Luiz Fernando

C S Durão

Helge Eichhorn
email: eichhorn@dik.tu-darmstadt.deanderl@dik.tu-darmstadt.de

Reiner Anderl

Klaus Schützer
email: schuetzer@scpm.unimep.br

Eduardo De Senzi Zancul

Integrated component data model based on UML for smart components lifecycle management: a conceptual approach

Keywords: Cyber-Physical Production Systems, Product Lifecycle Management, Data Model, Smart Products

Cyber-Physical Production Systems (CPPS) and Smart Products are considered key features in the development of the fourth industrial revolution. To create a connected environment in manufacturing based on CPPS, components must be able to store and exchange data with machines, and with other components and assemblies along the entire production system. At the same time, Smart Product features require that products and their components be able to store and exchange data throughout their entire lifecycle. Therefore, the aim of this paper is to present a preliminary integrated component data model based on Unified Modeling Language (UML) for the implementation of CPPS and Smart Product features. The development of the data model is based on requirements gathered both from the literature review and from corporate interviews with potential users. The results are still preliminary since the research results are part of a bigger research effort under an international collaboration network.

Introduction

The competitive situation for the manufacturing industry is marked by increasing product and process complexity associated with volatile markets and continuous shortening of innovation cycles. Both the increased complexity and the shortening of innovation cycles affect the development of new products, as well as the development of product related services. This scenario requires the development and management of more efficient and versatile production and logistic systems [START_REF] De Senzi Zancul | Industrie 4.0 -Volkswirtschaftliches Potenzial für Deutschland[END_REF]. These current demands are driving the development of what is being called the fourth industrial revolution [START_REF] Kagermann | Umsetzungsempfehlungen für das Zukunftsprojekt Industrie 4.0 -Abschlussbericht des Arbeitskreises Industrie 4[END_REF].

The world has already experienced three industrial revolutions. The trigger for the first one in the 18 th century was the widespread industrial use of steam engines. Less than 100 years later, manufacturing faced a second industrial revolution through the introduction of mass production systems. In the second half of the 20 th century, the introduction of programmable logic controllers in production inaugurated the third industrial revolution.

Recently, acatech (Deutsche Akademie der Technikwissenschaften -the German National Academy of Science and Engineering) proposed a program called Industrie 4.0 in order to lead the developments towards the fourth industrial revolution [START_REF] Kagermann | Umsetzungsempfehlungen für das Zukunftsprojekt Industrie 4.0 -Abschlussbericht des Arbeitskreises Industrie 4[END_REF]. Industrie 4.0 considers the introduction of the Internet of Things and Services approaches in manufacturing environments in order to establish production networks integrating products, production processes, production resources, and logistic systems in the shape of Cyber-Physical Systems (CPS).

The aim is to enable the production of Smart Products within Smart Factories, which are capable of exchanging information autonomously, to trigger actions, and to control each other partner independently. Thus, Smart Factories shall employ completely new approaches to production, driven and controlled by Smart Products. This way, Industrie 4.0 challenges companies and research institutes to direct their efforts in the implementation of three key features [START_REF] Kagermann | Umsetzungsempfehlungen für das Zukunftsprojekt Industrie 4.0 -Abschlussbericht des Arbeitskreises Industrie 4[END_REF]:

• Horizontal integration through value networks;

• End-to-end digital integration of engineering across the entire value chain;

• Vertical integration and networked manufacturing systems.

CPS will support the necessary collaboration among cyber environment and the surrounding physical world and its processes, constituting Cyber-Physical Production Systems (CPPS) [START_REF] Monostori | Cyber-physical production systems: Roots, expectations and R&D challenges[END_REF].

The manufacturing industry is already in the midst of a globalized economy scenario, which requires companies to shorten the time to market for new products, reduce costs and cater to customer requirements by offering a wide variety of product customizations. Adaptability and innovation potential are becoming vital factors to improve competitiveness within the demanding environment of today's ever-changing markets [START_REF] Westkämper | Wandlungsfähige Produktionsunternehmen[END_REF]. However, the challenges arising from the fourth industrial revolution, such as the developments considered in the scope of Industrie 4.0, have brought about not only new requirements to be met, but also tremendous opportunities for research as well as for the introduction of new commercial solution that can give rise to completely new markets. The key question is how fast research institutes and the industry can react to this scenario.

Planning to be prepared for the challenges and opportunities of the fourth industrial revolution, Brazilian and German partners started a collaborative research project called "Smart Components within Smart Production Processes and Environments -SCoPE", supported by the BRAGECRIM (Brazilian-German Collaborative Research Initiative on Manufacturing Technology) Program. The project started in August 2014 and this paper aims to present the initial efforts in the development of an Integrated Component Data Model (ICDM), which should represent the necessary product and process information to support the communication of Smart Products within a Smart Production Environment.

This paper is structured in five sections. Section 2 presents requirements for the ICDM gathered from literature review while section 3 discusses empirical requirements gathered from the industry. Section 4 presents the features of the ICDM preliminary version. Finally, section 5 discusses conclusions and presents future research efforts.

Data Model Requirements -Literature Review

Industrie 4.0 is a new approach to the manufacturing chain that aims to integrate factory systems and to create a self-controlled, intelligent environment [START_REF] Anderl | Industrie 4.0 -Advanced Engineering of Smart Products and Smart Production[END_REF]. A controlled environment can provide lifecycle information and close the product information lifecycle loop [START_REF] Kiritsis | Closed-loop PLM for intelligent products in the era of the Internet of things[END_REF]. It requires a flow of information connecting both the physical aspects of the factory floor and the data related to it, making up what is called CPPS [START_REF] Kagermann | Umsetzungsempfehlungen für das Zukunftsprojekt Industrie 4.0 -Abschlussbericht des Arbeitskreises Industrie 4[END_REF].

In a CPPS, the components communicate with assembly stations and other components [START_REF] Strang | Assembly Process driven Component Data Model in Cyber-Physical Production Systems[END_REF]. However, a CPPS is not about the union of the physical and the cyber. It is about the intersection of these worlds by transforming the physical into cyber and the cyber into physical [START_REF] Lee | CPS foundations[END_REF]. Data is collected by the CPS at different levels resulting in a massive amount of information that needs to be structured in a model [START_REF] Lee | A Cyber-Physical Systems architecture for Industry 4 . 0based manufacturing systems[END_REF]. The component data model is a key element of models in CPPS [START_REF] Strang | Assembly Process driven Component Data Model in Cyber-Physical Production Systems[END_REF].

The data model defines the component features and their relationships. It aggregates all relevant information of the product design [START_REF] Ramana | Data and knowledge modeling for design process planning integration of sheet metal components[END_REF]. The data model is not just about the product memory, but it also can influence production planning [START_REF] Strang | Assembly Process driven Component Data Model in Cyber-Physical Production Systems[END_REF]. However, the link between design and process planning remains a main issue [START_REF] Zhou | A feasible approach to the integration of CAD and CAPP[END_REF].

To cover different aspects of the literature on data models, a literature review was conducted. First, keywords were defined in relation to previous knowledge on the subject and the concepts of Industrie 4.0 and integrated manufacturing. The keywords, "Data model" and "CPS", were searched in the "Web of Science" and "Science Direct" databases considering a first filter that selected only articles of the manufacturing and computer science areas. This search resulted in approximately 6.000 articles.

A second filter was applied considering the similarity between the keywords of the database's articles and the present article keywords. The second iteration produced a total of 750 articles that required a third and deeper study of the article's abstract resulting in a total of 10 articles that were thoroughly analyzed. Considering the methodology proposed, it was possible to gather different data model propositions.

According to Dipper et al. [START_REF] Dipper | Defining, recognizing and representing feature interactions in a feature-based data model[END_REF], a data model should contain definitions of machine features in implicit terms such as depth, width, and length. Besides that, all features have an ID, the workpiece it belongs to and the related machine operations. Ramana et al. [START_REF] Ramana | Data and knowledge modeling for design process planning integration of sheet metal components[END_REF] suggest a geometric data model containing, besides ID, item coordinates, and item tolerance.

The inconsistency between the final component and the information available in the CAD database is a key issue to create an integrated platform [START_REF] Zhou | A feasible approach to the integration of CAD and CAPP[END_REF]. Based on that, Zhou et al. [START_REF] Zhou | A feasible approach to the integration of CAD and CAPP[END_REF] propose an integrated data model containing not only geometrical information, but also control data (key property, category, etc.), process data (hardness, roughness, etc.), function data (purpose, behavior, etc.) and management data (constraint, authority, etc.).

Following the premises of Zhou et al. [START_REF] Zhou | A feasible approach to the integration of CAD and CAPP[END_REF], Piccard and Anderl in [START_REF] Piccard | Integrated Component Data Model for Smart Production Planning[END_REF] propose an integrated data model that aims to reduce the gap between product model information and specific component data. Piccard and Anderl [START_REF] Piccard | Integrated Component Data Model for Smart Production Planning[END_REF] include general management information, identification, product structure and assemblies, requirements and functionality, geometry, product manufacturing information, product configuration, product status and presentation as essential parts of a data model.

A data model certifies that the decisions are based on individual information. To ensure that, Strang and Anderl [START_REF] Strang | Assembly Process driven Component Data Model in Cyber-Physical Production Systems[END_REF], propose a data model containing single part ID, single part dimension, single part tolerance, assembly ID, assembly station, assembler, assembling date, tools, order name, order specification and due date.

According to Kiritsis [START_REF] Kiritsis | Closed-loop PLM for intelligent products in the era of the Internet of things[END_REF], it is fair to say that the connection between the product and the manufacturer is lost at the moment it is delivered to the customer. Identifying products individually helps to create a sustainable supply chain controlling product information at any time of the lifecycle. To close the Product Lifecycle Management (PLM) loop, Kiritsis [START_REF] Kiritsis | Closed-loop PLM for intelligent products in the era of the Internet of things[END_REF] indicates that a data model should have the serial number, product type, product structure in addition to lifecycle phase and geometric information.

Companies spend a considerable amount of time trying to understand and apply the PLM approach. The correct representation of product information is a key factor in the implementation of PLM, according to Barbau et al. [START_REF] Barbau | OntoSTEP: Enriching product model data using ontologies[END_REF], who propose that a data model should include not only geometry information but also different aspects of the product. Considering these aspects, Barbau et al. [START_REF] Barbau | OntoSTEP: Enriching product model data using ontologies[END_REF] suggest that a data model should include function, behavior, structure, geometry, and material, assembly features, tolerances besides generalization, grouping, classification, and aggregation.

Information technology systems are used to deal with product data from the beginning of the development phase to the end of life. The majority of problems regarding the integration of data are caused by differences in the format of the information. Data models are employed to facilitate the portability and translation of information, according to Wang et al. [START_REF] Wang | DIMP: an interoperable solution for software integration and product data exchange[END_REF]. To promote systematic communication, a data model should possess function, behavior, structure, geometry, and material, assembly features, tolerances in addition to generalization, grouping, classification and aggregation [START_REF] Wang | DIMP: an interoperable solution for software integration and product data exchange[END_REF].

Analyzing the literature review, it is possible to define three main types of data model: manufacturing and assembly; integrated data model; and PLM data model. Manufacturing/assembly data model provides geometrical and process information in order to aid complete product manufacturing. The integrated data model provides information considering geometrical and management information aiming at mapping product status. PLM data model, on the other hand, proposes a data model containing information over the entire lifecycle of the product, from design to discard. Considering this classification, Table 1, summarizes the results and findings of the literature review.

PLM Data Model

Following the literature review, empirical data was gathered in order to provide companies view on data model requirements.

Data Model Requirements -Field Study

Initial informal consultations with industry partners have shown that there is growing interest for Industrie 4.0 technologies, in general, and components as information carriers, in particular. However, there are many reservations regarding feasibility, data security, and protection of intellectual property. These issues will be discussed in more detail in this section to derive the industry-specific requirements for the ICDM.

It should be noted though that this is still a matter of investigation and more detailed and formalized results can be expected in the future. At the time of writing two industryoriented studies are being prepared for publication by Anderl et al. within the project "Effiziente Fabrik 4.0" [START_REF] Anderl | Effiziente Fabrik -Effiziente Farbik 4.0[END_REF] and in collaboration with the Verband Deutscher Maschinen-und Anlagenbau (VDMA, German Engineering Association) [START_REF]Industrie 4.0 Forum[END_REF], which should provide additional information about industry requirements for smart components to the SCoPE project.

Centralized and Distributed Data Storage

It will possibly remain prohibitively expensive to embed significant amounts of data within every individual component in the near future. Therefore, centralized storage solutions for component data need to be investigated to enable quick adoption within the industry. On the other hand, mechatronic or cyber-physical components might already possess data handling capabilities and could easily accommodate additional component data (e.g. for maintenance purposes). Consequently, the ICDM needs to support centralized as well as distributed storage of component data.

Multiple Means of Component Identification

To connect centrally stored component data with the physical component, each component needs to be assigned a unique identifier (UID). Smart Production Units and Environments need to be able to retrieve automatically the component's UID. The consulted companies have expressed interest in enabling component traceability across the whole value creation chain. This might include physically demanding environments (e.g. hardening processes). Thus, the component identification technology must be suited to the production environments (e.g. Data Matrix codes instead of RFID for hightemperature environments).

Infrastructure Integration

Components as information carriers need to be integrated into the existing production and IT environments. This includes shop-floor systems such as Manufacturing Execution Systems (MES), field bus systems, and interfaces to programmable logic controllers (PLC) as well as office-floor systems like Enterprise Resource Planning (ERP), Product Lifecycle Management (PLM) and commercially available off-the-shelf databases.

Data Security

A potential area of conflict for many companies is the fact that while the smart components are produced and owned by the manufacturing company, external companies supply the equipment used in the manufacturing process. Since both parties have justified interest in ICDM data, the manufacturing company for improvement of their products and the equipment suppliers for the fine-tuning of production parameters, access controls must be implemented within the ICDM to enable collaborative use.

Especially if ICDM data is embedded into components in the future, strong encryption is mandatory to protect sensible data from unauthorized access.

Preliminary Data Model

Based on the literature review and industry's requirements it is possible to design a preliminary component data model considering that product information is gathered over the entire lifecycle. A component data model should contain, for instance, a unique identification, geometric information, process information, tolerance, product family and lifecycle phase. In addition, it should be able to integrate all the information coming from a wide range of sources over the lifecycle.

UML models provide stereotypes and constraints, together with syntax and semantics of the elements [START_REF] Tang | Data model for quality in product lifecycle[END_REF]. Considering the requirements gathered both from the literature review and from the field study, a preliminary ICDM is presented in Fig. 1. The ICDM is organized in five packages: The core model that handles basic administrative tasks also provides an extension interface for the additional partial models order, privileges, production, and specifications (Fig. 1).

Thus in the ICDM, a component is represented as an aggregation of its design specifications (specifications), production characteristics (production), access limitations (privileges) and customer's request (order) connected by an identification number as a primary key (core) to connect information over product lifecycle.

The product lifecycle starts with the properties provided by the design specifications such as the material characteristics, dimensions, and the production process plan that defines the planned activities to be executed within production. The specifications model is completed by requirements and simulation results from systems engineering contexts. The model is initialized with data obtained by interfacing with product data management (PDM) systems.

Fig. 1. Component data model using UML

The order partial model captures the information contained in enterprise resource planning (ERP) systems, including the customer for whom the component shall be produced and shipment information. Most importantly though the order data model captures customer-specific specifications and, therefore, enables customized production of the component within a smart production environment.

Throughout the physical production processes including manufacturing, assembly, and testing the production partial model is populated with data originating from manufacturing execution systems (MES). Process data such as responsible worker, utilized machines, workstations, and tools together with testing data such as measurements are captured to constitute observed production activities. This enables traceability of the component's production history and allows the optimization of future products and production processes.

To ensure that only authorized parties can access and modify the data within the ICDM the privileges partial model tracks all user (human users and smart systems) and user groups (departments, organizations, etc.) of component data together with their respective privileges.

Conclusions

The research initiative Industrie 4.0 aims to be a driving force of the 4 th Industrial Revolution. Its purpose is to create an intelligent production chain connecting the physical product with data collected over the product lifecycle and turning this data into information. The connection between physical and cyber is achieved through the utilization of Cyber-Physical Systems. To create value from the collected data, it is structured within the Integrated Component Data Model.

The component data model aims to create a representation of the communication between the component and its surrounding environment over its entire lifecycle. At the same time, it has to fulfill the requirements suggested by the literature review together with the main concerns of the industry. Based on these characteristics, the ICDM with its partial models is proposed linking the designed specifications (materials, dimensions, process plan, etc.); the production characteristics (process, testing, tool, observed activities, etc.); access privileges; and order details such as customer specifications. Additionally, the proposed model provides unique component identification and technology-agnostic data handling.

This paper represents the initial effort on the development of the ICDM within the collaborative research project "Smart Components within Smart Production Processes and Environments -SCoPE" supported by the BRAGECRIM Program. Next steps in this research stream include further development of the ICDM, prototypical implementation, and advanced scenario testing.

Fig. 2 :

 2 Fig. 2: Core package of the ICDM Apart from integrating the aforementioned partial model, the proposed core package, detailed in Fig. 2 handles administrative tasks including the provision of the unique component identification through technologies such as RFID or QR code and physical storage of the ICDM data.

Table 1 .

 1 Data Model characteristics according to literature review

	Author	Data Model Characteristic	Data Model Types
	T. Dipper, X. Xu, and P.	Depth, width, length, ID,	Manufacturing/Assembly Data
	Klemm [12]	workpiece, machine operation,	Model
		cutting strategies and cutting
		parameters.
	P. V. M. Ramana, K.V. Rao	ID, coordinates, and tolerance. Manufacturing/Assembly Data
	[10]		Model
	D. Strang and R. Anderl [7] Single part ID, single part	Manufacturing/Assembly Data
		dimension, single part	Model
		tolerance, assembly ID,
		assembly station, assembler,
		assembling date, tools, order
		name, order specification and
		due date
	X. V. Wang and X. W. Xu	Geometry, features, sketches,	Manufacturing/Assembly Data
	[15]	manufacturing, information,	Model
		assembly information, and
		drawings.
	X. Zhou, Y. Qiu, G. Hua, H.	Geometrical information,	Integrated Data Model
	Wang, and X. Ruan. [11]	control data (key property,
		category, etc.), process data
		(hardness, roughness, etc.),
		function data (purpose,
		behavior etc.) and
		management data (Constraint,
		authority, etc.).
	A. Piccard and R. Anderl	General management	Integrated Data Model
	[13]	information, identification,
		product structure and
		assemblies, requirements and
		functionality, geometry,
		product manufacturing
		information, product
		configuration, product status
		and presentation as essential
		parts of the data model.
	D. Kiritsis [6]	Serial number, product type,	PLM Data Model
		product structure besides
		lifecycle phase and geometric
		information.
	R. Barbau, S. Krima, S.	Function, behavior, structure,
	Rachuri, A. Narayanan, X.	geometry and material,
	Fiorentini, S. Foufou, and R.	assembly features, tolerances
	D. Sriram [14]	besides generalization,
		grouping, classification and
		aggregation.

Acknowledgments

The authors thank the Coordination for the Improvement of Higher Education Personnel (Capes), the Brazilian National Council for Scientific and Technological Development (CNPq), and the German Research Foundation (DFG) for supporting related projects. The authors also thank the companies involved for providing real case applications.


B Gurumoorthy

Amaresh Chakrabarti

Evaluation of methods to identify assembly issues in text

Keywords: Natural language text, diagnosis, sentiment analysis

The work reported in the paper is primarily aimed towards building a knowledge base for diagnosis of aircraft assembly process plans. The first step is to identify the presence of an issue in the text. Various existing methods that deal with issues in engineering are studied and their suitability presented. A study of sample documents across domains, including that of assembly, is then presented. Some key observations from the study are discussed, following which two main methods are explored for their suitability for detecting issues from documents. The first method is based on functional analysis of designs, which deems that an issue is the result of the violation of a function or a related parameter. The second is a Natural Language Processing technique called Sentiment Analysis that aggregates sentiments from individual words. The relative suitability of these methods is then discussed.

Introduction

Assembly is an important step in a product's lifecycle. It is an integrative step that sources inputs from design and part manufacturing. However, a number of issues that affect assembly could be avoided at the design and planning stage itself, if appropriate knowledge for this is available a priori. Such knowledge of issues, from previous experience, may already be recorded in various forms in case studies, issue reports, change requests and other such documents. A more general manifestation of this challenge of making legacy knowledge available is the closing of loop between two product life-cycles. Knowledge and experience recorded during one product life-cycle must be made available to inform the next or any future product life-cycle. It is, however, a challenge for an individual, team or organization to meticulously search, read, and understand all such documents. There could be hundreds, possibly thousands of such documents that may be present within a large organization. These documents could be varied in nature, and may be spread across many domains, and across various parts of the product life-cycle.

The final goal of the research reported in this paper aims to capture offline the knowledge present in these documents, and present an assembly planner with such knowledge in a contextualised form. The general framework for this work is shown in Figure 1.

Fig. 1. A framework for acquiring diagnostic knowledge from documents

Issues in product design and realization

Issues can arise at any stage in a product's life-cycle. Examples of such stages include design [START_REF] Hales | Five fatal designs[END_REF], manufacturing, and deployment. The work reported in this paper is focused on the manufacturing stage, assembly in particular. Guidelines have been proposed successfully for tackling such problems (e.g. DFMA guidelines [START_REF] Boothroyd | Product design for manufacture and assembly[END_REF]). However, the applicability of generic guidelines is not without challenges [START_REF] Lahtinen | Design for Manufacturing and assembly rules and guidelines for Engineering[END_REF]. The role of expert knowledge in design for manufacturing and assembly from various engineering areas has been stressed upon [START_REF] Lahtinen | Design for Manufacturing and assembly rules and guidelines for Engineering[END_REF]. Such knowledge helps to cover uncommon, but, important issues, and is useful in domain-specific contexts. For example, these could be issues that experienced personnel would have faced and documented.

The aircraft industry is the application area for this work. Due to the nature of the industry, assembly problems are not yet classified in a generic manner. One reason could be that it is still manual-assembly intensive, and hence a large number of cases of many varieties are present, as opposed to assembly-lines, where few, specific classifications of problems are possible. Our aim is to make use of documents that contain instances of issues, and automatically acquire required diagnostic knowledge. The paper is structured as follows. Section 2 looks at the objective of the paper and some existing methods for the same, followed by an initial study of documents to understand the representation of issues in text in Section 3. This is followed by exploration of two methods in Section 4 along with some examples. The conclusions of this exploration are presented in Section 5, followed by the future work in Section 6.

Current methods:

The objective of this paper is 'to explore and identify means for detecting an issue, wherever it is described in a text'. In engineering diagnosis, methods for identifying faults and issues, or their causes, have been studied for some time now. The following review looks into methods that have been proposed for identifying issues and causes rather than representing these, such as in the case of Ishikawa / Fishbone analysis.

One such method, often applied in industry, is the Root Cause Analysis Method. It is a four-step process [START_REF] Rooney | Root Cause Analysis For Beginners[END_REF]: collecting data, drawing up a causal chart, identifying the root cause, and finally, suggesting a resolution based on the root-cause.

Another means of dealing with issues is the Failure Modes and Effects Analysis (FMEA) [START_REF]Guidance for Performing Failure Mode and Effects Analysis with Performance Improvement Projects -QAPI[END_REF]. FMEA, by contrast, involves foreseeing the presence of various modes in which failure can occur. It is a managerial way of understanding a process, identifying possible failures in the process, and addressing some of them. Once again, it is a largely manual means of organizing people and documents, resulting in improved processes with reduced failures.

The method of Fault-Tree Analysis (FTA) is perhaps closer to what we aim to achieve. FTA is a formal deductive method of identifying causes of an issue [START_REF]Fault Tree Handbook with Aerospace Applications[END_REF]. Working backwards from the issue, a logical tree of the issue-cause relations is constructed. The final result is a probabilistic assessment of the likely causes and the chances of the issues occurring.

There has been previous work on modeling diagnostic knowledge in systems. Chandrasekaran et al [START_REF] Chandrasekaran | Deep'models and their relation to diagnosis[END_REF] propose two types of diagnosis. In particular they point to the knowledge required for one type, namely malfunction hypotheses, and the relations between observations and malfunction hypotheses.

Farley [START_REF]From free-text repair action messages to automated case generation[END_REF] acquired cases for a diagnostic system from free text repair action message by building a grammar based on the possible patterns in the text. However, in this case, the messages were not exactly in natural English, but rather in a coded form. Also, the aim here was to acquire cases, rather than knowledge pertaining to issues.

Since we deal with text sources, natural language processing techniques were also studied. One of them is Sentiment Analysis, where the objective is to find the overall sentiment of a piece of text by analyzing the individual sentiments of its words. For example, Taboada et al. [START_REF] Taboada | Lexicon-based methods for sentiment analysis[END_REF] calculate a semantic orientation based on sentiments of individual words, in combination with various modifying factors. Examples of such factors are valence shifters, such as intensifiers, downtoners and irrealis cases (Polanyi and Zaenen, in [START_REF] Taboada | Lexicon-based methods for sentiment analysis[END_REF]). A general English network of words (SentiWordNet) that indicates sentiment has also been in vogue for some time now [START_REF] Baccianella | SentiWordNet 3.0: An Enhanced Lexical Resource for Sentiment Analysis and Opinion Mining[END_REF]. This is a promising source of knowledge for realising whether a piece of text conveys a positive or negative sentiment. Commercial tools such as Lexalytics' Semantria are also quite effective [11].

Given the constraint of not having a very large data set, and the fact that our goal is to move beyond only identification of issues, many of the above methods present difficulties to the current task at hand. FMEA looks at functions of each system, then possible failures for each -it would not help us since we have to first detect failures and then associate them with a system. Root Cause Analysis does post-facto analysis, inferring the root cause after a detailed understanding of the causal factors involved, and is usually a manual task. Regarding FTA, it is not possible to identify the presence of the issue from a piece of text using this method. The functional representation and sentiment analysis methods are more promising from the perspective of this work. Both are explored in greater detail in the following sections.

Following a survey of existing methods, the need was felt to obtain a better understanding of means to identify issues in text documents. For this, it was necessary to understand how issues are represented in documents. In the next section we present a study on this.

Initial Study of Documents

The final goal of this work is to acquire diagnostic knowledge to a level at which it can be reused. To do so, the presence of issues and causes of issues in a text is important. The overall flow of the desired acquisition process is shown in detail in Figure 2. This schematic corresponds to the 'Knowledge Acquisition' step shown in Figure 1. In order to develop a means of acquiring diagnostic knowledge from documents, it was necessary to understand how such issues are recorded in text. For this, we surveyed a number of documents that are available in the public domain, and extracted portions that contain text where issues have been reported. A total of 20 such samples have been studied. These samples were from different domains, ranging from consumer complaints to bicycle maintenance, and riveting in aircraft assembly. Due to practical considerations such as corporate confidentiality, it is difficult to obtain documents from the industry. However, real examples, which are available in domains relevant to this work, have been carefully selected.

During the course of this study, the exact words (or phrases) which indicated the presence of an issue or an undesirable state were manually marked. Wherever it was found, the words (or phrases) that indicated one or more causes of the issue were also marked. The next step was to identify if any common pattern in the expression of issues and their causes in text emerges from such a study.

The following are some of the observations from the study: -There are some domain related key functions or parameters whose satisfaction or occurrence respectively (or negation) is seen as a problem e.g. "application has been declined" for a credit card domain; "brakes do NOT work properly" for a bicycle chain domain;

"rivet was too hard" for a riveting domain; "urinary chromium concentrations measured during BM-II were still higher than references from non-occupationally exposed populations" for a workplace safety domain; -There may be more than one issue expressed in a single sentence e.g. "chain will slip and skip"; "can cause slipping and may wear out drive train components" -There can be a linear chain of causes; it could be that one problem leads to another e.g. "rivet head cracked because rivet was too hard when driven" "front left wheel assembly suddenly collapsed" LEADS TO "the driver immediately lost all steering control" -Usually, a singular or small set of root causes is not found, unless a large number of cases are analyzed -The amount of background knowledge required to understand the occurrence of an issue is large. Unlike human understanding, it cannot be assumed that the proposed system will have enough knowledge to implicitly understand the issue. e.g. "known carcinogen to humans" -unless the word carcinogen carries a negative value of sentiment, it is unlikely to be understood that this is a problem (because cancer is eventually caused). -There are only three possible orders for causal reasoning -build up, or postanalysis or both. The causes of the issue may be explained by building up the final issue, the issue can be mentioned first followed by an explanation. And sometimes it can be a combination of the two. -It is not yet known if the set of root causes is already known or unknown -the condition is that they have to be identifiable with the system.

The potential use of these observations is that we can construct possible templates for detecting issues in text, and breaking down the text into necessary pieces.

Detecting issues in text

Following the analysis of documents containing issues, our next step was to identify what methods could be employed to identify these automatically, and whether they could be used as is, or needed to be modified for our purposes. Before carrying this out, it is worth explaining a particular detail of our work.

The need for diagnostic knowledge dictates the need to acquire knowledge of issues as well as knowledge about causes of issues. This implies a need to understand the contents of the document. This need, as well as the subsequent use of a logical form to do so using Discourse Analysis method has been shown in an earlier paper [START_REF] Madhusudanan | Segregating discourse segments from engineering documents for knowledge acquisition[END_REF]. Hence all such analyses of issues and causes in this work would be eventually performed on a logical form of text rather than the text itself. One example of a logical form (as given by the Discourse Representation Structure (DRS) tool Boxer [START_REF] Curran | Linguistically motivated large-scale NLP with C&C and Boxer[END_REF]) is as follows. For the sentence From the discussion regarding logical form presented above, we foresee two possible methods using which presence of issues in text could be identified in either text or its logical form. The first method is related to the use of functions as a means of identifying negative text segments. The second method can utilize the vast corpus of work from the domain of Sentiment Analysis.

Domain functions

As discussed in Section 3, the presence of a domain related function or parameter can be a clue for detecting an issue in text. The first of our proposed methods is to utilize this knowledge.

Functions of a product have been previously modeled using functional models [START_REF] Stone | Development of a functional basis for design[END_REF][START_REF] Chandrasekaran | Function in device representation[END_REF][START_REF] Chakrabarti | A Scheme for Functional Reasoning in Mechanical Conceptual Design[END_REF]. These may be useful for detecting undesired behaviour of a system by means of negation of the function or any of its parameters. Literature exists about representing such functions [START_REF] Stone | Development of a functional basis for design[END_REF], and building a basis of such functions for design [START_REF] Chandrasekaran | Function in device representation[END_REF]. Possible sources of function-related information include functional ontologies, such as Design Repository [START_REF]Design Repository[END_REF] and the SAPPhIRE-based ontology [START_REF] Srinivasan | Towards an ontology of engineering design using SAPPhIRE Model[END_REF].

Compiling such ontologies and resources is labour-intensive on its own. Hence, unless there are large-scale, domain relevant functional ontologies (that we are yet to access), it is not feasible to use this means to detect issues automatically in texts.

Utilizing Sentiment Analysis

As mentioned in Section 2, Sentiment Analysis is a useful domain for detecting the presence of issues. To test if this objective can indeed be satisfied, a number of tools were studied, alongside the test documents above. For the purposes of the study, the input given to the tools were samples from a document that contained riveting issues. The document was one that is available in the World Wide Web. The output of such tools is an assessment of the overall sentiment polarity of the document / sentence / phrase (whichever is supplied). The polarity may be positive or negative.

For example, Lexalytics' Sentiment Analysis tool [11] -Semantria -provides a comprehensive analysis of text, see Figure 3.

Fig. 3. Screen grab of Semantria's sentiment analysis

Similarly the performance of another openly available tool namely sentiment_classifier [START_REF]Sentiment Classification using WSD[END_REF] was also tested to see whether it can be used for our purposes. However, it was not trained separately and was used as-is, since the training exercise requires both domain resources and effort. Using sentiment_classifier, we manually tested the performance for our examples. Here, complete sentences were not used as input, since we wanted to compare the manual identification of text that indicates issues, with the tool's output. Parts of sentences that were found to indicate the presence of an issue were used as input. These were phrases, or combination of two or more phrases. Some example phrases and their semantic classifications are shown below. The inputs were the phrases that are in quotes and italicised.

"not properly lubricated.", "Bike squeaks when riding or pedaling", "rivet driven at a slant", were marked as neutral, "rivet head has some play" (also shown in Figure 4) "riveting tool damages metal" were marked positive.

"metal plates bulged because of poor fit.", "rivet head cracked because rivet was too hard when driven." "rivet could be too tight." were marked negative

The text "rivet driven at a slant" was also part of a larger text supplied to Semantria (Figure 3). In both cases, it was marked as neutral in sentiment. Similarly, for the text "body of rivet too short.", both Semantria and sentiment_classifier marked the text negatively.

Hence we see that some of the cases or parts thereof are covered correctly by such Sentiment Analysis tools. However, there are some domain-specific cases that do not give the desired output. For example, Semantria could not recognize the negativity in "one side of the rivet is flat". Similar is the observation with sentiment_classifier shown above in the cases where they were marked neutral or positive (e.g. "rivet head has some play"). The possible reasons could be, a) the absence of these words or phrases in the sentiment lexicon (or training data) with correct sentiment values ("rivet driven at a slant"), or b) semantic ambiguity (e.g. for "play" -unless there is an explicit recognition of which meaning of "play" is in use, and if it has a negative sentiment assigned).

From the above exercise, we can observe that current sentiment analysis tools are practical and usable. However, the accuracy is not high while trying to recognize domain-dependant texts. We have only yet tested this on small, but representative pieces of text. Though this still requires testing on a larger scale, sentiment analysis has a greater practical appeal compared to functional ontologies. The performance could be improved by training such classifiers on a domain corpus, or by enhancing the lexicon used (in this case, SentiWordNet). The first approach requires a large amount of training data, and considerable manual effort. The second approach is a current topic of research in literature, and has been indicated as being harder to build than general lexicons [START_REF] Kanayama | Fully automatic lexicon expansion for domain-oriented sentiment analysis[END_REF]. For example, one such method [START_REF] Kanayama | Fully automatic lexicon expansion for domain-oriented sentiment analysis[END_REF] starts with an available sentiment lexicon and expands it using unsupervised methods. To do so, clauses that indicate positive or negative sentiment called polar clauses are looked for. Thereon, clauses of the same sentiment in successive clauses are acquired. Another method constructs aspect-specific domain lexicon by using a general lexicon and an opinionated corpus, and treating the final formulation as a linear programming problem [START_REF] Lu | Automatic construction of a context-aware sentiment lexicon: an optimization approach[END_REF].

Nonetheless, Sentiment Analysis, for us, is a promising means of identifying issues from text, because of the available tools and resources for doing so despite the need for adapting these resources in a domain-relevant manner. Also, it is also suitable for the kind of natural language text that we work with.

Discussion and Conclusions

This paper has introduced the objective of the research -to acquire diagnostic knowledge, and identified the need for a method to detect locations in text where such knowledge may be present. Detection of issues has been explained as the starting point. Some example documents have been studied and some observations regarding issues have been made.

Based on the objective mentioned in Section 2 and observations drawn from documents about how issues are represented in text (See Section 3), two possible methods were explored -one based on functional descriptions of systems, and the other based on Sentiment analysis. The functional description based method has not been well explored. A disadvantage with this method seems to be the absence of large-scale resources such as function repositories and ontologies that can serve as a domain knowledge base. Hence, until the time such knowledge becomes openly available, it is practically difficult to exploit.

The second method, Sentiment Analysis, is found to be more practical. A good number of tools and resources exist for using this set of methods. The method was tested with some domain examples, and performed reasonably well. However caution has to be exercised in cases in which the language is highly domain-specific. Since some part of the issue-based knowledge is dependent on the context, a good amount of background knowledge in the form of domain related resources is necessary. This, in combination with sentiment lexicons, provided good background for our work. Additionally, some specific needs have to be met. The method should be able to also handle phrases. Moreover, identification of an issue is a starting point. Sentiment analysis will only tell us the polarity of the current text. The next step in understanding the issue would be identification of the cause(s) of the issue.

6

Future Work

We have discussed two possible methods to identify issues in a domain. This is one of several steps towards the eventual goal of constructing a diagnostic knowledge base.

At this point, Sentiment Analysis seems promising for identifying an undesirable state from a logical form. The next step is to implement and test the method. The implementation will require us to find a suitable means of extending the sentiment lexicon in a domain-specific manner, within our constraints. Some previous work about extending lexicons has been reported in Section 4.2.

To complete the reconstruction of an issue from text, it is necessary to realize the occurrence and causal relations using the logical form. Hence the current framework has to be extended for linking causes of the issue to the issue itself. It then has to be implemented and its performance tested.

Fig. 2 .

 2 Fig. 2. Procedure to construct diagnostic knowledge.

"

  An aircraft has many parts." x1, x2, x3 are the discourse entities, and the corresponding logical form is patient(x1,x2), agent(x1,x3),have(x1), parts.(x2), quantity(x2), aircraft(x3); (The patient and agent predicates are thematic roles for the entities (parts, aircraft) as expressed in the logical form. The 'patient' thematic role indicates something being affected, in this case, having parts. The other predicates indicate which variables are what, such as x3 being aircraft.)

Fig. 4 .

 4 Fig. 4. Example of using sentiment_classifier 0.6.

Acknowledgments. Acknowledgements are due to the authors and developers of the open tools that have been used in this work namely Boxer and C&C Tools, and Kathuria Pulkit for Sentiment-Classifier.


Michele Germani
email: m.germani@univpm.it@[f.gregori

Fabio Gregori

Andrea Luzi

Marco Mengarelli
email: m.mengarelli]@pm.univpm.it

Assessing social sustainability of products: an improved S-LCA method

Keywords: social life cycle assessment, S-LCA, sustainability

To preserve proper growth of the planet, industries have to increase sustainability of produced good according to the compliance and governance regulations for NPD (new product development). Sustainability concerns economical, environmental and social aspects; among these issues, the last theme is the less argued in literature and this paper focuses on the social life cycle assessment of products. One of the crucial aspects of S-LCA, is the definition of impact categories and involved stakeholders. This work, proposes a new S-LCA methodology, according to UNEP/SETAC framework. After the clarification of stakeholders, categories and general notions already known on S-LCA, a test case is shown where the new approach is implemented. In this use case, stakeholders from an Italian product line are analysed, then categories of attribution of social impacts are outlined. The paper offers a step-by-step procedure useful to verify the S-LCA theories currently available on a practical industrial case, defining also weaknesses that might be addressed in future studies.

Introduction

Nowadays one of the main challenges for product developers and designers is to meet sustainability standards. Beyond quality standards, products must fit with rigid rules to guarantee society's growth. This overall improvement passes through the sustainable development that found its basis on the following three pillars: People, Planet and Profit/Prosperity. A "sustainable" product should have low environmental impacts, with low costs and light human effects, in its entire lifecycle. Social life cycle impacts, that represent the social effect on people of a product or a service along its life cycle, are the less argued in literature with only a few articles that points out lacks and opportunities of this methodology.

To improve the knowledge about social issues estimations, in this paper, a revised social life cycle assessment (S-LCA) method is proposed, compliant with the UNEP/SETAC guidelines; its efficiency is tested on a real case study. The method developed offers a new way to acquire data for the inventory, redefining also some area of interests from UNEP's framework. After a clear definition of the goal of the analysis, the stakeholders involved into the lifecycle of the product are identified. The primary data are acquired through surveys, and the results are then put together, defining social impact indexes. Indexes are a crucial part for the S-LCA, in fact, a clear identification of standard indexes has not yet been discovered in literature; this methodology has been applied in a kitchen sink value chain to underline the importance of indexes that fits various social aspects that should be globally accepted. This paper is a part of an extended study about the proposed S-LCA methodology. In this work, the whole method is proposed, the inventory phase of the analysis and the relative use case application is explained.

SETAC/UNEP Methodology Overview

It is well known that the sustainable development should meet needs of the present situation without compromising the ability of future generations to meet their own [START_REF]Our Common Future[END_REF]. In this context it is important to take into account social issues that can be evaluated through a social life cycle assessment. As mentioned on UNEP/SETAC guidelines [START_REF] Andrews | Guidelines for social life cycle assessment of products[END_REF], a social and socio-economic Life Cycle Assessment (S-LCA) is a social impact (and potential impact) assessment technique that aims to assess the social and socioeconomic aspects of products and their potential impacts along their life cycle, from the extraction of raw materials, up to the product final disposal.

The work of Andrews et al. [START_REF] Andrews | Guidelines for social life cycle assessment of products[END_REF] provides the main recognized guidelines to conduct a socio-economic assessment; it provides a map, for stakeholders engaging in the assessment of social and socio-economic impacts of products life cycle. Moreover, the Methodological sheets for sub-categories in S-LCA [START_REF]United Nations Environment Programme and SETAC, the methodological sheets for subcategories in social life cycle assessment (S-LCA)[END_REF] provides a tool to conduct S-LCA studies giving detailed information for each subcategories introduced in the Guidelines, organized by stakeholder category. The methodology here proposed is rooted on the UNEP framework [START_REF] Andrews | Guidelines for social life cycle assessment of products[END_REF] and it is also inspired by the S-LCA methodological sheet [START_REF]United Nations Environment Programme and SETAC, the methodological sheets for subcategories in social life cycle assessment (S-LCA)[END_REF].

There are a few studies based on the S-LCA guidelines, and in some cases inadequacies in terms of inventory and interpretation of data are pointed-out. Franze and Ciroth [START_REF] Franze | A comparison of cut roses from Ecuador and the Netherlands[END_REF] provided a S-LCA study to compare the impacts related to the roses cutting between Ecuador and Netherlands. Results have been presented in a simple and intuitive manner, but a scale that measures the impacts has not been clearly identified. Differences in cultural heritage and conditions between the two countries affects the study, and the measurable gap between these two scenarios is not clearly defined. In 2013 Foolman and Ramjeeawon [START_REF] Foolmaun | Comparative life cycle assessment and social life cycle assessment of used polyethylene terephthalate (PET) bottles in Mauritius[END_REF] proposed a new method to aggregate and analyse the social inventory data; the categories and the sub-categories according to the UNEP-SETAC guidelines to perform a study on used PET bottles have been identified.

In literature there are few works that do not take into account the previous guidelines. One example is the work of Labuschagne and Brent [START_REF] Labuschagne | Social Indicators for Sustainable Project and Technology Life Cycle Management in the Process Industry[END_REF], which shows a framework where social sustainability criteria are introduced for the South African process industry, and a social impact indicator calculation has been presented. Calculations are based principally on statistics, therefore social footprint information is not available for all the considered categories. Umair et al. [START_REF] Umair | Social impact assessment of informal recycling of electronic ICT waste in Pakistan using UNEP SETAC guidelines[END_REF], provided a study to investigate the social impacts of informal e-waste recycling in Pakistan. The goal of the study is to provide input for improved decision making tools by collecting primary data on processes and practices using the UNEP framework for S-LCA, but the work is strictly limited to the application of the UNEP guidelines. Instead, Macombe et al. [START_REF] Macombe | Social life cycle assessment of biodiesel production at three levels: A literature review and development needs[END_REF], presented a work with the aim to analyse possibilities and development needed in a complementary approach. This approach represents the evaluation of social impacts in LCA by reviewing the general context, taking a closer look at the empirical case of three different raw materials: palm oil, forest biomass, and algae, within biodiesel production. This study has been conducted considering three different levels: company, region, state. At the same time, Martínez-Blanco et al. [START_REF] Martínez-Blanco | Application challenges for the social Life Cycle Assessment of fertilizers within life cycle sustainability assessment[END_REF], proposed a study in which S-LCA has been conducted to integrate social aspects into the environmental and economic assessments of fertilizers, comparing three different alternatives: compost, nitric acid and potassium nitrate.

Social aspects must consider as a fundamental task the stakeholders involvement. In this view, Souza et al. [START_REF] Souza | Definition of sustainability impact categories based on stakeholder perspectives[END_REF], proposed a study in which a methodology is developed and applied for stakeholder consultation regarding the selection of LCSA (Life Cycle Sustainability Assessment) impact categories, focusing on social and economic issues.

UNEP guideline highlights areas where further research is needed; the proposed work is also settled in this context. The stakeholders and related categories proposed by the guidelines are not easily evaluable in many assessment contexts. One example is the impossibility to consider the category "indigenous rights", for a EU context, because there is not indigenous presence in the European continent. The main issues identified on those documents concerns data inventory and their interpretation, which consist in the main challenge of the present work.

Here an improved method for a better data collection is provided, in order to collect in a more efficient way S-LCA inventory data. This method has been applied in a socio-economic assessment of kitchen sink production; this is also the first study that considers Italian companies and laws, for a S-LCA analysis.

Within the ISO 26000:2010 [11], stakeholders are defined as organizations or individuals that have one or more interests in each decision or activity of an organization. Moreover, stakeholders can be divided in clusters of people that have similar interests within the product systems boundaries. In a life cycle approach those categories are related to every product phase "from cradle to grave". UNEP identifies 5 different stakeholder categories:

1. Workers 2. Local community 3. Society 4. Consumers 5. Value chain actors Those categories are deemed to be the main group categories potentially influenced by the life cycle of a product in terms of socio-economic impacts. In the present work these categories are considered, but they are analysed with more accuracy thanks to the new proposed method.

Through this work, an improved method to perform social life cycle assessment analysis is proposed. This method fills the gap of the previous literature works that follow step by step the UNEP/SETAC procedure.

The strength of this new method is related to the development of an improved analysis in the inventory phase to obtain more effective results. In fact, a custom data identification procedure is driven by the specific analysis goal, and the procedure opens to a more thoughtful analysis. In other words, decisions that influence the analysis itself can be undertaken, pointing out certain social related topics. Moreover this procedure encourages the acquisition of specific data for each real use case. The S-LCA method proposed in this paper is outlined in Fig. 1. The figure shows the conceptual flow of the new S-LCA procedure developed. Goal definition. This is the starting phase of the method, where many analysis features are defined. In this first stage the subject of the social analysis, also called the goal, is decided. The goal is what the analysis wants to assess. Through the proposed method it is possible to assess social consequences of different scenarios; analyses can be focused on different levels, such as product development, specific processes, and overall company assessment. Depending on the analysis, the goal will be different and it will include different considerations. It is important to clearly define the goal in order to make effective decisions into the inventory analysis. In this phase the system boundaries are also defined.

Inventory.

Once the goal of the social analysis has been well defined, it is necessary to fulfil the data acquisition process. In this phase surveys are prepared. Surveys are modelled in relation to the identified areas of interest (AoI) and in relation to the selected stakeholders. The chosen areas of interest are the main drivers of the surveys definition phase. Different actors, depending on the stakeholder category, will then fill the surveys. This phase is one of the strengths of the proposed method, compared the methods already known in literature. As previously mentioned, in fact, some social data are often lost without the usage of a structured acquisition phase. The inventory proposed within this new method, covers the lacks of previous works, narrowing effectively the social issues related to the analysis. The definition of specific social Area of interests supports the data collection.

2.a Stakeholders selection. This is the first step for the inventory phase. Here, depending on the goal of the analysis, the stakeholders are defined. According to the S-LCA guidelines, stakeholders are intended as any group of person that plays a certain role contextually to the goal of the analysis. Stakeholders Categories here defined are the same proposed by UNEP/SETAC [START_REF] Andrews | Guidelines for social life cycle assessment of products[END_REF]. In this method, two main categories of stakeholders are considered: active and passive actors. Active actors are those clusters of people that act directly on the product/process analysed through the social assessment. Those people, through their action can influence social effects. Passive stakeholders are those that are socially affected by the value chain without directly interact with it (e.g. the local community members). Depending on the system boundaries previously defined, it is possible to make the stakeholder selection cut-off.

2.b Area of interest definition. An area of interest is here defined as a topic that is related to socio-economic consequence of a project, measurable through the analysis.

In relation to the goal some different area of interests are defined. For each stakeholder multiple area of interest could exists, therefore it is important to define correctly those areas to enquire the right questions to stakeholders. In the case study section different areas of interest are shown, some of these are generic, while other are customised for the present case study. AoI are identified in order to clearly define which clusters of data are needed for the social assessment. AoI are topics that will be examined in deep through surveys: each area of interest embeds many features that will be identified through specific questions.

2.c Surveys definition. The main tools used to acquire data for the analysis are the surveys. They are prepared according to the areas of interest and stakeholders previously selected. Each area of interest is depicted through 2-3 questions; each question highlights a feature related to that AoI. The AoI will be hidden into surveys: only data evaluators will know which AoI questions are referred to. Surveys are different for each group of stakeholders; customized questions are proposed through an online procedure to all identified stakeholders. Surveys follow strict rules: each survey is structured in a maximum of 20 questions with the aim to fill out the survey in a simple and quick manner. There are two types of questions: qualitative and quantitative. Qualitative questions are open-ended ore multiple-choice questions, quantitative ones are characterized by a scale based choice (mainly 0-5 points) or an exact number to insert (e.g. number of employers). Each survey contains more multiple-choice questions than the open-ended ones to facilitate the data interpretation phase.

2.d Stakeholders filling.

Once stakeholders receive surveys, they are entitled to fill up the survey. Answers will be anonymous, to guarantee privacy, obtaining objective responses not influenced by external pressures.

3. Data aggregation/interpretation. This phase aims to aggregate and interpret surveys results. A neutral S-LCA evaluator acts in this phase. It is important that the identified figure for this task does not belong in any way to the considered value chain, otherwise conflicts of interest occur. The main output coming from this phase is the creation of mid-point social indexes. The hard work of this phase is related to the aggregation of quantitative and qualitative answers. Mathematical algorithms will be implemented to aggregate surveys results; from the clustering of the results will emerge groups of answers having similar impacts for different stakeholders. The interpretation of those clustered answers aims to define mid-point indexes. Different answers for different stakeholders could then flow in the same mid-point index. In fact, each mid-point index is a measurable value that contains similar impacts coming from different area of interests.

4. Impact indexes definition. The final results of the analysis are presented and discussed. End-point indexes are made of different mid-points that are aggregated, to obtain a final value to measure the socio-economic impacts. As reported in the ISO 14040:2006 [START_REF]Environmental Management: Life Cycle Assessment, Principle and Guidelines[END_REF], an impact category is defined as a "class representing environmental issues of concern to which life cycle inventory analysis results may be assigned". This definition can be extended also to the social issue reported in the presented work.

Case study

Green Sinks, "Realization of green composite sinks substituting organic and mineral primary materials by recovered waste" is a project funded by the Life+ Programme of the European Commission. The product analysed, is made of acrylic composite materials, composed by methyl methacrylate (MMA), poly-methyl methacrylate (PMMA), and a mineral filler (quartz or cristobalite). The aim of the project is to experiment and demonstrate feasibility of 100% substitution of primary resources by treated waste and recycling of 80% of scraps and refuses produced by the process. This will allow to future market introduction of the first Green Sinks in the world. Recovered MMA, PMMA and mineral filler will permit the preservation of landscapes and primary resources use, recycling a large variety of pre-and postconsumer waste (PMMA, glass, quartz from stone industries). Life Cycle Assessment (LCA) methodology has been used both to evaluate the environmental benefits achievable, and to complete the evaluation of the overall sustainability. For this purpose an S-LCA analysis has been prepared, and this methodological framework has been reported in the paper. Now, the previously presented method is applied; and in particular in this paper is shown the procedure till the step of the areas of interest definition.

Goal definition

The goal of this S-LCA analysis, according to the project, is to identify and compare the socio-economic effects between the production of a green kitchen sink, and the production of a kitchen sink made by virgin raw materials. The considered system boundaries refer to the whole value chain of the production of the kitchen sink, "from cradle to grave".

Stakeholders selection

Here starts the inventory of this analysis. In this phase the actors that in a passive or active manner are related to the goal of the analysis are identified. The active stakeholders involved are suppliers, sink producer, costumers; the local community and society are the passive ones. In the following section the chosen stakeholders are detailed; Area of interests are shown only for Workers, Customers and Local community.

Stakeholders (Workers).

For this topic two types of questionnaires are presented with the aim to evaluate the social impact related to the manufacturing of the kitchen sink. Workers are here classified into two categories: people who work in the production area and people who work in the technical/management area. The questionnaires have the purpose to define the working condition, highlighting the workers satisfaction, health, safety, and other related social topics. For this stakeholder category the following areas of interest are identified.

Professional training: considering the continuous request of qualified work, the people training within the industry plays an important role in the growth of employees skills. In this view it is important to evaluate how many training hours are spent to also improve the employers' knowledge of standards and best practices about sustainable manufacturing.

Professional growth: the personal satisfaction is also related to this area, during the working period in the industry. The professional training done and the possibility to improve personal skills, making different activities can improve this topic.

Economic satisfaction: the salary is one of the most important aspects related to the worker satisfaction: the wage permits the worker to realise itself outside the industry, supporting himself and (eventually) his family. The trade-off among salary, the type of work and the number of work hours is a fundamental issue.

Social inequality: different treatment of the workers can bring to social inequality. Discriminations regarding sex, nationality, religion, are important themes debated over the last decades. In this area also the possibility for the worker to freely aggregate and set up associations, etc., can be included. In many cases the social inequality is related to different trade union representatives.

Risks at work: The aim of this area is to evaluate the risk level along the manufacturing activities. Risk is related to the environment in which the people work and the worker's behaviour. The number of accidents and injuries quantifies the risk level.

Diseases related to work: A crucial aspect is the birth of occupational illnesses. In fact, risks can be related also to operations that bring to diseases such as carpal tunnel syndrome, respiratory diseases, sight reduction etc.

Employment scenario: Social sustainability is also related to the stability of the workplace. The growth or the decrease of the workers employed can be measured by absolute values (trend of employment during 5 years for example) and relative values that represent rate of layoffs and hiring; the number of employees could remain the same but there is a continuous change of the people.

Work opportunities for the local community:

To guarantee a proper social growth the local community must be involved. In this case the number of employees who live in the local area represents the local involvement.

Stakeholders (Value chain actors

). All the actors not explicitly mentioned in other stakeholder categories, belongs to this one. In the case of a wide supply chain is important not to extend the complexity of questions related to this category because some data could be lose or wrongly estimated while acquiring information. Here, for this case study, are mainly involved suppliers of the sink producer.

Stakeholders (Society).

For this topic, no questionnaire would be submitted . However, an internal analysis would be carried out in order to assess the impact of the kitchen sink to the society. The society it is a very large group, therefore, it is necessary to clearly states the boundaries considered. In this study the society is intended as all the humans beings that are directly or indirectly involved along the kitchen sink life cycle

Stakeholders (Customers).

Once the sink has been realised, it is sold to a kitchens' manufacturer. This company is identified as the first customer of the sink manufacturer and in this study it represents the customer.

Three different areas of the company structure have been identified according to the functional organization model structure of a company: Purchasing, Manufacturing and Sales departments. These three company sectors have been chosen as they all interact with the kitchen sink from different point of views. Three different questionnaires have been here realized. For each subcategory one or more questions are proposed. The final customer, the one who buys the kitchen, is not included in this area of interest.

Health: The product traded should not influence in any case the state of health of the workers that are directly or indirectly related to it. Kitchen sinks are included within the ISO 19712-1:2008 [START_REF]Plastics Decorative solid surfacing materials -Part 1: Classification and specifications, International Organization for Standardization[END_REF], which establishes a classification system for solid surfacing materials according to their performance. The model under analysis is supposed to be compliant to the present regulation. There should be a sign "label" that confirms the approved status of the product.

Safety: The product should not be harmful for any operator that directly or indirectly operates with it. In this context, the list of possible interactions with the sink should not exclude the final customer (the one who buy the kitchen), even though he is out of the system boundaries. Potential danger should not be allowed. When this is not possible due to the nature of the product itself, it should be clearly stated which parts, or actions could procure safety issue.

Feedback:

The customer should be able to express a feedback on every purchase. The customer should not be scared to externalize his opinion. The seller should also give instruments and possibilities to do so. For example, the seller could ask for feedback on a traded product by using questionnaires and it should make the buyer comfortable in expressing his opinion.

Privacy: In order to foster a successful partnership and to ease the interaction between seller and customer, a certain amount of information must be shared. Nevertheless, privacy should always be respected. Meaning that the customer should always feel "safe" in sharing information with his seller. As an example, the kitchen sink manufacturer should provide a form that describes how sensible data from customer are treated.

Transparency: The customer is able to evaluate the performance of the product in a strict correlation to the social responsibility perspective. The seller/producer is able to show in a transparent way his attitude respect to sustainability issues. The customer on his side, is aware of the position undertaken by the producer/seller and thanks to that is able to think about political choices undertaken by the producer. For instance, the seller could show to his customers which sustainability actions or strategies have been undertaken in order to realise the kitchen sink.

Responsibility shares concerning after-use treatments: The producer sends production scraps for further treatment in developing countries. These wastes might be seen as potential danger for poor people that look for high value things into landfill plants. The producer should follow regulations concerning end of life management. As an example, the kitchen sink manufacturer could provide documents concerning the disposal of the product.

Imagine perceived from the final customer: The product contributes to define a positive image or a solid attitude towards sustainability issues. By acquiring/using the product, the customer itself is perceived from the outside, as someone which is aware of the importance of environmental issues.

Knowledge and awareness: The customer gains knowledge and expertise in the field of sustainable products. Its involvement in the "green" economy is enhanced by the trade of the kitchen sink or in general by the collaboration with the producer/seller. For instance, knowledge and expertise gained by the kitchen sink manufacturer could be shared as an aspiring action toward the involvement of "green" reaction within the kitchen assembler.

Post purchase engagement: The relation between the two parties is kept "alive" also when the transaction is over. The liaison is not merely constricted in the pure economic ambient but it goes beyond. The relationship level is deep and therefore involves both parties in a solid way, fostering future collaborations and initiatives. As an example, from the trade of the "green" kitchen sink, a collaboration in financed projects for sustainability could be proposed from both sides.

The table 1 represents an extract of the survey for this stakeholder category, according to AoI mentioned. In the table, the first column is presented to favour a proper link between AoI and questions, but it will be hidden when the survey will be proposed to end-users. The customer is free to express his own opinion without being afraid of the consequences?

Questionnaire for customers

Open answer

Privacy

The customer has no worries concerning the information shared with the producer/seller.

Yes No

The producer/seller safely keeps information shared by the customer.

Open answer

Transparency

The product comes with indexes that shows the contribution to social responsibility.

Yes No

The customer is informed concerning the attitude of the producer/seller toward sustainability.

Yes No

If yes, how much effort has the producer put in order to share this information? Open answer

The customer has been informed on the actual benefit of realizing a "green" kitchen sink.

Yes No Table 1. An extract of the survey proposed for the Stakeholder customers

Stakeholders (Local community)

. By previous remarks, for this case study, in this category are involved all of the people living nearby the area of the sink production plant (Plados, MC -Italy). Actors with different roles are identified within the community: People (anyone who lives within 8 km to the production plant), School institutions, Municipal Council. This differentiation is driven by the need to understand effectively social effects on the community from different points of view. Different surveys are developed to better understand the following area of interests.

Cultural growth: the contribution a company gives to the local heritage is defined by the efforts made to involve students and people for a value sharing. It is important for a company to establish collaboration with institution and schools in terms of students' internship or economical contribution to local cultural activity; this topic passes also by the amount of local assumptions the company board performs.

Smart Thinking: this area concerns the sharing culture the company embeds. The company often wastes a large amount of available energy or resources without sharing them with the local community. Resources include spaces, multipurpose rooms, gardens and tools; sharing these resources facilitates to build a smart community.

Social Sensitivity: here the focus is on the attention the company owns about weak categories, local associations and event of aggregation. Only an external point of view could attest the level the company has on this topic.

Communication: the ability of the company to interact with the community is here highlighted. The local community knowledge about the company means a leading role within the territory. Positivity or negativity emerged by the community, will affect the credibility and the image of the company itself.

Attractiveness: the interesting of people in being involved in the mission of the company means an industrial relevance on the territory.

Direct pollution: it is important to take into account this area of interest; healthiness of local territory has to be guaranteed by the company. A survey proposed to people living nearby the factory will demonstrate weather or not the company behaviour affects the integrity of the environment.

Conclusions and Future Work

This paper proposed a method to perform a social life cycle assessment with a "stakeholder approach". The new method is here applied for a case study on an Italian product: only a part of the method has been applied so far, but the whole method is depicted in this paper. The new method is based on the S-LCA worldwide guidelines; a more structured approach is here developed, taking into account mainly the inventory phase, overcoming weaknesses of guidelines into the inventory phase. The method here proposed is based on 4 different phases: following those steps an effective social assessment could be accomplished. This paper focuses on the second phase of the method, the inventory. The approach, for the inventory phase, suggests the definition of many reasonable areas of interests that could lead effectively the data acquisition phase. The study of the AoI has been performed according to the present analysis. The specifications of the involved territories are also taken into account to develop areas of interest that perfectly describe the social issues related to the analysis goal. AoI are settled to pursue the goal of the analysis into relative defined system boundaries. Within those areas, different questions for each stakeholder are developed in order to acquire some detailed information for a better understanding of social impacts. Many AoI designed in this work could be implemented in different case studies for future assessments. The generic AoI presented, thanks to similarities with the present case study (territories, industrial area, etc.), will be replicable for every S-LCA assessment, following the presented method. The described areas of interest could be also extended, identifying further significant AoI for future depth assessments. The use of the proposed method, according to the analysis goal, lead to a deeper understanding of the social sustainability. Through this method, social criticalities are clearly identified thanks to a detailed list of the areas of interest. In fact, the definition of the AoI lead to a better data acquisition during the inventory phase. The final results of the presented case study will be shown in future papers, when the surveys definition and their interpretation will be reported. Interpretation algorithm will be implemented to obtain measurable results. Through the analysis results, decision makers will be able to take effective choices in order to increase the social sustainability indexes within the boundaries of the analysis. Finally, this work is a part of a long-term research aiming at increasing the knowledge on the S-LCA topic, with the purpose to develop an efficient and unified social life cycle assessment method. This proposed method could represent a starting point for a software architecture aimed to perform socio-economic analysis.

Fig. 1 .

 1 Fig. 1. New S-LCA method structure The aforementioned method is based on 4 main phases: 1. Goal definition 2. Inventory a. Stakeholders selection b. Areas of interest definition c. Surveys definition d. Stakeholders filling 3. Data aggregation/interpretation 4. Impact indexes definition 1.Goal definition. This is the starting phase of the method, where many analysis features are defined. In this first stage the subject of the social analysis, also called the goal, is decided. The goal is what the analysis wants to assess. Through the proposed method it is possible to assess social consequences of different scenarios; analyses can be focused on different levels, such as product development, specific processes, and overall company assessment. Depending on the analysis, the goal will be different and it will include different considerations. It is important to clearly define the goal in order to make effective decisions into the inventory analysis. In this phase the system boundaries are also defined.

Acknowledgments. The work has been possible thanks to the collaboration with Delta srl, within the EU project co-financed by LIFE+ Programme.