Hans " Wortmann

A A Alblas

P Buijs

K Peters

Supply chain integration for sustainability faces sustaining ICT problems

Keywords: ICT, sustainability, supply chain integration 1

This paper is concerned with the role of supply chain management in sustainable operations. It argues that sustainability requires increased supply chain co-ordination using ICT for product life cycle management. Accordingly, the paper researches integration problems in supply chains due to ICT. It addresses the question, why operational decisions are hardly based on integrated information in supply chains. It argues that several current information/ communication technologies are essentially heterogeneous. Therefore, it is not easy to integrate these. The paper distinguishes (1) transactional systems (2) realtime monitoring/control systems (3) decision support systems (4) analytical systems. Integration problems should be studied by analysing the underlying characteristics of the technology.

Introduction

Information systems and information technology plays a key role in sustainable supply and return of goods. When companies want to prove that materials are acquired in a socially responsible way, they need information systems. When returning products have to be refurbished, these products have to be identified by suitable information systems. When companies want to perform a Life cycle Analysis (LCA), they are dependent on correct and appropriate information systems.

Moreover, the focus on supply chains and return networks requires interoperability of supply chain partners' information systems. Because of the fact that these partners change dynamically, the real requirement is that interoperability can be realized "on the fly" with hardly any human effort.

Current practice in supply and distribution networks is far away from this ideal. Despite of considerable investments in all kinds of ICT, the real decision makers are still deciding by rules of thumb based on flawed information, rather than by up-todate, correct and complete information. Apparently, there are still many ICT applications which are difficult to integrate, even in a single company. This phenomenon is the basis for the research question to be addressed in this paper: What makes ICT applications essentially different, creating sustaining integration problems?

In answering this research question, the researchers first of all corroborated their assumption, that supply chain decision makers are indeed facing heterogeneous systems. For this corroboration, in-depth interviews were held nine cross-docking firms. Section 2 reports about the findings. Section 3 presents an applications typology, based on the findings of Section 2. This leads to a characterization of heterogeneity (and therefore, difficulty in integration) which is elaborated in Section 4. Section 5 concludes the paper.

Information used by operational decision makers in manufacturing and freight logistics

After case studies in manufacturing [START_REF] De Snoo | Coordination activities of human planners during rescheduling: case analysis and event handling procedure[END_REF] and distribution [START_REF] Meyer | Intelligent products for robust monitoring and control of road-based logistics[END_REF], we had to conclude that it is apparently difficult for operational planners of goods flows to use scheduling systems. In order to understand better, what constitutes the problems of these planners, it was decided to proceed with more detailed studies.

Transportation is an essential part of supply chains and distribution networks of physical goods. Therefore, we decided to organize detailed interviews with 9 distribution companies in The Netherlands who were engaged in cross docking operations.

The results of these interviews can be summarized as follows. While managers of these operations considered the investments in operational ICT systems to be considerable, the planners would typically not rely on the information provided by systems, for the following reasons.

First of all, planners could follow individual trucks via GPS systems on screens with maps, but these systems would not provide insight into causes of delays or expected problems. Accordingly the planners preferred to be called or mailed by drivers and be informed on the context, expected problems and potential solutions Second, the planners would not trust transactional systems (ERP systems, warehouse management systems, distribution management systems), because these systems were not always up to date. This is also due to the fact that these systems are not integrated with the GPS systems. Third, the planners would not trust their scheduling or decision support systems, because these systems are fed by the transactional systems. Moreover, these systems take a snapshot of the transactional systems, which may be bypassed by reality. Finally, constraints as perceived by the planners always differ from the constraints modeled in these systems.

From these interviews, it may be concluded that heterogeneity (GPS, Voice, transactional, decision support) and varying time properties of data is indeed an issue in practice. NB: these findings concentrate on operational decision problems.

Conceptualization

Our conceptualization of enterprise information systems and information technology is outlined below (see Fig. 1). Note, that the increase of technologies over the last decades leads to a situation where technologies such as streaming video are nowadays covered under the header of ICT, while in the past, video streams were not considered as ICT applications.

Real-time monitoring and control systems

Real-time monitoring systems observe the value of certain variables, using sensors, cameras, microphones, GPS systems etc. Such systems generate basically streaming data. Although streaming data may be technically discrete, they can be thought of as continuous. Streaming data can be used to measure the behavior certain variables (temperatures, pressures, noise, etc.). The streams are kept in log files.

Streaming data may be in a form which is suitable for human processing (video, audio, text) or in may be in a form which is suitable for machine processing (usually numerical data). The latter may be transformed into the former.

As is well known, real-time control systems are automated systems able to influence certain variables via actuators with the purpose to keep other variables close to a specified set point or trajectory.

Real-time data can be used to generate discrete transactions for transactional systems (see arrow 1 in figure 1). However, generating transactions is not trivial, as will be discussed in Section 4.1. Alternatively, such transactions are posted by humans (see arrow 2 in figure 1).

Transactional systems

Transactional systems keep track of the relevant objects of enterprises. Such objects are e.g. freight, trucks, drivers, containers, locations, customers, orders and so on. The essence of transactional systems is that objects have a state. The state of an object changes in discrete steps (transactions). For example, a truck has either arrived or not arrived at a location. The freight either has been loaded or has not been loaded. The customer has accepted the shipment or has not accepted the shipment. And so on for all relevant objects.

Transactional systems are not built to capture full history. At each point in time, these systems contain the actual state of each object. In other words, the systems cannot recast the past (other than a few days, needed for database recovery in case of disaster). Of course, such systems keep track of the past via log files and archives, but conceptually it is not possible to delete, query or update the history.

Transactional systems are based on classical databases, whether relational or object oriented. The essence of such databases is a clear separation between the schema and the instances, corresponding to the distinction between types and instances in programming languages. The knowledge at type level defines the attributes and methods applicable for an object instance. Accordingly, transactional systems have explicit semantics as expressed in the database schema and in the business logic which is called when transactions are posted (these semantics and logic constitute an ontology in knowledge engineering parlance).

Finally, transactional systems are designed to be used primarily by humans. Although sometimes automatically generated transactions are applicable (see previous section), the primary goal is to inform humans on the state of affairs of objects relevant for the organization concerned.

Decision support systems

(Operational) decision support systems are systems which support operational decisions by software models. These models relate decisions to performance measures. In cross docking, such decisions allocate trucks to doors, they schedule trips, or they allocate shipments to trips. The models compute the best choices and provide advice to the decision makers, based on the state of relevant objects (shipments, orders, location of trucks ,…).

Decision support systems (DSS) have to take a snapshot of objects' states before they can start their calculations (see arrow 3 in figure 1). After such a snapshot, the decision maker may interact with the DSS to study trade-offs between different deci- sions. The models may vary in their mathematical sophistication or in the level of detail. The "snapshot" data are usually kept in fast computer memory (RAM) during computation.

As indicated by arrow 3, the DSS models of objects are based on transactional information. In many cases, the transformation of transactional information into DSS information is not easy. These problems are discussed in more detail in section 4.3. Reversely, decisions taken may have to be stored as objects in the transactional systems (see arrow 4). This involves peculiar problems, also discussed in section 4.3.

Analytical systems

Analytical systems are data warehouses where information of objects of the same type are stored. These systems typically collect information from transaction processing systems over periods of time. Accordingly, they provide insight into the dynamics of (aggregations) of objects.

Analytical systems use databases which are based on Online Analytical Processing (OLAP) technology. OLAP databases are often represented as cubes, which can be seen as tables with an additional time dimension. The tables are in themselves organized at different levels of aggregation. These systems can be queried for all kinds of analysis, but unlike transactional databases it is not possible to navigate between related object types.

Analytical systems are nowadays in use as the basis for many management reports, especially for strategic and tactical decisions. For operational decisions, they are only incidentally used. Our investigation in practice did not reveal high usage at operational level. It has been included here for the sake of completeness.

Analytical systems retrieve their information from transactional systems either within the company (see arrow 6 in figure 1) or from external sources. Section 4.2 describes some difficulties in this transformation. There is usually no information flowing back from analytical systems to transactional systems, with the exception of e.g. forecasts. The problems encountered here are similar to those related to DSS.

The above systems and their distinguishing features are summarized in Table 1. Integration problems between different types of systems

Integrating real-time systems and transactional systems

When data streams follow a structured format (e.g. by measuring known physical temperature, pressure, etc.), it is in principle possible to generate transactions for transactional systems, as depicted in figure 1 by arrow 1. Although this is possible, it is not trivial, for several reasons.

First of all, transactional systems are not designed to capture these streaming data, because these data are usually not meaningful as changing states of objects. Nobody is interested in the square meter where a truck resides at every other second. Rather, it is interesting to know if the truck arrives in time at its destiny, and whether it is able to load or unload; it is interesting to learn that the unloaded cargo is accepted by the next party; etc. This information is mostly still subject to human interpretation and therefore it still requires human data entry (arrow 2). The same holds for sensor data on equipment condition used in refurbishment decisions: it requires an explicit human decision to assess when to act.

Second, the usual differences in technical and syntactical standards between different IT applications will emerge. Although increasingly technology becomes available to generate interfaces, there is a long way to go before "plug-and-play" is reached.

Last but not least, there are always differences in semantics. One of the major sustainability challenges in the food industry is to decrease waste. Food may have a status "fresh" and a status "perished", but also some intermediate states. The nature of the states and conditions under which a product proceeds through these states may differ between applications (e.g. fish differs from fruit). Therefore, sensor data are unlikely to match the semantic categories applicable in specific cases without dedicated programming effort.

Therefore, the conditions of streaming data leading to a transaction may be programmed (e.g. by software on a truck), but such software posting transactions is customer-specific and expensive to build and maintain. With current state of the art, it cannot be configured or installed "on the fly" in e.g. third-party trucks which happen to transport the cargo concerned. Similarly, in the context of refurbishment and sustainability, a product being maintained or disassembled will not be automatically reported to the OEM or upstream in the supply chain. Rather this requires a separate (manual) transaction, for which many things have to be arranged organizationally.

Accordingly, the first sustaining problem encountered in ICT integration resides between streaming data and transactional systems (see arrow 1 in figure 1).

NB. The above arguments apply à forteriori to non-structured data. It is clear that e.g. video streaming data cannot easily be integrated with other ICT applications such as transactional systems. Basically, humans have to look at video data or listen to audio data and capture meaning. Attempts to create automatic alerts from audio data (e.g. with voice recognition) are encouraging, but this does not mean that in general transactions can be posted in transactional systems without human intervention.

4.2

From transactional information to analytical information Many decision makers will currently employ data warehouses for analysis of historical data or other time series. These data warehouses contain data of similar objects (e.g. sales data, quality control data or purchasing data) aggregated over certain dimensions such as time periods, geographies of market segments. The power of a data warehouse is that it enables quick drill down into data and/or slicing and dicing.

Analytical data are structured data, often based on transactional data from transactional systems; quite often, analytical data are generated for the archives of transactional systems. Because analytical data are derived from transactional data, they should both based on the same semantics (or ontology).

However, these semantics may yield problems. An obvious problem emerges when the semantics of a transactional system changes over time. In the context of supply chain management, the semantics of systems of different parties involved may also be different. For example, the notion of "sales figures" may be interpreted as "booked sales", "invoiced sales" or "earned sales" by different systems.

the context of sustainability, different interpretations of sustainability measures is a major problem. This is elaborated in [START_REF] Ingwersen | Can we compare the environmental performance of this product with that one? An update on the performance of product category rules and future challenges towards alignment[END_REF] where the term product category rules is used to denote standard values for environmental load calculated in LCA. The disadvantage of such standard is, that actual effort to reduce environmental load by companies is not acknowledged. Accordingly, there will be an ongoing debate on "numbers" related to sustainability issues in data warehouses.

We conclude that this semantic heterogeneity will be a sustaining problem in supply chains engaged in sustainability (see arrow labeled "6" in figure 1).

NB: some early work has been done to overcome semantic heterogeneity by automated semantic negotiations "on the fly", see Van Blommestein (2007).

From transactional to decision support systems

In contrast to data warehouses, which contain aggregated data related to one type of objects in past or future periods of time, decision support models perform calculations to support specific decisions. In sustainable product development, LCA calculations typically represent decision support. In manufacturing, DSS often simulate the future across multiple objects. In Logistics, these systems support planning or scheduling of logistical operations.

In contrast to analytical data, the data for these decision support models is based on a snapshot of transactional data at a particular point in time ("now"). Obviously, the semantic heterogeneity problem also plays in the context of creating data for decision support models from transactional data. However, there is an additional issue for operational decision making problems. This is due to the fact that the time proceeds while decisions are being taken. At the same time when the snapshot is taken, new transactional information becomes available which may change the picture to some extent. Accordingly, the decision maker is forced to keep an eye on actual developments while using the DSS model and while to taking the first decisions in real time.

No wonder that it is difficult to use such models (see arrow "3").

A DSS calculating LCAs suffers from similar problems in timing and semantics. An LCA is based on a snapshot of product data. This DSS is full of assumptions on coefficients for sustainability performance (from handbooks or LCI databases). Genuine improvements in sustainability will affect these coefficients, but in the DSS software, it is very cumbersome to adjust such coefficients. Moreover, maintaining correct engineering data is a challenge in itself. Ideally, such DSS for LCA should be interactive and integrated with PLM and ERP systems.

Conclusion

In this paper, several sustaining problems in supply chain integration have been identified. These problems are inspired by future requirements to information systems from expected sustainability developments. These problems are:

Creating automated transactions from streaming data (preferably "on the fly") Automated solution of heterogeneous semantics or ontologies between transactional systems and/or data warehouses and decision support systems Automated solution of the problem of changing snapshots for operational decision support systems Automated analysis of text and feedback of the results in enterprise systems.

Figure 1 .

 1 Figure 1. Conceptualization of enterprise information systems

Real

Table 1 .

 1 Summary of information system features

	Type of system	Structured or	Snapshot or	Continous or	Main way of
		un-structured?	time-varying?	discrete data?	data storage
	Analytical	Structured	Past until now	Discrete (often in	OlAP
	systems		(aggregated )	periods)	databases
	Decision support	Structured	Snapshot	discrete	In-RAM
	systems		and future		storage
	Transactional	Structured	snapshot	discrete	Relational or
	systems				object-oriented
	Real-time	Can be both	Past until now	Continuous	Files
	systems		(detailed)

Acknowledgement

This work was sponsored by the IOP ICPR (NL Min. of Economic Affairs)


Christos Emmanouilidis

Kari Komonen
email: kari.komonen@saunalahti.fi

Physical Asset Management Practices in Industry: Comparisons between Greece and Other EU Countries

Keywords:

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

Introduction

Physical asset Management is "the optimal life cycle management of physical assets to sustainably achieve the stated business objectives". The concept takes into account capacity considerations, design, investments, operations, maintenance of production equipment and disposals. One of the main tasks of Asset Management is to guarantee that the changing business requirements and physical assets match together in an optimal way, taking into account all life cycle aspects of equipment. In the present business environment, Physical Asset Management is becoming a key challenge for business organisations and has acquired more importance as a management function than ever before. However, the state of implementing Physical Asset Management best practices in industry is not sufficiently well recorded or documented. The current financial climate makes it all the more important to look into such practices and trends within European Industry [START_REF]Asset Management: the state of the art in Europe from a life cycle perspective[END_REF]. Among the tools that can be used to assess the maturity level of the Asset Management function is the PAS 55 model, developed by the Institute of Asset Management in the UK [START_REF]IAM: IAM PAS55:2008[END_REF]. Other initiatives have sought to map specific aspects of Maintenance Management practice in some countries, such as Italy [START_REF] Fumagalli | Computerized Maintenance Management Systems in SMEs: A Survey in Italy and Some Remarks for the Implementation of Condition Based Maintenance[END_REF] and Spain [START_REF] Carnero | Condition-based maintenance in small industries[END_REF]. A European-wide survey or a more global one over a common baseline was missing.

An initiative to support establishing the current status of Physical Asset Management was taken by the European Federation of National Maintenance Societies (EFNMS) European Asset Management Committee (EAMC), with the launch in 2011of a survey in order to map Physical Asset Management practices in EU industries [5][6]. An initial analysis of the obtained results has shed light into the current state of Asset Management practice in the EU and more work on supplementing the currently obtained results is foreseen [START_REF] Komonen | European Asset Management Practices: the results of EFNMS asset management survey[END_REF]. With Greece being among the countries severely hit by the financial crisis and with its industry just starting to switch to more export-oriented operation [START_REF] Feir | The Greek Economy: 3/2012[END_REF], it is of particular interest to focus on the survey results to draw some insight into differences between physical asset management practices in Greece compared to the rest of Europe. This paper presents an analysis of the main findings of such a comparison. An extension of the survey is planned for 2013-2014.

Physical Asset Management

Physical Asset Management integrates functions from different ISA-95 layers, including operational, tactical and strategic functions and therefore requires an integrated approach in decision making. It integrates asset creation and development, asset operation, upkeep of assets, repair and upgrading of assets, as well as recycling and disposals. An important part of asset development is the determination of capacity needs and capacity creation which involves investment planning and investment execution. Asset operation is the handling of assets for their intended function, mostly production, and especially the part of production operations that influences assets and their prevailing production capability. The third dimension, upkeep, stands for the maintenance function. Asset management strongly supports sustainable development by striving at efficient use of scarce resources taking into account market, technological, legal and normative constraints, market dynamics, as well as sustainability. Upgrades go beyond replacements, in the form of repair, renovation or remanufacturing. An important aspect of physical asset management is to strike the right balance between performance, cost and risk in pursuing the enterprise goals. It supports managing investments, capacity and production in a more efficient, better quality-assured, safer and more competitive way. There is a need for more efficient asset management to deal with the different sources of losses during the life cycle of production equipment and physical assets, thus demanding more efficient asset management, such as:

x economic lifetime is not in balance with technical lifetime x all processes are not functioning at the same operating rate x during process and product transitions production capacity of large concentration of assets is utilised x demand does not match with capacity x during installation and start of investment production losses may be huge

x Overall Equipment Effectiveness (OEE, including low availability, low speed and low quality rate) causes production losses x due to low flexibility of assets equipment is used in ineffective way (product mix and insufficient adaptation to demand fluctuations) x design and manufacturing of equipment is not up to date.

To drive improvements in Physical Asset Management practice, the current status needs to be identified and this has been the primary aim of the EAMC survey.

Physical Asset Management Survey

To carry out this exploratory survey EAMC launched a survey of European Industry, on the basis of a questionnaire designed to collect background information for the development activities in the area of Physical Asset Management and the maintenance function as a part of Physical Asset Management [START_REF] Komonen | EFNMS Asset Management Survey 2011: How organizations manage their assets in practice[END_REF]. Another objective was to offer European organisations insight into the mode of operations in Physical Asset Management within various business and technology environments. The survey questionnaire included the following groups of questions:

A.

Greek Industry Survey Findings

The analysis aims at indentifying the current status regarding Greek industry, compared to the rest of Europe. This is of interest especially since the current crisis puts on severe pressure for improvements in competitiveness, which currently, according to the latest figures from the World Economic Forum shows Greece to be a stubbornly persistently low performer [START_REF] Schwab | The Global Competitiveness Report: 2012-2013[END_REF]. This is despite Greece featuring at a relatively high ranking in terms supporting pillars, such as infrastructure, technological readiness and educational support. Due to the specifically negative macroeconomic environment, Greek industry would have to perform even better in terms of Physical Asset Management practices to raise its competiveness level.

The EAMC Asset Management survey has drawn participation from 33 Greek industries. These were distributed among the following sectors:

x Manufacture of food products x Manufacture of wood and of products of wood and cork, of furniture, etc x Manufacture of paper and paper products x Manufacture of coke & refined petroleum, chemicals & chemical products x Manufacture of basic pharmaceutical products and pharmaceutical preparations x Manufacture of rubber and plastic products x Manufacture of fabricated metal products, of machinery and equipment, of motor vehicles, etc. x Other manufacturing x Distribution of gaseous fuels through mains x Water transport / Other utilities x Service provider / facilities management Comparative results were obtained against 102 responses from other European countries. In general, some variations were found between Greek and other European industries in terms of organisational and management issues, as well as in terms of future drivers. No major variations were observed in the maturity of asset management activities. This may be an indication that physical asset management is not falling behind other European countries management, but other organisational and even legislative issues are considered as needing more improvement. When considering only manufacturing industries, more deviations are found. A first difference is observed in the lifecycle phase of industry wherein Greek distribution was more skewed towards slow-growth and mature to aging industry (Fig. 1). Another difference is on the planned operating rate of the production equipment. In Greece, the obtained responses indicate that this peaks at the middle-usage rage between 51-75%, whereas overall in responses obtained from all European countries, the responses indicate a much more skewed distribution towards higher operating rates (Fig. 2). One of the few deviations in the asset management practices was in the criteria for investment decisions. In Greece a clear majority of the respondents used investment costs-based decision making, whereas the other regions utilized more life cycle cost or profit based decision making (Fig. 3). A reason behind this tendency can probably be the deviating life cycle phase structure of the industry in Greece. In Greece, financial responsibility for physical asset management seems to be more on the shoulders of the maintenance function, whereas in the group of the other regions it is allocated to the asset manager, asset management teams or the top management. The extent of usage of asset management information systems, such as that of a computerised maintenance management system (CMMS) also appears to differ. Overall, a higher proportion of asset events tend to be registered in a CMMS in other countries, compared to Greece (Fig. 4).

The identification of risk-based maintenance requirements does not show a strong concentration either into low or high-priorities in industries (Fig. 5). Generally, most industries consider it to be in place, but there is a higher proportion of European industries that consider it effective and fully integrated with all other relevant aspects of asset management in all other parts of the organisation, whereas in Greece the same proportion considers it effective but more see it integrated. The best practice is missing in the responses from Greece. More deviations can be found if we focus on manufacturing industries. Other regions in Europe apply the follow-up of maintenance costs at the more detailed indenture level, compared to Greece (Fig. 6). Deviations are also found in producing annual asset management plans, acquisition of assets and ensuring new assets meet specified requirements. Respondents in Greece were not as satisfied with procedures as in the other regions.

Considering the drivers of asset management decisions and practices, business risk management is in general considered significant or very significant, with a smaller proportion in Greek industry considering it very significant compared to other countries. On the question whether these variations depend on real differences in asset management practices or whether they are due to variations in the business technological environment, multivariate regression analysis shows that. 'Expected economic age of production equipment' and 'business risk management as a driver' have the strongest influence on investment criteria (Fig. 7). Greek asset management practices have some impact on this factor, but statistical significance was only indicative, with explanatory power at 0.27 %. Table 1. Factors having impact on investment decision criteria Table 2. Factors having impact on financial responsibility for AM of existing equipment Business risk management as a driver, life cycle phase of main products and Greek specific practices seem to have some influence on financial responsibility for asset management activities (Tables 12). The overall explanatory power of the statistical model was 0.18 %. In the case of 'Maintenance costs calculation Greek specific practices seem to have some impact on the results, but the impact was not very strong. The same applies to 'Events registered in CMMS'.

Conclusion

Asset management within manufacturing is considered an increasingly important contributor towards enhancing production quality, safety, performance and efficiency, as well as environmental sustainability Nonetheless, the status of implementation of Physical Asset Management best practices in industry is not sufficiently recorded or documented. The EFNMS via its EAMC Committee has taken the initiative to seek to map the status of Physical Asset Management in Europe. This action is taken by the EFNMS Asset Management Committee (EAMC), with the launch in spring 2011of an EU-wide survey in order to map Physical Asset Management practices in the different kind of business environments in Europe. The survey has identified key areas for improvement among European industry, such as 'Risk & Review', 'Asset Knowledge' and 'Asset Management Strategy and Planning'. Focusing specifically on Greek industry, there is a need to place greater emphasis on the importance of specific driv- ers for asset management. Greater information integration and enabling technologies usage should be sought, while asset management linkage with production and asset utilisation performance aspects, such as production equipment planned rate should be better established. Other aspects such as risk-based prioritisation, lifecycle considerations and return on assets are worth improved attention. A greater push within Greek industry is needed to better establish links between asset management and upper management functions. Decision making should not be solely driven by acquisition and installation costs considerations, but life cycle costs and profits should be taken more into account. Asset management in Greek industry is advised to manage costs not only at a macro-level but at a lower asset level, and indenture could also reflect on this. The nature of the reported survey has been exploratory but is a good basis for developing future studies, providing benchmarking findings against which to seek to place current practices in the infrastructure sector and manufacturing industries.

Fig. 1 .Fig. 2 .Fig. 3 .Fig. 4 .

 1234 Fig. 1. Lifecycle phase of industry. 1. Emerging 2. Fast growth. 3. Slow growth 4. Mature. 5. Aging / Declining Fig. 2. Planned operation rate: 1: <25%, 2: 25-50%, 3: 51-75%, 4: 76-90%, 5: >90%

Fig. 5 .Fig. 6 .Fig. 7 .

 567 Fig. 5. Risk-based maintenance requirements: 0: least -5: maximum Fig. 6. The level, down to maintenance costs have been followed

  lines or best practices for classifying, collecting and storing asset knowledge 5. Asset management capability (3 questions) e.g. auditing and review the safety, efficiency and effectiveness of asset management activities 6. Asset management review and improvement (3 questions): e.g. measurement, development and monitoring the organisation's asset management performance C. Future driving forces of asset management activities Long term return on assets, OEE and legislation are among 10 proposed drivers. The questionnaire is available at: https://e-lomake.fi/lomakkeet/2033/lomake.html. The survey was implemented during spring 2011 and organisations representing 24 countries and 23 industrial sectors contributed the study.

Organisation and management principles 1. Organisations business environment (7 questions): e.g. rate of change, life cycle phase of the industry and principal business drivers 2. Organisation and decision making in the area of asset management (5 questions): e.g. financial responsibility of asset management, participation in various phases of investment processes 3. Management and control (7questions): e.g. use of aggregate and other key performance indicators for asset management, criteria for investment decisions 4. Asset knowledge management (4 questions): e.g. coverage of criticality analyses, use of asset history systems, working culture B. Maturity of management activities 1. Policy and strategy (4 questions): e.g. development of asset management policy and strategy, demand analysis, asset management plans 2. Whole Life Costing (WLC) justification and risk analysis (3 questions): e.g. evaluation of capital expenditure requirements for the whole lifecycle 3. Asset lifecycle management (6 questions): e.g. various lifecycle phases practices 4. Asset knowledge management (4 questions): e.g. definition of standards, guide-

Acknowledgements

The EFNMS Physical Asset Management Survey was designed by the EAMC Committee of EFNMS and supported by National Maintenance Societies and especially the Hellenic Maintenance Society (HMS) on Greek Industry. The authors wish to acknowledge the contribution of EAMC members on the survey results. The first author wishes to acknowledge partial support through GSRT (grant 09SYN-71-856, project WelCOM) and the support of P. Pistofidis for the survey of Greek Industry.


Ulysses Martins

Moreira Filho
email: moreiraf@uol.com.br

Pedro Luiz De Oliveira

Costa Neto

Critical analysis of the management system of hazardous solid waste generated in the city of Santos in the state of São Paulo 1

Keywords: Hazardous waste, Reverse Logistics, Management System

The world is undergoing major transformations with significant social, political and economic changes. Science and technology have evolved much from the twentieth century, and the intensification of industrial activity has its production techniques increasingly sophisticated, multiplying physical territory, what unfortunately had a negative impact on the environment. The objective of this study is to perform a critical analysis of the current Integrated Solid Waste System in the city of Santos in, the Brazilian coast, focusing on hazardous waste, identifying opportunities for improvement. Representative sectors governances were heard through the application of a structured questionnaire based on three pillars: Environmental Education for the population, Communication System and Infrastructure offered by the municipal authority. The results show weakness in the system of management in relation to environmental education and communication system, as well as absence of the main timeline of actions to achieve the proposed results.

Introduction

The Industrial Revolution, from about 1850, led to strong growth in world production consumption of goods, with emphasis on efficiency and effectiveness through the concepts advocated by the Classical School: Bureaucracy, Human Relations and Thinking Systems. These business schools saw the natural environment prevailed as a mechanic and deterministic thinking, nature being considered an obstacle to the society development. Science and technology have evolved much within the twentieth century, and the intensification of industrial activity had its production techniques increasingly sophisticated, multiplying physical territory, which unfortunately had a negative impact on the environment [START_REF] Melo | Government Portal, digital inclusion and the knowledge society[END_REF]. For decades the environmental degradation increased, being believed that the economic growth by itself would provide the best conditions due to society. However, it was observed that uncontrolled economic growth was causing irreparable damage to the ecosystems and these damages, at medium and long terms, should turn earth uninhabitable to humans [START_REF] Werner | Cleaner production: methodological concepts and definitions[END_REF].

Studying this situation, the United Nations -UN promoted the debate of environmental issues, and designed Prime Minister of Norway, Gro Harlem Brundtland, head of the World Commission on Environment and Development, to study this theme. As result of this labor, it was presented a report entitled "Our Common Future", which established the concept of Sustainable Development, based on three pillars: economic growth, social equity and environmental balance [START_REF] Onu | Our common future[END_REF]. This report was the basis for discussions of the United Nations Conference on Environment and Development, known as RIO-92 or ECO-92, thus allowing the creation of Agenda 21: Global Sustainable Development [START_REF] Melo | Government Portal, digital inclusion and the knowledge society[END_REF].

The evolution of environmental issues brought for discussion a new variable: sustainability. This requires a strong reflection about trying to understand the new reality, providing a paradigm shift. Since then, it was tried to integrate socially responsible practices, environmentally sound, traditional techniques associated with the production and management of the industrial sector, stimulating the society to consume products free from wastes and harmful effects to the environment. According to the United Nations, which used demographic estimates, the world population reached the milestone of 7 billion people in 2012. It means that among other necessary steps, for humans live harmoniously, it is imperative to establish public policies for solid waste management and deployment of an effective management system.

Objective

The objective of this study is to perform a critical analysis of the current Integrated Solid Waste System in the city of Santos presented by the municipal authority in 2011, focusing on hazardous waste, identifying opportunities for improvement.

Contextualization and Methodology

The city of Santos, founded in January, 26, 1546, has written in its flag "Patriam Charitatem et Libertatem Docvi". It is a port city, head of the Santos Metropolitan Region, located on the coast of state of São Paulo, Brazil. It houses the largest port in Latin America, which is primarily responsible for the economic dynamics of the city, together with tourism, fisheries and trade. In 2010, the Santos municipality had 419,757 inhabitants, according to the census conducted in the country. The United Nations Program for Development of 2000 placed the city of Santos in sixth place in the list of municipalities by the human development index, and it is currently the 17th richest city in the country. With the discovery and explotation of pre-salt project (oil laying very deep under sea devel) by Petrobras (the chief Brazilian oil enterprise), the city of Santos will house the headquarters of the Business Unit Gas Exploration and Production of Oil in Santos Basin. This certainly implies strong growth in business and commercial activities in the city, with its repercussions on the sustainability problem.

A case study conducted by the authors to evaluate the Integrated Management System of Solid Waste in the city of Santos approved in 2011, proposed by the Department of the Environment, on the subject of waste management hazards, especially fluorescent lamps, batteries, junk and X-ray slides, presents a systemic approach to evaluate the ability to manage the entire process as a system, identifying and understanding the network of processes and their interactions. Hazardous wastes have characteristics of flammability, corrosivity, reactivity, toxicity, pathogenicity, carcinogenicity, mutagenicity and teratogenicity, presenting a significant risk to public health [START_REF]IPT. Municipal waste: Integrated Management Manual[END_REF]. The aim of the project is to classify the responsibilities for achieving results, reducing barriers to better integration between the interested actors, in accordance with the principles of Quality Management.Therefore, in the case study, were heard governances representing the sectors of the residential and commercial buildings, and retail trade in Santos, represented by the presidents of its representative entities, by applying a structured questionnaire based on three pillars: environmental Education for the population, Infrastructure and Communication System offered by the municipal authority.

Theoretical background

This study is based on developing a critical analysis of the Integrated Plan Solid Waste Management in the Municipality of Santos in 2012, aiming to get compliance with the National Policy on Solid Waste Plan of 2010 and the Basic Guidelines of Sanitation of 2007 [START_REF] Seman | Integrated management plan of solid waste in the city of Santos[END_REF]. Solid waste or garbage, are the remains of human activity, considered by generators as useless. This waste is classified into three categories: hazardous, non-inert and inert [START_REF] Abnt | NBR 1004 -Solid waste classification[END_REF].

The integrated management of municipal waste is a coordinated set of regulatory actions, operational and financial planning, based on environmental and economic health criteria, to collect, segregate, treat and dispose the garbage in the city. Moreover, the municipal authorities are key pieces in the integrated management of municipal waste. They not only have the responsibility for implementing actions that aim to mitigate their effects, but also that of educating citizens, technicians and planners for this urgent need [START_REF]IPT. Municipal waste: Integrated Management Manual[END_REF].

It is estimated that the world population produces around 40 million tons of waste per year. Society requires the transformation of natural resources into products to benefit their needs in general. These products, when they reach the purposes for which they were created, the end of its useful life, are discarded. This whole process eventually leads to the deterioration of the natural environment, because the amount of solid waste produced by society is unquestionably one of the sources of environmental degradation [START_REF] Rao | Reverse logistics-Conceits and system components[END_REF]. To address this situation, from the 1980s the theme "reverse logistics" was explored with greater intensity at the academic, public and corporate levels, aligned with the philosophy advocated by the planet's sustainable development presented by the World Commission on Environment and Development.

In the 1990s, some world-renowned authors conceptualized reverse logistics as a "new area of business logistics, concerned to consider the multitude of logistical aspects of the return to the productive cycle of the different types of industrial goods made of these materials and waste industrial controlled by reuse of the well and its components or recycling of the constituent materials, giving rise to secondary raw materials which reintegrate the production process" [START_REF] Pereira | Reverse logistics and sustainability[END_REF].

The world changes at every moment. New products are launched in the market to meet the needs of people. This requires advanced technology and the use of natural resources. Consequently, the mindset of the consumer also changes in this process, because it requires changes in production processes and a new stance on ethics and social responsibility. Having a properly socially responsible business is a requirement of the current market. To be effective, this vision must be structured in the long term, ensuring benefits for future generations. In 1987, John Elkington proposed a management model known as "Triple Bottom Line", by which, the entire business management should be based on economic, social and environmental issues [START_REF] Rao | Reverse logistics-Conceits and system components[END_REF]. For these actions to be effective, it is imperative to have a change in the mindset of people and, therefore, of the population. For this, two actions are essential: Education and Communication. Corroborating this idea, the authors refer to the 13 th Deming point "Institute a strong program of education and self-improvement." According to this outstanding master on quality, training and self-development are important means for continuous improvement of people, both from the professional and personal standpoint [START_REF] Gitlow | Planning for quality, productivity and competitive position[END_REF]. Translating this point to a system for environmental management, environmental education is a very complex issue because, when dealing with a population, there is a great diversity in the level of knowledge and discernment in matters relating to the environment [START_REF] Falconi | The true power[END_REF].

On the other hand, a communication strategy is an essential part in the analysis of the life cycle of a product, based on the inventory of generates waste, since it directs to population the communication efforts related to the categories of impact and its main social and ambiental effects.

Method of control

For this study, the PDCA cycle was customized to evaluate better the Integrated Solid Waste System in the city of Santos, as shown in Figure 1 prepared by the authors.

The success of any management plan lies in the managers' ability of how to identify and solve problems. World Class Organizations have expertise in knowing how to apply the PDCA (Plan -Do -Check -Act) cycle throughout the organization. The PDCA cycle seems simple at first glance, and, in fact, is simple. However, those who use this method intensively over the years realize that, the more it is used throughout the organization, the more it is seen its complexity. This method allows: greater involvement of people, commonality of language and communication, understanding of each responsibility for the success of the system, continuous learning, use of several areas of science and uptake of best practices [START_REF] Amato | Production & Sustainability[END_REF]. Moreover, a communication strategy is a key part of the analysis of the life cycle of a product, based on the inventory of waste generated, for directing the communications efforts Figure 1-PDCA cycle adapted to the Integrated Solid Waste System throughout the organization, the more it is seen its complexity. This method allows: greater involvement of people, commonality of language and communication, understanding of each responsibility for the success of the system, continuous learning, use of several areas of science and uptake of best practices [START_REF] Amato | Production & Sustainability[END_REF]. Moreover, a communication strategy is a key part of the analysis of the life cycle of a product, based on the inventory of waste generated, for directing the communications efforts for the population towards to the selection of impact categories and its main social and environmental impacts.

Discussion and results

To comply with the National Policy on Solid Waste, 2010, the Department of Environment has developed the Integrated Management Plan based on the environmental management system established by ISO 14001:2004, with the control method of PDCA. Four grate goals were established in order to develop the integrated management: promoting the feasibility of reverse logistics flows to the waste generated in the municipality, expansion of discussion within the Metropolitan Agency in relation to waste management, development of contingency and emergency plans, and specific amplification of selective waste, deployed for each type.

The document is structured with an introduction by addressing the physical, economic and social aspects, a diagnosis contemplating an inventory of each type of waste and oversight responsibilities, goals for the integrated management, alternative use of reverse logistics for reuse or purification on of waste, investment proposals, operational environmental performance indicators, corrective and preventive actions to be practiced, contingency actions, environmental education programs and technical training, social communication and periodicity of quadrennial review.

The diagnosis for hazardous waste from the health sector is quite detailed, but for the waste under study is very superficial. The author has observed the absence of a guiding schedule of actions, thus existing the risk of becoming a "letter of intention".

In order to straiten the critical analysis, there ware elaborated three questions and applied to the society representatives mentioned in contextualization and methodology:

1. How do you evaluate the present Environment Education Program offered by the municipal authority to the population? 2. The communication system used by the municipal authority to inform population about the correct storage and disposal of dangerous waste is clear and objective? 3. The present infrastructure of reception and destination of dangerous waste in the city of Santos is adequate from the viewpoint of the reduction of Effects to environment? The participants agreed in affirming that the topics referred to the Ambiental Education issues to the population, Communication System and Infrastructure offered by the municipal authority are rather week and disconnected from society, what is worrying and must be improved.

It is not clear the system to collect hazardous waste among the population under study. However there are several companies with their efforts to capture and find a destination waste within their social responsibility programs.

Nevertheless, the structure proposed by the municipal authority was considered potentiality good and, if put in planned action, have a nice probability of success to protect environment. However, the analysis of the indicators proposed in the plan indicates partial default, which in the future will preclude the refinement of the waste inventory for proper mitigation.

The Integrated Solid Waste in the city of Santos, novelty implemented by the municipality, contributes to improve the quality of life of the population. The system is classified as level 2 (limited information or little understanding of the objectives of the improvements required, some good results available), according to the description of maturity levels established by ISO 10014:2006 -Quality management -Guidelines for realizing financial and economic benefits, focusing on the Principle of Quality "System approach to management" [START_REF]-Quality management-Guidelines for realizing finanial and economic benefits[END_REF].

Conclusions

Public sector has a peculiar quality management focused on the establishment of laws, ordinances and inspections. However, it is not enough from the perspective of modern management in the XXI century. The examined plan is not conclusive for the correct identification of the problems, which will inevitably contribute to the low productivity of the system.

Therefore, the authors propose to strengthen the systems approach to management, the adoption of the Theory of Constraints by Eliyahu M. Goldratt, which is a systemic management philosophy, based on the application of principles and methods of human organizations to Exact Sciences [START_REF] Cox | Theory of Constraints Handbook[END_REF].

It is fundamental the development efforts for an effective management practice through the PDCA model, considering the opportunities of improvement identified by the society representative governance. PDCA cycle has become a universal management concept, a true legacy, a tool whose owner is humanity in the service to identify and solve problems that are present in the globalized society.


Zaheer Tariq
email: zaheer.tariq@unibg.it

Sergio Cavalieri
email: sergio.cavalieri@unibg.it

Roberto Pinto
email: roberto.pinto@unibg.it

Determinants of Smart Energy Demand Management: An Exploratory Analysis

Keywords: Smart demand, collaboration, sustainability, intelligent systems, sustainable consumption patterns, energy demand management

The unprecedented rise of population with increasing energy consumption has necessitated the stabilization of dwindling energy resources to secure the provision of energy. Electricity production and distribution through smart grids is a key component in delivering reliable, efficient, and low-carbon energy for a sustainable development of the society, and to meet the growing demands of electricity. Smart electricity grid is a complex system of systems that requires sophisticated collaboration tools and intelligent techniques with active participation from all its connected users. A dynamic role and a proactive participation of consumers and societies with the integration of distributed energy resources are highly anticipated for the long-term sustenance of these grids. This paper discusses and identifies key determinants of the demand side sustainable collaboration in a smart way (resulting as what we address as smart energy demand) through the involvement of pro-active consumers.

Introduction

Cultural, social, and economic factors are increasingly modifying the consumption patterns, shifting the consumers' behaviors from a mere individual attitude to satisfy personal needs to a more collaborative process, resulting in a sustainable value creation. Thanks to a growing impetus in promoting sustainable consumption patterns, individuals are more conscious of their role in their communities, and are aware that their direct and collective participation can affect the current and future life of their society. Conversely, many sustainability strategies and corporate social responsibility (CSR) practices mainly address environmental aspects [START_REF] Berns | The business of sustainability: What it means to managers now[END_REF], neglecting the impact of global over-consumption [START_REF] Sheth | Mindful consumption: A customer-centric approach to sustainability[END_REF], and overlooking the role of the consumer and the societal involvement.

In our understanding, a holistic approach is required to effectively deal with the current challenges posed by the sustainable development principles: consumers (and their communities at large) should be explicitly encouraged to be directly engaged through a more participative and collaborative behavior, factually realizing a collaborative consumption strategy, that is a technology-enabled sharing of goods and services between consumers that requires enhanced forms of collaborations [START_REF] Sheth | Mindful consumption: A customer-centric approach to sustainability[END_REF][START_REF] Porter | The big idea creating shared value[END_REF][START_REF] Brokaw | Collaborative consumption: Drivers, systems, implications[END_REF].

Dealing with sustainability challenges finds its roots in a shift of mindset towards the achievement of more efficient consumption patterns. The support provided by properly designed collaboration platforms would increase the possibilities for producers and consumers to establish a mutual collaboration, seeking an equilibrium between supply and demand both in time (e.g. finding a customer when products are available, finding suppliers when demand upsurges) and space (e.g. finding a supplier close to a customer, or vice versa). Moreover, these common platforms can support pro-active consumers in acting interchangeably as users or providers of goods or services in their relation with other consumers according to the specific contexts and needs (i.e. privates that sells extra energy produced via photovoltaic panels installed on the house's roof).

As a result, changing the consumption patterns, moving towards a collaborative and sustainable process involving different participants, with distinct requirements and interests, requires a completely new approach to demand management. Under this premise, the nature itself of the demand changes, requiring a different connotation. Therefore, we refer to such a demand with the term Smart Demand, that is a demand signal coming from a collaborative process (see Section 2 for a detailed description).

The purpose of this paper is to provide a ground definition of smart demand, illustrating its main components. Along this, it analyzes the importance of active demand side participation in the case of energy demand management as result of the uprising role of smart electricity grid systems. Further, the paper highlights the key challenges under different dimensions. Attaining the benefits of smart grid technologies requires increased collaborative efforts between utilities and its connected customers. The smart grid concept supports the functioning of such energy systems with the integration of proactive consumers that contribute to the energy generation through their own systems, and share the excess with other consumers and utility grids.

The paper is structured as follows. Section 2 discusses the Smart Demand concept and the main components. Section 3 illustrates the smart demand applied in the energy sector, under the smart grids concept. Finally, Section 4 reports the conclusions, emphasizing some key challenges to be addressed by leveraging the smart demand concept.

Defining Smart Demand

The demand resulting from a collaborative consumption process differs substantially from the traditional forms of demand as usually conceived. For our purpose, we deem the term Smart Demand as a simple yet comprehensive term expressing the main characteristics of the demand signal expressed in a collaboration environment. In this environment, the individual demand can be shifted in time and/or compensated through other participatory efforts of the community. The "smartness" of the demand depends upon the smartness of the actions undertaken to reduce/shift/redistribute its profile under the collaborative systems by considering the mutual gains. We envision three main dimensions along which we characterize a smart demand:

Sustainability: Traditionally, much of the attention has been given to promote the sustainability design for production related impacts. Extensive efforts are also being made towards promoting those technologies and designs that can play an important role for moderating the overall consumption level and its related impacts [START_REF] Thorpe | Design's role in sustainable consumption[END_REF]. However, a systematic and holistic approach to address a social nature of consumption (such as promoting collaborative consumption) is still lacking in the user-centered and behavioral related design efforts. In view of this, there is a growing interest in accommodating the social-technical dimension towards consumption by applying social practices in the technical-design process [START_REF] Scott | Designing change by living change[END_REF]. To get the detailed insights, about users and their behaviors under user-centered approaches, different research methods and the ways of approaching users are being adopted from multi-disciplinary fields. With the efforts, based on cognitive and environmental psychology, behavior-based approaches include applying the persuasive designs, intelligent technologies, and design scripts [START_REF] Scott | Designing change by living change[END_REF].

Collaboration: Collaborative consumption is not limited to the communal use of products and services, but the primary objective is to promote and encourage active role of consumers towards sustainable communities. These communities collaborate to foster a sustainable development in different sectors such as infrastructure, mobility, energy, waste and food [START_REF] Rae | Energy autonomy in sustainable communities-a review of key issues[END_REF]. By adopting such consumption patterns, people get the ownership benefits with less personal burden, cost savings, and lower impact on environment, hence showing more captivating alternative to standard and traditional buying and ownership styles. Consumers, as well as their communities, need to be empowered with adequate collaborative platforms to play their dynamic role for such transformation. For such platforms, three primary systems are created that are redistribution markets, collaborative lifestyles, and product service systems [START_REF] Brokaw | Collaborative consumption: Drivers, systems, implications[END_REF]. To achieve vast cultural and commercial implications, a trust and user experience plays a vital part in the platform of collaborative consumption [START_REF] Porter | The big idea creating shared value[END_REF]. This participation yields benefits not only related to the environment; it also generates social (improved quality standards, improved health conditions, employment generation) and economic benefits (such as new products/service innovations, more choices to customers and financial incentives).

Intelligence: The literature proposes different terms regarding intelligent products that are often associated with the smart products, smart objects and can be used interchangeably [START_REF] Meyer | Intelligent products: A survey[END_REF]. Further, concept realization of smart products also varies according to the number of terms used to describe or relate to them [START_REF] Lopez | Adding sense to the internet of things: An architecture framework for smart object systems[END_REF]. Accordingly, in this paper, smart products and intelligent products are used as interchangeable. McFarlane et al. [START_REF] Mcfarlane | Auto id systems and intelligent manufacturing control[END_REF] defined intelligent products as the products that are able to represent their physical (locations) along information based (conditions) characteristics. Smart products can be defined as the products having the following characteristics [START_REF] Meyer | Intelligent products: A survey[END_REF][START_REF] Lopez | Adding sense to the internet of things: An architecture framework for smart object systems[END_REF][START_REF] Mcfarlane | Auto id systems and intelligent manufacturing control[END_REF]: (i) have its distinctive identity; (ii) sensing and storage ability towards measurement of systems; (iii) ability to identify and sense other relevant attributes of external entities; (iv) can have an interconnection and communication power with other smart objects; (v) have decision power (i.e. make the decisions about themselves and external objects). Smart and intelligent products play a central role towards enabling a platform for system integration through a collaborative sustainable process. Smart products and services can assist consumers in using the collaborative platforms and can help them to analyze their consumption and demand patterns [START_REF] Meyer | Smart devices for demand side power consumption management[END_REF].

By this vision, Smart Demand is a sustainable collaborative demand sensed through embedded product/system intelligence, under the network of information and communication technology. Such a demand, with the help of information technology network and smart/intelligent objects, provides the sensing ability to observe the collaborative demand shifts/variations.

By highlighting the importance of consumers, and their pro-active participation in a community, we emphasize the fact that individuals' demand are smartly connected (through intelligent products and systems) towards a collaborative demand which may provide substantial benefits for a sustainable development.

Smart demand for energy demand management

Electricity production through Smart Grids is a key component in delivering an efficient and low-carbon energy for the sustainable economic infrastructure [START_REF] Samad | Smart grid technologies and applications for the industrial sector[END_REF][START_REF] Gao | A survey of communication / networking in smart grids[END_REF]. Smart Grids can be defined as a cost effective electricity delivery infrastructure, enhanced with information and communication facilities (including technologies that facilitate the efficient integration of renewable energy sources) to enable more efficient, reliable and secured grid operations with an improved customer service and a cleaner environment [START_REF] Samad | Smart grid technologies and applications for the industrial sector[END_REF][START_REF] Moslehi | A reliability perspective of the smart grid[END_REF][START_REF] Ramchurn | Agent-based homeostatic control for green energy in the smart grid[END_REF].

Energy sustainability has a key influence in the domain of sustainable developments. It requires a shift from traditional energy sources towards renewable energy ones. A smart grid vision facilitates the development of sophisticated collaboration tools and intelligent techniques for increased member participations and for incorporating the decentralized and fluctuating energy sources. It can assist in reducing the excessive burden on the grid by creating a balance between supply and demand [START_REF] Ramchurn | Agent-based homeostatic control for green energy in the smart grid[END_REF]. This balance can be established through an effective demand side management (DSM) and proactive participants' efforts. DSM, an essential part of smart grids, is a cost effective tariff program for managing the electricity load that generates benefits both for utilities and consumers. Normally, under these programs consumers play a passive role because most decisions and actions are taken by utilities whilst consumers have little or no control. In addition, utility grids support the functioning of such energy systems with the integration of proactive consumers (also termed as prosumers) that contribute to the energy generation through their own systems, and share the excess with other consumers and utility grids. The proactive energy participants transform their passive role into the active role in the electricity generation and share the responsibility of reducing and optimizing the energy consumptions.

To achieve the true capability of a bidirectional flow of energy and information between utility grids and users, consumers along with their communities need to be encouraged for their involvement. It is estimated that their contribution (through demand response and renewable sources) will constitute from one-third to one-half of total smart grids benefits for sustainable energy [START_REF] Heffner | Smart grid -smart customer policy needs[END_REF].

Efficiency in energy sustainability refers to a curtailment in energy consumption without following the reduction in energy supply [START_REF] Schweizer-Ries | Energy sustainable communities: Environmental psychological investigations[END_REF]. As referred above, adopting the social nature of consumption that is from a product-based approach to a practiceoriented approach (for collaborative systems) can also considerably strengthen this energy consumption.

Seeking energy sustainability in a broader societal perspective with the support of proactive consumers requires consumption modification along socially connected efforts. These community efforts enforce sustainable practices leading to sustainable behaviors. Consumers should actively participate to achieve the sustainability targets (with mutual gains consisting of economic, social and environmental benefits) through collaboration (creating the demand flexibility) with the help of intelligence (as intelligent products and systems).

Smart demand for energy demand management is envisioned as based on a tripod of sustainability, collaboration and intelligence (as shown in Fig. 1). An increased level of collaboration through highly intelligent tools and techniques reflects improved efforts toward a sustainable development. A prosumer-to-prosumer collaboration through collective decision-making (against divergent interests and perspectives) may seek energy autonomy / self-sufficiency in sustainable energy communities [START_REF] Rae | Energy autonomy in sustainable communities-a review of key issues[END_REF].

The more consumers enter into the dynamic prosumers' domain, the more significant impact they can have on the smart grid infrastructure. In views of Vogt et al. [START_REF] Vogt | Market-based prosumer participation in the smart grid[END_REF] a grid becomes smart when all endpoints contribute to its operations. Where endpoints should not be restricted only to generators but must also include power users (consumers along prosumers). A smart demand can ensure that the amount of energy generated is equal to the amount of energy demanded. Along with the tripod vision, collaborative arrangements in the energy context are strongly intertwined with the expected level of sustainability and the form of intelligence required. Such integrated consideration is highlighted in Table 1.

However, thorough implementation of smart demand management into a specific system model requires its comprehensive analysis in terms of tangible and intangible determinants (such as behavioral, motivational and cognitive aspects in consumer collaboration towards energy sharing). Such contextual determinants can generate mutual gains in the societal coordinative efforts towards achieving the sustainability.

Whereas most work related in this perspective overlooks the comprehensive analysis and hence partially comments these dimensions in collective way as a peak load saving strategies. Such as [START_REF] Vinyals | Coalitional energy purchasing in the smart grid[END_REF][START_REF] Vasirani | Smart consumer load balancing: State of the art and an empirical evaluation in the spanish electricity market[END_REF] focus only on the collaboration as a collective energy purchasing by overlooking the consumers proactive participation and other determinants such as behavioral factors in creating the demand flexibility under energy sharing perspective.

Consumer to company

Consumers collaborate through their energy demand information sharing with the utilities to avoid consumption during peak hours, being beneficial for customer (as savings) and for utility (as low production costs). Both foster the sustainability as energy savings / conservations.

Consumer to prosumer

Collaboration that seeks energy efficiency (i.e. efficient usage of energy and / or opting alternative supply sources). For such DSM, participants integrate the network information (e.g. supply constraints) into their decision making towards consumption alterations.

Prosumer to prosumer

Collaboration for DSM by involving and promoting the role of prosumers in the network towards incremental capacity of the supply side leading to energy autonomy. It requires intelligent automation systems that enable fully autonomous decision-making against diverse self-interests.

Concluding remarks and future challenges

Leveraging on the smart grids concept, we posit that a pro-active participation of consumers through a collaborative process may provide substantial benefits for the long-term sustenance of the electricity infrastructure. Transferability of this process can also be envisioned in other contexts, as for example for the transportation demand management, which requires strong collaborative arrangements towards managing the travel / traffic demand (as modification and/or redistribution).

However, this potential role is determined by various factors (briefly highlighted in Table 2), ranging from self-interest layers to exogenous conditions that can induce the coordinated efforts of the users towards energy demand management. Smart demand illustration as energy demand management emphasizes the relationship of consumers along societal efforts towards energy sustainability. Such a relationship is heavily dependent on the self-interested participants, which may have an impact on the smart demand. Considering this, the same table provides a guideline for our future research to investigate about the most efficient coordination mechanisms by considering the optimality among dimensions of smart demand.

This paper raises some important aspects along different requirements, being the antecedent for building up the smart energy demand management model. The designing of this model does not only depend on the technological factors but also on those intangible factors that are related to consumers energy consumption and generation activities under the energy sharing context, being enabled by smart grids. Collectively absorbing these requirements into the system modeling for the demand side management may help to design proactive consumers' efficient energy management decision support system. Such system development can augment the smart grids economic impact.

Table 2. Future challenges to smart demand

Intervention Challenges

Sustainability

Consumers' limited knowledge regarding their energy usage patterns and its sustainability impacts can limit consumers' proactivity level. Embedded social practices, lack of societal pressures, lack of selfefficacy awareness level and existing satisfaction level do not allow them to pursue a change in their demand patterns. Investment costs regarding renewable energy sources, storage devices and system implementation costs.

Collaboration

Non-availability of tools to evaluate demand flexibility against technical and economic benefits and costs [START_REF] Bel | Technical and economical tools to assess customer demand response in the commercial sector[END_REF] Electricity market structures, legislative, administrative and regulatory barriers that suppress consumer participatory efforts. Consumer response fatigues and required attention to individual products / electrical appliances could also have adverse impacts.

Collaboration among participants raises the information security and data privacy concerns. Such unauthorized access of data can hinder successful implementation of collaborative platforms.

Intelligence (products and systems)

Costly access and overload of information with its required processing efforts can create hindrance for the participation. Lack of user-friendly technologies that can monitor real time usage and supply patterns assisting consumers to create demand flexibility. Technology costs and their financing options (such as smart meters) and without these consumer participation is not possible.

Fig. 1 .

 1 Fig. 1. Smart demand for energy demand management (Source: Authors)

Table 1 .

 1 Energy demand management through smart demand

	Collaborative arrangements	Integrated considerations


Günther Schuh
email: g.schuh@fir.rwth-aachen.de

Philipp Jussen
email: philipp.jussen@fir.rwth-aachen.de

Christian Fabry
email: christian.fabry@fir.rwth-aachen.de

Gerhard Gudergan
email: gerhard.gudergan@fir.rwth-aachen.de

Finding the optimal Operating Point for Service Production Processes via Simulation

Keywords: Service production, service process, simulation, service productivity

This paper presents a simulation approach for service production processes on the basis of which an optimal operating point for service systems can be identified. The approach specifically takes into account the characteristics of human behavior. The simulation is based on a system theory approach to the service delivery process. A specific use case of the simulation approach is presented in detail to illustrate how characteristic curves are deduced and an optimal operating point is obtained.

Introduction

Increasing operational productivity is the basis for improving competitiveness of industrial services [START_REF] Grönroos | Service Productivity. Towards a conceptualization of the transformation of inputs into economic results in services[END_REF]. Services are characterized by their intangible nature leading to a lack of storability [START_REF] Frietsche | Dienstleistungsproduktion[END_REF]. Furthermore, the customer is involved in the production process of the service. He is not only especially aware of the service production process, but also participates in the process as an external production factor [START_REF] Meier | Industrial Product-Service Systems -In[END_REF]. An analysis of productivity in service systems should incorporate both the quality and the process character of service delivery [START_REF] Waltemode | Productivity and Quality Assessment of Services within technical Product-Service Systems[END_REF]. The inclusion of quality into the assessment of productivity consequently leads to the inclusion of the factors time, cost and capacity. For an assessment of service productivity this ultimately means the consideration of all five dimensions and their interactions with one another [START_REF] Lasshof | Produktivität von Dienstleistungen[END_REF].

Past research results illustrate the ability and the value of simulation in the design of highly flexible business processes [START_REF] Mühlbauer | System Dynamics als Instrument zur Optimierung hochflexibler Geschäftsprozesse[END_REF]. In a service context, specific applications of simulation which aim at improving the operational understanding of the service production process have been researched scientifically [START_REF] Oliva | Developing operational understanding of service quality through a simulation environment[END_REF]. This paper presents a simulation approach for service systems. As a result of the simulation characteristic curves for services systems are obtained in order to find an optimal operating point.

The Elasticity of Capacity in Service Systems

The theory for industrial production mostly assumes constant process times which for example do not depend on the amount of load or input on a system [START_REF] Nyhuis | Logistische Kennlinien[END_REF]. For services, the process times are subject to larger fluctuations and actual productivity differs significantly from the theoretically calculated performance [START_REF] Höck | Dienstleistungsmanagement aus produktionswirtschaftlicher Sicht[END_REF]. The service delivery process has a determining human component, usually because a service process operates on employee labor as a major resource or capacity [START_REF] Fitzsimmons | Service Management[END_REF]. It is therefore essential to include the aspects of human nature into the consideration.

Humans are aware and therefore influenced by their environment and their current situation [START_REF] Chmiel | An introduction to work and organizational psychology[END_REF]. The productivity of a human depends not only on the capabilities of the human itself but also on the environmental state in which the human is being put into. For example, the human performance is viewed as a function of its ability and its motivation [START_REF] Campbell | Motivation Theory in Industrial and Organizational Psychology[END_REF]. According to the Yerkes-Dodson law the performance on a specific task is a direct function of the psychological or mental arousal [START_REF] Yerkes | The Relation of Strength of Stimulus to Rapidity of Habit-Formation[END_REF]. Similar relationships are known for the effects of stress on the work performance [START_REF] Hancock | Human occupational and performance limits under stress: the thermal environment as a prototypical example[END_REF]. [START_REF] Yerkes | The Relation of Strength of Stimulus to Rapidity of Habit-Formation[END_REF] In recent scientific research the term procrastination describes the impact of the organization of the work on the human performance [START_REF] Brunner | Elektronik ist anders -Ein Leitfaden für Unternehmer, Qualitätsmanager, Produkt-und Projektmanager mit wenig oder keinem Elektronikwissen zur erfolgreichen Entwicklung, Fertigung und Markteinführung von Produkten mit eingebetteter Elektronik und Software[END_REF] [START_REF] Techt | Critical Chain -Beschleunigen Sie Ihr Projektmanagement[END_REF]. Humans tend to put off more urgent tasks in order to do something else, which typically brings them more personal pleasure or enjoyment [START_REF] Steel | Procrastination and personality, performance, and mood[END_REF]. [START_REF] Ariely | Procrastination, Deadlines, and Performance: Self-Control by Precommitment[END_REF] showed that by setting multiple periodic deadlines instead of heaving one single deadline at the end, the performance for a specific task could be increased significantly. At the same time the average delay of the task was reduced [START_REF] Ariely | Procrastination, Deadlines, and Performance: Self-Control by Precommitment[END_REF]. Recent research suggest, that procrastination is not merely an issue of setting deadlines and organizing work, but occurs in various forms in all types of work settings and for most human beings [START_REF] Hammer | Differential Incidence of Procrastination Between Blue and White-Collar Workers[END_REF] [START_REF] Gupta | Time Perspective and Procrastination in the Workplace: An Empirical Investigation[END_REF].

Altogether the implications derived from scientific literature in the field of psychology led to two basic assumptions about the human component in the service delivery processes:

x Depending on the nature of the task, the time and the quality of a service process are not constant but show a certain elasticity towards environmental factors.

x As the human performance depends on a variety of factors (such as motivation, stress, arousal), an examination of service productivity should therefore consider the same factors as they determine the outcome of a service process.

Simulation Model Construction

In order to capture the characteristics of human behavior into a simulation model a system theory approach is taken. The system theory is used for modeling relationships in complex organizational structures such as productions systems [START_REF] Wiendahl | Betriebsorganisation für Ingenieure[END_REF]. A generic tool for structuring a system model is provided by the approach of Negele (1998), which was developed on the basis of system theory. The so-called ZOPH approach divides an overall system into four types of systems [START_REF] Negele | Systemtechnische Methodik zur ganzheitlichen Modellierung am Beispiel der integrierten Produktentwicklung[END_REF]: The target system, the object system, the process system and the actor system. Elements outside the system boundaries are part of the system environment. Figure 2 illustrates the elements and aspects of service production that are included in the simulation.

Fig. 2. Elements and aspects included in the simulation model

In this paper an application of the simulation approach for a specific use case is presented. The use case is based on a real technical service center. In the service center, which has the basic characteristics of a call center, technical service for specific technical products such as wireless routers is provided. Owners or operators of the products can call the service center in case of a malfunction of the product. The service center staff helps the customer via telephone and tries to solve the problem together with the customer. Problems can be detected not only in theory but are also addressed in a practical way because the service center staff can reconstruct the real configurations on a special workbench. If a problem solution can't be obtained on the phone, the customer is asked to send in their device. For this use case this is considered as a nonfulfillment of customer requirements.

Fig. 3. Simulation model for a service center

The following bullet points describe each of the factors included in the simulation model:

x The system has a certain constant capacity. The capacity is derived from the number of staff working in the service center. It's the theoretical amount of work (number of calls) a certain amount of staff can do in a fixed period of time.

x The input represents the amount of calls from customers to the service center. It is a fluctuating demand with a constant mean and standard deviation. x The input adds to a load for the system for a fixed time period. The load can be viewed as the workload (number of calls) during a fixed period of time.

x The utilization is derived from the comparison of the load and the capacity. The utilization is used to determine the effects of stress due to high workload. For the utilization a maximal value was included since for a given amount of capacity the workload can't be infinitely high. Also the telephone system of the service center has an automatic cap which only allows for a limited number of calls to be forwarded to the staff. Data provided by the service center allowed for the calculation of this utilization cap.

x The procrastination depends on the capacity and the load. If the load is bigger in comparison to the capacity a lesser level of procrastination will occur and vice versa. It is assumed that procrastination occurs while a task is performed (a member of the staff is answering a call from a customer). A practical example for procrastination during task performance in this case is exaggerated chatting with the customer (a certain amount of friendly conversation is imperative). The effects of procrastination will also occur in between tasks (staff taking longer breaks, chatting with other staff, etc.).

x The effective capacity takes into accounts the effects of procrastination and stress due to high workload. It manifests the concept of the elasticity of capacity in service systems. Although the capacity is kept constant the effective capacity varies depending on the workload. x The throughput is determined by the effective capacity. It is the performance (answered number of calls) in a fixed period of time. The throughput is subtracted from the load. x The opportunity costs arise, if the load in a fixed period is bigger than the throughput. In this specific case the difference between the load and the throughput (number of unanswered calls) can't be carried over to the next period and thus leads to opportunity costs. x The costs are the sum of the costs for the capacity and the opportunity costs. In this specific case all other costs are assumed to be constant and do not depend on the number of staff in the service center.

x The process time depends on the utilization and the level of procrastination. High levels of procrastination lead to increased process times. A high utilization leads to a decrease in process time, since a larger amount of work has to be done in the same fixed period of time.

x The fulfillment of performance criteria can be viewed as the number of tasks that are successfully performed. A successful performance depends on the fulfillment of the demands a customer has on a task. In this specific case the customer calls the service center and expects a solution to his or hers problem. If a solution can't be given (the product is still malfunctioning after the call and has to be sent in) there is no fulfillment of performance criteria. A high utilization leads to stress which leads to a lower average performance of the service center staff (diagnosing and solving a technical problem over the phone with the help of the customer with little or no technical expertise is categorized as a difficult task).

x The perceived quality depends on the fulfillment of the performance criteria and the process time. From a customer point of view a lesser fulfillment of performance criteria and longer process time mean a decrease in perceived quality. For this specific case the two factors are weighed against each other in a way that the fulfillment of the performance criteria has a significantly larger effect on the perceived quality than the process time. The effects of a non-fulfillment of the performance criteria for the customer are more significant since the product is still malfunctioning and the customer has to do the extra work of sending it in. Also the customer expects his problem to be solved by the service center but has not a certain expectation towards an amount of time the solution has to be given in (apart from as quick as possible).

x The overall output is determined by the throughput and the quality. For service processes the productivity cannot be judged solely on quantitative values but also has to take into account qualitative aspects (as pointed out in chapter 2). Therefor an output consisting of the quantitative throughput and the perceived quality is calculated.

x The productivity is the given by comparing the output and input (costs) of the service system.

Further key assumptions included in the simulation model are the following:

x For the actor system it was determined that no multitasking occurs (staff do not answer two phonecalls at the same time). Also each staff member is sufficiently qualified for the task and qualification does not differ significantly amongst the staff.

x An order control was assumed in a way that incoming calls into the service center are kept in a queue if no staff was available to answer the call. But only a certain amount of waiting time was allowed for the customer. After the customer has spent that time waiting in the queue he was disconnected and asked to try at a later time.

For the simulation model the waiting time of the customer was assumed to not play a significant role, since it was kept relatively small by the queuing system.

Finding the optimal operating Point via Simulation

For the presentation of the results in this paper all quantitative data was normalized due to data security and privacy issues of the service center's company. The results in this paper were generated in order to show the general principle of the characteristic curves and how an optimal operating point for a service system can be obtained. In order to generate results the capacity was given different values. For each value for the capacity a simulation run over a certain amount of time periods was conducted.

Fig. 4. Fulfillment of performance criteria for different capacities

Figure 4 illustrates how the fulfillment of performance criteria, as a major part of the perceived quality, changed for different values of capacity. For a capacity of 100 the maximal fulfillment of performance criteria is at 0.9 or 90%. This is due to the fact that for 1 out of 10 calls it was assumed that there was no solution to the problem and the product had to be sent in in any case. The fulfillment drops to 0.75 or 75% in periods with high demand and a large load. For a capacity of 80 the maximal fulfillment of performance criteria was seldomly reached. In most periods the fulfillment of performance criteria drops down to 0.75 or 75%. Due to the utilization cap, which was implemented in order to limit the workload for the service center staff, the fulfillment of performance criteria does not drop below this value.

Fig. 5. Obtaining the optimal operating point

In order to obtain the optimal operating point the productivity has to be maximized. For each value of capacity the resulting average productivity calculated over the time periods. By plotting the different capacities on one scale and the resulting averages for productivity on the other a characteristic curve is obtained. The optimal operating point for the service system is located at the top of the bell shaped curve. It is the point where the capacity of the service center is used most productively taking into account all the factors described in the simulation model.

Summary and Outlook

This paper has presented an approach for simulating service delivery systems. A major assumption for the simulation approach is that capacity derived from human workforce shows the same characteristics as human behavior does. Additionally, a use case for the simulation approach was introduced on the basis of which characteristic curves were described in order to find an optimal operating point for the service system. Further research could focus on including demand which not only fluctuates but also changes its mean over time. Also, a process chain with different types of processes with customer requirements and preferences to quality can be integrated into the simulation approach. The challenge would then be to assess and simulate the interactions between the different processes. Also further research could focus on avoiding or controlling the effects of human behavior (such as procrastination) in order to improve service productivity. For example the application of the principle of tact for service processes can be a solution.

Fig. 1 .

 1 Fig. 1. Yerkes-Dodson law[START_REF] Yerkes | The Relation of Strength of Stimulus to Rapidity of Habit-Formation[END_REF]