Sergio Cavalieri
email: sergio.cavalieri@unibg.it

Stefano Ierace
email: stefano.ierace@unibg.it

Nicola Pedrali
email: nicola.pedrali@unibg.it

Roberto Pinto
email: roberto.pinto@unibg.it

A Fourth Party Energy Provider for the Construction Value Chain: identifying needs and establishing requirements

Keywords: Sustainable manufacturing, Construction Industry, Energy efficiency, 4PEP (Fourth Party Energy Provider)

Today's building and energy management market is heterogeneous and complex. Most of the players in the construction market are not in possession of the managerial capability to fully control the dynamics that affect their energy costs in terms of energy sourcing and energy management. Moreover, construction industry needs to rely on a stronger technical and commercial expertise. On one hand, there is a need of an in-depth and extensive level of technical know-how that most of facility managers, property developers and building owners at private and public level scarcely hold. On the other hand, this industry is characterized by a fragmentation within the single tiers of the value chain. In this context, the paper aims at proposing a new vision of the building value chain towards a collaborative network led by a new player, namely the Fourth Party Energy Provider, acting as the "one-stop contracting and managing" operator, integrating resources, capabilities, best available technologies and practices for providing energy-efficient building solutions.

Introduction

The construction industry today plays a relevant role in the European context in which it is responsible for more than 11% of the European GDP and with 32 million people employed [START_REF]International Energy Agency: World Energy Outlook[END_REF]. Also from the environmental point of view, this industry plays a critical role since the buildings are the major actors in terms of energy consumption (more than 40%) and emissions (about 33%) at European level [START_REF]Cost optimality -Discussing methodology and challenges within the recast Energy Performance of Buildings Directive[END_REF]. In addition, the average age of buildings, which is very high, has a negative impact on the building energy performance. A more in-depth analysis needs to take into account also the rules provided by standardization organizations. On March 2007, the European Council underlined the need to increase energy efficiency within the European Union to achieve the goal of reducing by 20% the power consumption in 2020. This action plan has identified the significant potential for energy savings effective in terms of costs in construction. To achieve the goal of 20% reduction in energy consumption, the European Union has adopted directive 2010 31/CE [3] through which are set guidelines for improving building energy performance: (i) methodology for the calculation of energy performance, (ii) establishment of minimum requirements in terms of energy efficiency, (iii) nearly zero energy buildings, (iv) certification system of energy performance, (v) system control and independent experts. Despite the regulatory and standardization efforts, in the construction market most of the players are not in possession of the managerial capability to fully control the dynamics that affect their energy costs, in terms of energy sourcing and energy management. There is the evident risk for them to sustain high financial costs, not neglecting the indirect strong environmental and social impact. This complexity is further challenged by the need to have a building lifecycle management perspective, which stretches over a temporal dimension the structure of a construction value chain.

In addition, this specific value chain is characterized by a high fragmentation and heterogeneity of actors. As depicted in Figure 1, involved roles in this value chain are local authorities, capital providers, developers, agents, materials and equipment suppliers, contractors, engineers, designers, owners and users. Most of the operating companies are SMEs or even micro-companies normally specialized in the provision of a specific technology with often a short-sighted vision of their role within the value chain.

The complexity of interaction among these participants is one of the greatest barriers to energy efficient buildings. All the above mentioned players do have their specific impact on the energy consumption of a building throughout its life cycle.

Fig 1.

The actors involved in the construction value chain (adapted from [START_REF]Energy Efficiency in Buildings: Facts and Trends-Full Report[END_REF])

In this context, the paper aims at tackling some peculiar issues of the construction industry, considering in particular:  which are the current models for the evaluation of the building energy performance during its whole lifecycle (thus not only considering the usage phase);  the potential role of a new actor, namely the 4PEP (Fourth Party Energy Provider), who would act as the mediator between the constellation of companies operating in a construc-tion network and a generic client, in order to manage the whole construction lifecycle processes and master the enabling technologies required.

Nature of the construction value chain

In the construction value chain, a client has to interact with a multitude of suppliers, each providing a specific competence and accounting for a narrow slice of the overall energy bill. In some cases, the customer selects the manufacturer (contractor), the suppliers of specialist parts and the material suppliers. This raises up several issues since: (i) more independent organizations are involved, often with reciprocal conflicting interests; (ii) there are evident diseconomies in terms of transactional costs, having to relate with a multitude of players; (iii) single decisions are made in different phases of the building lifecycle with different counterparts, thus creating evident inconsistencies; (iv) it is not possible to define a fully comprehensive performance-based contract, but rather local service level agreements with the single service providers [START_REF] Akintoye | A survey of supply chain collaboration and management in the UK construction industry[END_REF], [START_REF] Vrijhoef | The four roles of supply chain management in construction[END_REF], [START_REF] Saad | A review of the progress towards the adoption of supply chain management (SCM) relationships in construction[END_REF].

A collaborative value chain approach is needed where the client could be involved from the early moments in co-creating the value of such a relation. Information and material flows relevant to energy efficiency and a comprehensive environmental assessment would be established between the players, and integrated collaboration with a common goal would replace isolated acting and self-optimization. These actors should have a life cycle vision of a building, very often neglected.

Fig 2. Building lifecycle: main phases and competences required

Two different models are identified in literature in order to perform a lifecycle energetic and environmental assessment: a) Multi-criteria Indicators models which take into account several dimensions affecting an environmental assessment such as: material and energy consumption, people "energy behavior", material recycling and re-use, pollutant emission reductions, water consumption. Hereafter the most relevant multi criteria models are reported:

 BREEAM (Building Research Establishment Environmental Assessment Method) developed by the Building Research Establishment, which takes into account seven criteria to perform building life cycle assessment: (i) energy; (ii) transport, (iii) pollution, (iv) materials (v) water, (vi) region features and (vii) health; for each criterion a specific evaluation is provided [8]

 LEED (Leadership in Energy and Environmental Design) which takes into account seven criteria: (i) site sustainability, (ii) water management, (iii) energy, (iv) materials, (v) internal environmental quality, (vi) innovation in design and (vii) regional priorities [START_REF]Foundations of the Leadership in Energy and Environmental Design, Environmental Rating System[END_REF].

 GBtool (Green Building Tool) which classifies a building using four hierarchical levels: (i) performance classes, (ii) performance categories, (iii) performance criteria and (iv) subcriteria [START_REF]GBtool User Manual[END_REF].

 CASBEE (Comprehensive Assessment System for Built Environment Efficiency) which evaluates the building energy performance in different building phases: (i) pre-design, (ii) design and (iii) post-design [START_REF]Comprehensive Assessment System for Built Environment Efficiency[END_REF].

b) Synthetic Indicators models, which take into account quantitative analyses rather than qualitative ones. The most relevant model in this category is represented by the life cycle assessment (LCA) methodology which provides a quantitative assessment of the "consumption" of a building in its lifecycle considering also the design and the end of life phases, traditionally neglected in the construction field [START_REF] Erlandsson | Generic LCA-methodology applicable for buildings, constructions and operation services-today practice and development needs[END_REF].

In table 1 a review of multi-criteria indicators and synthetic indicators is provided, underlying the main benefits and weaknesses of each model.

Model Main weaknesses Main strenghts Threaths

Multi criteria From the extensive literature review conducted by [START_REF] Ortiz | Sustainability in the construction industry: A review of recent developments based on LCA[END_REF], although LCA is recognized as an innovative methodology which could improve sustainability in the construction industry, it emerges that there has been a large number of LCA studies which merely deal with a specific part of the building life cycle. Only few of them really encompass the whole life span. In their concluding remarks, the same authors strongly maintain how entities involved in the construction industry must be proactive in creating environmental, social and economic indicators, which bring about building sector sustainability and promote the use of consistent construction practices.


What is missing is a common platform where all the different actors operating in a construction consortium could have a mutual understanding of their role and their contribution in terms of real added value and impact on the overall lifecycle of a building, not neglecting the involvement of the customer. Without this platform there is the evident risk that any environmental and energetic assessment would be quite myopic to the specific lifecycle phase (i.e. either on its construction or on its use) or peculiar to the instances and objectives of the single operator. In addition, it would be too generic, since it needs to consider also the habits and requirements of the users that will be living in the building during its existence. What could fill this gap is the definition of a building value framework based on the concept of a business model. In the most basic sense, a business model is the method of doing business by which a company can generate profit. It spells out how a company makes money or gets paid [START_REF] Chesbrough | The role of the business model in capturing value from innovation: evidence from Xerox Corporation's technology spin-off companies[END_REF], by specifying how it intends to create value to all the stakeholders [START_REF] Linder | Changing Business Models: Surveying the Landscape[END_REF]. Many authors argue that business models are not able to deliver long term goals due to their focus on short-term, internal and financial performance. This is particularly true for the current business models in the energy management and construction area, which are characterized by selfoptimization and a strong focus of the individual companies instead of focusing the attention on the value provided to the customer [START_REF] Kouloura | Energy management in buildings: A systems approach[END_REF]. The idea of the 4PEP Business Model -that will be described in the next section -is to overcome this issue in order to provide a comprehensive business model for the whole construction chain.

The proposed model: the 4PEP Energy Provider

This section is devoted to the description of a new acting role in the building value chain: the Fourth Party Energy Provider (4PEP). It represents an integrator that assembles the resources, capabilities, and technology of more organizations to design, build and manage a solution for fulfilling the specific needs and composite requests related to the energy aspects drawn from the construction market, by:  actively involving the customer in the creation of a value added solution;

 acting on the levers that impact on energy costs (mainly in terms of price and quantity);  selecting the best available technologies and practices for the specific situation;

 qualifying and selecting the key actors, according to their competences, assigning them a specific role in the emerging construction value chain;

 mastering all the dynamics that affect the building lifecycle;

 being the main responsible in the elaboration, monitoring and accomplishment of PBEE (Performance Based Energy Efficient) contracts, and relative KPIs towards the customer.

The term 4PEP finds its root in the homologous player in the manufacturing logistic context, namely the Fourth-Party Logistics (4PL) provider. The 4PL concept was put forward by the consulting group Accenture. The essence and core superiority of this concept lies in its ability to integrate the supply chain resources, through integrating the most high-quality resources (individuals) of the supply chain. A 4PL offers services considering a 360 degree view, which is not focused only on its ability to implement the recommendations it gives, but on all the technological and managerial options available in the market [START_REF] Yao | Decision optimization analysis on supply chain resource integration in fourth party logistics[END_REF]. In a sum, a 4PL provider manage and direct the activities of multiple 3PLs, serving as an integrator. In this way, a 4PL can leverage the whole supply chain network from an integrated perspective rather than from a specific, narrow perspective related to a single service category.

The main tasks performed by the 4PEP are not fully and consistently achievable if they are not supported by a specific toolbox of processes, methodologies and tools. The 4PEP would fill the current gap by adopting a Business Model Framework for the construction industry with a corresponding subset of tools and methods which empowers it to develop specific business models in order to evolve from offering standalone standard services to integrated solutions. As highlighted in Figure 3, the 4PEP Business Model Framework would rely on a specifically designed business model ontology, based on the Osterwalder and Pigneur's canvas, in order to share a common and standardised terminology and with an explicit definition of the relations between the constructs (i.e. from a technological, organizational, managerial, financial perspective) identified as a source of value for fulfilling the specific needs and requests from the market. The implementation of a 4PEP Business Model Software Platform would be crucial for gaining concrete results of this vision. It would provide the collaborative environment involving the 4PEP focal actor and the different stakeholders in the emerging creation of a customised business model and in simulating its affordability, robustness and durability throughout the whole building lifecycle.

In particular, it would support a stage-gate process, where "gates" or decision points are placed at specific phases of the 4PEP business model development process, and embed in its functionality a dashboard for monitoring throughout a contract lifecycle the main technical and financial KPIs in order to enable a prompt understanding of any deviations from expected targets SLAs (Service Level Agreements) and highlight eventual counter-measures.

Conclusions

This papers stems from the consideration that in the construction industry there is a need of an actor integrating competences and companies in this fragmented market. The presence of the 4PEP would quite relevant since it could act as the "one-stop contracting and managing" operator providing a direct and durable relation with the customer. The potential behind this concept is quite evident if we refer to public administrations where the low managerial capability, the multitude of contracts to deal with, as well as the need to maintain a continuous monitoring of their performance can become quite compelling and affect the overall quality of a public-private partnership. Some issues are also open: (i) which are the main competences and capabilities required for such an actor? (ii) is there a need of new professionals or are these skills already available in some companies (i.e. Energy Service Companies or within the same construction companies)? Moreover, for this research it is fundamental to focus also on the technology and tools for enabling the business models, in particular:  Building Technology Solutions: technologies related to building both in design, construction and usage phase (HVAC, advanced material, …);  energy performance models in order to monitor and improve the performance of the building;

 coordination and collaboration mechanism among the whole building value chain in order to involve the different partners towards the same main objective.

The next steps of this research will be mainly devoted to design the main constructs at the basis of the 4PEP business model and to apply this vision to a pilot study on the renovation of a building stock related to social housing.

Figure 3 :

 3 Figure 3: The 4PEP Business Model Framework

Table 1 :

 1 A comparison between Multi Criteria and Synthetic models

		Procedural simplifications	 Results clear and	 Diversification of the
		 Scoring systems	easily understandable	results according to the
		 Using quantitative and	 Possibility to carry out	used model
		qualitative indicators	self-certification	 Economic and political
		 Unreliable and misleading	 Very popular model	difficulties for its ap-
		results		plication
		 Using a prescriptive legis-
		lation
		 Need of a high number of	 Detailed and reliable	 Economic and political
		information	analysis	difficulties for its appli-
		 Analysis performed by	 Focus on the entire	cation
	Synthetic	specialists	building life cycle
		 Lack of data from literature	 Using quantitative
			indices
			 Use of performance
			norms


Stefan Hesse
email: s.hesse@sap.com

Volodymyr Vasyutynskyy
email: volodymyr.vasyutynskyy@sap.com

Martin Rosjat
email: martin.rosjat@sap.com

Christian Hengstler
email: christian.hengstler@sap.com

Modeling and Presentation of Interdependencies between Key Performance Indicators for Visual Analysis Support

Keywords: decision support, visual analysis, key performance indicators, user experience

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

Introduction

Nowadays, key performance indicators (KPIs) are an important instrument to analyze manufacturing systems. They allow the responsible experts or assigned staff e.g. at the production line to get quickly an overview and status of the production processes, detect possible issues, set targets and trigger actions. Accordingly, a lot of software tools support the analysis of KPIs by providing dashboards indicating the status of KPIs etc. Typical dashboards are shown in [START_REF] Sap | Simple, Affordable, and Open BI Tools for Everyday Use[END_REF] [2] and [START_REF] Siemens | [END_REF]. However, these tools are currently limited to a mere visualization of the KPI values. Dashboards lack in scalability for displaying all data. At the same time the processes in modern manufacturing are getting more complex and are tightly connected with each other, so that the processes and their KPIs cannot be considered separately. The analysis of the internal interdependencies between KPIs, which is necessary to assess the causes of possible issues and to make the correct decisions, is still not supported enough by the tools and depends a lot on the experience of human experts. This exposes a lot of potential risks to companies, which include making false decisions due to disregarding of important interdependencies, possible loss of expert knowledge due to human factors etc. Accordingly, this gap should be closed by providing the means for explicit modeling, visualization and analysis of interdependencies between KPIs. This paper describes the approach for visual support to analyze KPIs by providing a graph of KPIs showing their status and interdependencies, extended by several features to facilitate the visual perception of the users. The approach is a result of a joint project of several partners towards creating the production logistics and sustainability cockpit (PLANTCockpit, [START_REF]PLANTCockpit EU-funded Project[END_REF]). The approach has been prototypically implemented and tested on a use case from the automotive industry.

Motivation

In recent years the gathering, interpretation and analysis of business data has become an important key success factor. Single interpreted and calculated values over multiple information items are called key performance indicator (KPI). These indicators describe the state or value of a dedicated object in the business hierarchy. Examples for such indicators are the efficiency of a supplier, duration of the creation of a dedicated product, an error of a machine or the stop of a production line. The key challenges for managers are to have a consolidated and visual fast way of getting an overview about their responsible factory areas as well as potential problems and their causes. Nowadays, KPIs are analyzed using different business intelligence tools like SAP's Business Objects [1] [2]. BI tools can create reports based on data cubes e.g. from data warehouses. Typical reports of these multidimensional items are provided as two dimensional tables, charts or gauges and value based dashboards. Dashboards for KPIs show mostly single values and independent KPIs. As long as more values are stored in a KPI analysis table, these tables may become very complex and need a lot of explanations or background knowledge to be interpreted. This means that after a critical amount of KPIs is reached, those visualizations as tables and dashboards lose their comprehensibility and the semantics of KPIs are not further recognizable anymore. Also interdependencies between different KPIs (in this case cells of tables) are not visual recognizable by the expert within these tables. In most cases these interdependencies are not explicit maintained or even do not exist in common analysis tools, so that the relations between the KPIs (i.e. how they influence each other) are only known from human expert experience or generated by multivariate statistics methods.

Since the KPI queries are being created during long periods of time and by different experts, the background knowledge about the interdependencies can get lost over time. If no relations between KPIs explicitly exist, no visual links between the KPIs can be provided automatically.

A typical scenario of KPI analysis is when the cause of the state change of a KPI has to be investigated. For example if the change of a KPI (e.g. KPI A) is influencing another KPI (e.g. KPI B), the cause for changing KPI B can now only be found by background knowledge of the users, but not by direct visibility. This lacking of explicit visualization of logical and visual dependencies makes especially the route cause analysis very hard or impossible. As result, quick and profound decisions cannot be made. Our approach is intended to close this gap.

Related Work

Different key performance indicators have been defined in literature and applied in the practice, acquiring quantitative, measurable information and representing them as result of relations and facts [5] [6]. The further development of KPIs and KPI systems into a performance indicator reference model can be found in [START_REF] Hesse | Reference model concept for structuring and representing performance indicators in manufacturing[END_REF]. The application areas of KPIs include visibility of changes, measurement of current state, comparison of targets and actual business results, decision support, support for coordination and adequate presentation of the information [START_REF] Gladen | Grundlagen zum Performance Measurement[END_REF] [9] and [START_REF] Chryssolouris | Manufacturing Systems: Theory and Practice 2nd edn[END_REF]. Dedicated recommendations for visualization of KPIs can be found in [START_REF] Wenzel | Visualization for modeling and simulation: a taxonomy of visualization techniques for simulation in production and logistics[END_REF].

An investigation of different kinds of data visualization for expert support started in 1960s with the work of Jacques Bertin und Edward Tufte [START_REF] Bertin | Semiology of graphics[END_REF] [START_REF] Tufte | The visual display of quantitative information[END_REF]. Also approaches of graph theory for presentation of large data sets appeared [START_REF] Lamping | A focus+context technique based on hyperbolic geometry for visualizing large hierarchies[END_REF] [15] [16] [START_REF] Doganata | Visualizing meetings as a graph for more accessible meeting artifacts[END_REF]. As for now, graphs are a part of different analysis tools as well as visualization libraries. Graphs are also used for different purposes and analysis tools like decision trees, data structures, semantics (RDF), concepts etc. However, till now no approaches are known that support the analysis of KPIs from manufacturing processes using graphbased presentations. Moreover, the approach presented here also includes the method for creation of such KPI dependency graphs. Additionally different interaction features supporting the analysis are introduced.

4

Graph Model

Graph-based presentation of KPIs

We propose to introduce the graph-based presentation for KPIs to allow the explicit modeling and display of interdependencies. The interdependencies represent the mutual influences between KPIs, and a graph is an intuitive form for representation of such dependencies. Unexpected or undesired KPI state changes can be easily detected. In this way different kinds of analysis will be supported, including root-cause analysis of issues, what-if analysis of future KPI values as well as detecting of possible risks. The elements of the graph-based presentation are shown in Fig. 1. The nodes of the graph represent the KPIs itself. The edges between nodes present the dependencies. Both nodes and edges can have further attributes to represent additional KPI properties, e.g., values, status, etc.

KPI_2 KPI_3

4.2

Lifecycle of the graph-based presentation.

The realization of the proposed graph-based presentation consists of 2 main phases, a design and a runtime phase. Both phases include further steps which are described in the following.

Design-phase.

In the design phase the configuration of the tool and modeling of dependencies is done. This includes the definition of an overall dependency graph as well as the definition of different user views on the graph created before. First of all abstract KPIs acting as templates for concrete KPI instances are created. Following the dependencies between those abstract KPI templates are modeled. These templates represent generic KPIs without concrete instance relevant properties, e.g., connections to data sources in the business data warehouse. In this state, the design phase can use KPI hierarchies, reference models or results of multivariate statistics methods as input for the creation of the model. The next step covers the definition of user views. User views contain KPI instances derived from abstract KPIs, e.g., throughput of machine A, which are of interest for the user analysis. This step includes the refinement of the generic KPI properties with concrete values, e.g., the assignment of a query string to a data source. Using the dependencies modeled earlier between abstract KPIs eases the configuration in terms of modeling the dependencies twice or more times on instance level. Detailed information about the different abstraction levels to implement KPIs can be found in [START_REF] Hesse | Reference model concept for structuring and representing performance indicators in manufacturing[END_REF].

Runtime-phase.

This phase covers the presentation of user views with KPI instances and their values, states, etc. to the user. Depending on the type of analysis (status check, problem cause analysis, what-if analysis etc.), the user interacts with the dependency graph and visually inspects the KPI properties and their status.

Features for support of visual perception and user experience

To support the visual perception and the user experience, the graph is enhanced with the following interaction features: Drill-down possibilities. By clicking on a node, an analysis on its further properties can be made. Folding and expanding of nodes allow the display of a large set of nodes at the same time as well as root cause analysis by expanding and inspecting the relations between single nodes. Definition of different user views on top of the overall KPI dependency graph showing different sets of nodes and edges depending on the analysis task and user roles (like manager or dispatcher). For example, the manager is interested only in high-level KPIs like overall efficiency or quality ratio. For a dispatcher, the status of the production line (e.g. throughput of the production line) and single machines (working/not working) is important. By choosing the corresponding view, the user can immediately see only the KPIs and dependencies he is interested in and authorized for. Definition of sub-graphs and groups of nodes representing specific areas of interest. The sub-graphs or groups can be again folded to limit the visual overload for the user when viewing large graphs. Drag-and-drop of single nodes, node groups or sub-graphs to support easy creation of the KPI dependency graph as well as user views.

Highlighting of nodes or edges, e.g., based on values. For example, the color of a node may represent if the KPI values is within acceptable limits defined by matching targets (e.g., green) or exceeds these limits, (e.g., yellow or red).

Filtering (e.g. hiding or increasing opacity) of nodes according to different criteria. During the interactive analysis, the user may be interested only in a subset of KPI instances, which is supported by filtering. For example by setting the criteria "area=Painting", the user will see only KPIs from this area. Graph layout algorithms like Sugiyama or energy-based [START_REF] Eiglsperger | An E cient Implementation of Sugiyama's Algorithm for Layered Graph Drawing[END_REF] [19] can be used to adjust the presentation to the users' expectations. For example, for hierarchical KPIs should be intuitively presented by hierarchical trees.

Implementation

The proposed concepts are implemented in a prototype shown in Fig. 2 and developed in .Net. The prototype retrieves the KPI data and values from a connected business warehouse system out of test OLAP cube. The prototype has been applied on sample data of a use case from automotive industry and presents an exemplified user view. The implementation is based on the PLANTCockpit architecture described in [START_REF] Vasyutynskyy | Layered Architecture for Production and Logistics Cockpits[END_REF]. This component based architecture is designed for gathering, interpreting and analyz-ing data from heterogeneous data sources such as ERP, MES, Files, databases and data warehouses or other external systems.

The prototype implements the user experience features of graph visualization described in Section 4.3. Using the paradigm of Zoomable User Interfaces (ZUI), the expert can dive into large graphs with up to thousands of KPIs (see Fig. 3), still seeing most relevant parts. According to user's preferences large graphs, sub-graphs or groups of KPIs can be folded, expanded or repositioned by the user. Furthermore, the prototype allows the filtering of nodes using content, tags or states of the single KPI. In our prototype, the result of the filtering is shown in a decrease of the opacity of non-relevant KPIs. The filtering on the status of KPIs is depicted in Fig. 4. The automatic use of different graph layout algorithm allows the creation of KPI representations according to the user's expectations. The prototype was tested on the use cases of KPI analysis of manufacturing KPIs of a large automotive company. The prototype was connected to the data warehouse of the company, allowing presentation of the status of the real KPIs. The data ware-house contains thousands of KPI types as well as their current and historical values, which are usually accessed by data cube queries, based on which the table-and chartbased reports are created.

The KPI graphs have been created together with the experts of the company. Using our approach, the analysis of the huge amounts of KPIs could be managed in an easier way, facilitating and accelerating the work of human experts.

Conclusion and future work

The proposed approach will enhance the decision support by a higher visibility of respective influences and dependencies. It allows root-cause and what-if analysis for KPIs by utilizing links between dependent KPIs. With this capability the approach can be used for analysis of any complex business process, among others in the production or logistic management, and for production performance measurement. With the presented method, the implicit logical connections between KPIs are turned into visual dependencies. This allows the user to explore KPIs and their dependencies in a visual way. Using a graph for visualization and interpreting KPI data allows a much more interactive presentation than tables and reports. This interactive presentation is enhanced with visual support like filtering, drag and drop or zoom.

One of our future works will cover further enhancements in interactivity and a user test with KPI experts from the automotive industry to refine the assumed models and to enhance the user experience. This test will lead to new insights into the interpretation and realization of KPI results using our visual approach. An additional study will provide information about the effects of the approach for our use case partner.

Fig. 1 .

 1 Fig. 1. Elements of the graph-based KPI presentation.

Fig. 2 .

 2 Fig. 2. Sample user view of a KPI dependency graph of the prototype. States of KPIs are represented by different colors of nodes.

Fig. 3 .

 3 Fig. 3. Filtering support on different KPI states, names or tags.

Fig. 4 .

 4 Fig. 4. User support by folding of sub graphs.

Acknowledgment

The authors would like to thank the European Commission for co-funding of the research project PLANTCockpit (under the project number 260018).


Peter Almström
email: peter.almstrom@chalmers.se

Productivity measurement and improvements: A theoretical model and applications from the manufacturing industry

Keywords: Productivity, Performance measurement, OEE, KPI

At many companies, workers associate productivity or efficiency increase with something negative, it is interpreted as an increase in speed and the "sweat factor". Productivity is not only made up of the speed factor, but these misconceptions and lack of knowledge tend to put "a wet blanket" on all attempts to increase productivity. It is therefore important to clarify what productivity is and especially how it can be improved.

In general, the productivity at shop-floor level can be improved through improving the method, increasing the performance, and increasing the utilization. The design of the products and the amount of scraped products also affects the productivity in both manual tasks as well as work performed by machines. These aspects of productivity will be elaborated in the theoretical model and the industrial applications presented in this article.

Introduction

Productivity is an important factor to create wealth and well-being at national level as well as creating competitiveness at corporate level. But productivity itself is only a mean to create high standard of living for nations or high profitability for companies [START_REF] Sink | Productivity Measurement and Evaluation: What is Available? National Productivity Review[END_REF][START_REF] Tangen | Demystifying productivity and performance[END_REF]. Productivity can in general be defined as output over input. The choice of outputs and inputs are dependent upon the intended application. For example is the standard definition 1 for productivity at national level gross domestic product (GDP) over total amount of work hours for the total population. On a factory level the productivity may be defined as added value per employee and on cell or station level a typical productivity measure is the number of products produced per hour planned production time. Vora [START_REF] Vora | Productivity and Performance Measures: Who Uses Them? Production and Inventory Management Journal[END_REF] has made a study of the industrial use of different productivity definitions at three separate levels in manufacturing companies. The conclusion was that at first and middle manager level the most common productivity measure used was physical output per labor. Labor can be interpreted as number of people or planned working time. This article concerns productivity improvement at factory floor level, i.e. at station, cell, or line level. There are many ways to achieve these improvements and people associate different feelings and have corresponding attitudes towards productivity improvements. At many companies, workers associate productivity or efficiency increase with something negative. It is interpreted as an increase in speed and the "sweat factor". However, the sweat factor is only one way of increasing the productivity, and probably the least desirable one in most situations. Managers at different levels have often a low level of knowledge about productivity measurement and therefore also about its improvement. These misconceptions and lack of knowledge tend to put "a wet blanket" on all attempts to increase productivity in good times and productivity improvements are only discussed in bad times when the company is bleeding and at that time it's often too late. For these reasons it's important to clarify what productivity is and how it can be improved. In general, the productivity at shopfloor level can be improved through improving the method, increasing the performance ("the sweat factor"), increasing the utilization, improving the product design, and reducing the scrap. These aspects of productivity will be elaborated in the theoretical model presented in this article.

In order to measure the real productivity potential in Swedish manufacturing companies an assessment method was developed: Productivity Potential Assessment (PPA) [START_REF] Almström | Assessing and benchmarking the improvement potential in manufacturing systems at shop-floor level[END_REF]. Since 2005, close to one hundred PPA studies have been carried out in the manufacturing industry. The productivity improvement potentials have thus been assessed and measured for a wide variety of companies: large and small companies from different industries. The majority of the studies have been performed at suppliers to the automotive industry. The focus of PPA is on the utilization aspect of productivity, however it is not given that utilization has the greatest impact on the productivity. In most cases it is the method factor, i.e. the selected machines or the manual work method that represent the greatest potential. The potential for improvement in manual work methods is especially interesting, since substantial improvements often can be achieved without or with very limited investments. The manual work methods are often an effect of the production system design (or lack of intended design) or the management of the system. For example, it is common that the manual work is the bottleneck for machine operations; the machines must be loaded and un-loaded by operators, the operators perform the set-up work, and so on. Thus, the machine utilization is heavily affected by the manual work. However, very few companies in the Swedish manufacturing industry acknowledge this fact and actually design and measure manual work tasks in a systematic way. According to the PPA studies merely around 25% of the manufacturing companies are able to carry out work studies and set standard times in reasonably accurate way by using either time studies or a predetermined time system. The vast majority are either using operation times for similar products, set the times based on pre-production runs, or are simply guessing based on experience. The reason for this misconduct is a mix of the work organizations' and the salary systems' historical development and the managers' lack of insight and competence [START_REF] Almström | Why is there a mismatch between operation times in the planning systems and the times in reality[END_REF].

Theoretical productivity increase model

There are many ways to increase productivity. Taylor [START_REF] Taylor | Principles of Scientific Management[END_REF] was the most famous pioneer in the beginning of last century. He emphasized the productivity potentials in improving the methods used by introducing better tools and, more importantly, how the speed of people working could be enhanced greatly by proper motivation. Later were the socio-technical factors for motivation and productivity increase discovered in the famous Hawthorne studies [START_REF] Dipboye | Understanding industrial and organizational psychology: An integrated approach[END_REF]. In the last decades has the productivity been raised through increased automation and by outsourcing labor intensive work to locations with low salary level. The competence to carry out time-and motion studies vanished in the Swedish industry when new salary models and work organizations were introduced in the 1970-80ies. These days when Lean Production is the norm, the lost competence is needed again. Every company that tries to implement a Lean Production system will need standard work procedures and stable operation times at the lowest shop-floor level, as a base for continuous improvement and steady flows. The methods that were invented by Taylor and his followers (e.g. The Gilbreths, Gantt, Segur, and Maynard [START_REF] Freivalds | Niebel's Methods, Standards, and Work Design[END_REF]) are still valid today even though the purpose of using them (to set the salary in piece rate systems) is not the same today.

Regardless of method, improvement program, or production philosophy applied to increase productivity, it is important to know what factors that actually affect the productivity. In the following text will a model be presented that has the potential to pedagogically explain what productivity is and at the same time provide a concrete way to calculate a potential productivity increase.

Productivity model

Productivity can be improved through better methods (M), increased performance (P), and increased utilization (U). This can be expressed in the following equation:

(

Consultants ( [START_REF] Saito | Reducing labor costs using industrial engineering techniques[END_REF], [START_REF] Helmrich | Productivity Processes -Methods and experiences of measuring and improving[END_REF], and [START_REF] Sakamoto | Beyond world-class productivity -Industrial engineering practice and theory[END_REF]) have used this formula or parts of the formula at least since the 1960ies2 . However, it has not been described or analyzed in an academic context. The model has been developed and previously used for manual work only, but it is actually applicable for all kind of work, even if it's carried out by a machine. The method factor (M) is defined as the ideal or intended productivity rate. It is the inverse of the ideal cycle time for the specific work task. In order to determine the ideal cycle time for manual work tasks it is necessary to use a predetermined time system. There are a number of available systems and most of them are based on MTM [START_REF] Freivalds | Niebel's Methods, Standards, and Work Design[END_REF]. The MTM systems were in wide use in the Swedish manufacturing industry after WWII until mid-1980ies. At that time, the use was for setting times in piece-rate sala-ry systems. These salary systems have almost vanished now, and with them the competence of using the predetermined time systems. This has resulted in a situation where companies are unable to calculate an ideal cycle time before the work station is up and running. The time for the work task can then be timed with stop watch, but the resulting time will not be the ideal cycle time; it will be affected by the P and the U factor in equation 1.

The performance factor (P) corresponds to the speed the work is carried out at in relation to the ideal cycle time. For manual work the performance factor can be both below and above 100%. The normal speed in MTM is set to be valid for a "normal" person working at this speed for 8 h a day and for the whole working life without getting exhausted or injured. The performance rate is lower for not fully trained workers and for people with disabilities. For machine work can performance by definition never go beyond the ideal cycle time, i.e. 100%.

The utilization factor (U) represents the time that is spent on performing the intended work in relation the total planned time. Utilization can never go beyond 100%. The planned production time is usually defined as the paid working time minus planned stops, like weekly meetings or planned maintenance stops.

Expanded model

Apart from the three base factors in Equation 1, other factors that affect the productivity are perceivable. The quality rate (Q) or yield affects the productivity if the productivity is defined as the number of quality approved products produced per time unit. All production engineers (and obviously not all product designers) are aware that the product design has a very important impact on the productivity. This can be expressed with a design efficiency rate (D). These two extra factors together with Equation 1give the expanded model in Equation 2.

(

) 2
The quality factor (Q) is the yield or 100% minus the scrap rate. Yield is common in the electronics industry while scrap rate is normally used in the mechanical products industry. The quality loss is usually very small in comparison to the other productivity losses. However, the scrap rate has other implications as well; such that the errors need to be detected by a quality control system and that the scrap also is a waste of material and energy. The final factor in equation 1 is the design (D) factor. The factor for improving the design to decrease the cost of manufacturing and assembly is treated in the DFMA (design for manufacturing and assembly) literature. Specifically for assembly, Boothroyd et al [START_REF] Boothroyd | Product Design for Manufacture and Assembly[END_REF] have suggested a design efficiency factor. That factor is 100% if the design is ideal from an assembly point of view. The design factor is clearly outside the limitation to the factory floor, and in practice it is very hard for a manufacturing company to affect in many cases. In fact, contract manufacturers have normally little to gain and possibly a lot to lose by suggesting design changes to the customer com-pany. The contract manufacturer will lose their value adding work since the customer most likely will decrease the price paid because of the design change.

Applications

The focus of the applications has been to find and demonstrate how simple, low cost changes, in the production will affect the productivity factors and because of the multiplying effect will provide substantial increase in productivity. The special application to machine work will also be discussed.

A fictive case based on today's average and best practice

The first application example is fictive in order to demonstrate the three factors in equation 1 and their multiplicative effect. The example is based on an assumed situation with facts based on the average figures in today's industrial practice compared to best practice. The figures are based on previous studies using the PPA method and subsequent improvement studies carried out in Swedish industry [START_REF] Almström | How to realize the productivity potentials in the manufacturing industry[END_REF].

Consider an assembly work station. A specific work task takes 3 minutes or 180 s to carry out according to a well-defined standard work description. However, the worker does not work at standard speed (100%) but rather a bit lower; at 90%. The utilization is 65%, 15% of the time is spent on supporting activities like moving the material to and from the work area, planning and reporting, and cleaning. The remaining 20% of the paid working time is personal time and breaks.

The method can be improved 30% with small means, mainly by putting material closer and minimizing the number of steps. The cycle time decreases to 180 s -30% = 120 s. The performance shall always be on 100% if not special conditions apply, e.g. work performed by a not fully trained employee. The utilization can increase to 81% assuming that the personal time and breaks can be lowered to 9% which is a normal agreed level and the supporting tasks can be reduced to 10% using a better method for those tasks as well. The resulting productivity increase can be calculated step by step assuming one hour production of the particular product (table 1).

The total productivity improvement by going from the average level to the best practice level identified in the PPA studies is over 100%. This example, based on realistic improvement levels, clearly demonstrates the importance of the multiplicative effect of the small improvements in each factor.

Improvement of manual assembly

This application is from a manufacturer of heat, ventilation, and air condition (HVAC) units for vehicles like buses and trucks. The production is highly customized and the products are manufactured and assembled in relatively small batches, typically around 10 units in each batch. The study was a part of a government supported program to suppliers to the automotive industry and it was carried out by professional consultants.

The study commenced with a PPA study of the factory's general productivity potentials. The final assembly area was selected for the work sampling study and that revealed that the utilization rate (U) for carrying out the actual assembly work was 60%. The remaining time was split equally on supporting (necessary) activities, e.g. fetching material from the warehouse and reporting, and personal time. To formally measure the method improvement potential is not a part of the PPA study. However, while the work sampling is conducted it usually becomes obvious to the experienced analyst if the method potential is large. After the initial PPA study a total of 10 consultancy days were spent on analyzing the method in detail and involving the operators in the work to improve the assembly area's productivity.

The analysis was delimited to the assembly of the factory's volume products; two varieties of ventilation units. The performance factor (P) was measured to be close to 100%. The productivity was 0,27 units/h and 0,44 units/h for the two products before the improvements. The present assembly was conducted at stations and in batches, where the worker assembled several units in parallel. A lot of time was wasted on walking to and from the material storage areas. The improvement was to transform the station assembly into a simple flow, where the work was divided into three parts and corresponding workstations were designed. The workers were involved in the design and they constructed some simple material racks out of scrap material from the production. The new work method was documented in work instructions and the assemblers started to work according to the new standard. Virtually no investment apart from workers' time was needed.

The productivity for the two products was improved to 0,37 units/h and 0,68 units/h respectively. This corresponds to a productivity increase of 37% and 54%. The improvement was verified in running production. The performance rate was most likely 100% afterwards as well. The utilization was not measured afterwards, but it is likely that it improved slightly due to less external material handling. The personal time and break time was probably not changed at all since that was a matter of culture and old habits. In other words; almost all of the productivity increase was thanks to the method improvement. That confirms what has been concluded from similar studies [START_REF] Almström | How to realize the productivity potentials in the manufacturing industry[END_REF]: It is much easier to improve the M factor than trying to improve the U factor, even though the potential is considerable in that factor. The U factor is affected by the company culture and the management of the production. That takes a lot of time to change compared to involving the operators in method improvements.

Improving machine work productivity

The theoretical model can be applied to machine work as well, but the definitions are a bit different to that of manual work activities. The M factor is defined as the inverse of the ideal cycle time. Ideal cycle time is the fastest time either recorded or simulated for a particular job in the machine. In practice it is hard to determine the ideal cycle time for a running production, especially if manual tasks are a part of the work cycle. The prepared times (e.g. from a CAM simulation) can be used in many cases, but it is not certain that used machine programs are optimal and "ideal". The ideal cycle time is important for calculating OEE (Overall Equipment Effectiveness) [START_REF] Nakajima | Introduction to TPM -Total productive maintenance[END_REF]. The OEE ratio is commonly used in the Swedish manufacturing industry and it is included as one parameter in PPA. The basic definition of OEE is the ratio between the time that was spent on producing a certain amount of quality approved products and the planned production time. OEE can be calculated by multiplying the availability (A) which is the planned production time minus larger stoppages and break-downs, the operation efficiency (O) (including both small stops and speed reductions), and the yield (Q):

(3)

The OEE figure is not a productivity measure, there is no M factor. The A factor plus the small stops of the O are the same thing as U losses and the speed reduction part of O is the same as P losses. That gives the following relations:

Conclusions

The presented productivity model has great practical importance for industry. There is a need in industry too understand what productivity is and how it is defined and measured. The equation is simple, yet it pedagogically describes the important factors that can be improved in order to increase the productivity. The model is well founded on the experiences from the PPA studies and it has been validated through applications by academics and consultants.

  5 explains the applicability of Equation 1 (and 2) to machine work.

Table 1 .

 1 Calculation of productivity increase step by step for 1 h = 3600 s production (p/h = products per hour).

	Factor	Productivity before	Productivity after
	Method improvement	3600/180 = 20 p/h	3600/120 = 30 p/h
	Performance improvement 20×0,9 = 18 p/h	30×1 = 30 p/h

See e.g. Eurostat (http://ec.europa.eu/eurostat).

According to Bengt Isaksson who was employed at Maynard's in Sweden in the 1960ies and Shigeyasu Sakamoto who has been employed at JMAC in Japan for a long time.

Acknowledgment. This research is carried out within the Sustainable Production Initiative and the Production Area of Advance at Chalmers University of Technology. The support is gratefully acknowledged by the authors.


Petros Pistofidis
email: pistofid@ceti.gr

Christos Emmanouilidis

Profiling context awareness in mobile and cloud based engineering asset management

Keywords: Context Awareness, Asset Modeling, Mobile & Intelligent Services

This paper presents an analysis of the potential and a methodology for handling context events and adaptations in maintenance services. A significant number of industrial IT systems employ data models and service frameworks whose design specifications where drafted on the basis of functional requirements for non context-aware systems. Such systems were modular in nature but little care was taken for providing data fusion in a context-aware manner. Though system intelligence might have been a feature for a subset of such systems, two major requirements kept implementations away from wider adoption: (i) refactoring and reengineering of the system's base services (ii) customising and modifying the structure of a data model tightly connected with interprocess organisation. This paper discusses a methodology for injecting industrial asset management systems with modelling semantics and software mechanisms that enable context awareness via portable clients and applicationagnostic data fusion services.

Introduction

Increasingly computational logic is aggregated inside IT systems that serve industrial processes to enhance their capacity for offering adaptive services. Their corresponding platforms exhibit an aggressive shift to embody and exploit intelligent mechanisms for the organization, correlation and fusion of field data into application knowledge [START_REF] Samanta | Prognostics of machine condition using soft computing[END_REF]. Both tasks introduce high-capacity processing and require a capable infrastructure to serve them. Such requirements become a costly overhead especially when involving scaled semantics and rich parameter-lists for capturing highly focused contexts. Engineering asset management constitutes a domain containing several systems that already benefit from such mechanisms, many of which currently advertise services ranging from smart condition monitoring to enterprise-level decision support. The question at hand is whether such complexity can be easily handled by rigidly implemented systems and how recent software trends that decouple intelligence from the application context, can facilitate a more modular implementation.

This paper proposes an operational shift of focus for the above systems, addressing the feasibility and potential of adopting components that exploit the dynamics of modern IT technologies. After building a case for the versatility and scalability of contextualized services, we explain how a simple semantics framework can facilitate legacy systems to annotate their existing model and compose functionally beneficial contexts. This simple enrichment can fuel mobile components that track and detect context events, and respond to them with application oriented calibration of maintenance services. Such a modeling evolution can lead to the identification of simple context patterns and binding rules that offer elevated knowledge of the process. At the same time, a new meta-context can emerge and become available for monitoring and system-adaption. Computational and operational challenges of multi-site and multiindustry contexts point at the exploitation of cloud services. The features of modern cloud frameworks are discussed and specific interfacing components are analyzed to provide a conceptualization of how cloud's adoption is feasible by maintenance IT.

Context awareness in engineering asset management

From a system's design perspective, the semantics of context awareness in engineering asset management, though tightly connected with the data model, essentially derive from functional and non-functional specifications of the information system. Positioning assets and personnel, as well as any ICT smart agent, in a scaled manufacturing shop-floor, constitutes part of the asset management context. Indirect and direct localization options offer effective detection, introducing highly capable components within tools already available in mobile maintenance practice [START_REF] Emmanouilidis | Mobile Solutions for Engineering Asset and Maintenance Management[END_REF]. Location awareness brings added value to positioning events and translates their impact in the system. This impact may involve a wide range of functional adaptations and reconfigurations, irrespective of the flat semantics defining the underlying legacy system.

As with most context aware systems, the design of a maintenance-oriented one should follow a few essential steps. As a first step, serving a practical understanding of the maintenance context, its semantics should be associated with three core concepts: location (spatial locality), identity (actor locality), and time (time locality). In order to build a more focused context and extend its semantics, an extraction of functional dynamics must result from defining the desired system's response.

Context awareness is not limited to location awareness, nor is it bound by limitations to sensed parameters. The concept of context is addressed by semantics of any direct, computed or inferred factors [START_REF] Emmanouilidis | Mobile Guides: Taxonomy of Architectures, Context Awareness, Technologies and Applications[END_REF]. The practice of data analytics on large volumes of maintenance records has gradually revealed that the fusion of new parameter sets may result in discovering new meta-contexts [START_REF] Roy | Supporting pervasive computing applications with active context fusion and semantic context delivery[END_REF] [START_REF] Chen | Data-centric middleware for context-aware pervasive computing[END_REF]. The importance of each new context is assessed by the correlation of events with maintenance semantics. The fusion of machinery condition parameters can fuel the identification of new context instances, such as the machinery states. On a different level, fusion of a maintenance plan's Key Performance Indicators (KPIs) with maintenance personnel expertise, can lead to discovering contexts that optimally balances performance with utilization of human resources. Scenarios of context awareness addressed in our research include:

• A technician's route during inspection tracked directly or indirectly through GPS or RFid beacons. Machinery proximity can be inferred, enabling system to adapt the mobile interface, providing instant access to maintenance history and a list of pending tasks associated with the specific units. • When abnormal readings are taken and unknown conditions are inferred, the monitoring agents switch to "alarm" status. A gateway reports to personnel, while sensor monitoring is adapted/ calibrated to a new collective operation profile. • When an engineer edits the maintenance plan with a corrective task, he may also allocate spare parts and assign a task to personnel. The system captures the session path and presents equipment parts, recommending available personnel and actions.

There are three basic constituents in the described cases: (a) the context, (b) the context event and (c) the system's response. The data schema should define the participation of each maintenance data type in context-related events, as well as map a response or response rule to the identified context. The internal components should incorporate or facilitate processes and services responsible for compiling the context states, detecting the events and executing the system's adaptation. These context interpretation and calibration services are integrated in mobile and server side components respectively, providing the final set of "smart" maintenance services.

A context aware e-maintenance architecture

The majority of recent IT architectures are developed with the modularity required for fast and effective adoption of the aforementioned mechanisms. The serviceoriented nature of modern condition monitoring, asset tracking and maintenance management systems enables context awareness to be achieved by the hosting of a supplementary pool of services and data entities. Our research studies the extent at which legacy and modern systems can facilitate and tune federated services for mobile and cloud-based context awareness. We propose the semantics of a context-organizing sub-schema template that can be easily customized and imported in existing models of industrial systems. We discuss how mobile applications can serve a context identification/adaptation engine that captures and responds to context events, occurring in a complex shop-floor or a multi-site enterprise. Finally the role of cloud computing is assessed by evaluating emerging frameworks offering application-agnostic services for data clustering and classification [START_REF] Owen | Mahout in Action[END_REF].

WelCOM Maintenance Platform and Piloting Testbed

The conducted research constitutes part of the WelCOM (Wireless sensor networks for Engineering asset Life-Cycle Optimal Management) project [START_REF] Pistofidis | A Layered E-Maintenance Architecture Powered by Smart Wireless Monitoring Components[END_REF]. The WelCOM platform utilizes a smart sensor infrastructure for machinery condition monitoring, while interfacing with a CMMS (Computerized Maintenance Management System), to deliver a context aware asset management tool (Fig. 1). An Intelligent Maintenance Advisor client handles context aware operations by exploiting the mobility of portable devices with mechanisms able to record the user's location, environment and system session. The client adapts visually and functionally to changes in the apparent context of a service request. The same client enables maintenance staff to select parameter semantics that profile new monitored contexts. These contexts are processed by the Knowledge Management sub-System, utilizing a set of local or remotely accessed data fusion services. The Intelligent Maintenance Advisor offers an abstraction layer between maintenance practice and computations, by providing a profiling tool that translates maintenance engineering and management specifications into appropriate method invocation for modeling services. Context modeling and identification, as well as context-based adaptation are key elements in the WelCOM approach.

Fig. 1. WelCOM e-Maintenance Architecture

The development of WelCOM's mobile client drives an assessment of direct and indirect methods for inferring maintenance related contexts. One of our main testbed sites is the Kleemann's lift testing tower. This tower is equivalent to a seventeen floor building, and comprises four testing units. The WelCOM intelligent advisor should be able to monitor, map and analyze a set of condition parameters related to lift operational parts and components (Fig. 2). The lift testbeds themselves are technical structures composing an integrated lift machinery installation.

Fig. 2. WelCOM Testbed Planning

Context in Mobile Maintenance

A range of context models have been investigated by research initiatives and development processes. While the first seek to explore the depth and breadth of context semantics, the later battle their way through implementations that can deliver functionality to facilitate handling of these contexts. Maintenance related models are connected with indicators of operational performance and descriptive power, providing a solid base for context aware systems. The extensibility of these models is achieved through compliance to established schemas (MIMOSA [START_REF] Mimosa | Machinery Information Management Open Systems Alliance[END_REF]) and the native importing (multi-inheritance) features of the modeling technology (XML schemas). While earlier and recent models, address context modeling with respect to a single application or application class, engineering asset management constitutes a problem space that allows scaling of semantics at a generic level. This derives from the ranged scope of the corresponding services that start from shop-floor maintenance tasks to strategic level KPI analysis. The WelCOM maintenance advisor is designed to be configured by a context engine that detects and adapts to the following context types (Fig. 3):

Fig. 32. Context in Mobile Maintenance

• User Context -It maps the semantics behind the information included in user profile, system preferences and logged activity. Profile information designates personnel's expertise, maintenance roles and system credentials. Preferences express a set of property values that allow user-defined adaptation of services and console personalisation. Logged activities contain a list of both completed and pending tasks, along with checked/unchecked notifications. • System Context -It holds the semantics for describing device features, along with other non-functional information, such as the connection status and network avail-ability (e.g. availability of Wi-Fi networks and/or 3G signal strength), power level and system performance/status, etc. • Environment Context -It addresses the location coordinates or code of the personnel proximate position (asset/segment => position), along with a timestamp of his presence there. It also includes a set of property values that describe constituents of the sensed industrial environment (temperature, noise, humidity, light). • Service Context -The context of the user assigned functions and services. It contains the semantics about the service participation in a larger workflow (sequence number in maintenance plan), its criticality/priority and its dependences with other services/functions. A set of property values that define maintenance specific task parameters, such as constraints and available supporting material. • Social Context -This context describes the dynamics of group participation and collaboration between industrial agents such as personnel. The semantics here draw a constantly updated linked graph, where flow of knowledge and authority identify individual skills for co-operative efficiency and effective supervision. In order to enhance both maintenance personnel positioning and asset proximity, we explore the potential of using NFC-enabled devices with corresponding tags. Currently the vastly adopted android OS supports the 3 rd version of NFC framework, while the mobile market offers a range of tablets with native features for read/write operations on NFC-tags. Facilitation of such technologies can lead certain benefits:

• The utilization of solid and non-fragile portable units, capable of operating in harsh industrial environments and by maintenance practicing hands. Introducing state-ofthe-art features, such robust tablets can operate as a final product/service host, without strict handling policies. Former systems lacked casing protection and handling versatility due to connection limitations (tablet + GPS receiver/RFid reader). • New context aware maintenance clients, developed on-top of a uniform NFC framework. Such implementations ensure: ─ Interoperability: cross device coupling and context data exchange between proximate personnel. Technicians passing reports to engineers, for collaborative authoring and validation, serving and powering the social context. ─ Portability: migrating users' session state, such as pending maintenance tasks, alert notifications and system preference, from the tablet to his smartphone. Synchronizing a single rich session between shop-floor tablet, the client pc and the personal smartphone, serving and powering the user context. ─ Compliance: exploiting the wide range of supported tags, offered by the NFC framework to program multiple associations. One tag to multiple assets and multiple tags to one asset. Optimal collaboration with the driver-set of other sensor can detect and map a detailed environment context. ─ Scalability: NFC framework is integrated with state-of-the-art devices driven by the latest versions of android (and Window8) operating system. Such versions include the most recent libraries for data presentation, UI composition and wireless coupling. They effectively incorporate the features capable of performing unique client adaptation for the maintenance service context.

Context Aware Maintenance Cloud

The concept of cloud and its serving technologies have only recently found application in the engineering asset management field. The term "sensor cloud" has emerged to offer the first maintenance cloud tool in modern IT architectures [START_REF] Melchor | A design of flexible data channels for sensor-cloud integration[END_REF]. A sensor cloud is introduced as the method to outsource storage of maintenance data, supported also by proper tools that semantically annotate and organise the collected volumes. A cloud approach comes to offer two main advantages: (i) the provision of scalable storage and service instances, hosted in (ii) third party infrastructures. In maintenance systems, scalable resources may became crucial, when multiple diagnostics and modelling services are invoked by different monitoring agents (software/personnel). Aggregating the execution of such complex tasks demands high availability of resources. Few of them can easily dominate the capacity of a limited infrastructure. To address such a challenge many industries overtook the indoor development of custom distributed diagnostics, operating them on top of enterpriseowned/maintained servers/clusters and backup systems. The multifold cost of such an investment is where the second cloud feature comes to serve. Cloud providers enable enterprises to escape the cost of running their maintenance system on top off owned storage and processing facilities. The Quality of Service (QoS) defined in a cloud Service Level Agreement (SLA) has proven to be much more reliable and costefficient than most self-maintained centres. The question then is how secure is for a competitive industry to outsource such core dynamics that support its manufacturing edge, the maintenance process.

Cloud services have evolved to deal with security issues of data analytics, by supporting the abstraction layer of application-agnostic frameworks. This means that cloud providers can now efficiently host generic modelling services, whose analytic power can be extensively configured to provide highly focused results. In this scenario, apart from the service hosting/handling infrastructure, the development cost of base services is also escaped by the client industry. Such a cloud-based maintenance system only requires the following components:

• The Intelligent Computation Broker -Knowing and analysing the capabilities of the employed cloud services, this subsystem is responsible for orchestrating an appropriate workflow of service invocations that eventually returns the desired results. It operates as an engine that profiles specialised tasks of maintenance intelligence into sequenced calls of application agnostic analytics. • The Data Resource Allocator -This subsystem handles the encryption, wrapping and exchange of maintenance data. Such tasks ensure that the output of one cloud service is properly formatted and securely passed to lead the input of the next one in the workflow. It is also responsible for monitoring and imposing the data caching, replication and migration policies between the cloud storage sites.

The target of the WelCOM's cloud side is to assess how thin and modular our maintenance advisor can be, by exploiting cloud instead of local resources. Currently, frameworks such as Mahout offer ready-to-use cloud services, addressing core tasks in context analytics (classification, clustering and batch-based collaborative filtering).

Our goal is to calibrate and facilitate such services in order to provide, consume and/or extend maintenance services.

Conclusions

Context awareness is a design methodology that promotes adaptation in customised IT services and content. In the last few years, many field adoptions have been studied and evaluated, while enabling technologies and techniques progressed to enable new context dimensions for maintenance management and condition monitoring. By employing them, modern maintenance systems can offer advanced adaptive services build upon application-agnostic cloud frameworks. This paper described the Wel-COM-platform's semantics, design and implementation plan for a contextualised mobile client of a distributed model that constitutes a maintenance mobile cloud.

Acknowledgement:

The collaboration with all project partners and especially Kleemann Lifts for providing the application case, as well as Atlantis Engineering and Prisma Electronics for contributing to the application scenario setup, within the context of the GSRT projectt 09SYN-71-856, 'WelCOM', are gratefully acknowledged.


Takashi Tanizaki
email: tanizaki@hiro.kindai.ac.jp

Improvement Method of Service Productivity for Taxi Company

Keywords: service productivity, taxi company, rearrangement working time, simulation, staff scheduling

We propose improvement method of service productivity for Taxi Company of rearranging taxi drivers working hours. There are two ways a taxi company gets customers. One is to deliver taxi for a telephone request, and the other is to get customers on street. In Japan, there are many taxi companies where the ratio of the former method is higher. Therefore we research rearrangement method of the taxi drivers working hours so as to operate many taxis with many telephone requests. We find it effective strategy to increase taxi drivers in midnight and early morning with many customers per taxi instead of decreasing taxi drivers in daytime with few customers per taxi.

Introduction

In recent years, it became very severe to get customer between taxi companies in Japan. The number of taxis is increased, because it was easy to start Taxi Company from 2002 by regal revision, but customers decreased. As a result, income of a taxi per day was decreased (see Fig. 1). Then taxi companies introduce various method to gain customers, for example, customer membership, information system for efficient taxi operation [START_REF]Ministry of Land, Infrastructure, Transport, and Tourism[END_REF] [START_REF] Sugiyama | Bus & Taxi in Deregulation Age[END_REF]. There are many taxi companies which introduce a taxi operation system using GPS and radio system to shorten taxi waiting time. But taxi waiting time couldn't be shortened, if the operation demands of a taxi exceed the number of taxis which can be worked. Furthermore, there is a problem that suitable service arrangement method is not established to the operation demands of a taxi. From above reason, we analyze telephone requests data and taxi operation data from the Taxi Company, and research taxi driver arrangement method to improve service productivity.

In this paper, we analyze present situation of the Taxi Company which we research (we call Company A in this paper), research improvement method of rearranging taxi drivers working hours using computer simulation, and verify validity of our method.

2

Subjects of Company A

Taxi Driver's Working Hours for Company A

Leaving and returning garage time according to taxi driver's service pattern of Company A are shown in Table 1. Taxi driver's service pattern consists of four groups, such as Shift, Day, Fixed time, and Night. In Shift, taxi drivers change service pattern Ⅰ, Ⅱ, Ⅲ, Ⅳ, and Free by every other day. In Day, taxi drivers work from Monday to Saturday. In Fixed time, taxi drivers change working group A and B in each week. They work on Monday, Wednesday, and Friday in group A, Tuesday, Thursday, and Saturday in group B. In Night, taxi drivers work on "Monday, Wednesday, Friday" and "Tuesday, Thursday, Saturday" in each week or work on Friday and Saturday every week. Furthermore, there is the following detailed rule.

(a) Although Free is originally no working day, if the taxi driver wishes, they can freely work between 7:00 and 24:00. (b) Since the cleaning car time is also contained in working hours, taxi drivers finish the taxi operation 1 hour before returning garage time. However, they drive taxi as working hours only on Friday and Saturday from the 20th till the end of the month, because there will be many customers.

Subjects of Company A

There are two customer acquisition methods for Taxi Companies in Japan. One is to deliver a taxi for a telephone request, and the other is to get customers on street. For the former method, it is required to deliver a taxi immediately by telephone a request. For the latter method, it is required to allocate taxis for a place with many customers who need a taxi. In Japan, there are many taxi companies whose customer acquisition ratio of former method is higher than latter one. We research a service system with which many taxis work in time zone with many telephone requests.

In order to extract present subjects, we analyze the number of telephone requests per taxi in every hour (we call it load) shown in [START_REF]Ministry of Land, Infrastructure, Transport, and Tourism[END_REF].If load is high, taxis for telephone requests are short, and if low, taxis are enough.

ijk ijk ijk T R L = (1)
L ijk : Load per taxi on i-month j-day k-time R ijk : The number of telephone requests on i-month j-day k-time T ijk : The number of taxis which can be worked on i-month j-day k-time

We get telephone request data from Company A, and calculate the number of telephone requests per hour, day of the week, and load based on (1), respectively. From Fig. 2 and Fig. 3, we find out subjects of Company A as follows.

(a) The peak hours of the number telephone requests and load differ (See Fig. 2). (b) The distribution according to day of the week of telephone requests and load is almost the same. They are high on Friday and Saturday (See Fig. 3).

Improvement Method of Customer Acquisition

From present data analysis, if the number of taxis which can be worked on high load time zone or day of the week is increased, "delivering a taxi immediately for a telephone request" will become possible, and service productivity will increase. Increasing the number of taxi drivers increases labor cost, and it may become rising income and falling profits. Therefore, we decide to rearrange the taxi driver's working hours without increasing the number of taxi drivers. This problem is formulated as staff scheduling problem. There are several research results for nurse scheduling problem which is one of the staff scheduling problems. In previous research, optimum solution method to minimize the number of constraints violation was researched [3][4]. In this research we research method to minimize the summation of load described in [START_REF]Ministry of Land, Infrastructure, Transport, and Tourism[END_REF]. Therefore we choose computer simulation analysis to work many taxis in the time zone with many telephone requests. In addition, we research the methodology which can be applied to the other taxi companies.

Computer Simulation Cases

It turns out that the peak hours of the number of taxi which can be worked and telephone requests differ. We research service system so as to increase taxi drivers with high load and decrease taxi drivers with low load according to five simulation cases on condition that the service pattern is not so much different from the present one.

(a) Case1

[Method] Every leaving and returning garage time of all service patterns are shifted 1 hour before.

[Purpose] The number of taxi drivers with high load, such as 6:00 to 7:00, is increased.

(b) Case2

[Method] Leaving and returning garage time of Day and Fixed time are shifted 1 hour before.

[Purpose] The number of taxi drivers with high load, such as 6:00 to 7:00, is increased, only on Day and Fixed time because those flexibility of change is high in Taxi Company A.

(c) Case3

[Method] Taxi driver's working time of 12:00 to 16:00 in Fixed time is changed to 3:00 to 6:00 or 22:00 to 2:00.

[Purpose] The number of taxi drivers with high load, such as 3:00 to 6:00 or 22:00 to 2:00, is increased. The number of taxi drivers with low load, such as 12:00 to 16:00, is decreased.

Non-working hours

Working hours From 12:00 to 13:00

From 6:00 to 12:00, From 13:00 to 21:00 From 13:00 to 14:00 From 6:00 to 13:00, From 14:00 to 21:00 From 14:00 to 15:00 From 6:00 to 14:00, From 15:00 to 21:00 From 15:00 to 16:00

From 6:00 to 15:00, From 16:00 to 21:00 (d) Case4

[Method] Taxi driver's working day of Monday to Thursday in Fixed time is changed to Friday or Saturday.

[Purpose] The number of taxi drivers with high load day, such as Friday or Saturday, is increased.

(e) Case5

[Method] According to the Taxi Company A's know-how, taxi driver's working pattern is changed as follows.

・ Taxi driver's working pattern Free on Friday or Saturday is changed to working pattern Ⅰ on Sunday or Monday. ・Taxi driver's working pattern Ⅲon Friday or Saturday is changed to working pattern Ⅳ on Sunday or Monday.

[Purpose] The number of taxi drivers with high load day, Friday or Saturday, is increased. We verify all considerable patterns about change of time, day of the week, or the number in each simulation cases (see Table 2). Furthermore, in case3, considerable pattern is subdivided by non-working hours between 13:00 and 16:00 (See Table3).

We find out the best taxi drivers working hours which minimizes the summation of load described in (1) from 42 simulation patterns.

Simulation Results

Top 5 patterns in 42 simulation patterns using telephone request data in 2010/1 to 2010/10 from the Company A are shown in Table4. All of them belong to Case3. Load average and load variance of top 5 patterns are improved compared with the present situation. Load per hour of the best pattern, such as 4 hours before, is shown in Fig. 4. We find the following results compared with the present situation shown in Fig. 2. The load between 4:00 to 5:00 and 6:00 to 7:00, which is high in the present situation, decreases. Especially, the load between 6:00 to 7:00 decreases to less than 1.0 from 1.4. It causes a little enough time in taxi operation. Taxi driver must drive a round trip under 40 minutes to achieve 1.4 taxi operations per 1 hour. On the other hand, taxi driver drives a round trip in 60 minutes to achieve 1.0 taxi operation per 1 hour. The load between 12:00 to 16:00 remains 0.6 times per hour. Namely, load doesn't increase in daytime steeply.

It turns out that it is effective strategy to increase taxi drivers in midnight and early morning with high load instead of decreasing taxi drivers in daytime with low load so as to gain customers who reserves taxi by telephone requests.

Verification of the Validity of Simulation Results

We verify the validity of simulation results using the other data from the Taxi Company A. We use all of the taxi operation data (e.g. telephone requests and getting customers on street) in 2010/2 to 2011/1. The reason for using all of the taxi operation data is for checking whether a problem appears for in customers who get a taxi on street in daytime, when the number of taxi which can be worked in daytime is decreased.

Top

Subjects of Applying New Strategy to Taxi Drivers

From above discussion, it is effective strategy to increase taxi drivers in midnight and early morning with high load instead of decreasing taxi drivers in daytime with low load. Applying this strategy, load descried in (1) decreases and service productivity is improved. There are non-working hours in working hours of Fixed time. For example, taxi drivers work from 3:00 to 12:00, don't work from 12:00 to 16:00, work from 16:00 to 21:00 in the best pattern. We suppose that it is difficult for taxi drivers to receive this service pattern. Therefore, we design new service pattern for Fixed time.

We divide one working hours of Fixed time into two working hours, such as "a.m. working hours" and "p.m. working hours", rotate working groups of Fixed time across Sunday (See Fig. 6). This service pattern enables taxi drivers to secure the same working hours per one month as the present service pattern, without non-working hours between working hours.

Conclusions

We research service methodology so as to improve service productivity in Taxi Company. For the above mentioned purpose, we research improvement method of rearranging taxi driver's working hours so as to work many taxis with many taxi delivery demands by telephone requests. Using telephone request data from Taxi Company A, we analyze present situation of Taxi Company A, and perform simulation analysis so as to minimize the taxi delivery demand per taxi and hour. As a result, it turns out that it is effective strategy to increase taxi drivers in midnight and early morning with high load instead of decreasing taxi drivers in daytime with low load. Finally, we consider the management measure to subject of applying this strategy to taxi drivers. We will research the optimal arrangement of the taxi waiting position in the taxi operating area which is another measure so as to improve service productivity of Taxi Company.

Fig. 1 .

 1 Fig. 1. Income of taxi per day

Fig. 2 .Fig. 3 .

 23 Fig. 2. The number of telephone requests (left Fig.) and load (right Fig.) per hour

  before(Service pattern: Day), (b)1 hour before(Service pattern: Fixed time), (c)both (a) and (b) 3 (a)1 hour before, (b)1 hour after, (c)2 hours before, (d)2 hours after, (e)1hours before and 1hour after, (f)3 hours before (g)3 hours after, (h)2 hours before and 1 hour after, (i)1 hour before and 2 hours after, (j)4hours before (k)4 hours after 4 (a)A *2) :Mon,Wed,Fri B:Tue,Fri,Sat or Thu,Fri,Sat, (b)A:Mon,Fri,Sat or Wed,Fri,Sat B:Tue,Thu,Sat, (c)A:Mon,Fri,Sat B:Tue,Fri,Sat, (d)A:Wed,Fri,Sat B:Tue,Fri,Sat, (e)A:Mon,Fri,Sat B:Thu,Fri,Sat, (f)A:Wed,Fri,Sat B:Thu,Fri,Sat 5 (a)One taxi driver is changed, (b)Two taxi drivers are changed, (c)Three taxi drivers are changed *1) Leaving and returning garage time are shifted 1 hour before. *2) "A" means Group A.

Table 4 .Fig. 4 .

 44 Fig. 4. Load per hour (4 hours before)

  5 patterns of 42 simulation patterns which are same case as 3.2 are shown in Table5. Load average and load variance of top 5 patterns are improved compared with the present situation. Although the 3rd place and the 4th place of ranking are changed, top 5 patterns are same patterns as results in 3.2. Load per hour of the best pattern, such as 4 hours before, is shown in left Fig. of Fig.5. We find the following results compared with the present situation shown in right Fig. ofFig5. The load between 6:00 to 7:00, when load is high in the present situation, decreases to 1.2 from 2.0. It causes a little enough time in taxi operation. Taxi driver must drive a round trip under 30 minutes to achieve 2.0 taxi operations per 1 hour. On the other hand, taxi driver drives a round trip in 50 minutes to achieve 1.2 taxi operations per 1 hour. The load between 12:00 to 15:00 remains 1.0 times per hour. Namely, load doesn't increase in daytime steeply.

Fig. 5 .

 5 Fig. 5. Load per hour (4 hours before (left Fig.) and present situation (right Fig))

Fig. 6 .

 6 Fig. 6. Improvement of service pattern

Table 1 .

 1 Leaving and returning garage time

	Service pattern		Leaving	Returning	Total working hours
	Shift	Ⅰ	7:00	1:00	18hours
		Ⅱ	8:00	2:00	18hours
		Ⅲ	8:00	3:00	19hours
		Ⅳ	12:00	8:00	20hours
	Free	7:00	24:00	17hours
	Day		7:00	19:00	12hours
	Fixed time		7:00	21:00	14hours
	Night		20:00	4:00	8hours

Table 2 .

 2 Considerable patterns for each case

Table 3 .

 3 Example (1 hour before)

Table 5 .

 5 Top 5 patterns of all the taxi operation data

	Rank	Pattern	Working hours	Load average	Load variance
	1	4 hours before	3 -12, 16 -21	1.207361	0.365542
	2	3 hours before, 1 hour after	4 -12, 16 -22	1.216663	0.367898
	3 4	3hours before	4 -13, 16 -21 4 -12, 15 -21	1.218303 1.219509	0.371616 0.373924
	5	2hours before, 2hours after	5 -12, 16 -23	1.219803	0.356295
	-	present situation	-	1.262712	0.394881

Fr

St.