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Second International Symposium on Engineering Systems MIT, Cambridge, Massachusetts, June 15-17, 2009

Challenges and Trends in Distributed Manufacturing Systems: Are wise engineering systems the ultimate answer?

Claudia-Melania Chituc1 and Francisco José Restivo2 Department of Informatics Engineering of the Faculty of Engineering of the University of Porto and LIACC – Artificial Intelligence and Computer Science Laboratory, Rua Dr. Roberto Frias,

Porto, 4200-465, Portugal

Copyright © 2009 by [C.-M. Chituc and F.J. Restivo]. Published and used by MIT ESD and

CESUN with permission.

The growing instability of the business arena, advancements of the information and communication technologies, and increased competition determined manufacturing enterprises to change their way of pursuing business. As consequence, new paradigms for manufacturing engineering systems have emerged. The aim of this article is to present preliminary results of an inter-disciplinary (on-going) research and development project focusing on the design, specification, performance modeling and implementation of an intelligent self-healing self-adaptable self-improving manufacturing engineering system (named wise manufacturing system). Main manufacturing paradigms are briefly presented, emphasizing their main strengths and weaknesses. The proposed system architecture towards a wise manufacturing engineering system is introduced, underlying main challenges and open issues. A cost model is also presented. Keywords: engineering system; distributed manufacturing system; wisdom; artificial intelligence; self-adaptable self-improving engineering system; subjectivity; wise information and communication technology; system architecture; performance assessment; cost model.

1. Introduction Engineering systems is a field of study focusing on the complex engineering of systems, within a human, societal and industrial context [1]. Manufacturing engineering systems are a particular class of engineering systems, targeting manufacturing processes and related activities within a manufacturing enterprise, a network of enterprises, and/ or the whole supply chain. 1 Post-Doctorate Researcher and Teaching Assistant (http://www.fe.up.pt/~melania), Department of Informatics Engineering, Faculty of Engineering of the University of Porto (FEUP/DEI) and LIACC – Artificial Intelligence and Computer Science Laboratory; Rua Dr. Roberto Frias, 4200-465, Porto, Portugal, cmchituc@fe.up.pt. 2 Associated Professor, Department of Informatics Engineering, Faculty of Engineering of the University of Porto (FEUP/DEI) and LIACC – Artificial Intelligence and Computer Science Laboratory; Rua Dr. Roberto Frias, 4200-465, Porto, Portugal, fjr@fe.up.pt.

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With the growing instability of the global business arena and increasingly aggressive competition, there is a corresponding increase in the complexity of engineering systems and challenges faced by manufacturing enterprises. Although current developments in the area of distributed (manufacturing) engineering systems bring a significant contribution, the results achieved so far are not a sufficient approach to the needs of modern manufacturing enterprises. This drives the need to take a broader and more holistic view on distributed manufacturing engineering systems (e.g., considering engineering-management aspects [1], economic and technical issues, environmental drivers, social implications). This article pertains to address challenges of distributed manufacturing engineering systems, with a holistic approach. The aim of this article is to present preliminary results of an inter-disciplinary (on-going) research and development project focusing on the design, specification, economic performance modeling, and implementation of a wise manufacturing system. Main manufacturing paradigms are briefly presented, emphasizing their main strengths and weaknesses. The proposed system architecture towards a wise manufacturing engineering system is introduced, emphasizing main challenges and open issues. A cost model is also presented. The article concludes with a section addressing the needs for further research.

2. Manufacturing Systems This section presents a brief overview of the main requirements of a manufacturing engineering system, followed by main paradigms on manufacturing. Main challenges and trends are introduced. 2.A. Main Requirements Main requirements to be satisfied by manufacturing systems have been summarized in [2] and [3], and they include: full integration of heterogeneous software and hardware within an enterprise and across a supply chain; open system architecture to accommodate new (hardware or software) sub-systems; communication within an enterprise and across enterprises; embodiment of human factors into manufacturing systems; quick response to external changes and unexpected disturbances from internal and external manufacturing environments; fault tolerance at the system and sub-system level (e.g., so as to detect and gracefully recover from system failures and minimize their implications on the working environment); agility; re-configurability; scalability. Additionally, today’s manufacturing enterprises face several challenges (e.g., determined by changing clients’ demands, high diversity of standards for e-communication, increasing competition) with which they have to deal with and remain competitive. Although current developments in the areas of manufacturing and (distributed) engineering systems bring a significant contribution, the results achieved so far are not a sufficient approach to the needs of modern manufacturing enterprises and several issues are still unsolved, such as: seamless interoperability in networks of enterprises (e.g., [4], [5]), scalability, optimal decision making, self-adaptability, self-improvement).

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2.B. A Brief Overview of Manufacturing Systems Paradigms Different views on classifying the periods of development in manufacturing exist. Jaikumar [6] identified six epochs of manufacturing by reviewing the events in terms of approaches to process control (e.g., accuracy, precision, reproductability, stability, adaptability, versatility), where technology development is the trigger to a new epoch. Mehrabi et al. [7] divided the evolution of manufacturing systems in terms of manufacturing techniques, identifying three epochs: pre-numerical computer control (pre-1970s), computer numerical control (1970-1990), when several manufacturing techniques emerged (e.g., Kaizen, just-in-time, lean manufacturing, total quality management), and knowledge epoch (post-1990). The glue that binds the collection of heterogeneous and distributed machines/ equipments into a manufacturing system is the information, which is stored, transmitted, processed, analyzed and interpreted by information systems and humans, and the underlying technology that provides support in performing these operations. According to [6], manufacturing technology is the technology of process control, which combines machines and human labor to control a manufacturing process. Main paradigms of manufacturing engineering systems (with emphasis on their strengths and weaknesses), and their underlying technologies are presented in Table 1. Central production planning/ manufacturing systems rely on centralized communication, are rigid, lack scalability and robustness, and have a high cost of integration. Mass Production Systems place emphasis on the reduction of products’ costs and full utilization of plant capacity. This manufacturing approach resulted in inflexible plants, associated with work-in-process and finished goods inventories. Computer-Aided Design (CAD)/ Computer Aided Manufacturing (CAM) systems integrate different tools (e.g., e-mail, multimedia, 3-dimensional CAD geometry viewer) in a distributed multimedia-designing environment through the Internet (e.g. [8]). In CAD, the computers are used in the design and analysis of products and processes. In CAM, the computers are used directly to control and monitor the machines/ processes in real-time or offline to support manufacturing operations (e.g., process planning). Computer integrated manufacturing (CIM) systems have been used to integrate different areas within manufacturing enterprises. They use a graphical user interface within a programming environment and incorporate multimedia packages to facilitate the dissemination of product information (e.g. [8]). With the evolution of the information and communication technologies and standards, adoption of the Internet, growing instability of the business arena, and increased competition, manufacturing systems boundaries are extended from a factory towards various types of network relationships. As consequence, enterprises’ mission and business strategy have also changed, e.g., from product competitive advantage towards collaborative added value, and the way enterprises perform business have been transformed (e.g., migrating from traditional practices to e-business, and the information related to manufacturing processes is transmitted over the Internet). Thus, different paradigms (or philosophies) emerged, such as lean manufacturing, agile

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manufacturing, flexible manufacturing, re-configurable manufacturing systems, e-manufacturing (e.g., [9], [10]), self-organizing decentralized manufacturing systems, holonic manufacturing systems, distributed virtual manufacturing systems, balanced automation manufacturing systems, biologically-inspired manufacturing systems.

Table 1. Main Paradigms for Manufacturing Engineering Systems

Nr. Crt.

Manufacturing System/

Paradigm

Focus Main Strengths Main Limitations Technology/ Approach

1. Mass production Reduction of product cost; Full utilization of plant capacity.

Full utilization of plant capacity.

Inflexibility; Do not cope with today’s requirements.

2. CIM Integration of computers and computer-based tools.

Use of computers to support different activities (e.g., design).

Do not cope with today’s requirements.

3. Flexible Manufacturing a variety of products on the same system.

High diversity of manufactured products.

Expensive; (Usually) include unnecessary functions/ software; Subject to obsolescence.

4. Reconfigurable Rapid adjustment of production capacity and functionality.

Modularity; Integrability; Convertibility; Diagnosability; Customization.

Include unnecessary functions/ software; Subject to obsolescence.

5. Intelligent Systems enhanced with human intelligence (e.g., concerning decision making).

Acceptance. Missing access to a body to interact with, and learn from environment [25], [26].

Agents.

6. Holonic Holons [15]. Preserves the benefits of hierarchy and heterarchy structures.

Centralized control. Agents, Petri-Nets.

7. Balanced automation

Optimal mix of machines and humans.

Balance of automated and human-based activities.

Agents.

8. Bio-inspired Based on bio-inspired approaches.

High adaptability, Flexibility; Often work well even when a desired task is poorly defined,

Early phase – need further developments.

Agents, bio-inspired computing, evolu-tionnary computing and related algo-rithms, bio-robotics, swarm intelligence.

9. Wise Wisdom-enhanced manufacturing systems.

Early phase (Expected: considerable increase in performance).

Early phase; this approach needs further research and development.

Wise information and communication technologies (wICT); evolvable hardware; cloud computing.

Manufacturing systems built on the concept of lean manufacturing were focused on continuous improvement in product quality while decreasing product costs. Flexible manufacturing systems

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place emphasis on manufacturing a high variety of products using the same system. Agile manufacturing systems have as main concern quick response to changes (e.g., [11]). A review of agile manufacturing systems is available in [12]. Main drivers for agile manufacturing are analyzed in [13]. Although lean and agile manufacturing concepts sound similar, they have different approaches to manufacturing engineering systems. While lean manufacturing is a response to competitive pressure with limited resources, agile manufacturing represents the response to complexity brought about by constant change. As emphasized in [12], lean is a collection of operational techniques focused on a productive use of resources, while agility is an overall strategy focusing on thriving in an unpredictable business environment. Reconfigurable manufacturing systems are manufacturing systems designed to rapidly adjust their production capacity and functionality, in response to new circumstances (e.g., changes on product demand, production of a new product on an existing system, integration of new process technology into existing manufacturing systems), by rearranging or changing its components (e.g., [14]; [7]). A historical summary of key events related to reconfigurable manufacturing systems is available in [7]. Unlike dedicated machine systems and flexible manufacturing systems, reconfigurable manufacturing systems do not have a fixed capacity and functionality, and they are designed through the use of reconfigurable hardware (e.g., modular machines) and software (e.g., open-architecture control). Flexible, agile and reconfigurable manufacturing systems concern the adaptation of the manufacturing system to new market conditions. While flexible manufacturing systems address expected changes, agile manufacturing systems concern adjustment to unexpected changes or events. It is a partial overlapping between agile and reconfigurable manufacturing systems; however, while agility concerns changes for the entire enterprise, re-configurability refers to the responsiveness of the production system to change. Intelligent manufacturing systems are manufacturing systems enhanced with human-like capabilities (e.g., human-like decision making capabilities). A review of Internet-based manufacturing system, with emphasis on distributed intelligent manufacturing is available in [8], and a review of the application of agent systems in intelligent manufacturing is available in [3]. Holonic manufacturing systems rely on the concept of holonic systems introduced in [15]. Accordingly, a holon is an identifiable part of a system with a unique identity, and consists in sub-ordinate parts belonging to a larger whole. In a holonic manufacturing system, a holon is an autonomous and cooperative manufacturing block for transforming, transporting, storing/ validating information and physical objects (e.g., [16], [17]). Despite the great promise of holonic (and multi-agent) systems, they did not make significant inroads in manufacturing plants in use today (e.g., [18]) mainly due to: lack of widely accepted standards; their implementations cover partially the manufacturing landscape; the use proprietary standards and mechanisms; the concept of centralized decision is difficult to accept.

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Balanced automated manufacturing systems are characterized by an optimal mix of automated and human activities. They are designed considering the socio-economic context and other factors, such as: production cost, level of required flexibility, desired product quality (e.g., [19]). Biologically inspired manufacturing systems address challenges in complex (unpredictable) manufacturing environments based on bio-inspired approaches, tackling aspects on self-organization, learning, evolution and adaptation (e.g., [20], [21]). They easily adapt to unforeseen changes in the manufacturing environment, usually work well even if a desired task is poorly defined, and achieve global behavior through interaction among (many) units (e.g., [22]). Accordingly, there is no central designer in such an engineering system; rather, a collection of alternatives compete against one another in an attempt to solve a given problem, and through the process of natural selection the best solutions tend to bubble to the top. Agents, bio-inspired computing, evolutionary computation, bio-robotics, swarm intelligence are the most relevant technologies and approaches for bio-inspired engineering systems [23]. The automated design of bio-inspired engineering systems, automatic adjustment (e.g., to continue operating when one of the units stops working or sudden changes in the manufacturing environment occur) and resilience are among unsolved challenges. 2.C. Challenges and Trends Manufacturing engineering systems evolved in order to meet several objectives, such as: reduction of cost; reduction of lead times; easy integration of new processes, sub-systems, technology and/ or upgrades; interoperability; reduction of production waste, production process and product environmental impact to ‘near zero’; fast reconfiguration; fast adaptation to expected and unexpected events. An extensive survey on manufacturing systems allowed the identification of the main current trends for manufacturing systems, which can be summarized as follows: specialization, characterized by an extensive focus on core competences; outsourcing; transition from vertical to horizontal structures (e.g., concerning management systems), from highly centralized to decentralized structures (e.g., where an individual element, unit or sub-unit is enhanced with decision making/ intelligence capabilities); evolution towards self-properties or self-sufficiency (e.g., self-adaptation) which generally occur at low levels. Manufacturing systems with these characteristics have a high level of integration, are easy upgradable, evolvable and adaptable (e.g., to new market conditions). In this context, several challenges have been identified, such as: achieving seamless interoperability, since information availability and e-communication are critical for distributed heterogeneous manufacturing engineering systems; the development of technologies and applications to support all the requirements of current distributed manufacturing systems; competitiveness: the enterprises should remain competitive, e.g., in terms of costs (e.g., life-cycle costs, investments) vs. payoffs; adequate equipments and machines (e.g., sensors) adequate to new manufacturing paradigms; sustainability (e.g., to consider environmental concerns into design); technology, equipment and manufacturing systems’ selection (e.g., to evaluate various systems configurations based on life-cycle economics, quality, system reliability); integration of humans with software and machines; non-functional properties, e.g., fault tolerance; openness, self-adaptability; each unit/sub-unit/ element of the manufacturing system should independently

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take optimal wise decisions (e.g., concerning resource utilization, incorporating scheduling algorithms, planning and control execution techniques), having a goal-driven and cooperative behave; performance assessment. Concerning future trends: it is rather difficult to forecast long term trends for manufacturing engineering systems. However, it is possible to indicate future trends for relatively short term, and directions for further research. Among research and development areas which will have an increasing attention are: scalability, seamless interoperability, algorithmic engineering systems biology (e.g., [29]), and manufacturing systems enhanced with human capabilities, such as wisdom-enhanced manufacturing systems.

3. Wise Manufacturing Systems 3.A. Main Characteristics A wise manufacturing engineering system represent a wisdom-enhanced3 intelligent manufacturing system, which has the ability to solve problems (e.g., related to control, collaboration) and make decisions taking into consideration human values and human’s subjectivity. In this research work (which reflects results of an on-going research and development project), a wise manufacturing system is an intelligent manufacturing engineering system (that is a system which has the ability to solve problems and make decisions taking into consideration human values and human’s subjectivity), which tends to be self-improving (e.g., able to monitor and assess the economic performance of a manufacturing process, unit or sub-unit, diagnose the causes of lower performance and take appropriate decisions for the overall unit/ system’s performance improvement), self-adaptable (e.g., by detecting abnormalities and taking decisions to recover from them), and self-healing (e.g., being able to monitor itself, diagnose causes of failure and recover from them, and may concern a single service or manufacturing unit, or address a more global level, such as manufacturing network). The idea of self-adaptation is to not new; e.g., the concept of self-adaptive software is related to the field of evolutionary computation (e.g., [27]). Accordingly, self-adaptation enables the algorithm to dynamically adapt to the characteristics of a problem and cope with changing environmental conditions. The novelty of this approach described in this article is related to self-improvement (especially from an economic perspective) and wisdom-enhancement. Additional requirements (besides the ones identified in Section 2) of such systems include:

- Constant data integration, to assure real-time capturing and loading from different operational sources;

- Highly available analytical methods and tools based on an analysis engine that can consistently generate and provide access to current business analyses at any time. Such analytical tools and applications are aimed at supporting real-time operational and tactical decisions, and they should be completely connected to the operational/ manufacturing units of the manufacturing system and information and communication technology platform;

3 This characteristic may be related with Penrose’s explanation of consciousness [28], or elan vital concept, as introduced by Chalmers [29].

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- Detection and correction of abnormalities, e.g., active abnormalities detection and decision engines to correct/ heal from these abnormalities, that can trigger tactical or operational decisions for specific events or tasks encountered in the manufacturing environment;

- High adaptability (e.g., of the architecture/ information and communication technology platform, manufacturing processes) to changes of the external business environment, and internal operational/ manufacturing environment;

- High availability; - Scalability, e.g., since the number of performance and manufacturing requirements will

increase with the acceptance of new orders, with the deployment of analytical applications enabling tactical and operational decision support;

- Instantaneous detection/ awareness of unexpected events (e.g., due to changes in the external business environment and internal manufacturing environment);

- Real-time response to unexpected events captured in the environment (e.g., business, manufacturing);

- Wisdom-enhanced capabilities, e.g., to support decision making.

3.B. Towards a Wise Manufacturing System An overview of our approach towards a wise manufacturing system is illustrated in Figure 1. The trigger is the reception of an Order (1). The Orders Management Unit (2) is responsible for the reception, creation and sending of order-related e-documents (e.g., order, order change request, order cancelation). The received orders may be accepted or rejected; the System Management Unit (3) takes the final decision, based on the information received from the Negotiation/ Agreements Management Unit (4). The negotiation and agreements setting (with clients, suppliers and outsourcing companies) is performed by the Negotiation/ Agreements Managing Unit (4), e.g., concerning quantities to be delivered, delivery and payment due date. The agreement is reflected in a collaboration agreement document, which is negotiated between the client, supplier or outsourced company, and the manufacturing enterprise. It is aimed that both the negotiation and agreements setting are automatic. The Technology Management Unit (5) assures secure and reliable e-transmission of e-documents over the Internet, between the manufacturing enterprise and clients (e.g., concerning orders of final products), suppliers (e.g., concerning order of raw material), and outsourced companies (e.g., concerning the delivery of partially finished goods, or execution of specific operations) and manages the centralized repository. Based on the orders received and the materials supplied, the ordered products are manufactured. The System Management Unit (3) involves automatic and human-based activities and coordinates all units. The Production/ Manufacturing Unit (8) is responsible with the actual manufacturing of the ordered products, by executing specific manufacturing processes. It combines both human-based and automatic activities. It receives as input (raw) material and/ or components from suppliers and finished (or semi-finished) products and components from outsourced companies. Based on the decision received from the System Management Unit (3), the final products are shipped to the clients, or kept in warehouses.

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Figure 1. Proposed architecture for a wise manufacturing system

A manufacturing enterprise may subcontract other enterprises to perform specific operations (e.g., in order to lower costs). The Outsourcing Management Unit (7) manages the business relationships and business processes with the outsourced companies (e.g., the selection of companies to perform specific operations; to keep track of the fulfillment of all the tasks specified in the collaboration agreements established with these enterprises). The Monitoring Unit (9) tracks the execution of the manufacturing processes. It executes a software program responsible for detecting/ sensing the current state of the business and operational manufacturing environment, monitoring the manufacturing-related business processes, in execution, for determining if the manufacturing system’s behavior is within acceptable threshold values of the manufacturing systems parameters (e.g., concerning economic performance), for capturing (unexpected) events and continuously informing the Analyzer (10) of the current situation (e.g., desired, undesired and unexpected events). In addition, the Monitoring Unit (9) may send to the Analyzer and System Manager alerts (e.g., when critical situations occur). The Manager/ System Management Unit (3) may view the ‘health’ status of the production system (e.g., monitoring of the overall manufacturing unit), and the ‘health’ status of individual production units/ sub-units, processes and resources.

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The Analyzer (10) unifies the information received from the Monitoring Unit (9) before the actual data processing begins. The received data is interpreted (e.g., what indicates the captured data). The Analyzer executes a software program which selects the metrics/ indicators which may be calculated with the received information (e.g., based on a set of predefined metrics stored in the local repository). It analyzes which is the significance of the captured data for the current situation of the manufacturing company (e.g., concerning the execution of the manufacturing processes), based on the values obtained for the performance assessment indicators/ metrics, by comparing the attained values with threshold values of key indicators. Based on the results attained for the key indicators, the Analyzer identifies which improvement decisions (for the overall manufacturing enterprise, and/ or for specific units) could be made, what risks may arise. The Analyzer determines whether the manufacturing system and all its units operate within a range of acceptable values for a set of parameters (e.g., related to economic performance, noise, optimal use of resources). Based on this information, a prediction of the performance and assessment of the risks for changes in the environment are made. The Analyzer indentifies the areas of improvement (e.g., manufacturing processes that can be improved), and makes (self-improving) decisions (e.g., assignment of resources) aiming at attained the desired values for the selected parameters. Thus, the Analyzer’s software capabilities should include decision support, on-line analytical processing, statistical analysis, forecasting and data mining. The Analyzer takes the appropriate wisdom-enhanced decisions (e.g., based on the expected values of the parameters and the actual quality degradation of these parameters) and transmits them to the Architecture Model Unit (12) and Repair Handler (11), Violations of constraints are handled by a repair mechanism: the Repair Handler (9). Based on the information received from the Analyzer, the architecture of the system may be adapted by the Architecture Model Unit (12) (e.g., when the values obtained for certain parameters fall outside well established limits), and the changes are propagated to the (operating) manufacturing unit. In this way, the Architecture Model Unit and Repair Handler support reasoning of the production system. 3.C. Cost Model A predictive cost model is proposed in this sub-section, which is derived from the cost model proposed by Chituc and Nof [30]. This proposed cost model is aimed at studying a wise manufacturing system. The costs incurred in manufacturing a product are the costs associated with all activities performed by the enterprise during the manufacturing process. Costs represent a useful parameter in assessing a system’s performance. The next paragraphs refer to costs incurred by making use of a wise manufacturing system to manufacture a product. Thus, other costs associated to manufacturing (e.g., raw material and components acquisition; e-communication costs; outsourcing costs; overheads; warehousing costs) are not addressed in this study. To develop a cost model for a wise manufacturing system the cost model proposed by Chituc and Nof [30] has been extended, as follows. The recovery cost from [30] corresponds to (self-) healing costs in a wise manufacturing system. Other costs (besides the costs identified in [30])

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have been considered here: costs associated with self-adaptation, Cadapt, and cost associated with self-improvement, Cimpr. Like in [30], let pj be the probability of an unexpected event u to occur, which prevents the manufacturing of product g. Let Cp be the cost associated in case pj is positive (e.g., disturbances occur). Let rj be the probability that the consequences of the event u can be ‘healed’, so that the product g is manufactured, and let Cheal be the corresponding additional (self-)healing cost. With these assumptions, the total cost of manufacturing a unit of a product (considering costs which are incurred by using of a wise manufacturing system) (Cwms) can be modeled as illustrated in Eq. (1), where Cm represents the total manufacturing cost to produce a unit of a product4.

Cwms = Cm + pj × Cpj + pj × rj × (Cheal + Cadapt ) + Cimpr (1) where 0 " pj, rj " 1 and j ∈N. Eq. (1) illustrates that unexpected events determine an increase of the cost, e.g., the necessity to perform self-healing and self-adapting actions determine an additional cost. Additionally, when the probability of unexpected events to occur is increasing, the costs are increasing. In such circumstances, the pressure to execute improving operations may increase. Let gi be the final product ordered (where i indicates that the order of product g has been received from an enterprise/ client i). Let qi be the quantity ordered be client i. The manufacturing enterprise may receive different orders of this product from different clients, who may request different quantities. Then the total cost (TCwms) of manufacturing qi units of the product from all n received orders is illustrated in Eq. (2).

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n

i 1

TCwms = �=

n

i 1

qi × Cwms (2)

A restriction could be set here: qmin " qi " qmax, where qmin and qmax are the minimum and maximum quantities of an ordered product (e.g., an order is accepted only if qi # qmin). This approach has to be interpreted with care. Firstly, this is a simplified cost model (and the reality is more complex). Secondly, this predictive cost model has been elaborated considering the received orders refer to a single product. Thirdly, in order to assess the performance of a wise manufacturing system, the prospective costs have to be analyzed in correlation with the expected payoffs.

4 Cm includes costs related to the acquisition of raw materials and components, labor costs, e-communication costs, and other costs related with the manufacturing process, which have not been detailed here because the aim of this sub-section is to model specific costs related to the use of a wise manufacturing system.

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3.D. Challenges and Open Issues From an engineering system perspective, this approach raises a number of challenging research problems, concerning Monitoring, Interpretation, Resolution, Self-adaptation, system Wisdom-enhancement, and Economic performance assessment, as follows:

- Monitoring: Which parameters should be monitored? Is it possible to build a reusable monitoring mechanism which can be easily incorporated in an existing system? How to design manufacturing processes, systems, sub-systems and/ or software components that can be easily monitored?

- Interpretation: How to interpret monitored information? How to determine if and when the manufacturing system needs to be adapted or improved? How to identify the source of an abnormality? What system architecture is more suited so that to attain a desired level for a set of selected parameters? How to set threshold values for selected parameters? How to determine which is the ‘optimal’ value for a certain parameter?

- Resolution: How to (self-)repair a manufacturing system once an abnormality has been identified? How to select the ‘optimal’ (self-)healing actions from a set of (predefined) healing actions? Is it guaranteed that the repairs will improve the system’s performance? How to reconcile conflicting (self-)healing actions obtained from different (self-)healing models? Can and should the manufacturing system be improved even when no specific errors or abnormalities have been detected?

- Self-adaptation: How to design and implement a manufacturing engineering system which allows dynamic (self-) adaptation of its elements? What actions should be performed when abnormalities occur during the process of self-adaptation? Which parameters to evaluate when taking a self-adaptation decision?

- Wisdom-enhancement: How to design and implement a manufacturing engineering system supporting wisdom-enhanced decisions? What technologies to use to implement such a system?

- Economic performance assessment: How to asses the economic performance of a wise manufacturing engineering system? What metrics/ performance indicators should be considered?

In addition, several open issues for this approach are identified: When overheads occur to each element (e.g., Repair Handler, Monitoring Unit, Analyzer), which is the most appropriate action to be performed? To what extend can costs be minimized when building a wise (e.g., self-adapting self-healing) manufacturing system? How much is improved the economic performance when using a wisdom-enhanced manufacturing system compared to other approaches? However, the greatest challenge will be the actual implementation of such a wisdom-enhanced system. Such an approach requires appropriate software and hardware elements. Evolvable hardware (e.g., [31], [32]), cloud computing, bio-inspired and service oriented computing are promising directions, which will be explored. Despite the numerous developments of information and communication technologies, even the most advanced ones are not yet mature enough to solve complex real-world problems. Evolutionary algorithms are at their infancy, with many obstacles yet to overcome (e.g., making them more automated) [22]. Existing developments (e.g., related to the implementation of bio-

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inspired manufacturing systems, attempts to hybridize engineering systems by combining bio-inspired computing with formal methods) hold a great promise through the next generation of engineering systems. Although such approaches have been applied only to simple problems, there is hope that they may be scalable to large/ complex problems. However, as emphasized by [26] the field of artificial intelligence has been labeled as failure, since the produced artefacts cannot actually be confused with a living organism (e.g., models helped modeling biological engineering systems better, but the produced models never worked as well as biology). Maybe wise information and communication technologies (wICT) can be regarded as the new generation of information and communication technologies, encompassing the theories and applications of artificial intelligence, pattern recognition, learning theory, data warehousing, data mining, knowledge discovery, swarm intelligence, adaptive management, bio-inspired methods, cloud computing. wICT may be regarded as an inter-disciplinary field, involving techniques developed not only in computers science/ engineering, but in other related communities, e.g., economics, mathematics, cognitive science, neuroscience, operations research. A promising step towards wICT is the World Wide Wisdom Web (W4) (e.g., [33], [34]).

4. Conclusions and Future Work

Despite the developments in the area of engineering systems and advancements of information and communication technologies, current (manufacturing) engineering systems fail to address all the needs of today’s manufacturing enterprises. Current trends of manufacturing engineering system are towards enhancing machines with bio-inspired and human abilities (e.g., intelligence, wisdom, cognitive functions), and in hiring (fewer) highly skilled employees. However, this trend has to be closely accompanied with (positive and negative) human, social and environmental consequences. This article reflects results of an on-going inter-disciplinary research and development project towards a wise manufacturing engineering system. A proposed system architecture has been described, which support self-adaptability, self-healing, self-improving and wisdom-enhanced capabilities. Main challenges concerning monitoring, interpretation, resolution, self-adaptation, system wisdom-enhancement, economic performance assessment, and implementation issues are discussed. A cost model is also presented. Wise information and communication technologies (wICT) are envisioned (through the prism of current technologies and approaches) as supporting technologies for the implementation of a wise manufacturing system. wICT may represent a paradigm shift, driven from artificial intelligence techniques, the Wisdom Web (W4), autonomy-oriented computing, service-oriented computing, cloud computing, swarm intelligence. It will yield the new tools and infrastructures necessary to support wise manufacturing systems. As technologies evolve, and simulation, modeling and prototyping techniques mature, the hope is that manufacturing such a wise engineering system will become relatively straightforward. However, this requires research and

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development combining different disciplines, e.g., economics, computer science, cognitive science, neuroscience, systems engineering, biology, mathematics. Brooks [26] pointed out four possible causes for failure in building systems inspired by various aspects of life, which should be considered when attempting to bridge the gap between non-living and living systems: some parameters might be wrong; the build models might be below some complexity threshold; lack of computing power; missing something fundamental and currently unimagined when modeling biological systems. Future research work will carefully consider these remarks. Future research and development work will focus on modeling the performance of such a wise manufacturing system and identifying specific metrics for performance assessment, system modeling, and the selection of technologies which may support actual implementation of such a wise manufacturing system. Main challenges and open issues identified (which have been presented in Section 3.C) will also be addressed. Acknowledgments The authors acknowledge the financial support from the Artificial Intelligence and Computer Science Laboratory (LIACC), and the Department of Informatics Engineering of the Faculty of Engineering of the University of Porto (FEUP/DEI) and LIACC for the facilities offered to pursue this research work.

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