Teresa Taurino
email: teresa.taurino@polito.it

Agostino Villa
email: agostino.villa@polito.it

Multi-Agent Systems for Production Management in Collaborative Manufacturing

Keywords: Collaborative Manufacturing, Multi-Agent systems, Game Theory

The paper aims to analyze multi-agent management structures in Small-Mid Enterprises (SME), such to investigate how collaborations among agents could be improved. A SME manager has generally to face with three issues: data management, organization of the production phases, interactions with mid-level management (denoted by "agents"). Data management is necessary in order to have a clear representation of the many types of data about products, orders and resources of the company. The organization of production phases consists on the management of the production process phases from design up to recycling. Based on a clear representation of the product life cycle, the manager will be able to organize the interactions between these agents (each one dedicated to handle a process phase) so as to facilitate the collaboration, and to make effective each mid-level management as far as it is concerned with controlling the operations to be carried out in each process phase. A model of the mid-layer multi-agent management negotiation, according to the "game theory" viewpoint, is proposed, and its main characters are analyzed, in view of its application.

Introduction

In today global markets, factory managers are under pressure to optimize technological processes and to reduce production costs. Adequate tools and sufficiently robust procedures do not support Small and Medium Enterprises (SME) in the organization of work and in the management of the product life-cycle phases. On the other hand, a number of formal approaches have been developed for solving individual processing/managing problems in manufacturing, but their utilization in SMEs find obstacles in the insufficient knowledge of managers and enterprise owners [START_REF]Annual Report[END_REF][START_REF]A Road Map to the Development of European SME Networks -Towards Collaborative Innovation[END_REF].

A SME manager generally face with three issues: how to have a clear representation of data concerning products, orders and resources; how to organize the various phases of the production process, from design up to recycling; how to interact with employees to whom management and control duties have been assigned.

These problems have a common point: use a clear representation of the product life cycle, highlighting the sequence of process steps [START_REF]amePLM -Advanced Platform for Manufacturing Engineering and Product Lifecycle Management[END_REF]. Using this representation, the manager will be able to reform / re-engineer the management structure of his / her company based on the following logic: a) To organize the stages of the product's life cycle, divide them into management duties, and assign each stage, as management duty, to a mid-level manager of the company with precise responsibilities and authority; b) To organize the interactions between these mid-level managers (one dedicated to handle a phase) so as to facilitate the collaboration between the mid-level managers themselves and therefore make them more profitable for the company; this organization aims to optimize the "enterprise integration", i.e. cooperation within the enterprise; c) To make effective the management of each mid-level manager as far as it is concerned with controlling the operations to be carried out at each stage; this corresponds -with reference to the "production" phase, for example -at shopfloor management, that is, the collaboration between manufacturing resources within a shop-floor.

The two points b) and c) generally represent the greatest difficulty for a top manager / owner. Instead, the organization of the product life cycle phases and the operations to implement them is a simpler task for top management of SMEs since, often, the top manager is also the founder of the enterprise and the "initial designer" of the first product that the company manufactures.

This work is therefore devoted to the analysis of an organizational structure designed to support the manager in solving the problems mentioned in b) and c).

Some Hints on the Multi-agent Structure of a Manufacturing SME

The multi-agent modeling approach of a manufacturing mid enterprise is here discussed with reference to the main results of the EU project ame-PLM -Advanced Platform for Manufacturing Engineering and Product Lifecycle Management [START_REF]amePLM -Advanced Platform for Manufacturing Engineering and Product Lifecycle Management[END_REF].

As above mentioned, product and production engineering in mid industrial companies are typically fragmented across different functional units, dedicated to the complementary phases of a product life cycle. A typical scheme of these phases is in Figure 1, with reference to a generic product and to its main life cycle phases. With reference to Figure 1, conceptual design and preliminary design activities are typical of the SME manager / owner, while other activities involve several mid-level managers, as illustrated in the following figures referred to the "detailed design" phase (Figure 2), the "production" phase (Figure 3), and the "utilization" phase (Figure 4). The group of mid-level managers that can be recognized in Figures 234, are embedded into a network of interactions, each one corresponding to a negotiation. In fact, every mid-level manager has the duty of achieving two goals: (i) to optimize the efficiency of the plant / service / shop-floor managed by himself; (ii) to give the maximum possible contribution to the enterprise profit.

The first goal could be individually obtained by any mid-level manager (for brevity denoted in the following "agent"), irrespective of any interaction with the others, but the second goal is of prevailing importance. That generates a two-layer approach in order to manage the enterprise activities: (1) a higher-layer interactions among agents, i.e. negotiations with the aim of obtaining the strongest possible coordination of tasks and efforts; (2) a lower-layer individual optimization of the operations required at each phase.

With reference to the higher-layer negotiation, this has to be based on the network of interactions linking agents, as well as on the presence of a "moderator / broker", who will be the owner / top manager. The presence of the "moderator" ensures that negotiation is a converging process towards a common goal [START_REF] Dresher | The mathematics of games of strategy -Theory and applications[END_REF]. The kind of agents' network can favor the negotiation process, busting conflicts between agents wanting to maximize their own goals, as in case of the managers in a SME network [START_REF]Managing Cooperation in Supply Network Structures and Small or Medium-sized Enterprises: Main criteria and tools for managers[END_REF][START_REF] Villa | Supporting collaboration in European Industrial Districts -The CODESNET approach[END_REF][START_REF] Michaelides | Collaboration networks and collaboration tools: a match for SMEs?[END_REF].

An example of agents' network, that can be derived from Figure 234, is illustrated in the following Figure 5:  two partial negotiations, one between the "plant manager " in Fig. 3 and the "project manager" in Fig. 2, and the other between the "plant manager" and the "program manager" in Fig. 4;  one loop transferring requests for improvements from the "program manager" towards the "project manager". Therefore, it is now necessary to discuss a formal model of the multi-agents negotiations, oriented towards the evaluation about when an active collaboration among agents could be activated. modeled by the game theory [START_REF] Dresher | The mathematics of games of strategy -Theory and applications[END_REF], according to the idea that any agent (in practice, a mid-layer manager) wants to be competitive but must assure his/her best contribution to the enterprise profit.

Multi-agent Collaboration Model based on Game Theory

Then, the requirement from any agent of the network is to understand the payoff of the part of production system managed by himself. In order to evaluate this payoff, it is necessary to adopt a network model based on "cooperative game theory" that shows the different way of "players" (here, the agents) to interact together and cooperate.

A cooperative game (𝑁, 𝑣)is constituted by two elements [16]:  the finite set of players 𝑁 = {1, 2, … , 𝑛}, i.e. the enterprises that compose the network;  the characteristic function v, that associates to each subset 𝑆 ∁ 𝑁 a number 𝑣(𝑆) that represents the value created by each subset of players. 𝑣(𝑁)is the total value created, i.e. the value created when all the players in N cooperate together.

Given the set 𝑁 and a specific player 𝑖 ∈ 𝑁, the marginal contribution 𝑴𝑪 𝒊 of player i is:

𝑣(𝑁) -𝑣(𝑁\{𝑖}) = 𝑀𝐶 𝑖 (1)
The marginal contribution of player i is the amount that the total value would lost if the payer do not belong to the network.

Given a cooperative game (𝑁, 𝑣), an allocation 𝒙 is a sequence of numbers (𝑥 1 , … , 𝑥 𝑛 ) where 𝑥 𝑖 is the value received from the i-th player.

An allocation is individually rational if 𝑥 𝑖 ≥ 𝑣{𝑖} ∀ 𝑖 = 1, … , 𝑛.

An allocation is efficient if

∑ 𝑥 𝑖 𝑛 𝑖=1 = 𝑣(𝑁) (2)
An allocation (𝑥 1 , … , 𝑥 𝑛 ) satisfies the principle of the marginal contribution if

𝑥 𝑖 ≤ 𝑀𝐶 𝑖 ∀ 𝑖 (3)
An allocation is in the Kernel of the game if it is efficient and so that

∀ 𝑆 ∈ 𝑁: 𝑥(𝑆) ≥ 𝑣(𝑆) (4)
An efficient allocation is in the Kernel if and only if

𝑥(𝑆) ≤ 𝑀𝐶 𝑆 (5)
From these definitions and considerations, we can demonstrate the following theorem.

THEOREM

The global value generated from the cooperation of all players in set N is greater or equal than the sum of the values generated from subsets of N:

𝑣(𝑁) ≥ 𝑣(𝑆) + 𝑣(𝑁\𝑆) (6)
Dim. Let us consider 𝑥(𝑆) an allocation of subset S that is in the Kernel and it is individually rational: 𝑣(𝑆) ≤ 𝑥(𝑆) ≤ 𝑀𝐶 𝑆 ⇒ 𝑣(𝑆) ≤ 𝑀𝐶 𝑆 [START_REF] Michaelides | Collaboration networks and collaboration tools: a match for SMEs?[END_REF] This means that the marginal contribution that a player or a subset of players in N gives to the network is greater than the value that all the players would generate playing by alone without cooperation.

The definition of marginal contribution is 𝑀𝐶 𝑆 = 𝑣(𝑁) -𝑣(𝑁\𝑆), so by replacing this expression in the previous equation we obtain:

𝑣(𝑆) ≤ 𝑀𝐶 𝑆 = 𝑣(𝑁) -𝑣(𝑁\𝑆) ⇒ 𝑣(𝑆) ≤ 𝑣(𝑁) -𝑣(𝑁\𝑆) ⇒ 𝑣(𝑁) ≥ 𝑣(𝑆) + 𝑣(𝑁\𝑆) (8)
Thus, the value generated by the cooperation of all the players is greater than the sum of the values generated by any subdivision of players into groups. This condition will assure the convenience for the system of players (i.e., the multiagent system) to collaborate.

Some Concluding Remarks

From the point of view of practical application in a multi-agent structure, the above approach to organizing the network of agents by maximizing the gain (7) could reflect in supporting agents with higher efficiency. This theoretical solution, indeed, implies some practical defect, among which that of generating an unbalanced network, with some agents, well organized and with greater impact on the system performance, mainly supported, against some others, not so equipped and assessed.

This consideration suggests that the above "game-theory-based" formal model should be used for clarifying the concept of multi-agent network design, but it must be followed by a validation of the resulting negotiations and of the effectiveness of the collaborations of agents together, as discussed in some EU-funded projects [START_REF]GRACE -Integration of process and quality control using multi-agent technology[END_REF][START_REF]ARUM -Adaptive Production Management[END_REF]. This further step, even if it could be done by applying a simulation tool, needs a deeper analysis of the completely multi-agent system, as well as of the interactions between the two layers, with a more accurate definition of the lower-layer individual optimizations.

Figure 1 .

 1 Figure 1. Main phases of the product life cycle and their macro-activities.

Figure 2 .

 2 Figure 2. The detailed design phase.

Figure 3 .

 3 Figure 3. The "production" phase.

Figure 4 .

 4 Figure 4. The product "utilization" phase.

Figure 5 .

 5 Figure 5. Simple negotiation scheme among the three agents, with the "Moderator" busting conflicts between agents.

  Based on the above considerations, now the agents network of negotiations


Antonio Palmiro Volpentesta
email: antonio.volpentesta@unical.it

Alberto Michele Felicetti
email: alberto.felicetti@unical.it

Salvatore Ammirato
email: salvatore.ammirato@unical.it

Intelligent Food Information Provision to Consumers in an Internet of Food Era

Keywords: intelligent food services, internet of food, food-in-context awareness, food information provision

Food information is a crucial tool for facilitating consumers in decision-making activities related to their consumption process. Recent advances in "Internet of food" technologies (such as food sensors, cloud computing, food data analysis, and mobile app technologies) makes possible to conceive new consumer information platforms. The rationale is to empower consumers by letting them get more relevant food information than they usually obtain through on-product labeling, mass media or other traditional channels. In this paper, we envisage a new generation of food information provision services, called intelligent food services (IFSs), which would be responsive to consumer's expectations and information needs. We outline IFS structure and main features as well as constitutive elements of user-IFS interaction context. Particularly, we focus on food-in-context awareness capability and we discuss its influence on consumer and IFS behaviors.

Introduction

Food consumer behavior has changed substantially over the past decades. The contemporary food consumer, particularly the European consumer, has become more critical and outspoken, as he/she may be concerned with many heterogeneous attributes of food products. Along with, he/she demands and expects to get more relevant, accurate and reliable information necessary for his/her food decision making or learning.

These information needs may regard many heterogeneous elements pertaining to wellbeing, health, convenience, environmental, social, cultural, ethical and political concerns as consumers' choices are driven by many different motivations related to enjoyment (e.g. sensory quality, conviviality), health (e.g. diabetes, allergies, obesity), religion (e.g. halal, kosher), environment safeguarding or other special lifestyle (e.g. organic, vegetarian, no genetically modified foods). Recent research has shown that: a) consumers who are environmental conscious and ethically concerned, give high value to food information relating to environmental and social issues [START_REF] Ghvanidze | Consumers' environmental and ethical consciousness and the use of the related food products information: The role of perceived consumer effectiveness[END_REF]; b) consumers who want to make healthful food choices are more interested in information about food nutrition attributes, food origin and technological conditions under which the food has been produced and processed [START_REF] Lando | Helping consumers make more healthful food choices: consumer views on modifying food labels and providing point-of-purchase nutrition information at quick-service restaurants[END_REF]; c) as a reaction to frequent insertion of fraudulent, adulterated, misrepresented, and mislabeled food products into retail-toconsumer portion of food chains, many consumers strive for more and reliable information that guarantees the authenticity of their foods [START_REF] Downey | Advances in food authenticity testing[END_REF].

The challenge of food information management is to deliver the information that enables consumers to make appropriate choices according to their own individual objectives as they are involved in food consumption activities (planning, purchasing, preparation/cooking, eating and disposal/tidy-up). An effective and efficient food information service should deliver the right information to the right people, at the right time, and at the right place.

This challenge is particularly complex as it could require to meet a broad range of alternative information needs that are largely driven by the context in which food consumers make their decisions. This context may include many different entities (such as consumer, food, artifacts, and environment) and their attributes (such as identity, location, time, and activity currently involved in) depending on the situation they are relevant to.

Services that deliver massive and generic food information have a limited chance of success since they carry high search costs for the consumer and can hamper the ability to detect and correctly identify information that is needed in his/her decision or learning process [START_REF] Verbeke | Impact of communication on consumers' food choices[END_REF] [START_REF] Bialkova | Standing out in the crowd: The effect of information clutter on consumer attention for front-of-pack nutrition labels[END_REF]. Employment of conventional channels (e.g., store shelf and package labels) allows neither to retrieve actionable information from sources that are appropriate for the current consumer's situation, nor, more in general, to provide personalized food information.

Today's consumers have access to a wealth of mobile app-based services that provide them with food information (food traceability, nutrition advices, recipes and purchasing support). However, the information provision of these services is neither contextualized nor consumer-centered. Other than the capacity to adapt to consumers' mobility, it does not seem much different from usual food information provision through conventional media or Web sites [START_REF] Volpentesta | Evaluating web interfaces of B2C e-commerce systems for typical agrifood products[END_REF] [START_REF] Volpentesta | Analyzing App-Based services providing consumers with food information[END_REF].

A promising channel could be represented by a new generation of food information services which would empower consumers to have control over their own food as well as would be responsive to their contextualized expectations and information needs. These services could be based on the exploitation of recent advances of "Internet of food" technologies (such as food sensors, cloud computing, food data analysis, and mobile app technologies). The entry in the consumer market of some smart food applications and devices are clear signals in this direction: automated "food scanner" or "food sniffer" for the biomolecular food analysis, mobile apps that use camera photos for the quantification of food volume/weight/portion size or for the assessment of food quality, smart digital kitchen scale apps, intelligent package for food traceability or for improvement of food safety, and many others. Although these technology tools focus on specific functionalities and their data/information exchange with other systems is limited, they are at the cutting edge of current food information technology and give some clues about future food information services to consumers.

In this paper we envisage these new food information services that we call intelligent food services (IFSs), and we argue that they are characterized by having the following capacities:



Towards Intelligent Food Information Services

"Internet of Food" is an offshoot of the Internet of things. It can be viewed as a network of smart food things, i.e., food-related objects and devices which are augmented with sensing, computing and communication capabilities in order to provide advanced services. Smart food things include sensor-equipped information artifacts (e.g., food labels with RFID or NFC tags), time-temperature indicators and other sensors on packages to detect spoiled foods, sensor devices that spots bacterial infection in food and water, kitchen devices that generate a record of compliance with food safety protocols, wearables to count bites and estimate calories, and so on.

In past years, research scientists have devoted considerable resources to the development of analytical tools for a quantitative and/or qualitative analysis of various food aspects, such as the identity of product and ingredients, region of origin and/or species, quality attributes and variety of ingredients. Until recently, their approaches had to be implemented in a laboratory with large and expensive equipment to get reliable data. New technological developments, especially in food sensor miniaturization, have made available hand-held and low-cost devices to capture food data or food-related entities data (e.g., data from label, package, container, environment), and to communicate them with a specialized smartphone/tablet app. Despite their miniaturization, these devices incorporate an analytical precision and resolution almost equivalent to bench-top instruments [START_REF] Sorak | New developments and applications of handheld Raman, mid-infrared, and near-infrared spectrometers[END_REF]. These advances are forging the convergence of consumer and enterprises technologies, and they are contributing to development of the "Internet of Food". Moreover, they make possible to conceive a new generation of food services that, throughout this paper, are referred to as intelligent food services (IFSs). These services are capable of carrying out intelligent functions (like sensing and monitoring, food data acquisition and analysis, food information searching and reasoning) to facilitate food decision-making.

The intelligence is essentially due to two main service characteristics:

 context aware. By leveraging on context-data acquisition and outcomes of a machine learning or a crowdsourcing process, IFSs are capable to identify and understand enough of a consumer's current situation in order to address his/her specific food information needs;  knowledge leverage. By reasoning with identified current context items and available food knowledge resources (from simple food facts to food knowledge bases or food ontologies) IFSs are able to provide the user/consumer with personalized and context-sensitive food information. Due to these characteristics, IFSs are most likely to be more effective than conventional food information services (product labeling, mass media or other traditional channels). For instance, they can help consumers with meal planning that is personalized according to their health condition and other factors such as food cost and affordability, and they can warn consumers about allergens or intolerance forms of food they are going to ingest, based on their known medical history.

On one hand, they empower consumers with information that was not previously accessible. Providing information on the safety and quality of foods and processes, as well as on issues around environmental, social, and ethical aspects, they let consumers have transparency on which trust can be built. In this sense IFSs could represent a means to create social food awareness. On the other hand, they reduce the consumer's cognitive load needed to interact within the context in which decisions are made.

Several real case examples (see next subsection) suggest a general framework describing IFS application systems. From the infrastructural point of view, IFSs could rely on cloud/app/sensor-based solutions. Service applications could run on a cloud computing infrastructure and be deployed via mobile device so they could interact with the user/consumer, food items and food-related entities through an app that has access to user's mobile device resources (e.g. built-in device sensors and local databases) or is connected to an external sensor device. Besides, they could interact with a sensor networked environment (e.g. a place where sensors are attached to food-related objects and connected to a wireless network) in order to detect food-related events. Having this in mind, we introduce a conceptual architecture for an IFS system, which is visualized in Figure 1.

This structure can be considered as a kind of backbone for a new class of applications supporting intelligent food information provision. The IFS system front-end is the interface between the user/consumer and the IFS system back-end, and it is responsible for interactions with the external environment (user's request formulation, sensor data acquisition, and information presentation/visualization to the user); the IFS system back-end analyze context data versus domain-specific context knowledge (e.g., mining knowledge from lookup databases of previously identified food images, or of previously classified NIR spectra of food samples) to determine the current context configuration; successively, it exploits general food knowledge to adapt food information provision to the current user/consumer situation.

Domain-specific context knowledge can be acquired by using supervised learning from a training set of food samples data (e.g., previously classified NIR spectra of food samples) or by mining knowledge from a crowdsourced food properties database (e.g., a lookup database of previously identified food images).

Real Case Examples and Usage Scenarios

Over recent years, some pioneer companies have entered the food information market, exploiting benefits of IoF in order to provide consumers with food information. Such companies are bridging the gap between recent research advances in various scientific fields (such as spectroscopy [START_REF] Sorak | New developments and applications of handheld Raman, mid-infrared, and near-infrared spectrometers[END_REF][9], machine vision [START_REF] Casale | Review: Near infrared spectroscopy for analysing olive oils[END_REF] [START_REF] Steele | An overview of the state of the art of automated capture of dietary intake information[END_REF], hyperspectral imaging [START_REF] Kong | DietCam: Automatic dietary assessment with mobile camera phones[END_REF], odour analysis [START_REF] Pu | Recent progress of hyperspectral imaging on quality and safety inspection of fruits and vegetables: a review[END_REF] and taste analysis [START_REF] Peris | A 21st century technique for food control: Electronic noses[END_REF]) and industry.

In what follows, we propose some usage scenarios, derived from real-world examples, to clarify how IFS are currently provided by these companies.

Usage scenario 1 A user is currently purchasing some meat in a store. He/she needs information about the freshness of that food. The user scans food with an odour-analysis based device (Smart Food Thing), able to acquire data about volatile organic compounds (VOCs) emitted by food [START_REF] Jha | Detection of Adulterants and Contaminants in Liquid Foods-A Review[END_REF]. The odour-analysis device is coupled to the user's smartphone via mobile app. Data are sent to a cloud-based engine able to perform a pattern recognition analysis to classify some food characteristics of that food item. Results of classification are processed by IFS back-end. The mobile app tells the user about the level of food freshness of the meat he/she analyzed.

FOODsniffer (http://www.myfoodsniffer.com/) and CDx inc. (www.cdxlife.com) are two examples of companies providing this kind of IFS.

Usage scenario 2 A user is going to eat a cooked meal in a restaurant. He/she needs information dealing with his/her diet. The user takes multiple pictures of food by means of his/her smartphone. That device acquires data about surface condition and volume of that food item. A data analysis unit, by mining knowledge from a food properties database, is able to recognize multiple food in user's plate and infer the mass of food products. IFS back-end processes food information exploiting a food knowledge database (e.g., nutritional facts tables). Moreover, data on user's food activities (e.g. historical data on previous food assumed the user during the day) and user profile (e.g. health related issues) are processed in order to provide the user with customized information. A mobile app informs the user with about the amount of calories and nutrition in his/her plate and the number of calories he/she can still consume in the rest of the day.

Calorie Mama (http://www.caloriemama.ai) and Lose it! (https://www.loseit.com) are two examples of companies providing this kind of IFS.

Usage scenario 3 A user is going to cook some food in his/her kitchen. He/she needs information about calories and nutritional facts of that food. The user takes a picture of food by means of his/her smartphone, acquiring data about surface condition of that food. Data analysis is based on a real-time crowdsourced process, as the picture is analysed by expert volunteers that manually recognize food items. Data about nutritional facts are processed by IFS back-end and information on calories and nutritional facts is provided through a mobile app.

An example of solution based on crowdsourced analysis process is represented by Rise (https://www.rise.us/).

The Interaction Context

Internet of Food (IoF) is an offshoot of the Internet of Everything. It can be viewed as a network of smart food things, i.e., food-related objects and devices which are augmented with sensing, computing and communication capabilities in order to provide advanced services. Smart food things include sensor-equipped information artifacts (e.g., food labels with RFID or NFC tags), time-temperature indicators and other sensors on packages to detect spoiled foods, sensor devices that spots bacterial infection in food and water, kitchen devices that generate a record of compliance with food safety protocols, wearables to count bites and estimate calories, and so on.

An interaction is generally understood as the effect two or more entities will have upon their counterparts as they perform actions on each other. The user-service interaction context is a set of entities with their relevant properties (called items) characterizing the situation the consumer is currently involved in, while he/she is interacting with the service [START_REF] Volpentesta | A framework for human interaction with mobiquitous services in a smart environment[END_REF].

In user-IFS interaction context modeling, four broad classes of context entities should be taken into account:

 user: a consumer who requires the information necessary for his/her food decision making or learning process as he/she is involved in food consumption activities in a real spatial environment; he/she interacts with an IFS through interface devices located in the environment;

 food: foodstuff that the consumer is interested in. Food related stimuli are perceived by the user and food characteristic data are acquired by the IFS during the interaction;  IFS interface devices: they comprise devices that are used by the IFS system: a) to take input from and deliver output to the user in the foreground of user's attention. These include food information artifacts (FIAs1 ), such as labels, tables, RFID chips, and NFC tags, that allow the user to get their contained information in the place where the artifacts are located; b) to take input from the user in the background of user's attention, while he/she is involved in food consumption activities, such as many wearables for food intake monitoring [START_REF] Bi | AutoDietary: AWearable Acoustic Sensor System for Food Intake Recognition in Daily Life[END_REF]; c) to take food and environment data with or without requiring user's action or attention. These include sensor equipped food packaging and containers, food appliances, as well as small tools designed for food-related functions (e.g. kitchen or cooking utensils);  environment: physical and organizational environment where interactions take place (e.g. a home kitchen, a restaurant, a food shop, etc.). Environmental conditions have direct or indirect influence on consumer and service behaviour during the interaction. Physical properties, like light, humidity, temperature, localization, spatial layout of the environment, may change quality attributes of the food or consumers' perception of quality in food; organizational aspects, like rules, shop opening hours, working time, may drive the provision of information from the IFS. According to many approaches for using and modelling context information, items of the above context entities fall into five categories [START_REF] Zimmermann | An operational definition of context[END_REF]:

 Identity. A model of this item allow the service, or the user, to identify a context entity that is needed to know in the interaction with the counterpart.

In particular, the user determines food identity by elaborating his/her food sensorial perceptions, like colour and appearance, taste, and odour, while the IFS system determines food identity by elaborating food data coming from instrumental measures of physical, chemical, biological, and microbiological food properties;  Time. This item refers to temporal aspects of context entity properties. Item values may range from a current time representation to a complete time history of entity properties. When referred to food, values of this items allow to recognize or predict food quality over time. For instance, a time series of values of properties, like temperature, pH, or microbial growth, may help the user or the service to recognize when food is at its nutritional best and is safe to eat and when it should be disposed of to avoid ill health;  Location. A model of this item may be quantitative or qualitative, and item values may represent current and previous positions of context entities in absolute or relative terms. When referred to food, location is a fundamental item of models for geographical traceability or geographic-based origin determination of food products.

 Activity. A model of this item describes fundamental changes of entity attributes that occur when a food activity is performed. In particular, changes of food characteristics, like temperature or size, may let the user or the service to determine the state progress in a current consumer food activity (e.g. cooking); as a further example, chewing sounds may let a service to classify the food a consumer is eating.  Relations. This item refers to relations a context entity has established with other context entities. A relation between two context entities expresses a semantic dependency that emerges from user-service interaction situations that these context entities are involved in. Relations between food and other entities are essentially relations of spatial proximity (e.g., relative distance and orientation). For instance, food data capture is strongly dependent on the relative distance and orientation of the food product and the sensor device.

Food-in-context Awareness

Interaction context-awareness refers to the ability of the service (respectively, user) to acquire context item information and to use it in the interaction with the counterpart; context item information is acquired by capturing context data (respectively, sensory/cognitive cues), analyzing them, and exploiting some context knowledge.

In an interaction session, context awareness has a significant influence on the behavior of the consumer, as service user, and on the behavior of the IFS, as food information provider. As matter of fact, context changes can trigger IFS/user actions on the counterpart, and context cues or data can support IFS/user to interpret actions performed by the counterpart. IFS and user mutually increase their context awareness, as they interact with each other. In particular, Fig. 2 shed light the role of context in an interaction between user and IFS, i.e. it further focuses on the Consumer-IFS interaction module reported in the functional architecture (see Fig. 1).

Fig. 2. Contextualized user IFS interaction.

Food-in-context awareness is context awareness limited to food items, including relations with other context entities. In what follows we discuss more deeply us-er/consumer and service food-in-context awareness and their roles in the interaction. Successively, we summarize main technical approaches that can be used to make an information service food-in-context aware, as they are aimed to automatically determine information on food characteristics and performances.

User/consumer food-in-context awareness. It is the user's ability of knowing and experiencing contextual food items, i.e., sensory properties (such as appearance, taste, and odor) of food in the interaction context. Signals from contextual food can trigger a user's information need that in its turn can trigger a request to a food information service. For instance, the presence of a new food product can trigger the user need for nutrition or health information about that product (e.g. what vitamins have).

More important, in a contextualized interaction the user attempts to make sense of data and information delivered by the service provider, within his/her current situation and his/her own knowledge acquired from past experiences. Contextualization is absolutely essential both to let the consumer make appropriate choices in his/her food consumption activities in which he/she is currently engaged and to enable consumer's informal learning process that is crucial for his/her future related behavior.

User food-in-context awareness is at the core of user contextualization, since it provides the user with sensory and cognitive cues that allow him/her to refer delivered data and information to food items in the interaction context:  value-laden information embedded within a food product can be effectively disclosed only by associating the perception of food-related stimuli (e.g. smell, flavor) with the logical information provided by the service;  information content of labels/tags can be properly interpreted only by taking into account spatial relations between food products and these labels/tags. Think of a label on a food-shelf; we automatically refer the label information content to the closest food product on the food-shelf, and the label information content changes meaning as the label has moved to another place;  food information provided by the service can be directly validated only by knowing specific physical conditions (e.g. light, humidity, temperature, localization, spatial layout, package integrity) or organizational features (e.g. operation rules, shop opening) of the environment surrounding food products the information content is referring to. For example, the "best before date" indicates the last date by which a product flavor and/or quality is best, but what is the value of this information if the product package has been opened? Service food-in-context awareness. It is the ability of the service to capture and analyze food data versus domain-specific food knowledge in order to acquire information on food in current context. Food data coming from instrumental measures of physical, physico-chemical, and kinetic food properties are correlated with sensor properties such as color, appearance, taste, odor, and sound.

Service food-in-context awareness is at the core of context-adapted information provided by an IFS that creates value in letting consumers make better informed food choices, while they are involved in related food activities. Context-adapted information provision is characterized by the following dimensions:

 context-triggered. Changes of some food items can be detected and used by an IFS as a means to trigger context data acquisition that, in turn, can lead to timely information provision, i.e., information delivered when and where desired or needed by the consumer. For instance, an IFS may alert the user of a sensed condition within a refrigerator that food spoilage may occur.  context-situated. In a current interaction session, an IFS information provision makes exophoric reference to context entities (e,g, food products or food-related objects) surrounding the user/consumer who, in his/her contextualization process, is required to resolve this exophoric reference as well as to assign meaning to spatial constructs. Service context awareness lets an IFS make easy and smooth the consumer's contextualization process by eliding spatially-retrievable information and inserting a minimal amount of unnatural clarification statements, in a food information provision. For instance, in a food supermarket, consumers could be guided towards what they're looking for, by using simple spatial deictic expressions in utterances such as "take a left" or "head towards the stairwell"  context-focused. The IFS satisfies contextualized needs of information, i.e., consumer's needs of information that can be derived from the knowledge of current food items. This kind of adaptation couples contextualization and personalization, as addressing not just decontextualized consumer's needs, but what the consumer needs in the current context (e.g., in that place, at that time). It exploits an explicit context model that takes into account many context entities and it is broader than the only consumer model limited to personal characteristics such as preference, background, attitudes, and lifestyle. For instance, an IFS can suggest restaurants or food stores based on the user's preferences and current location and time.

Conclusions and Future Works

Internet of food based solutions are promising a huge impact on both food business and food consumers. In this paper, the envisaged intelligent food services constitute a part of the Internet of food vision that is grounded on recent researches and technology advances.

In this work, we proposed a functional framework such services. We provided also some usage scenarios to clarify how emerging companies in food information market exploiting benefits of IoF to provide context based information services. Moreover, we have introduced and discussed the concept of food-in-context awareness as a crucial ability for these services in order to provide consumer's situation adapted information.

In our opinion, this constitutes a contribution to realistically conceive innovative food information services that could let consumers be more food aware and could help them to make more informed decision when involved in food related activities.

The emergence of these services poses new challenges and problems but it also gives new business opportunities for tech startups in the Food Information Ecosystem. A critical point concerns with the consumer acceptance of these new technologies. Future research will be addressed to apply the Technology Acceptance Model (or its extensions) to evaluate the individuals' acceptance of this new generation of food information services.

In the paper we have presented some real-case applications, however a comprehensive study of companies operating in the food information market is missing. Further research will be devoted to conduct a survey aimed to understand technology foundation and business models of startup companies leveraging on IoF technologies, and to identify potential trails leading to the emergence of IoF based ecosystems.

Fig. 1 .

 1 Fig. 1. Conceptual structure of an IFS application system.

. According to[START_REF] Smith | Aboutness: towards foundations for the Information Artifact Ontology[END_REF], a FIA is a physical entity expressly created to bear food information.


Sofia Freitas De Oliveira

António Lucas Soares

A PLM Vision for Circular Economy

Keywords: Product Lifecycle Management, Circular Economy, Collaboration, Information Management, Footwear Industry, Industry 4.0, IoT, Big Data

Due to growing concerns with sustainability issues and the emergence of the Circular Economy (CE) paradigm, combined with recent technological changes and consequent increase in competitiveness, there is a pressing need to redefine the Product Lifecycle Management (PLM) approach. PLM needs to incorporate aspects that would enable the shift to this paradigm, such as enhanced collection and evaluation of information coming from production processes, distribution, retail, consumers, and collaboration in an extended enterprise context, by implementing enabling technologies such as the Internet of Things (IoT) and Big Data. This paper proposes a vision, based on the state of the art, for a CE enabled PLM, having the Portuguese footwear industry scenario as a reference.

Introduction

Today, the concept of Product Lifecycle Management (PLM) is being redefined by the shift from the mass production paradigm to a new personalized, on demand, customer-driven and knowledge-based production paradigm [START_REF] Ferreira | Product lifecycle management in knowledge intensive collaborative environments: An application to the automotive industry[END_REF], which requires the enhancement of information coming from design, production processes, distribution, retail, consumers, among the whole product lifecycle, from its initial inception to its end of life. The increase in complexity of products has led to enterprise unbundling, with most companies focusing on their core activities and externalizing support activities, leading to a value chain distribution among several organizations. In this decentralized knowledge intensive environment, collaboration between the stakeholders involved in all product lifecycle activities, in an extended enterprise context (network of retailers, consumers, producers, among others) is a challenge that needs to be addressed in PLM, enabled by technologies such as the IoT and Big Data. At the same time, new challenges related to growing concerns with sustainability issues and the increasing significance of Circular Economy (CE) concept are emerging, which require improved products that can be used for longer, with multiple lifecycles. This scenario poses challenges to future PLM systems, especially those intended for SMEs, which have limited internal resources and strongly rely on collaboration with external partners, suppliers and customers. This paper proposes a vision for a CE enabled PLM, targeting SMEs needs, having the Portuguese footwear industry as a reference in order to illustrate the current scenario, and is structured as follows. The Portuguese footwear industry, which consists mainly of SMEs, is a success story in the Portuguese industry, being responsible for the greatest positive contribution to the Portuguese trade balance. Leading European footwear players, such as Portugal and Italy, pursue a strategy based on technological innovation, quality-competition, and product differentiation. Differentiation is a crucial factor for the fashion cluster, as a result of accelerated changes in consumer preferences and fashion trends [START_REF] Batista | Assessing the Competitiveness of the Portuguese Footwear Sector[END_REF]. These changes require a reduction of the footwear development cycle, with repercussions to companies' strategies and PLM approaches.

The volume of business growth in the footwear sector witnessed in recent years, together with product differentiation and a decrease in time to market, translates into an almost exponential increase of references, each one with a reduced production volume. This new reality, while enhancing new opportunities for the most dynamic footwear companies, entails enormous organizational and technical challenges regarding product data management throughout the entire product lifecycle.

Moreover, the increase in complexity of footwear products led to specialization and consequent unbundling. Currently, few footwear companies internalize all their processes, leading to a value chain distribution among several organizations (from the end customer, either an individual store or a distribution chain, through the manufacturer and its partners, often in the form of subcontractors, to raw material suppliers), which greatly increases the need for interaction between agents, with large amounts of data, information and knowledge being constantly exchanged. In this heterogeneous and dispersed environment, the alignment of people, information and processes poses a big challenge. In order to be competitive in the described context, companies must manage their value chains effectively, with the aim of reducing lengthy product development cycles and costs, while improving quality [START_REF] Terzi | Product lifecycle management -from its history to its new role[END_REF].

Traditionally, the management of this dynamic in SMEs is done in a very ad-hoc way, without any specific methodologies, usually by using tools designed for personal productivity (text documents, spreadsheets, e-mail messages). This approach, in addition to requiring a huge consumption of human resources, greatly limits the ability to exploit the value associated with the increase in data exchanged, and its transformations into information and even knowledge. Many SMEs have strong gaps in management and execution of activities with a strong collaborative nature, both horizontally (intercompany relations along the value chain) and vertically (information exchange between business processes within the same organization).

Today's knowledge-intensive product development setting requires a computational framework that allows the acquisition, representation and reuse of product and process knowledge. In a manufacturing environment, all product information should be shared along the value chain and continuously synchronized with updates. During product utilization, product data must be used by the service chain in order to provide support. Sharing and managing product data, information and knowledge is the cornerstone of PLM [START_REF] Terzi | Product lifecycle management -from its history to its new role[END_REF]. PLM is defined as business strategy for building and sustaining a product-centric knowledge environment, seeking to integrate people, processes, resources and information. A PLM environment allows collaboration between multiple stakeholders of a product along its lifecycle [START_REF] Stark | Product Lifecycle Management[END_REF]. Over the last two decades, PLM emerged as a strategy to enable end-to-end product development [START_REF] Raconteur | Supporting the "Circular Economy[END_REF]. Ideally, companies manage products from cradle to grave as part of their PLM strategy, according to a linear model in which products are developed, manufactured, used and disposed. However, environmental problems such as biodiversity loss; water, air and soil pollution; resource depletion; excessive land use, among other sustainability issues, are increasingly compromising the earth's lifesupport systems, pressing the need to transition to more sustainable paradigms [START_REF] Geissdoerfer | The Circular Economy -a new sustainability paradigm[END_REF]. To address these issues, the concept of Circular Economy, while not entirely new, has recently been gaining importance. Based on different contributions [START_REF] Geissdoerfer | The Circular Economy -a new sustainability paradigm[END_REF] define Circular Economy as "a regenerative system in which resource input and waste, emission and energy leakage are minimized by slowing, closing and narrowing material and energy loops. This can be achieved through long-lasting design, maintenance, repair, reuse, remanufacturing, refurbishing and recycling" [START_REF] Geissdoerfer | The Circular Economy -a new sustainability paradigm[END_REF]. Ongoing European Project FUTURING -Futuring European Industry Training. The Pillar Actions describe some complimentary ways to implement CE -Regenerate, Share, Optimize, Loop, Virtualize, Exchange. A fundamental principle of circular economy is that after the utilization phase, products should be returned to manufacturers instead of being disposed, creating new flows of recyclables with extremely high values. Therefore, circular economy will require new PLM enabling solutions and supporting services [START_REF] Raconteur | Supporting the "Circular Economy[END_REF].

According to a study conducted by McKinsey & Company [START_REF] Bouton | The circular economy: Moving from theory to practice[END_REF], companies can benefit largely from consumer-focused lifecycle thinking and cross-functional collaboration, as it helps capturing more value from the resources they consume. Some companies are already adopting circular economy practices to create durable products that are easy to reuse or recycle and profitable, by devising a highly collaborative product development process that helps determining sourcing requirements, production methods, marketing, sales and other aspects of how products are created and managed at the end of their lives. However, these aspects are far from being a reality in PLM solutions designed for SMEs, and even in PLM solutions overall [START_REF] Abramovici | Next Generation Product Lifecycle Management (PLM): Integration of Practice-Oriented Knowledge Technology: Trends and Prospectives[END_REF] [START_REF] Messaadia | PLM adoption in SMEs context[END_REF]. The rest of the paper is organized as follows: sections 2, 3 and 4 cover a brief literature review regarding PLM and its main aspects: data and information management and collaboration; section 5 provides a vision for the support of Circular Economy in PLM; the paper concludes with final remarks on the contributions of this study, and interesting fields for further research.

PLM and PLM Systems

In the last few years, PLM has become the paramount management approach in engineering in the manufacturing industry [START_REF] Abramovici | Next Generation Product Lifecycle Management (PLM): Integration of Practice-Oriented Knowledge Technology: Trends and Prospectives[END_REF]. According to Stark [START_REF] Stark | Product Lifecycle Management[END_REF], PLM is the business activity of managing a company's products all the way along their lifecycles; from the very first idea until products are retired and disposed of, in the most effective way. PLM is an integrated paradigm that includes a consistent set of methods, models and tools for managing product information, engineering processes and applications along the phases of product lifecycle [START_REF] Abramovici | Next Generation Product Lifecycle Management (PLM): Integration of Practice-Oriented Knowledge Technology: Trends and Prospectives[END_REF]. The core functionalities of PLM systems include information management, process management and application integration, as well as a set of additional PLM methods and tools such as engineering collaboration support, user access management and data analysis, reporting and visualization [START_REF] Abramovici | Next Generation Product Lifecycle Management (PLM): Integration of Practice-Oriented Knowledge Technology: Trends and Prospectives[END_REF].

As clearly defined by Stark [START_REF] Stark | Product Lifecycle Management[END_REF], the generic product lifecycle has five phases: Imagine, Define, Realize, Support/ Use, Retire/ Dispose. During the imagination phase, the product in an idea. In the "Define" phase, ideas are translated into detailed descriptions. By the end of the realization phase, the product is ready be used by the customer. During the use/ support phase, the product is being used by the customer. Ultimately, the product reaches a phase in which it is no longer useful, and is retired by the company or disposed by the customer. The specific activities that occur across the lifecycle phases can be different, depending on the particular industry. PLM activities enable a company to grow revenues by improving innovation cycles, reducing time-to-market for new products, and providing support and new services for already existing products [START_REF] Stark | Product Lifecycle Management[END_REF]. PLM can improve the development of new products and reduce manufacturing costs by controlling products' data throughout their lifecycle [START_REF] Li | Big Data in product lifecycle management[END_REF].

The main weakness of current PLM solutions is poor support of product lifecycle phases and activities besides product development [START_REF] Abramovici | Next Generation Product Lifecycle Management (PLM): Integration of Practice-Oriented Knowledge Technology: Trends and Prospectives[END_REF]. Another problem of available PLM solutions is their very high complexity and the huge customizing efforts that are necessary. The total costs involved in the installation, usage and maintenance of PLM systems means that the most powerful and popular PLM systems are only affordable to large companies, and a mirage for SMEs [START_REF] Baptista | PLM4all -A novel approach for product lifecycle management of mechatronic equipment in SME[END_REF]. In recent years, light and low cost PLM solutions have started to appear in the market for SMEs [START_REF] Stackpole | Vendors Try to Rightsize PLM for Smaller Businesses[END_REF]. However, according to studies carried out by several authors [START_REF] Messaadia | PLM adoption in SMEs context[END_REF], SMEs continue to consider PLM solutions complex, with high cost and high dependence on software services, which constitutes a barrier to the adoption of this technology. In this context, one of the biggest challenges in PLM is the reduction of costs related to IT technology and infrastructure investment, as well as the development of easy-to-use, intuitive software with features that are very oriented to the real needs of organizations, with the objective of enabling their adoption by SMEs, which represent 99% of the European Union's manufacturers [START_REF]European Commission, Internal Market, Industry, Entrepreneurship and SMEs[END_REF].

Data and Information Management in PLM

The Industry 4.0 term, which originates from a German government strategy project [START_REF] Acatech | Recommendations for implementing the strategic initiative Industrie 4.0[END_REF], defines the complete digitization and integration of the industrial value chain.

The technical foundation for Industry 4.0 is the combination of information and communication technologies with automation technologies, embodying the Internetof-Things (IoT) and Internet-of-Sevices (IoS). The combination of these technologies enables high levels of network communication, encompassing people, equipment, products, services, as well as business-to-business communication. This way, it becomes possible to create intelligent supply chains, covering all phases of the product lifecycle -from the initial idea to the end of its life, through development, production, use and service [START_REF] Acatech | Recommendations for implementing the strategic initiative Industrie 4.0[END_REF]. The Reference Model for Industry 4.0 -RAMI 4.0, consists in a three-dimensional coordinate model that describes all the important aspects of Industry 4.0, in order to reach a common understanding of required standards and use cases. According to RAMI 4.0, presented in Fig. 1-RAMI 4.0, the complex interrelationships can be decomposed into elementary clusters: "Layers" axis, "Life Cycle & Value Stream" axis and "Hierarchy Levels" axis [START_REF] Hankel | Industrie 4.0: The Reference Architectural Model Industrie 4.0[END_REF]. the hierarchical levels of business information systems connected via the Internet, and the IT structure of an Industry 4.0 component. The description of the product lifecycle is done considering that a product has a lifecycle as a virtual "type" (during the design and development phases) and as a physical instance (after the manufacturing phase, through use, service and end of life). During the entire product lifecycle, as a type and an instance, the data generated in the process chains is processed by PLM systems, creating product-service centered collaboration among the connected world [START_REF] Contact | RAMI 4.0: the three-dimensional reference architecture model[END_REF].

Due to technological advances and the emergence of the Industry 4.0 paradigm, which generate and must deal with extremely high volumes of data, new opportunities for the application of PLM in areas such as SMAC (Social, Mobile, Analytics, Cloud), Internet of Things (IoT), Big Data and Smart Products have recently emerged. PLM has been evolving continuously towards a digital paradigm, under which products are managed throughout their lifecycle using digital information and communication. The reporting of product performance using technologies such as the IoT, GPS and RFID, which allows real-time perception of its performance and customer satisfaction level, is a pressing trend [START_REF] Stark | Product Lifecycle Management[END_REF].

The aim of the IoT application in PLM is to establish a smart information cycle along the whole product lifecycle. From data sensed and communicated by smart products, manufacturers can understand products' behavior when they are being used by the customer. From the analysis of product data, innovative new products and services can be identified in order to better fit customer's requirements. In the product's end of life, components can be identified and tracked, which can lead to better decisions being taken regarding how to treat products in an environmentally correct way. These aspects can reduce costs and improve compliance with environmental rules and regulations [START_REF] Stark | Product Lifecycle Management[END_REF].

Top PLM manufacturers are already defining visions for enabling digitalization in their PLM solutions. Siemens Automation is building a "Smart Innovation Portfolio", with the aim of activating digitalization in manufacturing. Siemens AG's "Digital Enterprise Software Suite" extends digitalization from development and operation through production [START_REF] Siemens | [END_REF]. PTC FlexPLM, a powerful retail oriented PLM, is leveraging PTC's own IoT technology, ThingWorx [START_REF]PTC Flex PLM[END_REF]. However, the integration of these aspects in PLM is at an early stage, and far from becoming commonplace in PLM solutions for SMEs. According to Li [START_REF] Li | Big Data in product lifecycle management[END_REF], in recent years researches related to the increase of data have been conducted in the field of data mining and knowledge management. However, the utilization of Big Data in PLM significantly falls behind other areas. Most manufacturers do not store data or have limited knowledge about how to use these data, which makes the manufacturing chain not efficiently connected, with data being generated, transmitted and stored unsuccessfully. Additionally, the existing studies about Big Data and PLM are exclusively concerned with the development lifecycle phase, and have not been explored from the whole lifecycle perspective [START_REF] Li | Big Data in product lifecycle management[END_REF].

Collaboration in PLM

In nowadays global economy, manufacturing companies must be considered in the context of their input to the whole value chain. Extended or virtual enterprises, which consist of connected chains of suppliers, manufacturers, assemblers, distributors and customers, compete to provide customized products and services to competitive markets [START_REF] Browne | Extended and virtual enterprises -similarities and differences[END_REF]. Virtual enterprises and manufacturing networks have been largely discussed in the last decade [START_REF] Ferreira | Product lifecycle management in knowledge intensive collaborative environments: An application to the automotive industry[END_REF]. According to Camarinha-Matos [START_REF] Ferreira | Product lifecycle management in knowledge intensive collaborative environments: An application to the automotive industry[END_REF], "a Virtual Enterprise represents a temporary alliance of enterprises who join to share skills or core competencies and resources in order to better respond to business opportunities, and whose cooperation is supported by computer networks". An extended enterprise is defined as a particular case of a virtual enterprise, which arises when an organization "extends" its boundaries to all or some of its supply chain stakeholders [START_REF] Ferreira | Product lifecycle management in knowledge intensive collaborative environments: An application to the automotive industry[END_REF]. Due to the growing complexity of products, their variability and the continuous transformation of customer needs, product lifecycle has become increasingly challenging, requiring collaboration between heterogeneous teams, holding expertise in different fields [START_REF] Penciuc | Towards a PLM interoperability for a collaborative design support system[END_REF]. Internally, collaboration among associated departments, such as marketing, development and production, is the most crucial factor in the successful on-time delivery of new products to market, which is one of PLM goals [START_REF] Oh | A collaboration model for new product development through the integration of PLM and SCM in the electronics industry[END_REF]. Externally, collaboration among various stakeholders, such as customers, suppliers and other partners, such as service suppliers, is also a contributing factor to improve processes efficiency and customer satisfaction.

Therefore, the modern product lifecycle is characterized by an interdisciplinary interaction between a large number of actors, intra and extra organization, such as departments, customers, suppliers and partners, in an extended enterprise environment. Traditionally, PLM solutions focus on collaboration between various departments of the same company. However, as a business integration platform, PLM systems must take into account the entire network of partners and customers, in order to improve information access and sharing inside the company and outside the company [START_REF] Soto-Acosta | A case analysis of a product lifecycle management framework for SMEs[END_REF]. This way, PLM can provide the capability of collaborative creation as well as management and use of product data, information and knowledge in a virtual enterprise environment, merging people, processes and technology [START_REF] Ming | Mass production of tooling product families via modular feature-based design to manufacturing collaboration in PLM[END_REF].

Siemens PLM software solution for apparel, footwear and accessories provides the supply chain stakeholders (buyers, designers, merchandisers and managers) with a central location to effectively manage the fashion product development process, enabling collaboration across the supply chain [START_REF] Siemens | [END_REF]. PTC Flex PLM integrates internal and external teams, as well as third-party customers [START_REF]PTC Flex PLM[END_REF]. However, fashion industry PLM solutions aimed at SMEs, such as Infor Fashion PLM, are more focused on devising collaboration within the enterprise, by linking planning, design, development and execution on a common platform [START_REF]Infor Fashion PLM[END_REF]. The effective implementation of an extended PLM raises a concerning challenge, which is the need to process information gathered from multiple sources into knowledge for making effective decisions in real-time. This technical challenge addresses the goal to integrate the collaborative network partners' information systems, so that cooperation can occur flawlessly [START_REF] Ferreira | Product lifecycle management in knowledge intensive collaborative environments: An application to the automotive industry[END_REF]. The interaction between the various agents is marked by the use of IT tools such as CAD, CAE and CAM, among others, which creates a very heterogeneous data scenario [START_REF] Abramovici | Next Generation Product Lifecycle Management (PLM): Integration of Practice-Oriented Knowledge Technology: Trends and Prospectives[END_REF].

Vision for the Support of Circular Economy in PLM

As evidenced in the previous sections, recent work shows that PLM is evolving from a system mainly supporting product development and engineering processes to a platform destined at encompassing the whole product lifecycle. This future PLM platform is about integration, collaboration and connectivity of devices, across disciplines and enterprise borders, supported by enabling technologies such as the IoT and Big Data [START_REF]ResCoM -Resource Conservative Manufacturing[END_REF]. Scarcity of materials and energy resources, combined with rapid degradation of the environment, is pressing the reformulation of economic and industrial systems. As briefly mentioned in the opening section, nowadays most manufacturers still follow a linear model of production, in which materials are extracted, turned into products, used and discarded, with their value being lost, according to a "take, make, use and throw away" approach. The Circular Economy approach, which includes reuse, recycling and remanufacturing principles, presents an opportunity for manufacturers, since it enables the creation of flows of valuables capable of generating new business opportunities. However, there are challenges in closing the loop [START_REF]ResCoM -Resource Conservative Manufacturing[END_REF], particularly the information gaps along the end-of-life stages of product lifecycle. The development of efficient and robust PLM systems for Circular Economy will require excellence in IT, supporting collaboration among the extended enterprise and management of multiple lifecycles [START_REF]ResCoM -Resource Conservative Manufacturing[END_REF]. In this context, a Circular Economy PLM Strategy is defined along these lines [START_REF] Vila | An Approach to Sustainable Product Lifecycle Management (Green PLM)[END_REF]: Mission: To provide products capable of satisfying customer needs, while taking full advantage of the company's sustainable production system and innovation competency, considering all impacts along the lifecycle. Vision: To generate data, information and knowledge within the supply chain and to manage it from the perspective of sustainable product design and development, manufacturing, distribution, use and service and recycling and recovery. Objective: To share data, information and knowledge of all the product lifecycle phases among all the stakeholders (internal and external); to encourage collaboration between customers, designers, producers and suppliers; and to enable sustainability through environmentally conscious products and processes. Fig. 2. CE enabled PLM concept illustrates the concept of managing the entire product lifecycle from a Circular Economy outlook, supporting and driving the value chain. This concept is focused on the footwear industry scenario. resilience [START_REF] Weetman | A Circular Economy Handbook for Business and Supply Chains. Repair, Remake, Redesign, Rethink[END_REF]. The use of the IoT technology is especially important during the Manufacturing, Sales and Retail and Use and Service product lifecycle phases, as shown in Fig. 2. CE enabled PLM concept. During the Manufacturing phase, the IoT helps optimizing production efficiency, by using data across the manufacturing value chain. During the Use and Service phase, the IoT enables companies to track their products, offering numerous opportunities to get an improved understanding of products behavior, as well as to manage their related services. At the store level, during the Sales and Retail phase, the IoT enables the collection of valuable data regarding consumers' preferences.

Due to recent technological advances and the evolution of PLM into a digital paradigm, under which products are managed using digital information and communication, the volumes of data generated along the whole lifecycle are increasing at a high rate, which poses an opportunity for the application of Big Data in PLM [START_REF] Stark | Product Lifecycle Management[END_REF]. There is the need to analyze and extract information from the large volumes of data collected along the product lifecycle, in order to capture knowledge that can lead to improvements in product lifecycle's activities all along the supply chain. Therefore, a CE enabled PLM should support IoT and Big Data technologies integration. Types of Products. A next generation PLM should support "smart products", embedding information devices such as RFID or on-board computers, capable of tracking and tracing all the product information over the entire lifecycle in real-time. PLM solutions should also manage intangible products such as software or services, and in a more complex level PSS (Product Service Systems) [START_REF] Abramovici | Next Generation Product Lifecycle Management (PLM): Integration of Practice-Oriented Knowledge Technology: Trends and Prospectives[END_REF].

Currently, there are concluded and ongoing R&D projects that address product and stores sensorization. Shoe-ID project, concluded successfully, addressed the incorporation on RFID technology in the initial phase of the footwear production cycle, in order to simplify the whole supply chain follow-up, from manufacturing to the final consumer [28]. The ongoing project FASCOM aims at creating a solution that enables the collection of sales related data from the store and its access by product developers, manufacturers and distributers [START_REF]CTCP[END_REF]. Data collected from the store regarding the perception of products by customers, as well as reasons for lost sales, can provide valuable information to designers and manufacturers that can lead to improvements in the development of new products. The integration of these technological solutions with PLM solutions is not yet explored and therefore is an area for further research. A CE enabled PLM should support smart products and smart stores, connected by the IoT. Product Lifecycle Phases and Stakeholders. As previously stated in Section 4, currently the focus of SME oriented PLM solutions is within one manufacturing company, which may include various distributed locations [START_REF] Abramovici | Next Generation Product Lifecycle Management (PLM): Integration of Practice-Oriented Knowledge Technology: Trends and Prospectives[END_REF]. A CE-enabled PLM approach for footwear industry should integrate all supply chain stakeholders along the whole product lifecycle: logistics suppliers, materials suppliers, retailers, manufacturers, customer and recyclers. Specific models, methods and tools from actual PLM solutions are mainly focused on product design and development, providing little or no support to other lifecycle phases [START_REF] Abramovici | Next Generation Product Lifecycle Management (PLM): Integration of Practice-Oriented Knowledge Technology: Trends and Prospectives[END_REF]. CE-enabled PLM platforms must provide support in managing other downstream product phases such as manufacturing, distribution and logistics, sales and retail, use and service, recycling and recovery, focusing on value-added activities. A CE enabled PLM system should consider not only the management of a single life cycle but multiple product life cycles in which the products used can be changed or reconfigured in a sustainable way. These aspects are represented in Fig. 2. CE enabled PLM concept. As previously mentioned, PLM platforms must support smart, connected products and stores enabled by the IoT, improving product design through feedback on in-use performance and durability. This information flow should continue seamlessly through reuse to the end of use, with further feedback to the designer and producer, supporting improved design and material choice. In the design and development phase, components normalization and modularity aspects must be considered, thinking of the products end-of-life from an early stage, which can include reuse, remanufacturing, recycling and disposal. In the CE context, the use and service phase is particularly relevant, since the product and its related services must be managed in order to provide customer support. CE enabled PLM systems should focus on generating product data during utilization and end-of-use phases, which is more complex than generating product data at the development and manufacturing phases. Data, information and knowledge flows break down after the delivery of the product to the customer, preventing the feedback of data, information and knowledge from service and recycling providers back to designers and manufacturers [START_REF] Kiritsis | Closed-loop PLM for intelligent products in the era of the Internet of things[END_REF]. CE PLM solutions should close this information gap, feeding information about in-use and end-of-use to inform product development, materials selection and support circular business models. The recycling and recovery phase is characterized by various scenarios, such as product reuse with refurbishing, components reuse with disassembly and refurbishing, material reclamation with disassembly and disposal with or without incineration, material reclamation without disassembly, remanufacturing [START_REF] Kiritsis | Closed-loop PLM for intelligent products in the era of the Internet of things[END_REF]. Therefore, in the recycling and recovery phase PLM systems must be capable of managing activities related to materials recovery, recycling, remanufacturing and disposal. Virtual Enterprises and Industrial Symbiosis. Industrial symbiosis engages traditionally independent industries in a collaborative approach to competitive advantage by involving tangible exchange of materials, energy, water, and byproducts. The key elements to successfully implement industrial symbiosis are collaboration and the synergistic potential offered by geographic adjacency [START_REF] Chertow | Industrial Symbiosis: Literature and Taxonomy[END_REF]. Relating this concept to the virtual enterprise concept, there are some opportunities in extending PLM not only to the company's supply chain, but also to other companies with whom they have resources synergies. The aim would be to establish a knowledge base that would involve a network of enterprises, offering the necessary conditions to efficiently promote the sharing information regarding materials, water, energy and infrastructure, with the intention of achieving sustainable development in a collaborative way [START_REF] Romero | Green Virtual Enterprise Breeding Environments: A Sustainable Industrial Development Model for a Circular Economy[END_REF]. This is especially relevant for SMEs, which have limited internal resources, since it reinforces their capacity without the need to make large investments. Fig. 2. CE enabled PLM concept illustrates the concept of connecting multiple enterprises with symbiotic relationships, incorporating this data in PLM.

Conclusions

A CE enabled PLM would be a fundamental tool to improve companies' competitiveness in a collaborative and sustainable way, particularly footwear industry SMEs, which have complex interactions along the supply chain. Due to the growing market demands in this field, translated by product differentiation and a reduction in time to market, as well as increasing environmental awareness, there is a need to implement a new PLM paradigm. This circular PLM paradigm should be focused on generating data, information and knowledge within the organization and its stakeholders and manage it along the product's multiple lifecycles, from the perspective of sustainable product design and development, manufacturing, use and service, recycling and recovery. Technologies such as the IoT and Big Data will have a major role in enabling collaboration throughout the whole lifecycle. Although the design and development phase can be defined as the main phase responsible for driving the sustainability impact [START_REF] Vila | An Approach to Sustainable Product Lifecycle Management (Green PLM)[END_REF], future works must be done regarding its connection with other critical lifecycle phases such as use and service and recycling and recovery. Currently, efforts are being made in the development of technological solutions aimed at supply chain integration and smart products and stores. However, the integration of these technologies with PLM is nonexistent and therefore should be explored. The integration of a knowledge database regarding industrial symbiosis in PLM is also an area for further research.

Fig. 1 -

 1 Fig.1-RAMI 4.0 RAMI 4.0 maps the relationship between the lifecycle of products and value streams, the hierarchical levels of business information systems connected via the Internet, and the IT structure of an Industry 4.0 component. The description of the product lifecycle is done considering that a product has a lifecycle as a virtual "type" (during the design and development phases) and as a physical instance (after the manufacturing phase, through use, service and end of life). During the entire product lifecycle, as a type and an instance, the data generated in the process chains is processed by PLM systems, creating product-service centered collaboration among the connected world[START_REF] Contact | RAMI 4.0: the three-dimensional reference architecture model[END_REF].Due to technological advances and the emergence of the Industry 4.0 paradigm, which generate and must deal with extremely high volumes of data, new opportunities for the application of PLM in areas such as SMAC (Social, Mobile, Analytics, Cloud), Internet of Things (IoT), Big Data and Smart Products have recently emerged. PLM has been evolving continuously towards a digital paradigm, under which products are managed throughout their lifecycle using digital information and communication. The reporting of product performance using technologies such as the IoT, GPS and RFID, which allows real-time perception of its performance and customer satisfaction level, is a pressing trend[START_REF] Stark | Product Lifecycle Management[END_REF].The aim of the IoT application in PLM is to establish a smart information cycle along the whole product lifecycle. From data sensed and communicated by smart products, manufacturers can understand products' behavior when they are being used

Fig. 2 .

 2 Fig. 2. CE enabled PLM concept The vision for a CE-enabled PLM proposed in this research encompasses the CE dimensions defined in the scope of the FUTURING approach, represented by Science & Technology, Business & Innovation, Human Being & Society, Policy & Finance, Environmental Sustainability and Education & Training Building Blocks (BB) and the Pilar Actions (PA) needed to implement CE -Regenerate, Share, Optimize, Loop, Virtualize, Exchange. The vision is grouped into four categories: Enabling technologies; Types of products; Product lifecycle phases and stakeholders; Virtual enterprises and industrial symbiosis. Each category is described below. Enabling technologies. There is a consentual opinion among various authors [4][5][7][27] that digital technologies are the main drivers to the transition to the Circular Economy paradigm in PLM. The Internet can link together multiple supply chain partners, improving transparency and helping optimize cost, service, agility and

  1 defines an approach for the definition of CE that is structured in Building Blocks, Pillar Actions and impacts on Profit/ Planet/ People, covering the extent of CE. The Building Blocks represent the different dimensions of CE, defined as Science & Technology, Business & Innovation, Human Being & Society, Policy & Finance, Environmental Sustainability, Education &

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Acknowledgments

The research leading to these results has received funding from the European Union´s Horizon 2020 -The EU Framework Programme for Research and Innovation 2014-2020, under grant agreement No 723633.