening of product lifecycles [START_REF] Garetti | Organisational change and knowledge management in PLM implementation[END_REF]. Further, in a globalized world, products are often designed and manufactured in several locations worldwide, in "extreme" collaborative environments.

To deal with these challenges and maintain their competitiveness, companies and professional organizations need employees to own a basic understanding of engineering practices, and to be able to perform effectively, autonomously, in a team environment [START_REF] Chen | Web-based mechanical engineering design education environment simulating design firms[END_REF]. Traditional methodologies for design projects (i.e. with collocated teams and synchronous work) could be effective until a few decades ago, but they are insufficient nowadays. Thus, engineering education has changed in order to provide students with some experience in collaborative product development during their studies. It is essential to train students to Computer Supported Collaborative Work (CSCW) [START_REF] Pezeshki | Preparing undergraduate mechanical engineering students for the global marketplace -New demands and requirements[END_REF], and PLM is a means for students to structure their design methodology. Indeed, before starting an efficient professional collaboration, future engineers must be mindful of how this approach works, and how tasks can be split between stakeholders. Thus, from an educational point of view, the PLM approach can be considered as a sophisticated analysis and visualization tool that enables students to improve their problem solving and design skills, as well as their understanding of engineering systems behaviour [START_REF] Chen | Web-based mechanical engineering design education environment simulating design firms[END_REF]. Moreover, PLM can also be a solution to face one of the main problems in our educational system: the fragmentation of the knowledge and its lack of depth [START_REF] Pezeshki | Preparing undergraduate mechanical engineering students for the global marketplace -New demands and requirements[END_REF].

The main research question from here is: "How can we, as engineering educators, respond to global demands to make our students more productive, effective learners? And how can PLM help us to achieve this goal?". At the state of the art, the information about PLM education is fragmented. Hence, the aim of this paper is to propose a survey structure to collect quantitative data about the existing university courses in PLM, identify the most common practices and possible improvements to closer adhere to the needs of manufacturers.

The remainder of the paper is organized as follows: in section 2, an analysis of literature concerning recent changes in educational practices in engineering education is presented and the state of the art of PLM education is settled. Then, the survey structure is presented in section 3. The results are presented in section 4: data collected from Italian universities are presented, as well as the results of the test performed in France to validate the survey structure. Finally, in section 5, some conclusive remarks and hints for future work are provided.

State of the art

In literature, there is no evidence of a complete and full review of how PLM is taught in higher institutions around the world. Still, partial works can be found. Gandhi [START_REF] Gandhi | Product Lifecycle Management in Education: Key to Innovation in Engineering and Technology[END_REF] presents the educational strategy employed by three US universities. Fielding et al. [START_REF] Fielding | Product lifecycle management in design and engineering education: International perspectives[END_REF] show examples of PLM and collaborative practices implemented in higher education institutions from the United States and France. Sauza et al. [START_REF] Sauza-Bedolla | PLM in a didactic environment: the path to smart factory[END_REF] performed a two-step research. The first attempt consisted in a systematic research of keywords (i.e. PLM education, PLM certification, PLM course, PLM training) in the principal citation databases. Nevertheless, the analysis of scientific literature was limited to some specific programs of a limited quantity of countries. For this reason, the research was extended to direct research on universities' websites. The inclusion criteria for institutions was the attendance to one of the two main events in scientific and industrial use of PLM: (i) the IFIP working group 5.1 PLM Inter-national Conference, and (ii) Partners for the Advancement of Collaborative Engineering Education. The review process covered 191 universities from Europe, Asia, America and Oceania. It was found that there is a high variety in the topics that are presented to students, departments involved in the course management, the education strategy and the number of hours related to PLM.

The analysis presents useful insights. However, the research methodology based on website analysis was not sufficient and may present some lacks. In some cases, websites did not present a "search" option and this limited the accessibility to information. Moreover, during the research, some issues with languages were experienced: not all of the universities offered information in English, and for this reason, the universities were not considered. In some other cases, information was presented in the curricula that can be accessed only to institution members. The specific didactics nature of this study is precisely in that it brings researchers and professors from engineering education to work explain their vision of how PLM is taught. The objective is to get real participatory innovation based on integration of the PLM within a proven training curriculum in engineering education. One step further, we prone that by stimulating the desire to appropriate knowledge, innovative courses are also likely to convince a broad swath of students averse to traditional teaching methods and much more in phase with their definition as "digital natives" [START_REF] Prensky | Digital natives, digital immigrants. Part 1[END_REF] This paper is intended to be the first step of a broader effort to map the actual situation of PLM education around the world. This contribution presents the methodology employed to scientifically collect information from universities. Before going global, a first test has been made to evaluate the robustness of the tool in the authors' countries of origin, where the knowledge of the university system structure was clear.

Methodology

In order to get insights on the state of the art in PLM education, a survey structured in three parts has been prepared.

The first part is named "Presentation": the recipient is asked to state the name of his institution and to provide an email address for possible future feedbacks. Further, he is asked to state whether he is aware about the existence of courses in PLM in his institution or not, and if he is in charge of such courses. In case of positive reply, the recipient is invited to fill the subsequent part of the survey.

The second part of the survey aims to collect objective information to describe the PLM course. In particular, the following data are required:

- Finally, in the third part of the survey, subjective data are collected to measure the interest of the recipient in teaching the PLM approach and the interest of the students in this topic (both in a likert 1-5 scale). Further, an opinion about the duration of the course is required (not enough/proper/excessive) and whether the presentation of applied case studies or the contribution of industrial experts are included in the course. A space for further free comments is also available.

The
The invitations to fill the survey been organized in two steps. First, a full experiment has been made in Italy. The official database owned by the Italian Ministry of Education and University has been accessed to identify the academics to be involved. In Italy, academics are grouped according to the main topic of their research. Therefore, the contacts of all the professors and researchers working in the closest topics to PLM have been downloaded, namely: (i) Design and methods of industrial engineering; (ii) Technologies and production systems; (iii) Industrial plants; (iv) Economics and management Engineering; (v) Information elaboration systems; (vi) Computer science. This research led to a database consisting of 2208 people from 64 public universities. A first invitation and a single reminder have been sent; the survey, realized through a Google Form, has been made accessible online for 2 weeks in January 2017.

The second step consisted in inviting a small set of academics from French universities through focused e-mails: 11 replies have been collected. 11 replies have been collected. Further, a similar survey has been submitted to French companies employing people that has been attending a Master in PLM in the years 2015 and 2016.

Survey data analysis 4.1 Results from the Italian sample

The overall number of replies from Italian academics is equal to 213, from 49 different institutions. Among this sample, 124 people do not have information about PLM courses in their universities; therefore, they were not asked further questions. The 89 respondents aware about a PLM course belong to 36 universities; among them, 40 professors are directly involved in teaching PLM. A synthetic overview of the results is provided in Fig. 1; the map of the Italian universities in which PLM is taught is shown in Fig. 2. Type of course. The teachers involved in teaching PLM state that this topic is mostly dealt in broader courses, such as Drawing, Industrial Plants, Management. Practical activities. Among the 40 PLM teachers, 25 of them do not use software to support their educational activity. Some courses deploy Arena, Enovia, the PLM module embedded in SAP, Windchill. Other solutions, developed by smaller software houses are also used. Among the respondents, no one uses Aras Innovator, a PLM solution that has a license model inspired by open source products. However, in the majority of the teachers (27), industrial case studies are presented to show the role of PLM in managing product information and to provide students with a practical demonstration of the possible benefits coming from its implementation. Furthermore, interventions from industrial experts, aiming to show the practical implications of the theoretical notions taught in frontal lectures, are planned by 21 teachers.

Interest in PLM. The interest of students in PLM is variable: the replies are equally distributed among "Low" or "Fair" (25 occurrences) and "High" or "Very high" (25 occurrences). The interest of replicants in PLM is variable too: 34 people replied "Strongly low", "Low" or "Fair"; 34 people replied "High" or "Very high"; the remainder sample states "I don't know". As expected, the interest in PLM of people teaching this topic is high: 29 people replied "High" or "Very high" (out of a sample of 40 teachers).

Results from the French sample

On the French side, 11 replies were collected from 7 different Universities and School of Engineering. All the respondents teach PLM courses in their Universities. Similarly to the Italian sample, PLM is mostly taught in the M.Sc. level: beside a Master course, one B.Sc. and 8 M.Sc. courses were mapped. Most of the courses (8) are devoted to Mechanical Engineers. In 6 cases, a specific course is designed for PLM; further, in the Ecole supérieure d'électricité settled in Châtenay-Malabry the so-called 'PLM week' is organized. The duration of the PLM courses mainly ranges between 32 and 64 hours, which is an appropriate duration, according to the teachers; conversely, in the broader courses, the time spent in teaching PLM is lower than 6 hours. The only Master mapped through the survey is held in Ecole Nationale Supérieure d'Arts et Métiers (Paris): the duration is equal to 350 hours, with high interest of the participants.

A reduced version of the survey was also sent to a small set of French companies to map internal courses in PLM.7 replies have been obtained.: 3 were from large companies in the field of aeronautics, textile and consulting, and 4 were small-medium companies from the PLM and BIM sector. 57% of these companies declare they have courses dedicated to PLM. The name of the courses are various. In particular, a textile enterprise has course structured in 11 modules as business process:

1

Conclusions

The present paper presented a methodology for a systematic overview about university education in PLM. A survey has been submitted to all the Italian academics performing research and teaching activities in fields related with PLM. The percentage of respondents in the Italian experiment was approximately 10%, which is in line with the expectations of the authors: these replies enabled to identify PLM courses in 36 different universities, mainly located in the north-central part of the country, which is characterized by a higher density of industries. However, to have a successful realization of the survey a complete database of university teachers is mandatory.

The proof-of-concept realized on the French sample led to good results: no criticalities have been found in the survey. Hence, the next steps of the work are the creation of the recipients database and the full-scale experiment. Then, the experiment can be replicated in other countries, to have a more exhaustive picture about PLM education. We plan to rely on Bloom taxonomy of objectives to sharpen the skills taught in PLM courses [START_REF] Bloom | Bloom taxonomy of educattional objectives[END_REF].

Our research question was: "How can we, as engineering educators, respond to global demands to make our students more productive, effective learners? And how can PLM help us to achieve this goal?". A first insight given to this research question is the proposal, as an ultimate goal, of the creation of a network made of PLM teachers, that will enable mutual exchange of expertise, teaching material, exercises and practices. To reach this goal and to wider our approach to IFIP WG 5.1 community, a first step could be the creation of shared storage space for documents that allow any user to teach PLM at any level.

  level at which the course is taught (among B.Sc, M.Sc, Ph.D, Master); -The curriculum in which the course is taught (free reply); -At which year the course is taught, and the overall duration of the curriculum (values constrained between 1 and 5); -The department in charge of the course (free reply); -If PLM is taught in a devoted course (Yes/No) or as a topic in a broader course (Yes/No); -The name of the course (free reply) and its duration; -If software training is included (Yes/No) and which software is used.

  Degree level. In the sample of 36 universities, PLM is taught at different levels. The Master of Science is the most common: 53 courses have been identified. In 22 cases, PLM is also taught at the Bachelor level. Furthermore, there are 4 courses devoted to Ph.D. candidates and 2 Masters are organized. The latter two Master courses are organized in the Polytechnic universities of Torino and Milano; however, the first one has recently moved to University of Torino. Curricula. There is a variety of curricula involved in teaching PLM. Course for Management Engineering and Mechanical Engineering are organized (23 occurrences each). The area of Computer Science is also involved (23 occurrences): topics concerning the architecture of PLM systems, or the so-called Software Lifecycle Management are taught. Moreover, PLM courses are also provided in Industrial Engineering (6 occurrences), Automotive Engineering (B.Sc. at Polytechnic University di Torino) and Building Engineering (Ph.D. course at Politecnico di Bari).

Fig. 1 .

 1 Fig. 1. Synthesis of the results obtained through both the Italian and the French PLM teachers.

Fig. 2 .

 2 Fig. 2. Map of the Italian universities in which PLM is taught.

Acknowledgments

The authors are grateful to the colleagues and industrials that replied to the survey.