Matthieu Lauras
email: lauras@enstimac.fr

Hervé Pingaud
email: pingaud@enstimac.fr

Jacques Lamothe
email: lamothe@enstimac.fr

An approach to diagnose local and collaborative supply chain processes

Keywords: supply chain management, SCM, framework, diagnosis, processes, data analysis, cooperation

It is often really difficult to capture and exploit knowledge about a given supply chain, covering all types of flows and business processes. Supply chain managers have frequently to make diagnosis and need realistic representations of their supply chain. We work on a methodology that tries to help them in this way. First, a framework is designed to capture information on multiple dimensions of supply chain, from structural to operational aspects. Then, a data analysis approach is detailed and put into practice to explain the differences between current and ideal entities, so that progress actions can be identified.

Introduction

Supply Chain Management (SCM) plays a major role in increasing performance and competitiveness of companies. [START_REF] Simchi-Levi | Designing and Managing the Supply Chain[END_REF] define SCM as "a set of approaches utilised to efficiently integrate suppliers, warehouses, and stores, so that merchandise is produced and distributed at the right quantities, to the right locations, and at the right time, in order to minimise systemwide costs while satisfying service level requirements." SCM is made up of two main areas (according to [START_REF] Stadtler | Supply chain management and advanced planning: basics, overview and challenges[END_REF]:

• network integration: management of cooperation between entities of the network and development of their ability to work together

• network coordination: management of each entity of the supply chain, in order to control flows effectively, and achieve the supply chain goals. [START_REF] Beamon | Measuring supply chain performance[END_REF] and [START_REF] Gunasekaran | A framework for supply chain performance measurement[END_REF] explain that important supply chain features have been ignored by the control theory. Performance control, and more specifically cost control, are limited due to the composite nature of the Supply Chain (SC). Several authors have studied recently these network based organisations with this perspective [START_REF] Aviv | The effect of collaborative forecasting on supply chain performance[END_REF][START_REF] Dudek | Negotiation-based collaborative planning between supply chain partners[END_REF][START_REF] Villareal | Understanding and managing shared projects in SMEs networks[END_REF]. SCM not only tries to control the performance of individual predefined processes, as well as expected collaborative processes, it also has to deal with continuous improvement of these processes in order to maintain them at a high level of integration. So, the network is not a frozen architecture: each node has sufficient autonomy to adapt its own processes, and could behave like an inductor of transformation.

Cooperative game theory put forward the weak balance between shared and individual objectives of partners as an explanation of these adaptation needs. In consequence, keeping control at a satisfying level involves an ability to supervise individual and collaborative processes, and eventually to adapt some of the performance variables.

In this paper, a methodology is proposed that allows a SCM to build a realistic representation of the system and to make diagnosis using this representation.

In a first part, a literature study identifies fundamental components which are relevant to get a supply chain diagnosis. The objective is to define a framework based on questionnaires that helps supply chain managers to make investigations. Integration and coordination metrics are defined on the basis of answers made to the set of relevant questions. Data are collected by meeting actors in their workplaces, or produces through appropriate links to the information system (especially through requests to databases). It provides a large amount of well structured supply chain data.

Then, in a second part, the relevance of our approach is restricted to a two-stage supply chain with a centralised control structure. Individual entities are supposed to be linked to a unique central node as it is usually the case in worldwide companies that distributes the same kind of goods all over the world. Each partner of such a supply chain can have some similarities with others, even if long distance separate them. So, we propose to use multivariate data analysis methods in order to extract information from the large spectrum of variables that characterise the broad and spread population of entities. The ability to discriminate groups of entities with similar processes is very useful for the central and main control entity. We will show these effects on an industrial case study.

From supply chain's fundamentals to a diagnosis framework

Because we need to understand the operating modes of the supply chain, we have to collect and organise data. However, due to the problem of size, direct observation without methodology is fastidious and inefficient on a large scale SC. We consider naturally the question of how to prepare and make an investigation. Investigation supports have not been a subject of discussion in the scientific literature on SCM. If there are widely known reference frameworks: SCOR model or Global EVALOG, for example, it seems that they are mainly used as audit supports rather than diagnosis supports. We mean that they try to validate what is supposed to be a correct execution with respect to best practices or standards established before. By contrast, diagnosis will not refer to predefined proposals. Diagnosis just relates to existing operating modes without any reference to a preset frame, and focused on a set of elementary questions: what? when? where? who? with which? how? with what? and why?

The concept of framework can be used for a SCM diagnosis. It will concentrate on the structure of subjects that should be treated in order to design a particular questionnaire starting from a generic and well defined database of questions. We will not detail here the generic questionnaire. In the following, we discuss only supply chain topics that have to be unified in order to define a structured framework. The topics will be presented by sections into the framework. If local process models are quite easy to capture, it is not the case for collaborative processes. This last point will be more specifically studied.

Flows of entities through a supply chain

Various approaches and definitions [START_REF] Stadtler | Supply Chain Management and Advanced Planning[END_REF][START_REF] Mentzer | Defining the supply chain management[END_REF][START_REF] Arnold | Introduction to Material Management[END_REF] consider SC as co-ordinated system that manages flows. [START_REF] Cooper | Characteristics of supply chain management and the implications for purchasing and logistics strategy[END_REF], for example, define the SCM as "an integrative philosophy to manage the total flow of a distribution channel from the supplier to the ultimate user".

These flows are financial, informational, or physical flows. A reference framework for supply chain diagnosis must provide a definition of flows (informational, physical and financial), to qualify the objects to be managed in the supply chain ('What?').

As can be seen in Table 1, the physical flows are classified depending whether they are upstream flows relative to suppliers, internal flows relative to an intermediate node, or rather downstream ones relative to customers. Descriptive variables that will be quantified or qualified are listed for each type of flows.

Key functions of a supply chain

Handfield [START_REF] Handfield | Supply Chain Redesign: Transforming Supply Chains into Integrated Value Systems[END_REF] or [START_REF] Simchi-Levi | Designing and Managing the Supply Chain[END_REF] consider that without a foundation of effective organisational relationships among supply chain partners, efforts to manage the flow of materials or information across the supply chain are likely to be unsuccessful. A sequence of agreements between entities -of one or more companies, from the first of the suppliers to the consumers -has to be existed. Many models have been developed using this approach [START_REF] Kearney | Management Approach to Supply Chain Integration[END_REF][START_REF] Croxton | The supply chain management processes[END_REF]Supply Chain Council, 2004;[START_REF] Chen | Towards a theory of supply chain management: the constructs and measurements[END_REF]. At a macroscopic level, all these authors state clearly that a supply chain is just a collection of interrelated functions: Procurement, Manufacturing, Distribution, Global planning, Marketing and Sales. Naturally, processes can be thought as particular detailed representations of such macro functions. Inspiring by [START_REF] Chopra | Supply Chain Management[END_REF] proposal, we could split them into three categories: functions that transform physical flows following a logic of stream (supply, manufacture, distribute), functions that concern the customer relations (sale and marketing) and then, functions that control the activities (planning). The ultimate objective is to understand how the supply chain performance is achieved through the combination of these functions (transverse processes). [START_REF] Heikkila | From supply chain management: efficiency and customer satisfaction[END_REF] identifies two main categories of related study: works on the chain structure, and studies of the network behaviour. With a similar approach, [START_REF] Stadtler | Supply chain management and advanced planning: basics, overview and challenges[END_REF] proposes two fundamental concepts. On the one hand, SCM 'coordination' is defined like a type of business process management: planning and scheduling decisions are made to control flows. It is based both on Information Technologies, and on business process models. On the other hand, SCM 'integration' refers more specially to business process design at the level of the network. For example, choice of network entities, and of selected activities are typically included in this second category.

As an evidence, the functions appears to be common denominators of these two facets of SCM. [START_REF] Stadtler | Supply chain management and advanced planning: basics, overview and challenges[END_REF] identifies three main components for:

• the supply chain coordination: processes, Information Technologies and planning methods

• the supply chain integration: partners characteristics, supply chain network design and management.

2.3 Key components of SCM 'coordination'

The processes

In an integrative supply chain strategy, business processes create value for customers. But these processes reach beyond the boundaries of the firm by drawing suppliers and customers into the value creation process [START_REF] Vickery | The effects of an integrative supply chain strategy on customer service and financial performance: an analysis of direct versus indirect relationships[END_REF]. [START_REF] Handfield | Supply Chain Redesign: Transforming Supply Chains into Integrated Value Systems[END_REF] say that organisations must have a detailed understanding of current processes to embark in the promising world of SCM. A process is a suite of activities that uses resources to allow the transformation of inputs to outputs. Whatever the function under study, this is a problem to identify how its is realised, by executing the given set of activities, or even the set of processes. [START_REF] Hammer | Re-engineering the Corporation[END_REF] were among the first to recognise a process as a set of activities directed towards the production of a result representing a value for the customer. [START_REF] Croxton | The supply chain management processes[END_REF] and [START_REF] Manthou | Establishing an integrated virtual logistics network[END_REF] affirm that SCM is increasingly being recognised as the integration of key business processes across the supply chain. The concept of value for the customer supposes that a performance can be measured, and requires at the outset a commitment on each activity. This is absolutely necessary for the process management in order to define continuous improvement. The representation of processes is often done using a cartography, that can be seen as a map on which process interfaces are shown.

Information Technologies (IT)

Providing decision-makers within the supply chain the right information, in the necessary format, and in a timely manner is a major challenge. Particularly, to success this challenge could be viewed as a major strategy to counter the so-called 'bullwhip effect' [START_REF] Lee | The value of information sharing in a two-level supply chain[END_REF]. To do that, supply chains use Information Technologies (IT) that include all the techniques of collection, storage, treatment and data dissemination whatever they are. It's a fact, IT transform the way exchange-related activities in supply chain are performed, and the nature of linkages between them [START_REF] Palmer | Information intensity: a paradigm for understanding web site design[END_REF]. Progress made in the few decades in data-processing has deeply upset the management of companies and supply chains in terms of modularity, accessibility and inclusiveness [START_REF] Upton | A path-based approach to information technology in manufacturing[END_REF]. The main difficulty clearly consists in making individual information systems becoming collaborative information systems. [START_REF] Lee | The value of information sharing in a two-level supply chain[END_REF], [START_REF] Cachon | Supply chain inventory management and the value of shared information[END_REF] and [START_REF] Vickery | The effects of an integrative supply chain strategy on customer service and financial performance: an analysis of direct versus indirect relationships[END_REF] propose some good practices when using IT, that summarises clearly all the difficulties of the exercise even if they appear as really simple principles. Firstly, the value carried by an exchanged data should be meaningful for its user, this is the right information at the right place (in order to avoid problems such as grossly inaccurate demand forecast, low capacity utilisation, excessive inventory, long lead times and delivery frequencies, and poor customer service). Secondly, the quality of an information is contextual, the situation to which the information is attached has an effect on its value, and any evolution of the situation can require an actualisation of the information. Thirdly, an ideal system should span all functions and organisations throughout the entire supply chain in order to enhance collaboration and coordinate activities [START_REF] Handfield | Supply Chain Redesign: Transforming Supply Chains into Integrated Value Systems[END_REF][START_REF] Chronéer | The impact of supply chain information and networking on product development in Swedish process industry[END_REF]. So a SCM diagnosis must consider IT maturity in the SCM. This implies three aspects: information systems architecture; information viability (quality with respect to time); and relevance of information.

Planning

The planning process concerns each function of the supply chain. A part of operations management is always done inside the company with limited transparency for the supply chain. Nevertheless, a global planning function also exists in order to centralise and integrate some key decisions. The basic idea, as proposed by Supply Chain Council ( 2004), is to distinguish global decisions (same for all entities) from local ones. The planning process seeks to make suitable decisions in order to satisfy customer's demand, while using resources in the best way [START_REF] Dudek | Collaborative Planning in Supply Chains: A Negotiation Based Approach[END_REF]. Planning is split into several time horizons depending on degrees of smoothness in the analysis of the demands compared to the capacities. This decomposition provides some chance to do what is expected in terms of resource allocations, and 'guarantee' that the assigned orders will be completely satisfied [START_REF] Chopra | Supply Chain Management[END_REF]. [START_REF] Arnold | Introduction to Material Management[END_REF] note several properties necessary to implement a planning process:

• among them, the various steps of a planning shall be necessarily supported by specific methods -formal or not, structured or not

• to be efficient, planning rules must be defined to solve conflicts, and find a balance between loads and capacities

• finally, control of budget is crucial in order to approximate real financial possibilities, and to help in making decision (investments, cost controls).

Synthesis

We 2.4 Key components of SCM 'integration'

Partners

A partner is an actor, internal or external, individual or collective, specialised or general practitioner who works in a collaborative process [START_REF] Poirier | Supply Chain Optimization: Building the Strongest Total Business Network[END_REF]. A partner can be a distributor, a subcontractor, a provider or a supplier. A collaboration between partners is a mutual ongoing relationship that involves a high level of trust, commitment over a period of time, long-term contracts, joint conflict resolution, and the sharing of information, risks and rewards [START_REF] Ellram | Supply chain management: the industrial organization perspective[END_REF]. [START_REF] Poirier | Supply Chain Optimization: Building the Strongest Total Business Network[END_REF] explain that the choice of a partner must be based on customer requirements.

The added value analysis of the potential partner competencies compared to existing competencies in the supply chain [START_REF] Stadtler | Supply Chain Management and Advanced Planning[END_REF] is a main criteria for selection. A potential partner must prove its ability to meet expressed requirements of the supply chain. This can be achieved by its competencies, knowledge, resources or know-how. [START_REF] Vickery | The effects of an integrative supply chain strategy on customer service and financial performance: an analysis of direct versus indirect relationships[END_REF] underline that partners work together to ensure high product quality and low costs, with benefit sharing. Moreover, because the customer requirements could evolve, it is often necessary to check the adequacy of partner contributions to actual demand. It should be stated that this process is not limited to simple economic criteria, but must also integrate all kind of capabilities [START_REF] Arnold | Introduction to Material Management[END_REF]).

Management of a supply chain network

A supply chain gathers various entities which are not under the same legal heading.

The question arising then is how the network is controlled. Very often, a natural leader appears [START_REF] Stadtler | Supply Chain Management and Advanced Planning[END_REF] for various reasons such as financial capacity; knowhow on products or processes; value added to the product. According to the same authors, decisions taken by the leadership of this central entity will be accepted by other entities of the supply chain without discussion. In other cases, the supply chain can be managed by a steering committee. It is composed of representatives of the supply chain and is managed according to predefined collective rules and processes [START_REF] Villareal | Understanding and managing shared projects in SMEs networks[END_REF]. [START_REF] Benton | The influence of power driven buyer/seller relationships on supply chain satisfaction[END_REF] stress a particular risk associated to management of the supply chain network. They explain that some dominant firms may behave with opportunity, in such cases performing individually will always be preferred to partnership, and submitting to a supply chain partner is accepted only when the balance of power makes it necessary. As for any management activity, the network management activity can be analysed through:

• its environmental constraints: laws, regulations, benchmark and best practices

• its internal history: individual entities lay out, their own culture, procedures and the supply chain as a whole

• the evaluation of performance and the continuous improvement.

Performance evaluation is not itself sufficient: it is only one of the links in management process. In order to control a system, position, target and resources must be known at the same moment.

2.4.3 Nature and organisation of the collaborative network [START_REF] Williamson | The Economic Institutions of Capitalism, The free press[END_REF] characterises two extremes forms of governance: perfectly competitive markets and vertically integrated hierarchies. [START_REF] Chen | Towards a theory of supply chain management: the constructs and measurements[END_REF] indicate that an intermediate form of governance is the network. Network firms are characterised by strong linkages between supply chain members with low levels of vertical integration [START_REF] Rigatto | Difference between supply chain structures within a given industry: insights from the Brazilian peach canning industry[END_REF]. A reference frame of collaborative states has been proposed in [START_REF] Lauras | Méthodes de diagnostic et d'évaluation de performance pour la gestion de chaînes logistiques[END_REF]. This proposal showed that the collaborative components could be characterised by four fundamental elements:

• defining a common objective, object of the agreement, beyond simple individual interests: combining competences for the achievement of a particular project, supporting a continuous activity

• going beyond individual limits: bringing solutions to the intrinsic limits of each entity is necessary (forces and weaknesses of each entity to achieve the common objective)

• qualifying methods of agreement: forms of concretisation, formal or not, of cooperation (contracts, commitments and constraints, perimeters and horizons)

• having a common risk management: locating the options to be taken and anticipating the risks associated with operations in order to identify consequences and responsibilities if an event occurs.

In a logistic contract [START_REF] Lauras | Méthodes de diagnostic et d'évaluation de performance pour la gestion de chaînes logistiques[END_REF] identifies two main factors that distinguish various states of agreement:

• The objects shared by partners: are they data, data processing or both of them?

• The temporality of cooperation: the cooperation can be made either occasionally on demand, or frequently with objects exchanged with a predefined process, and lastly can be made continuously with objects regularly shared?

Synthesis

In addition to Tables 1 and2, another table could be defined inside the framework to diagnose the SCM integration of each function and explain when? where? who? what? with which? how? and why? of collaborative operations (Table 3).

Global architecture to structure a SCM Diagnosis

From all these elements (cartography of flows, function analysis: coordination and integration), we can define a global architecture that allows to make a relevant SCM diagnosis (Figure 1). We can remark that we have added a branch to this architecture in order to qualify the relationship between each node of the supply chain and the environment (culture of the company, history, local regulation, etc.). Actually, these dimensions influence directly the supply chain's behaviour.

Formulation of a data analysis problem for supply chain diagnosis

A study of supply chain starts with observations made on the field by means of exchanges with actors representative of the various entities, and using our framework. Once this collection realised, rough information is aggregated for all (or some of) the entities. These data could be broken down into quantitative elements (flows, states of inventory or capacity), but also into qualitative elements broken down into functions: Supplying, Manufacturing, Distributing, Sales, Marketing, Planning. Then, in order to extract 'the essential' information, statistical tools, and more particularly data analysis tools, offer the means of classifying the whole of the criteria by explaining the differences between the entities. Priorities, action levers, horizon. . .

Hypothesis and principles

In this study, we consider a supply chain defined as a simple twofold tree; so that the entities (i.e., the nodes) can be classified by level. It is assumed that entities of a given level may have the same role (added value for final customers), the same local functions and can be brought together into a single category. It should be normal that they work with similar processes. However sue to a lot of factors (including culture, market, history, . . . ), it is not the case. Here, data analysis is used to help in the characterisation of roles and in the classification of processes. From another point of view, managers need to evaluate the gap to best practices. Virtual 'best practices' entities are therefore added to the data: experts answer their questionnaire considering how such 'best practices' could be implemented. Consequently, data analysis is used to enlighten the most significant topics that explain the gap to the 'best practices'.

Elements of theory for data analysis

Several methods of data analysis are available [START_REF] Bryman | Handbook of Data Analysis[END_REF]. Into this set, the statistical exploratory or descriptive subset of methods is appropriate to synthesise and summarise the information included in the data. Their role is to highlight the main features of an observed population and to suggest assumptions about their classification. We can divide the descriptive statistics into [START_REF] Bouroche | L'analyse des données[END_REF][START_REF] Bryman | Handbook of Data Analysis[END_REF]:

• classification methods (to form groups of individuals)

• factorial methods (to reduce the number of characteristics)

• Principle Component Analysis (quantitative)

• Correspondence analysis (qualitative)

• Canonical analysis (bond between characters)

Here, we aim to create typologies of individuals (taxonomy of entities) starting from an observation of the population by a whole set of characters (variables that are answers collected during the investigation), trying to answer the question of whether they are similar or different. Our study clearly requires factorial methods. The art then consists in controlling a compromise between a model of data representation (based on linear correlation between pairs of characters) and the values of the data themselves.

The Principle Component Analysis (PCA) and the Multiple Correspondence Analysis (MCA) are two complementary factorial methods that can be applied. The PCA treats the quantitative variables, while the MCA treats the qualitative variables.

Global approach

On this basis, we develop an approach in four steps [START_REF] Lauras | An approach to define local and cooperative typologies of supply chain processes[END_REF]) (Figure 2):

1 Analysing the internal processing modes of each entity in order to select the entities that have the same local characteristics. We want to know which entities behave similarly locally. As the nature of the data must be taken into account, the analysis is broken down into three phases:

a First an analysis is made on the quantitative elements using a Principle Component Analysis (PCA). This highlights key quantitative variables that explain the major quantitative differences between the entities (effect of scale in particular).

b Then a Multiple Correspondence Analysis (MCA) is carried out to analyse the qualitative characteristics by function. Key qualitative variables are thus obtained by function.

c Finally, a global analysis is performed on all the key variables of the entities. It enables us to define global classes of entities. It also enables us to measure which are the functions or quantitative elements that mainly explain the differences between classes. This is relevant only if a big enough ratio of the initial data is expressed in the final categories.

The questions are: Are all these categories necessary? Should management standardise local processes of the entities involved in a given category? Are the classes obtained at phase 1.c coherent with those obtained per function at step 1.b? 2 Analysing the cooperative agreements existing between entities. Here the idea is to define typologies of cooperation. Here the analysis is broken down into three phases in the same way as step 1.

3 Checking whether the agreements are coherent with typologies of local processes: The logical assumption is that a series of individuals of the same category, therefore having the same local characteristics, should be found in the same cooperation class.

A test of coherence crossing typologies of local processes and typologies of cooperation identify the variations with respect to this assumption. If the typologies have poor similarities, this leads to a great diversity in the modes of management. Here the management should decide which are the relevant collaborative processes for each class of local entities.

This coherence test can also be carried out per function. The relevance of doing this is twofold:

• On the one hand, less information is lost as the comparison is made on the results of the 1.b and 2.b analysis

• On the other hand, global incoherence can result from entities being gathered into classes that differ from one function to another. Therefore, the test by function shows the source of incoherence more precisely. Finally some complementary investigations can be made to explain incoherence, and to determine the improvement projects that can solve it.

4 From the literature on the subject, identifying 'best practices' cooperative processes. So, virtual entities are qualitatively characterised according to these 'best practices'. Then, step 2.b and 2.c are performed again. This enables us to identify key variables that mainly explain the distance between actual cooperative agreements and 'best practices': they are the characteristics to be modified in order to converge to the best practices.

Finally, from a manager point of view, the interest for this approach is twofold: first, it enables to define classes of entities so that the management can be adapted per class. Secondly, key variables are identified that define these classes: they are the characteristics that have to be changed in order to move an entity from a class to another. Now as time passes, the supply chain evolves: entities local or cooperative characteristics change, new entities appear, others disappears. In this situation, two cases are supported by the data analysis approach:

• The complete methodology applied once again. This is long and costly as all the data must be collected again or even actualised. But it provides a guarantee on the representation of the chain.

• Key variables, as identified at steps 1.a, 1.b, 2.a, 2.b are assumed to be the same, even if entities' value change for them. Here a simple questionnaire restricted to questions to the key variables. It enables a quick and efficient gathering of the data. But only the 1.c, 2.c and 4.c analysis can be obtained.

Moreover, in both cases, managers can be interested to compare analysis at different dates. As data change, PCA and MCA made for different dates may result in changes in axes definition. In this situation, the manager should choose the axes definition of a given date and project all the entities evaluation (new and old ones) on these axes.

Here the risk is that the projection loses a lot of information, if the supply chain key variables have changed drastically from on date to another.

A pharmaceutical and cosmetic supply chain case study

Application is dedicated to a pharmaceutical and cosmetic company and has been performed in collaboration with its supply chain department. The supply chain management asked for a diagnosis of the cooperation between the manufacturing plants (central entity) and the distribution system in the foreign countries (Figure 3). All the analyses led on the application case cannot be presented here. Nevertheless, two illustrative analyses will be developed in the following:

• quantitative data on local processes of distributors: physical flow analysis (PCA results for step 1.a)

• qualitative data on cooperation processes: gap analysis with VMI and CPFR best practices (MCA results for step 4.c).

Figure 3 The supply chain under study (see online version for colours)

4.1 From the framework to the questionnaire A preliminary investigation was carried out with the central entity to prepare the meeting with distributors, on the basis of our reference framework for diagnosis.

We particularly focused on Distribution (Manufacturer), Procurement (Distributors), and Global Planning functions. The Sales, Marketing and Manufacture functions will be less detailed. This phase consisted in some contact actors from Quality Management, Sales Administration, Distribution, Legislation, Supply Chain, Data-processing. A set of 120 questions have been made up to cover 300 topics of the reference framework (a question can inform several aspects of the system studied). These 300 topics were identified after reducing the framework by omitting irrelevant sections [START_REF] Lauras | Méthodes de diagnostic et d'évaluation de performance pour la gestion de chaînes logistiques[END_REF]. For obvious confidentiality reasons, we are not able to reveal here the exact contents of these questions. We can nevertheless give key points that have to be observed throughout the questionnaire:

• Questions about Flows: What is the inventory value (final products and promotional articles)? How many units (weights, volumes, containers)? How many dataprocessing users are used? How many customers (by type)?

• Questions about Distribution: How is the activity of distribution scheduled? Which kind of contract has been subscribed to by customers (inductors of costs, duration)? What are the delivery cycle times (targets and noted)?

• Questions about Procurement: which methods and which tools are used for the procurement planning? What is the frequency of the head office's deliveries?

• Questions about Global Planning: How are forecasts established? Which dashboard is set up to follow the activity?

• Questions about Production: What is the process of complementary packaging (activity, means, instructions, inputs/outputs, performance)?

• Questions about Marketing: Are there, locally, created promotional articles? Which brands are marketed?

• Questions about Sales: What are the various types of orders (specificity)? What is the organisation of the sales networks?

Investigation

Our investigation focused on twelve European distributors and their main manufacturer, to acquire a knowledge of the companies, theirs organisations, cultures and operations.

For each distributor, an on-site mission lasting three days enabled us to collect precise and exhaustive qualitative data: interviews with local managers of each function and of their correspondent at the central entity. We also extracted, from the local databases, the inputs and outputs of each distributor in order to detail a physical and informational cartography of flows (dynamic). Additional quantitative information (static and financial informational flows) was obtained at the same time from the local financial control managers. The rate of reply to the survey exceeded 85%. From the elements collected, we retained 147 explanatory characters of the local processes, and 143 explanatory characters of the cooperation processes implemented between the central entity and its distributors.

Example of local processes analysis: quantitative flows -PCA method

A PCA was first carried out on the totality of the numeric variables relating to physical and informational flows (12 individuals and 26 characters). The characters under study are mainly references, orders, lines of orders, types of parcels/pallets, inventory levels, manpower, number of sales networks, number of customers as well as seasonally ratios. We can see from the start that two axes represent more than 85% of the information (Table 4). This allows us to interpret the results accurately and reliably.

The first axis shows that the following volumetric data move in the same direction: orders, multi-reference parcels, backorders, lines of orders, stocks, references, units, picking operations and numbers of commercial networks. It thus appears that the main part of the orders is characterised by flows of 'detail' (parcel multi-references, picking operations). We also note that no character is opposed to this tendency. This last very significant point underlines the fact that any increase in volume on an unspecified criterion automatically generates increases in the same proportions in the other inductors. For the next step of our analysis, the use of only one variable among these will be sufficient (we will call it 'detail flow').

The second axis shows that the other type of orders which are characterised by flows known as 'complete' (complete parcels, complete pallets), are linked together and move in the same direction. It is nevertheless necessary to counter balance this assertion by the low representativeness of this axis (vs. axis 1). The concepts of seasonality and a number of references are opposed. We can explain this point as large volumes are generally In its positive part, axis 2 puts forward some very reactive and flexible systems (short frozen planning horizons, weekly meetings, unit orders, small integrated information systems). Less reactive and flexible systems (long frozen planning horizon, highly integrated information systems, large order lots) are characterised by negative contributions.

The MCA distinguishes two groups of individuals in terms of their relationship to the head office, in terms of best practices (class 1):

• observations of class 2 (agreements with pharmaceutical distributors), although already subject to collaboration and cooperation, must relax their processes in order to gain flexibility and reactivity

• observations of class 3 (agreements with cosmetic distributors) must progress from a simple state of agreement (elementary communication, coordination or collaboration) to a more complete state (advanced cooperation, coordination or collaboration), while still preserving the qualities of reactivity and flexibility which characterise them today.

Conclusion on the application

Our proposal helped the supply chain managers in three ways:

• The reference framework enabled them to be confident in the quality of the questionnaire

• The analysis on the collected data enabled them to figure out the state of their SCM

• The comparison with VMI and CPFR processes enabled them to identify differences and what could be changed. But, it did not enable them to evaluate potential added value. This last evaluation still relies on the management interpretation.

Conclusion

Some gaps in knowledge and in methodologies which are used to investigate supply chains have been identified. This paper proposes a solution to progress in SCM diagnosis. It includes two main components:

• a structured reference framework for collecting the data that explain the mechanisms of given SCM processes and particularly collaborative processes

• an approach, based on data analysis tools (PCA and MCA), to extract and analyse relevant information included in those data in a given SC architecture.

The object of this methodology is to structure the diagnosis step and thereby prepare the next phases of a continuous improvement step for supply chain processes. It makes it possible to structure, understand and exploit the awareness and weakness of supply chain entities and supply chain interactions.

The relationships between a pharmaceutical and cosmetical product manufacturer (central entity) and its European infrastructure of distribution services (a set of national entities) have been focused on. The proposed diagnosis appears to be useful and enables the definition of progressive improvement in the standardisation of some distribution local processes and also in the cooperation agreements with the central entity.

Several questions arose, which still need to be answered:

• Can management accept divergences between taxonomies of agreements and those of local processes?

• Is it necessary to privilege certain states of agreements?

• Should the 'actual' states of agreements systematically develop into the best practices?

• How we can develop an 'a priori' use of our proposal in order to identify the various trajectories of evolution?

Besides, a significant piece of information is missing to answer these questions: performance evaluation has been addressed in other related papers by the same authors [START_REF] Lauras | Méthodes de diagnostic et d'évaluation de performance pour la gestion de chaînes logistiques[END_REF][START_REF] Lauras | Performance evaluation of supply chain processes according to determinants[END_REF].

Figure 1

 1 Figure 1 Global architecture for a relevant SCM diagnosis (see online version for colours)

Figure 2

 2 Figure 2 Global methodology to identify typologies (see online version for colours)

Figure 5

 5 Figure 5 Factorial plan -MCA analysis -agreement processes (see online version for colours)

Table 1

 1 Referential framework to diagnose supply chain physical flows

	Sections	Sub-sections	Main topics
	Physical flows
	The upstream flows
		1 Receipted units	Units, weight, palets, orders, receipts. . .
		2 Stocks	Raw material average inventory,
			max & min inventory (palets, units). . .
		3 Staff	Kind of contracts (permanent, temporary. . . )
		4 Product numbers Number of references, by brand, by kind
			of products. . .
		5 Suppliers	Number of suppliers, repartition by channel. . .
	The internal flows
		6 Produced units	Units, weight, fabrication orders, . . .
		7 Stocks	Average in-process inventory,
			min & max inventory (palets, units). . .
		8 Staff	Kind of contracts (permanent, temporary. . . )
		9 Product numbers Number of references, by brand, by
			kind of products. . .
		10 Sub-contractors Number of sub-contractors, repartition
			by channel. . .
	The downstream flows
		11 Distributed units Orders, picks, shipments, order-lines, weight. . .
		12 Stocks	Average available inventory,
			min & max inventory (palets, units). . .
		13 Staff	Kind of contracts (permanent, temporary. . . )
		14 Product numbers Number of references, by brand, by
			kind of products. . .
		15 Third-party log. Number of 3rd party, repartition of the flows. . .
		16 Customers	Number of customers, repartition of
			sales and units per channel. . .

Table 2

 2 Referential framework to diagnose local processes

	Sections	Sub-sections	Main topics
	SCM coordination
	Processes
		1 Activities	Added Value, primary operation,
			temporality. . .
		2 Resources	Human resources, Financial resources,
			material resources. . .
		3 Inputs / Outputs	Object, Results of the process,
			periodicity. . .
		4 Controls	Objectives of the activity, link with the
			strategy, periodicity. . .
		5 Local performance	Dashboards, objectives, exhaustiveness,
			interest. . .
	IT
		6 Informational systems	Softwares, needs adequacy,
			architecture. . .
		7 Information reliability	Updating of data, documentations,
			relevance of use. . .
		8 Adequacy with strategy	Needs adequacy, limits, projects. . .
	Global planning
		9 Budgetary management	Investments, fixed asset turnover,
			reporting, costs reduction. . .
		10 Demand Management	Load / capacity analysis, product family,
			horizon / frequency. . .
		11 Methods and tools	Procedure, formulation, precision. . .
		12 Sources	Exhaustiveness, relevance, update. . .
		13 Decision levers

Table 3

 3 Referential framework to diagnose cooperative processes

	Sections	Sub-sections	Main topics
	SCM integration
	Partners
	(internal and external)
		14 Specifications	Competences, place and plants. . .
		15 Choice criteria	Quality, cost, cycle time,
			policy, geographic. . .
		16 Needs expression and control Comittments, dashboards. . .
		17 Reliability, Step of progress	Performance control,
			projects, synergies. . .
	Control
		18 Leadership	Decision centre
			(individual, comittee), autonomy
			level, arbitration rules. . .
		19 External constraints	Laws, regulation. . .
		20 Agreement performance	Dashboards, objectives, interest,
			exhaustiveness. . .
	Co-operative network
		21 Common objectives	Definition, nature
			(structural or trend). . .
		22 Individual limits	Competences, resources,
			capacity. . .
		23 Agrement details	Contract, horizon,perimetre. . .
		24 Risks management	Risks anticipation,impact
			evaluation, responsability. . .
		25 Objets manipulés	Data or treatments
			(characteristics, exhaustiveness,
			using). . .
		26 Temporalité	Exchange or share,
			Update frequency. . .

related to pharmaceutical products intended for wholesalers, whereas volumes of 'details' are related to cosmetic products for retailers. This last activity is mainly based on seasonal products (solar, thinners for example). In addition, the cosmetic catalogue naturally includes many more references (promotions, innovations). This analysis shows that the activity of an entity can be divided into two distinct components: management of large volumes, and management of smaller volumes. Each component is the consequence of the local market structure. From this PCA, we can define four classes (Figure 4):

• the pharmaceutical distributors: 2, 4, 9 and 12 as well as the homeopathic distributors 5 and 10

• the cosmetic distributors: 6 and 8

• the cosmetic distributors: 1, 3 and 11

• the pharmaceutical distributor 7.

It can be stated that unlike other distributors, the pharmaceutical distributor 7 shares an activity between two types of identified flows. Distributor 7, indeed, also supports the marketing of family medication products (i.e., those one which are naturally close to cosmetics) for its country. In all the other countries, this activity is controlled by cosmetic distributors. Moreover, cosmetic distributors 6 and 8 can be distinguished from the others by the much greater volumes that they deal with.

Data analysis of the cooperative distribution processes flows: qualitative elements -MCA method

From step 1, 15 macro-characters representative of the distributor local processes have been obtained.

At the same time, step 2 highlighted 12 macro-characters representative of the cooperation processes: 3 resulting from the PCA are carried out on the quantitative characters [type of transport ratio, product specificity ratio, volumes], 2 relate to the 'marketing' function [new product development, co-marketing], 4 relate to supply chain planning [interoperability of tools, flexibility, performance in service rate and absolute value] and 3 relate to the supply chain execution [costs, reception capacities, transportation cycle time]. These steps also result in a taxonomy of the local and cooperative processes. The test of coherence (step 3), on each function, regarding different classes of cooperation agreements, and those of local processes, revealed that: cooperation processes on marketing, sales, and supply chain planning functions were coherent with the local distributor classes, while distribution execution and supplying functions were not. This enabled us to focus specific investigations on these functions for the distributors that were at the source of these incoherencies.

On the basis of research on cooperative processes in supply chains [START_REF] Aviv | The effect of collaborative forecasting on supply chain performance[END_REF][START_REF] Dudek | Negotiation-based collaborative planning between supply chain partners[END_REF], we develop here step 4 of our approach. Articles which review cooperation techniques [START_REF] Martin | Infopartnering: The Ultimate Strategy for Achieving Efficient Consumer Response[END_REF][START_REF] Seifert | Collaborative planning, forecasting and replenishment: How to create a supply chain advantage?[END_REF] stress that the main concepts used by companies of nowadays is the ECR and its principal tools. We thus sought to locate the existing cooperative agreements in relation to these concepts. We consequently created two distributors artificially (VMI (subsidiary n • 13) and CPFR (subsidiary n • 14)) whose elementary character values were estimated by accordance with best practices. Step 2 of the approach was launched again.

The analysis in Figure 5 relates to twelve observations: real observations (except the two homeopathic distributors) and two fictive observations. On its positive part, axis 1 characterises cooperation agreements based on sharing data and/or treatments. It highlights the tools used within the framework of these relations (MRPII integration, common coding, integrated information systems). Negative contributions highlight the existence of agreement processes that are not formalised or are not supported by structured information systems or processes. They are 'in communication' or 'in collaboration' states.


Matthieu Lauras
email: matthieu.lauras@mines-albi.fr

Jacques Lamothe
email: jacques.lamothe@mines-albi.fr

Hervé Pingaud
email: herve.pingaud@univ-jfc.fr

A business process oriented method to design supply chain performance measurement systems

Keywords: supply chain, performance measurement system, PMS, business process management, enterprise modelling, control theory

While performance management forms an integral part of the concept of supply chain management, designing a consistent and powerful performance measurement system (PMS) at the level of an enterprise network is still proving very problematic. In this paper, a business process oriented approach is proposed to tackle problems of heterogeneity between partners. Special attention is paid to the representation of both decisional and operational activities, taking into account the links between them. Based on this framework, a method is applied to select, implement and publish the key performance indicators (KPI). We propose a classification of KPI into three categories related to management abilities: ambition, reality and facility. These classes form a consistent set to support decision making and process control at a network level. A business case is used to show the potential of this approach.

Introduction

For a number of years, specialists [START_REF] Beamon | Measuring supply chain performance[END_REF][START_REF] Stadtler | Supply Chain Management and Advanced Planning[END_REF][START_REF] Gunasekaran | A framework for supply chain performance measurement[END_REF][START_REF] Chan | A review of performance measurement systems for supply chain management[END_REF]Neely et al., 2006;[START_REF] Gunasekaran | Performance measures and metrics in logistics and supply chain management: a review of recent literature (1995-2004) for research and applications[END_REF][START_REF] Akyuz | Supply chain performance measurement: a literature review[END_REF] have highlighted the limitations of solutions based on the performance measurement systems (PMS) in use today. Very important supply chain features, like business process and information partner heterogeneities or the limited accessibility to information, for example, have been underestimated or, in some cases, not considered at all. These authors point out that there is little knowledge available on PMS and the choice of the right key performance indicator (KPI) in the open literature on supply chain environments [START_REF] Beamon | Measuring supply chain performance[END_REF]. More recently, other authors [START_REF] Gunasekaran | Performance measures and metrics in logistics and supply chain management: a review of recent literature (1995-2004) for research and applications[END_REF][START_REF] Alfaro | Business process interoperability and collaborative performance measurement[END_REF][START_REF] Akyuz | Supply chain performance measurement: a literature review[END_REF] have confirmed that academics and practitioners are still in need of a new PMS which can handle the requirements of the new supply chains.

Progress on supply chain performance measurement requires an outlook encompassing the extended enterprise, a state of mind that emphasises a collaborative win-win policy between respective supply chain partners. [START_REF] Lee | Creating value through supply chain integration[END_REF], [START_REF] Al-Mudimigh | Extending the concept of supply chain: the effective management of value chains[END_REF] and [START_REF] Alfaro | Business process interoperability and collaborative performance measurement[END_REF] stress that the first step in developing an efficient supply chain is to improve the performance of disparate internal systems and processes responsible for managing and coordinating the interactions in the value chain. They show that interfacing activities locally, without a systematic overview, may result in failure, as it will be dependent on an exclusive use of internal measurements. [START_REF] Folan | A review of performance measurement: towards performance management[END_REF] note effectively that the development of disparate measurement systems may result in superfluous and incompatible performance evaluation. Because companies are moving towards a more integrated type of operations management across their supply chains:

• it is becoming necessary to measure performance on the various parts of the supply chain in a consistent way, i.e. with the support of a clear, logical and intelligible framework in which the PMS will work [START_REF] Lohman | Designing a performance measurement system: a case study[END_REF] • there is a need to quantify performance for the supply chain as a whole and refer properly to different problem dimensions.

Explicit knowledge of a supply chain can be gained by business process modelling, which has been recognised as a good practice to reduce heterogeneity problems. Once they are drawn up, collaborative processes between supply chain partners provide a common acceptance of the business and define the many interfaces that need to be managed. However, most of the time, the standard representation of business processes is limited to operational and support processes. The operational processes are sequences of activities that are executed using qualified resources that transform inputs (material or immaterial flows) into outputs, and that are under control. Support processes are responsible for the availability of the means required for the execution of operational activities. We seek to demonstrate later in this paper that, while this primary basis of knowledge is useful in defining KPIs, the PMS claim that it has a capacity to manage cannot be accepted without an investigation into decision-making activities. Therefore, in our approach, we emphasise decision-making representation and try to prove the added value for the design and operation of the PMS. Decision-making processes are a foundation of management activities. They deliver the control information that the operational activities need (when the execution will start, what the objectives are). But they are also in charge of selecting, booking and monitoring resources for the predicted executions. [START_REF] Folan | A review of performance measurement: towards performance management[END_REF] specify two kinds of requirements for managing performance with business processes:

• a procedural performance measurement framework shall evaluate the performance of each activity and process

• a structural performance measurement framework shall evaluate the performance at each level of the organisation (internal and external).

In accordance with the above, we propose a complete and systemic methodology to help practitioners to define a new PMS for SCM, in terms of both procedural and structural performance measurement. Our contribution is basically twofold:

• producing rules that guarantee relevant and systematic KPI definitions according to supply chain business process knowledge (process repository)

• effectively supporting the improvement of supply chains through an original performance analysis framework.

The paper is organised as follows. In the next part, we discuss performance measurement concepts in the perspective of organisation networks. Later on, we will try to analyse available knowledge on PMS and associated methods. This analysis will show some strengths and weaknesses of well-known methods, especially in the context of management applications. An outline of a new PMS for SCM will be explained in the continuity of these mixed results. Then, we propose a six-step method to put the PMS into practice. This method is founded on enterprise-modelling concepts. The performance determinant is introduced as a key variable for the management, acting as a cornerstone between decision and action, and we will show that its sensitivity on performance can be assessed with an inference mechanism on appropriate process model maps. Finally, we give some feedback about an implementation of this PMS on a real supply chain business case.

Process oriented supply chain PMSs

It is commonly agreed that a manager follows a four step approach to control a system. [START_REF] Smith | Performance management and operational research: a marriage made in heaven?[END_REF]: define the set of KPIs; measure performance; analyse responses with respect to objectives; act to satisfy the objectives. There is clearly a kind of analogy with the closed loop system in cybernetics control theory. The three elements which address the problem (the set point, the measurement itself and the control variable) can be interpreted in a management context (see Figure 1, inspired from [START_REF] Lohman | Designing a performance measurement system: a case study[END_REF]:

• The set point represents the expected state of the controlled operating system. It is the expected trajectory and can be interpreted as a contribution to the expected objectives on the value chain.

• The measurement reports on the real state of the operating system. It is used to check the difference with the set point and this gap analysis corresponds to a diagnosis.

• The control variable value is adjusted with the aim to reduce the above difference, in the most efficient manner and in the future. The control loop is considered closed at this point, when the measured variable and the control variable are dependent. The control variable may be compared to a decision taken on the basis of the diagnosis.

Obviously, when this closed control loop representation is roughly applied to complex systems such as supply chains, the analogy quickly reveals some drastic limitations. Human factors and decision making are so important in SCM that a descriptive picture of the system would probably be drawn with more confidence using an open loop. But even if comparing decision makers as a complex control transfer function is an awkward position, we have to keep in mind how important this analogy is to understanding PMS in the tricky situation under study. Several authors have reviewed the literature on performance measurement, performance metrics, performance evaluation and/or performance management for enterprise networks or supply chains [START_REF] Chow | Logistics performance: definition and measurement[END_REF][START_REF] Neely | Performance measurement system design[END_REF][START_REF] Neely | Performance measurement system design: should process-based approach be adopted?[END_REF][START_REF] Beamon | Measuring supply chain performance[END_REF][START_REF] Bourne | Implementing performance measurement systems literature review[END_REF][START_REF] Kleijnen | Performance in supply chain management[END_REF][START_REF] Folan | A review of performance measurement: towards performance management[END_REF][START_REF] Chan | A review of performance measurement systems for supply chain management[END_REF][START_REF] Gunasekaran | Performance measures and metrics in logistics and supply chain management: a review of recent literature (1995-2004) for research and applications[END_REF][START_REF] Alfaro | Business process interoperability and collaborative performance measurement[END_REF][START_REF] Martin | On measuring company performance within a supply chain[END_REF][START_REF] Akyuz | Supply chain performance measurement: a literature review[END_REF]. Both accounting management and operations management have dealt with these subjects. Theories and methods cover the whole range of performance influences on decision levels, from the strategic to the operational decisions [START_REF] Lohman | Designing a performance measurement system: a case study[END_REF].

In the following sections, we begin by a study of KPI design on SC operational processes. Then, we conduct a study of the relationship between performance and decision making, with an enterprise model oriented approach, with reference to the control loop. In our third part, we try to reconcile the two faces of the problem by exploiting complementarities using the control system analogy.

Performance of supply chain operational processes

The idea of identifying an activity as the atomic entity onto which the performance must be achieved is not new. At the scale of a single organisation, activity-based costing and activity-based management (ABC/M) have promoted this idea for a long time. They are concerned with information on costs and margins [START_REF] Cokins | Activity-based cost management: an executive's guide[END_REF]. ABC assigns costs to activities on the basis of their use of resources and then allocates costs to products according to their ratio of activity consumptions. If the principle seems intuitively obvious, the access to pertinent information in order to implement the method is frequently a limitation [START_REF] Ghalayini | An integrated dynamic performance measurement system for improving manufacturing competitiveness[END_REF]. Moreover, the large volume of information processed by the method is controlled by the definition of KPIs which are supposed to be meaningful for ABM through the use of well structured dashboards (showing progress of actions applied to strategy, value perceived by the customer, performance of competitors, the quality and the improvement steps). It is quite clear that the extrapolation of such approaches to networks of cooperative organisations is not easy, because, for example, the different kinds of costs are vital pieces of information that cannot be spread over the set of partners without potentially negative effects on business transactions. In addition, some ABM dashboard dimensions (i.e., customer, competitor) could become fuzzy. This is an illustration of blockages that any extension of PMS to SCM has to deal with.

The holistic process PMS [START_REF] Kueng | Process performance measurement system: a tool to support process-based organizations[END_REF], the performance prism [START_REF] Neely | The performance prism: the scorecard of measuring and managing business success[END_REF], the process-based framework [START_REF] Chan | Feasibility of performance measurement system for supply chain: a process-based approach and measures[END_REF] or the Fraunhofer approach [START_REF] Bourne | Implementing performance measurement systems literature review[END_REF] are initiatives that have already developed the idea of PMS design based on business processes at the scale of an enterprise or a network. The pertinence of KPIs is enhanced by the knowledge captured in process representation. Process models are oriented graphs of activities that naturally provide a solution to allow aggregation of data and to quantify KPI. This is emphasised by the fact that a business process model frequently has many levels of decomposition, and that KPI can be distributed among those levels at approximately the right scale of activity detail.

As previously stated, in the context of a supply chain, a business process representation is a necessity to check the common agreement of businesses and put a frontier between what can be shared among partners and what cannot be shared. Because of this search for agreement, the supply chain operations reference (SCOR) model has received a lot of attention from practitioners. The SCOR model (Supply Chain Council, 2003), provides a unified representation of supply chains with five general processes: plan, source, make, delivery and return. Each process can be refined into sub-processes, which are themselves decomposed into sub-sub processes, and so on. The first granularity levels of the reference model are generic enough and can be used to compare several companies of a given sector. Three categories of KPI are proposed in the SCOR model dashboard [START_REF] Bolstorff | How does SCOR measure up?[END_REF] depending on what they are related to:

• customers (service, flexibility, effectiveness-swiftness)

• internal processes (costs and efficiencies)

• shareholders (profitability, return on investments).

Although these performance indicators are attached to processes and well formulated, they remain very difficult to set up within an existing supply chain. If the decomposition process on a macroscopic level is general enough to be applied to many cases, the more detailed levels (level 2 and especially level 3) may be difficult to identify using the SCOR elementary processes, because specificities increase with the granularity of process representation. In addition, the SCOR model does not 'try to close the loop', and there is no suggestion that the KPIs should be used to manage the system. Thus, without a method, it could be rather difficult -even random in some cases -to define, the necessary corrective actions on this basis alone.

Performance and decision making processes

A large number of PMS methods link performances to decisions. We can quote for instance, the strategic PMS [START_REF] Vitale | New process/financial scorecard: a strategic performance measurement system[END_REF], the performance measurement questionnaire [START_REF] Bourne | Implementing performance measurement systems literature review[END_REF][START_REF] Chan | A review of performance measurement systems for supply chain management[END_REF], the strategic measurement analysis and reporting technique system (Cross and Lynch, 1989) or Cambridge University's PMS method [START_REF] Bourne | Implementing performance measurement systems literature review[END_REF]. They insist on the need to split decisions into many levels depending on their weight on the organisation and their time effect. They also examine the sensitivity between KPI variations and alternative decisions by direct investigation.

But if information on performance is condensed in KPIs, it is also possible to synthesise information on decision using the well-known performance determinant, a concept first introduced by the balanced scorecard method (BS) [START_REF] Kaplan | The Balanced Scorecard: Translating Strategy into Action[END_REF]. The performance determinant could be defined as the 'decision variable' that mainly controls an activity. The 'decision variables' of an activity ensure that the system is effectively controllable and avoids conflicts between the decisions made within the system. The concept of the determinant identifies a sub-set of these decision variables that should have an influence on the performance of the activity. Consequently, the main criterion to select determinants is the sensitivity evaluation of their influence on the system (activity). BS focuses on strategic management, and even if a reference to internal processes is proposed as an improvement policy, there is very little information about the method to achieve it.

Representing maps of decisions in an organisation is a difficult, if not unfeasible, task. Time decomposition of decisions is normal practice in management science. However, for procurement, production and distribution systems like supply chains, it is possible to have a more structured approach to the chain of decisions with respect to both business process and time. Enterprise modelling has produced results in this area. In particular, the GRAI method has been developed using this idea to produce a two dimensional map (a grid) of the decision centres inside the organisation. The columns of a GRAI grid are the main business processes (called functions) of the organisation while the lines are the time periods. A cell of the grid (intersection between a function and a time period) defines the perimeter of decision-making and is called a decision centre. Two types of arrows are used between the decision centres to show how some decisions are influenced by others, on the one hand, and how information are distributed on the grid after a decision making, on the other hand. The representation of a propagation mechanism on the grid defines a partial preset order between decision centres, which we shall hereafter refer to as decisional processes.

GRAI appears very attractive in the perspective of an extended PMS framework. ECOGRAI is a set of methods which contributes to the improvement of a company's performance through GRAI enterprise modelling [START_REF] Ducq | Coherence analysis methods for production systems by performance aggregation[END_REF]. These methods allow a system of indicators to be designed and implemented. Indicators, limited in number, are distributed in the structure of decisions inside the grid. The links that we are searching for with the closed loop analogy are known explicitly. ECOGRAI presents a five step approach:

1 modelling the management structure and the decision centres (GRAI grid) 2 identifying the objectives of each decision centre 3 extracting the determinants that a decision-maker can use to achieve his goals 4 proposing performance indicators 5 developing, establishing and maintaining the information system of the indicators.

So, ECOGRAI suggests that the performance should be associated with the variables of decision, explicitly called performance determinants. If we come back to the control loop analogy, and if we agree to compare our problem with a multivariable (MIMO loop: multiple inputs, multiple outputs) control problem, ECOGRAI provides a kind of architecture that defines the connections between the measurements and the control variables.

Although the formal framework of ECOGRAI seems to be a powerful tool, it does not offer analysis axes for the causal relationship between decisions and actions in order to facilitate the decision making, neither does it integrate the vision of operational processes proposed by previous methods. We could say that the other part of the MIMO loop, the connections between control variables and subparts of the operating systems, are missing. Consequently, determining the sources of an actual deficiency from this model remains based on the know-how of the decision-maker. Furthermore, tentative use of GRAI enterprise modelling techniques on a supply chain is still in its infancy (even if the five processes of the SCOR first level are natural candidates for a node of a network application, the structure of the network is difficult to address in only one dimension). [START_REF] Folan | A review of performance measurement: towards performance management[END_REF] explain that the control could be broken up into several levels of different range:

Outline of the PMS architecture

• the operational control that relates to the regulation of a physical process compared to a pre-established instruction

• the tactical control that consists in adapting the existing resources at a particular state

• the strategic control that aims to correct variations in comparison with the strategic objectives.

The principle of the articulation between these various levels was approached in the works of [START_REF] Lohman | The Effectiveness of Management Information[END_REF], [START_REF] Lohman | Designing a performance measurement system: a case study[END_REF] and [START_REF] Folan | A review of performance measurement: towards performance management[END_REF]. For instance, [START_REF] Lohman | Designing a performance measurement system: a case study[END_REF] have proposed a schematisation by a double control loop depicted in Figure 1. The strategic and tactical loops are amalgamated.

A KPI is a piece of input information to the diagnosis activity that the decision maker has to carry out. Performance management is regularly fed, and decision making triggered, by KPIs. Decisions on how to perform an activity are a result of the performance management. We use a process typology that distinguishes decisional processes and operational processes as a basis of PMS architecture. The first type determines the performance stakes through the control instructions that will be transferred to operational activities and resource management. The second type is, however, the main subject of concern since it governs the achievement of objectives and is the performance 'inductor'.

A key point in PMS architecture is to keep a clear vision of the relationship between these two types of process. This constraint is depicted on the lower part of Figure 2. Respectively from the right side to the left of this scheme, the KPIs must be:

• associated with the operational processes or activities (as proposed by ABC or SCOR);

• articulated around the performance determinants, which represent the most sensitive decision variables (as proposed by BS);

• defined in closed connection with the various decision processes (as in ECOGRAI).

Operational processes

Decisional processes

Determinants

Analysis Axes

One problem remains to be solved in order to complete the definition of the PMS architecture. The distributed nature of processes along a supply chain is a source of difficulties when KPIs have to be classified and published. We have argued that ECOGRAI gives a first part of the answer to that question by its structure of links between KPIs and performance determinants. However, the relations to the operational processes need to be studied in order to complete the framework.

The approach to tackle this last problem was based on the idea that a decision is a set of instructions intended both for an operational activity on what must be performed at the end of the execution, and for support processes on the resources that should be provided to allow this execution. Using a SADT/IDEF0 diagram to model an operational process (functional process representation) or an activity, as shown on Figure 3, we develop a systematic rule to structure KPI definitions. The rule uses concepts of relevance, effectiveness and efficiency (REE) introduced by [START_REF] Jacot | A Propos de l'évaluation Économique des Systèmes Intégrés de Production[END_REF]. REE is based on comparison between the objectives/results/resources, a triptych that basically aims to discuss the performance of an activity. Effectiveness measures whether the results of the activity meet its objectives. Efficiency expresses whether the resources were well used to reach the results. Relevance measures the appropriateness of the means to the objectives. Despite its simplicity, it is surprising that this kind of analysis has received little attention in the past. Applying this rule to the whole system gives an opportunity to make factorisation (a form of aggregation) easily. Moreover, it provides a simple solution to the problem of the relationships to the decision centres. Therefore, the PMS architecture will include three axes of operational process performance analysis, reported on the upper part of Figure 2. This approach remains generic, and takes the opposite direction to the more specific definitions proposed by many methods, such as BS, for the axes of analysis.

Designing the supply chain PMS

PMS in operation

Returning to Figure 1, we can now describe the interface between the different parts of the PMS architecture and explain how information is processed along the control loop.

As stated earlier, a decision-making centre (upper part of the figure) has to produce controls and to allocate means to a whole or part of an operational process (seen as the fulfilled function). For this purpose, a decision-making centre determines values of decision variables from external information and from the results of the performance evaluation, so as to satisfy objectives and respect constraints. At the operational level, the performance evaluation (P) will consist in comparing the measurements taken on the outputs (Ou) of the operational process activity with the corresponding objectives (O) considered as instructions. If an anomaly is noted between the measurement and the objective, then one or more decisions (D1) are taken to correct the dynamic behaviour of the system. Thus, a unit of decisions D1 will have to define controls (C) and/or means (M) for the implied activity.

The second loop relates to the decision-making centre of higher levels (tactical and strategic). The time-constants are greater, and the information is more compact. These levels work on a prediction of the behaviour (described by a program). This program is a set of instructions and allocations of resources for a given period of time.

Inspired by [START_REF] Akkermans | Relevance assumed: a case study of balanced scorecard development using system dynamics[END_REF], we propose to use influence diagrams coming from the system dynamics theory proposed by [START_REF] Forrester | Principles of Systems[END_REF] to explain the propagation of information. It is important to notice that we do not use influence diagrams in a quantitative manner in this paper. System monitoring is clearly beyond our scope. We simply focus on the dissemination of information from the operational business processes to the decisional business processes.

Figure 4 shows such a diagram in analogy with Figure 1, i.e., for a simple case of two decision-making centres superposed, and connected to only one function at the operational level. This figure clearly shows the originality of our approach to tackle the problem of performance evaluation at a network level. As described in the previous sections, our proposal consists in defining performance indicators according to the transverse business processes of the supply chain and not with regard to the different services or departments of each company (and so to base the performance evaluation on reliable information). But our contribution (as described in Figure 4) also consists in controlling the dissemination of this information from the operational level to the decisional level in order to be able to assess performance reliably:

As previously stated decisions taken at the operational level (D1 determinant) have an effect on the capacity of the means and on the levels of requirements assigned. They directly impact the outputs, which are also influenced by the inputs of the activity.

Compared to the objective O1, a performance variable (P1) translated the effect of the value of the output variable (Ou). The more the outputs conform to the objectives, the better the performance will be (effectiveness). But, the performance is also evaluated in conjunction with the allocation and the use of the means (efficiency). We thus establish a relation between the means variable (M) and the performance (P1).

A similar approach enables us to understand the effect of the higher decision levels. Thus, it appears that the performance (P2) relating to the activity controlled from this decision-making centre is the comparison of the whole of the outputs (Ou) with the objectives (O2), under condition of the means (M).

The decision-making which generates values of D1 (resp. D2) starting from the recorded performances P1 (resp. P2) and from the postulated objectives O1 (resp. O2), is done with respect to Constraints Ct1 (resp.Ct2).

Expressiveness of performance analysis axis

Using the comparison included in the triptych REE, we decided to introduce three new categories of performance indicators, called 'points of view', which are intended to help reasoning during the diagnosis phase of PMS operation. We use KPIs that are built with reference to the performance determinant values. The definitions of these three classes are as follows:

• The 'ambition point of view' evaluates the appropriateness of the controls to the performance determinants. The objective is to evaluate the feasibility of the controls assigned to the activity. In other words, does the activity have the means to match its ambition? This information allows the detection of unexpected controls or determinants unsuited to the mission. Here we are measuring the distance from the determinants to the instructions. It is important to notice that the controls are not some decision variables but only the objective or the constraint the activity has to reach. A consequence of a bad result on this point of view might be a modification of the objectives assigned to the activity or the implementation of a more effective process.

• The 'reality point of view' evaluates the appropriateness of the activity results to the determinants. The objective is to check whether the activation of the determinants produces the expected results. We measure here the cause-effect relationship of the determinants on the results. Specifically, this point of view assesses whether the decisions that are supposed to control the activity and its results are adapted or not. A consequence of a bad result on this point of view might be a change in the decisionmaking process (transition from a classical Procure and Demand process to a Vendor Managed Inventory process for instance).

• The 'facility point of view' evaluates the appropriateness of the resources to the determinants. The objective is to identify what the impact of the determinants is on the means used by the execution of the operational activity. This information allows the detection of insufficient or oversize resources. We measure here the cause-effect relation of the use of resources due to the determinants. In other words, the facility point of view measures the quantity of energy (human resources, costs, machines, etc.) the determinant has to use in order to produce the activity's results. A consequence of a bad result on this point of view might be some new investments (new machines, new capabilities, etc.).

These three points of view are not independent. Without ignoring these interactions, our proposal simply provides a way to analyse these relationships. Figure 5 is a translation of Figure 2 with this new set of analysis axes.

Putting PMS into practice on supply chain

Figure 6 is a useful aid to understanding the implementation of the proposed PMS. We summarise the work with the following six-step approach:

1 Define the SC decision process maps using a set of interconnected GRAI grids: specify through this formulation the various time periods, the functions and the decision centre. Associate the performance determinants to the decision centre. The resulting part of a model makes it possible to specify, for each decision centre, the temporal elements to be considered in the definition of indicators (minimal periodicity of measurement and horizon of projections).

2 Complete the process model representation with the SADT/IDEF0 operational processes for each identified functions. This work defines the links between functions, resources, material flows, and controls.

3 Allocate the performance determinants to the functions. This consists in propagation, for each horizon, of the decision levers onto the operational process model.

4 On the basis of the models, decision-makers define their performance indicators (results and determinants). In parallel, they also prepare their diagnosis ability by defining the three points of view (ambition, reality and facility). The use of standard formulations coming from experts or best practices [START_REF] Bowersox | Logistical management', The Integrated Supply Chain Process[END_REF]Supply Chain Council, 2003;[START_REF] Neubert | Performance measurement for the supply chain: methodology to define a supply chain dashboard[END_REF][START_REF] Gunasekaran | A framework for supply chain performance measurement[END_REF]) is recommended.

5 Implement the dashboards in the information systems. Perform, automatically if possible by an executive information system, their calculations according to the temporalities.

6 Manage the supply chain using performance measurement: use the portfolio of indicators to lead supply chain continuous improvements, particularly on cooperation links. This can result in redefining responsibilities (decision-making), operational or decisional processes or action levers which force a return to Step 1.

We can remark here that we do not propose any KPI in this research work. Our proposal consists only in structuring a method to help decision-makers to define reliable KPIs at a network scale. But our proposal could of course also be used at a single enterprise level (which is a more simple case) whereas the traditional PMSs discussed in the previous part are generally designed for a single company and could not be extended at a supply chain level.

Application to a pharmaceutical supply chain

The proposed PMS has been implemented and tested on a business case, an international pharmaceutical supply chain. The system under study is made up of a world central entity that manages its production units and central distribution centres, and a set of national entities that manage their local markets, procure products from the world entity, and possibly transform products locally. We give here some details about this experiment.

A local entity

Steps 1 and 2: decision and process models

From the temporal decomposition of the decisions (GRAI grid of Figure 7), six decision levels have been identified: political (new markets, shareholding, R&D or partnership decisions); business orientations (organisations, long-term investments, business plan decisions); strategic (medium-term investments, product developments, sales and operation planning decisions); tactical (short-term investments, sales supports, production plan decisions); operational (resource adjustment, scheduling and customer management decisions); real time (realisation and forwarding decisions). A strong coordination appears between the world and the local entities, at the 'political' and the 'orientation' levels only. However, local entities are independent of the other levels for their decision making.

At the political level, each decision centre designs the strategy associated to its function and only sends information to the other functions. At the « orientation » level, each decision centre must organise its processes and align the required means in order to put the chosen policy into action. At the strategic, tactical and operational levels, planning activities enable the coordination of sales with production and distribution.

During the second step, for each decision level of the GRAI grid, the decision makers formalise their vision of the operational processes in a SADT model. For each process, all the activities, inputs/outputs, resources and instructions were identified. If we consider the example of people in charge of the 'business orientations', they have identified four activities to describe, from their point of view, the physical level in the world and the local entities : to manage customer relationships, to make, and to develop products, and finally to distribute (see Figure 8).

Steps 3 and 4: PMS

In this part, we consider the activity 'to make' viewed by the decision-makers of the 'business orientations' level (see Figure 9). Its inputs and outputs are those identified in Step 2. Kind of resources which are concerned by this activity are:

• material: production plants, production lines (engines and conditioning), elementary components and raw materials;

• human: individual and collective skills, organisation (shifts, temporary employees…)

• supports: information systems and technical tools (quality control…).

Experts agreed that the determinants for this activity come from the decision centre 'to make orientation in production'. The name of the activity can be replaced by the performance determinants:

• to formalise a strategic orientation of the means (human, financial and material): to decide opening/closing-down of a production plant or a production line, to decide on a training plan and/or recruitment of particular skills, to contract with a provider, etc

• to adapt the planning and execution of processes: to revise the internal planning procedures, to design a new rule to manage the inventories, to decide on an arbitration rule to manage priorities in case of stockout, etc.

The controls for the 'to make' activity are those expressed in the GRAI grid for 'to make orientation on production' by its leader, decision centre 'production policy design' (arc ). The objectives are to minimise the costs on the one hand, and to maximise the service level on the other hand. They are coherent with the outputs of the activity (manufactured items).

• Then, KPIs and points of view have been formulated on the basis of the existing indicators (but now validated and contextualised), of best practices (SCOR model essentially) and complementary proposals. Figure 7 illustrates the result of this step.

The use of the resources is constrained by financial means, and by those available, and allocated to this activity (both in the field of operations and investments). Finally, the performance indicators and the points of view that were defined with the supply chain experts are:

• Results indicators: product flows are followed-up both in term of number of units by plant (measure of the total activity) and the stock out rate (measure of the service quality). • Determinant indicators are the effective costs for adjusting resources (subcontracting, investments, shifts, overtime, etc.) and measures of the process duration (inventory rotation of the final products and elementary components, total cycle time). • Ambition point of view (bond between determinants and instructions): these indicators measure the impact of the determinants ('to adapt the resources' and 'to parameterise the processes') on the objectives of costs and services. The indicators from this point of view measure the deviation between the objective of cost allocated to this activity and the reality of the costs (expressed in cash and in terms of covers of inventory rotation) engaged by the determinants of this activity. The objective of service is here translated in terms of cycle time of the whole manufacturing activity. Therefore, the 'ambition' indicators on service measure the relative variations in terms of cycle time of the activity. • Reality point of view (bond between determinants and results) measures whether the determinants are effectively used to make products for customers and particularly track non-value-adding activities. Four criteria are proposed and are associated either with the determinant 'adaptability of the resources' (complete orders delivered on time, obsolete units/total units) or with the determinant 'to parameterise the processes' (productivity in made units/day, returned units/total units). • Facility point of view measures the appropriateness of resources to determinants.

Indicators depend on whether human or technical resources are under concern. With regard to human resources, the choice was to track the need for extra resources to run the process (temporary employees, subcontracting) measuring both the underestimation of means and the need to activate the determinants. As regards technical resources, indicators measure the efficiency of the means (hours effectively used/hours available, non-quality rate).

The approach was repeated on all activities identified by functional modelling. Nearly 130 KPIs and 250 points of view were defined on the whole supply chain. While this number was high, the distinction between result, determinant and point of view indicators clarified the use of indicators by managers. Result indicators are mainly used for reporting. Determinant indicators are used for analysing the coordination of functions, while points of view indicators are used to analyse the source of problems which have been detected using other indicators. These indicators have also been used to justify and select a set of projects in the organisation of the supply chain. In return, these projects forced changes to the models and consequently led to a reformulation of the indicators. By this stage, indicators are considered as stable and Steps 5 and 6 are under implementation.

Conclusions

In this paper, we have shown how to define a PMS, in a supply chain context, that allows an explicit link to be established between decision and action at the network level. Our research has focussed on the definition of relevant KPIs in transverse business processes and on the development of an original framework to analyse supply chain performance more efficiently. Specifically, our contributions can be summarised as followed:

• identification of good practices according to PMS problem statement in supply chain

• following a control system analogy, design of a pertinent supply chain PMS oriented towards decisional and operational processes, centred on the performance determinant concept and based on enterprise modelling methods • proposal for three new axes of performance analysis: ambition, reality and facility points of view • application to a pharmaceutical supply chain.

The suggested method brings together the advantages of the principal existing methods that define PMS, and tries to add some specific points: a way of identifying causes of (non-) performance (points of view), practical tools to diagnose and define action plans in order to better manage supply chain processes. Finally, our proposal helps to identify problems and opportunities and to formulate solutions. With this proposal, we respond to one of the new needs for performance evaluation identified by [START_REF] Gunasekaran | Performance measurement and costing system in new enterprise[END_REF].

Given that we have applied them to only one case, we have not performed a complete verification of the suggested methods, and thus three axes of complementary research now need to be performed:

• The appropriation of the model by the practitioners. If companies today, by using this approach, find one of the tools that will enable them to control their supply chain processes better, we need to examine the real capacity of our proposal to adapt to the rapid changes in their contexts and organisations. • The implementation supposes that the SCM integrates the concept of performance management. This does not necessarily apply in today's companies (especially in the pharmaceutical world). Such integration certainly generates significant changes in processes and in organisations. This must be taken into consideration during the implementation process. • Our study was started on the major assumption that the system under study was already established. One should now consider extensions, integrating the possibility of (re)designing the whole or part of the system, in terms of organisation or of process definition.

Figure 1

 1 Figure 1 The double control loop

Figure

  Figure 3 Relevance, effectiveness, efficiency triptych

Figure 4

 4 Figure 4 Performance influence diagram

Figure 5

 5 Figure 5 Points of view for performance evaluation

  Figure 7 GRAI grid of the world and local entities Functional view for the 'orientations' decision level (see online version for colours)

	Horizon Ext. Info. Design Sales Production Distribution Int. Info. Ext. Info. Design Sales Production Distribution Int. Info. Figure 8 make POLITICAL H = 10 years P = 1 year Market Study Benchmark Policy Design Policy Design Policy Design Policy Design Market Study Policy Design Policy Design Policy Design Policy Design ORIENTATIONS H = 5 years Market Study make orientations P = 1 year Benchmark make orientations make orientations make orientations Organisation & systems Market Study make orientations make orientations make orientations make orientations Organisation & Systems World Central Enitty Local Entity Product flows Distribute manage customer relationship Products flow Valid distribution orders Production orders & Forecasts World central entity	STRATEGICAL Develop H = 18 month Products P = 1 month Family Forecasts make SOP Plan load Histories Develop Products Family Forecasts make SOP Plan load Histories develop Products World Products portfolio Financial and	TACTICAL Develop H = 12 month Support P = 1 month Product forecasts Quota allocation Make Master Prod Sched. Plan Capacity Histories Develop Support Product forecasts Plan Prod. & Procurement Plan Capacity Histories manage Financial and Service flow Innovation Needs service flow	OPERATIONNAL Manage Schedule Schedule H = 1 month Entities Prod. & Deliveries P = 1 week Accounts Procurment Manage Customer Accounts Schedule Prod. & Procurment Schedule Deliveries customer relationship Local distribution orders Market Needs develop Local	REAL TIME Record make H = 1 week Orders, products P = real time Invoice Stock, Pick & deliver Record Orders, Invoice Stock,make products & procure Stock, Pick & deliver make Distribute to final products customer Products Procurement requirements Product Portfolio Production orders & Forecasts