Model-based design of exploitation guidelines

Original Article

Proceedings of IDMME - Virtual Concept 2010 Bordeaux, France, October 20 – 22, 2010

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Model-based design of exploitation guidelines Raphaël Chenouard, Catherine da Cunha, Florent Laroche, Alain Bernard

IRCCyN 1 rue de la Noe 44321 Nantes Cedex 03 Phone/Fax : 02 40 37 69 30 E-mail : {raphael.chenouard}@irccyn.ec-nantes.fr

Abstract: Piloting a complex system (product, process and/or organisation) aims at reaching the expected performances. This can be achieved by applying an exploitation guideline that consists of the appropriate performance drivers. This paper stresses the potential of reverted models for the exploitation of complex systems. The difficulty of this approach mainly results from the existence of several multidisciplinary models that have to be jointly considered.

In this approach KPIs are considered as requirements used to compute optimized performance drivers. Thus, models have to be reverted: expected key performance indicators (EKPI) become entry parameters and performance drivers become computed values (figure 2).

Key words: Modelling, control, Key performance indicators, performance drivers, constraint programming. 1- Introduction

This paper focuses on the issues relating to the exploitation of complex systems. Piloting them following a classical approach is often not enough to deal with the complexity of the knowledge to manage and to process. An exploitation guideline is represented by a set of values that describes the parameterization of the system. Such guidelines can be evaluated considering key performance indicators (KPIs): consumption, production rate, benefits, etc. The parameter values are then the performance drivers (PDs) of those KPI (see Figure 1) and the global optimization of PDs requires a loop over this process.

Figure 2: Reverted problem

The next section introduces the objective of our work and it is followed by a brief state of the art. Then, section 4 details our proposition before the conclusion. 2- Objective

Complex systems (product, process and/or organisation) are difficult to manage. To consider the expectations of all beneficiaries (investors, clients, workers, etc.) multiple key performance indicators must be studied. Extended organisations for example have to deal with constant re-configuration decision. When a parameter (external alliances, internal projects, competition pressure, technological evolution,) changes, the performance of the organisation as a whole is impacted (see section 4.2 for an example). Difficulties emerge when changes occur simultaneously. The lack of mixed model and the joint impact of multiple evolutions cannot be foreseen. This complexity of evaluation (both multi-levels and multidisciplinary models) has boosted the number of parameters

Figure 1: Variety of performance drivers and KPI.

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Model based design of exploitation guidelines

that have to be considered to estimate ex-ante the performances of a system. Furthermore these performance drivers may have joint impacts: this study must therefore be global. 3- Context and state of the art 3.1- -Models and KPI/PD

In this paper, we consider a model of a system as any representation of the knowledge relating to it that allows us to manage and drive it. Models are mainly based on a set of variables and a set of relations between them. Figure 3: Domain models for the same system.

A system transforms an input state (or input objects) into an output state (or output objects). The dynamic of this 3.2 - Predictive Knowledge based models transformation can be modelled through performance drivers [MP1]. The concept of performance drivers is an extension of Expert knowledge is a great source of performance. By the concept of “cost drivers” defined by Porter [P1]. formalising this tacit information, firms found a way not only to predict the evolution of complex systems but also a lever Several kinds of variables are standing in a model representing to influence it. a system: The work of Ammar-Khodja extends the MOKA PDs: Performance drivers are the features of a system and its methodology to enable the formalisation of expert technical environment that influence the final performances and that knowledge [AP1]. This process of knowledge capitalisation cannot be modified without generating performance was enriched by Candlot. The MARISKA method enables to fluctuations. Performance drivers are thus causal factors. Thus, make this process a continuous one [CP1]. performance drivers can be not only environmental variables, status variables, action variables but also organizational Recent works around product life cycle addressed the issue capabilities (cf. section 3.2). of knowledge and knowledge support (documents for instance) management. PLM tools permit to link KPIs: Key performance indicators are measurable quantitative performance drivers (such as product) with value [LB1]. values that characterise the functioning of a system. Cost and resources consumption are among the most used KPIs. Design (of a product, a process or an organisation) can also be supported by models. The FBS-PPRE model can be AVs: Auxiliary Variables are introduced in models in order to extended to address conceptual design problems [CS1]. Early link PDs and KPIs evaluation of the value of knowledge can guide designer to identify good potential alternative [BX1]. This classification of variables may be compared to the one made for instance during the design process of a product: To illustrate this let’s consider the example of the Belgian design variables may be considered as PDs, criteria may be program MOPSEA [VM1] which aimed at creating a new viewed as KPIs and others variables used in mathematical emission model for sea vessels, i.e. a model that links the key models are auxiliary ones [VN1]. As an example in the Design performance indicators of air pollution (gas discharges) to for X (DfX) approach (see [MK1] for a synthesis) X represents the usage setting of ships (figure 4). the KPI (which can be unique or multiple). DfX is a holistic approach (interdisciplinary and considering the whole For this particular model, examples of AVs can be emission product). This characteristic makes it difficult to explicit links factors, i.e. relationships between the amount of pollution between design variables and KPI. The design is then not produced and the amount of fuel burned. based on making explicit those influences but on design guidelines (often represented by good/bad practices). The This activity based model was created using not only appropriateness of the resulting design to the KPIs is then statistical information about technological aspects of sea tested through simulation/estimation methods. vessels but also the knowledge of experts (e.g. port operators) who were able to specify the model to include Since a complex system may be represented using several Belgian specificities such as the geometry of Belgian models, piloting it requires taking into account PDs and KPIs harbours which are of paramount importance to evaluate the from all models (see Figure 3). gas discharges for manoeuvring, loading and unloading in harbours.

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The greatest challenge regards the inversion of models: the inputs being the expected key performance indicators (EKPIs) of systems and the output the parameters of exploitation guidelines (Figure 6).

Figure 4: Example of a domain model: MOPSEA 3.3 - Organizational capabilities

Organizational capability is defined as “a know how to act, a potential of action which results from the combination and the coordination of resources, knowledge and competencies of organization through the value flow, to fulfil strategic objectives” [SR1]. According to them, that results from the creation of a guide of practical knowledge which is then Figure 6: Reversed integrated model. transmitted to the different organizational entities to ensure In our approach, we mainly use Constraint Satisfaction coordinated and collective progresses. Problems (CSPs) being defined as a tuple of 3 sets: variables, their domains and the constraints to consider [RB1]. CSP This management tool eventually permits to stress the links modelling languages are declarative languages where between knowledge and competence element (performance constraints are acausal. Indeed, a constraint is a declarative statement where any variable can be considered as an entry drivers) and performance [RD1]. or a computed variable. 4. Proposition 4.1 – Integrated model

Previous researches enable to define and implement methodologies to model: knowledge, economical and human factors within firms. These conceptual blocks were used for simulation and decision aiding tools (KBS, PLM, Value Chain Simulation, etc). The remaining scientific barriers lie in the integration of those elementary modelling (figure 5).

CSPs are only the modelling part of the Constraint Programming (CP) field. CP also gathers generic and efficient algorithms to solve these models. In our approach, we mainly consider mixed CSPs (discrete and continuous variables). The corresponding solving algorithms are based on interval arithmetic and interval analysis [M1]. In most CP solver, operators in constraints have an inverse operator, which is defined as its relational inverse operator [C1,G1] and symbolic rewriting operations are applied on constraints [GB1]. For instance, the following constraint allows directly computing y:

Difficulties lay not only on the alignment of models or metamodels. The results of partial models have to be coherent, y = cos(x). which may be difficult for performances modelled by more than one partial model. Such issue is also encountered in Then, it is reformulated to reduce x current domain: product design with multiple point of view on the product to consider [Y1]. x = cos-1(y), where cos-1 is the inverse relational cosine computed as follow:

{x ∈ D

}

∃y ∈ D y : y = cos( x) .

Most arithmetic and trigonometric operators and functions have an inverse relational operator. Nevertheless several kinds of constraints operators cannot be used in CSPs like the derivative when considering differential equations. However, even if a constraint cannot be reverted, if its variables occur in other constraints that can be reverted, consistent domains can be computed by the constraint propagation process.

Figure 5: Integrated model.

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Moreover, a CSP can be viewed as a conjunction (logical operator “and”) of all the constraints to satisfy. Then, the logical disjunctive operator “or” is not always available in CP solvers. Then, we can change PDs or KPIs in a CSP model without any extra work to rebuild it. This solving paradigm offers flexible modelling abilities where each variable in a model can be considered as a PD, a KPI or an AV depending on the context we use the model.

Figure 8: Reversed MOPSEA model inputs and outputs.

4.2 - Application on ships driving

Other models will also be used. They will for instance focus We are involved in the design of a Decision Support System on energy production and on the buoyancy of the ship and its (DSS) to be used on complex ships (e.g. military ships, cruise global behaviour. Our major challenge is to make consistent ship, tankers) in order to limit their ecological impacts such as all the reversed models to consider and to globally minimize the ecological impact while computing the corresponding greenhouse gas discharges. guidelines for the ship. The approach implemented by this DSS is a direct application of our generic approach. Indeed, it has to define the relevant and optimized parameters considered in the driving of a ship that fit the KPIs corresponding to the ecological impacts of the ship and the achievement of the current mission objectives (see Figure 7).

Conclusion

In this paper, we introduce the main difficulties appearing when piloting complex systems or complex organizations. We propose to reverse classical models so that key performance indicators are used to determine the corresponding performance drivers. Thus, we can define exploitation scenarios (i.e. PD values) that fit the objective thresholds (KPI values) we want to reach. In future work, we have to implement this approach while considering several kinds of models for the same system. Considering a ship, we notably want to take into account a propulsion model and an electrical model. However, this approach can be applied in other domains, like complex industrial process optimization considering at the same time the supply chain, economical models, etc. 7- References

[AP1] S. Ammar-Khodja, N. Perry, A. Bernard, “Processing Knowledge to Support Knowledge-based Engineering Systems Specification”, Concurrent Engineering: Research and applications (CERA), 16, 1, 2008, p 89-101. [BX1] A. Bernard, Y. Xu, “An integrated knowledge reference system for product development”,, CIRP Annals Manufacturing Technology, 58, 1, 2009, p.119-122.

Figure 7: Project main implementation scheme.

Several kinds of models have to be used to compute the relevant PDs corresponding to the navigation instructions and guidelines. For instance, a CSP model focused on greenhouse gas discharges has been defined [LY1] (represented by figure 8), which aims at easing the exploitation by reducing the size and complexity of the exploitation guideline intervals.

[C1] J. G. Cleary. Logical arithmetic. Future Computing Systems 2, 2,1987, 125–149. [CP1] A. Candlot, N. Perry, A. Bernard, S. Ammar-Khodja, “Case Study, USIQUICK Project: Methods to Capitalise and Reuse Knowledge”, in Process Planning, in Methods and Tools for Effective Knowledge Life-Cycle-Management, pp 487-506, A. Bernard,S. Tichkiewitch (Eds.), Springer, ISBN: 978-3-540-78430-2, 2008. [CS1] F. Christophe, R. Sell, E. Coatanéa, M. Tamre, “System Modeling combined with Dimensional Analysis for Conceptual design”, in Proceedings of the 8th International Workshop on Research and Education in Mechatronics R.E.M. 2007 8th International Workshop on Research and Education in Mechatronics 2007, p. 242–247, 06 2007.

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[G1] F. Goualard, “Gaol: Not Just Another Interval Arithmetic Library”. Laboratoire d'Informatique de Nantes-Atlantique, http://sourceforge.net/projects/gaol/. [GB1] L. Granvilliers, F. Benhamou, “Algorithm 852: Realpaver: an Interval Solver using Constraint Satisfaction Techniques”, in ACM Transactions on Mathematical Software, 32(1):138-156, 2006. [LB1] J. Le Duigou, A. Bernard, J.-C. Delplace, S. Gabriel, “Global Approach for Technical Data Management Application to Ship Equipment Part Families”, Cirp Journal of Manufacturing Systems and Technology, 01 2009, p. 185–190. [LY1] V. Larroudé, P.A. Yvars, D. Millet, R. Chenouard and A. Bernard “Inversion of emission model using constraint propagation on tables and intervals - Application to ShipEcodesign”, IDMME 2010 - Green Engineering, Design and Innovation, Bordeaux 2010. [MP1] M. Mauchand, N. Perry and A. Bernard, “Specification management for the cost contraint optimisation in microelectronic design”, International Journal of Manufacturing Technology and Management, 15(3/4), 284297, 2008. [M1] R. Moore, “Interval analysis”, Prentice Hall, Englewood Cliffs, N. J., 1966. [MK1] H. Meekamm, M Koch, “Design for X”, Chap 12. of Design process improvement, J. Clarkson, C. Eckert (Eds), Springer, 2010. [P1] M. Porter, “Competitive advantage creating and sustaining superior performance”, FreePress, 1985. [RD1] Ph. Rauffet, C. Da Cunha, A. Bernard, “Designing and managing Organizational Interoperability with organizational capabilities and roadmaps”, 5th International Conference on Interoperability for Enteprise, Software and Appliccations (IESA), 2009. [RB1] F. Rossi, P. van Beek, and T. Walsh, “Handbook of Constraint Programming”. Elsevier, 2006. ISBN 0-444-527265. [SR1] G. Saint-Amant, L. Renard L., “Premier référentiel de connaissances associées aux capacités organisationnelles de l'administration électronique”. Management International, Vol.9, 2004. [VM1] M. Vangheluwe, J. Mees, C. Janssen, “Monitoring programme on air pollution from sea-going vessels”, Belgian science Policy 2007. [VN1] Y. Vernat, J.-P. Nadeau and P. Sébastian, “Formalization and qualification of models adapted to preliminary design”, International Journal on Interactive Design and Manufacturing, 4(1), 1-80, 2010. [Y1] P.A. Yvars, “A CSP approach for the network of product lifecycle constraints consistency in a collaborative design context”, Engineering Applications of Artificial Intelligence, 22, 961-970, 2009.

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