- Email: [email protected]
A formal modelling of control processes
European Journal of Operational Research 109 (1998) 377±389
A formal modelling of control processes Regis Dindeleux a
a,b,*
, Lamia Berrah
a,c
, Alain Haurat
a
Laboratoire de Logiciels pour la Productique (LLP-CESALP), 41, Avenue de la plaine, 74016 Annecy Cedex, France b ATOS 1 Avenue Newton, Clamart, France c IPI-ENSGI-INPG, 46, Avenue F elix Viallet, 38031 Grenoble Cedex 1, France Received 1 March 1997
Abstract This study concerns the performance management of manufacturing processes. Such processes are decomposed into two types: the operating processes and the control ones. While an operating process has to realise the deliverable products, a control one is designed to plan, control and supervise the execution of the activity of the operating processes. In this sense, one approach of control is discussed. The proposed model is based on the exchange relation concept between a customer and a supplier. The dierent control parameters are de®ned from this point of view, such as the activities assigned objectives and the performance indicators associated with them, the control activities and their functions, the control means... this for both the technical and economical levels of control. These ideas are applied to some problems encountered in manufacturing processes. In particular, aluminium metallurgy processes are considered. The performance of these processes are driven by many parameters such as the quality of the raw materials, the conformity of the tools used, the elements to assemble, the know-how of the operators. Ó 1998 Elsevier Science B.V. All rights reserved. Keywords: Control; Simulation; Formal process modelling; Multicriteria decision support systems performance indicators
1. Introduction ± Context Manufacturing companies have to deal with a strong competition between comparable manufactured products and exacting customers. Their survival depends, on a ®rst level, on an external performance which is measured by the customer
*
Corresponding author. Fax: 33 50 66 60 20; e-mail: [email protected]. 0377-2217/98/$19.00 Ó 1998 Elsevier Science B.V. All rights reserved. PII S 0 3 7 7 - 2 2 1 7 ( 9 8 ) 0 0 0 6 4 - 2
satisfaction. Then, this satisfaction is translated into an internal performance, essentially characterised by a quick reacting to changing demands, a continuous improvement of the quality and the delivery of the products, a high productivity of the resources, etc. leading thus to cost reductions. To satisfy the new requirements of competitiveness and performance, traditional management systems which are based on functional organisations and ®nancial control have quickly shown their limits. Companies nowadays turn towards a
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management by processes ± in contrast to a traditional functional one ± based on technical performance indicators which directly measure the process enactment [1,2]. Moreover, decision support systems which are able to react to any event, such as malfunctioning, urgent orders, etc. are needed. Moreover, simulation can be useful in the area of the decision support for the analysis, design and exploitation of manufacturing processes. This explains that simulation tools [3,4] currently proposed on the market are more and more eective and user-friendly. However, these tools essentially concern the modelling of the operating processes. These tools do not yet allow to model and simulate in a coherent and simple manner the control processes and their connections with the operating systems [4]. This lack also limits the analysis of the impact of the control driven on the performances of the operating processes. Indeed, often at the technical level, decisions must be taken with regard to more than one criterion. The problem is how to select the alternative which best satis®es all the criteria involved in the decision system. Multicriteria decision supports are very helpful in this context. However they must be handled by models which allow a simulation of what would happen for each considered alternative. This study focuses particularly on the modelling ± for the simulation ± of manufacturing processes. The enactment of a manufacturing process is faced with many kinds of malfunctioning, drifts and other uncertainties (e.g. machine failure), being the sources of non-performances, such as delays on the deliveries, losses with regard to global manufacturing capacities, intermediary inventories. Therefore, it is advisable to supervise and control the processes, by using a decision support system able to quickly detect, diagnose and then react to any malfunctioning. The detection step must be necessarily supported by performance indicators. These indicators allow the observation and the reporting of what is happening during the operating process enactment. Whenever one drift is detected by any performance indicator, supervisors can immediately react, rather than passively wait for the subsequent ®nancial evaluation. But often, for one detected malfunc-
tioning, more than one decision can be eventually taken with regard to more than one parameter and more than one constraint. This is the problematics of every control system. Moreover, knowing that a process can be seen as ``a set of partially ordered set of activities executed to realise a given ®nality of or within a company'' [5], its control naturally concerns the enactments of the activities. More precisely, each activity in a process is seen either as a supplier to the next one and or as a customer to the previous one [2,6]. From this point of view, two kinds of performances are distinguished for any activity [5] (see Fig. 1). · An external performance which illustrates the realisation of the objectives expressed by the customer (delivery, quality, quantity, etc.); this notion extends the customer satisfaction concept to all the activities of the company. · An internal performance, related to the execution of the dierent activities (productivity, etc.). In this context, the control problem can be summarised in the following points: · What are the external and internal objectives (i.e. the external and the internal performances to reach)? · How to realise these objectives? · How to control the enactment of the activities concerned by the assigned objectives (monitoring and action)? While the answer to the ®rst question is provided by strategic and economic analyses, the two others precisely concern the industrial (technical) control. In this context we propose a modelling approach of the manufacturing processes. This approach is based on a generic model of the process, which is
Fig. 1. External and internal performances of an industrial activity.
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speci®ed according to the kind of process (operating or control). The links between the operating process and the control one are done by the socalled control points, which handle both the information given by the performance indicators and the control actions. 2. The industrial process modelling The ®rst step in this study concerns the industrial (operating and control) process modelling. In accordance with the CIM concepts, each entity, in particular the process, can be seen through its functionally on the one hand, and its behaviour on the other hand. Moreover, the level of details is de®ned according to the visibility that the users have. The scope of functional modelling concerns the description of the dierent activities which may occur in a company. The behaviour modelling concerns the control ¯ow within a company. 2.1. Functional aspects of the process The functional aspect can be seen through the structure of a partially ordered set of activities considered in order to realise an objective linked with manufacturing. An activity [7,5] is the association of an objective (considered as a sub-objective of the process global objective this activity is part of), a set of products, an operation, an order, the means allocated to this operation and a couple of actors. The latter is the exchange relationship between two actors, where the ®rst actor realises the activity for the second one (see Fig. 2).
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The objective. The objective can be de®ned through three facets [8]: the data facet (What: products and quantities to deliver), the economical facet (How much: cost) and the lead time facet (When: deliver dates). Example 1. Let the objective be expressed by: delivering 20 aluminium coils, in a delay of a week, for 200 monetary units. The products. A product is seen through a set of properties. Each property is composed of an attribute and a domain of values associated to this attribute. A ¯ow function allows to associate quantities to vectors of articles (products + quantity article). Example 2. Concerning the objective previously described, the products are the coils, the aluminium sheets, etc. The operation and the orders. An operation transforms a set of input products into a set of output products. Every operation can therefore be interpreted as a transformation of unitary input product ¯ows into unitary output product ¯ows. Quantities associated to operations are de®ned by linear combinations of unitary ¯ows relative to articles (i.e. products + units) of the set of products. The order function allows to associate quantities of output products to the set of quanti®ed input products of an operation. Example 3. The operation which is necessary to produce aluminium coils is the laminating (cooling) one, by using orders such as: 10 tons of aluminium provide 10 sheets. The actors. An actor is an individual being within the company able to enact a certain number of operations using his competence and abilities. Example 4. The competence required to produce the coils is the laminating know-how. The means. Means can be physical, informational or economic resources. We de®ne a means with the help of a set of properties and services.
Fig. 2. The activity components.
Example 5. Laminating requires a ¯attening mill (service) whose velocity is 5 m/s.
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2.2. Behaviour aspect of the process From the behaviour point of view, the process enactment can be seen through an executed sequence of activities [5,9]. This sequence is realised through exchange relationships between customers and suppliers, who are in fact the actors of the activities. During its execution, the operating activity is ``communicating'' with the process control through messages. The availability of the product to be transformed, the means to use and the given actors are controlled. The customer actor gives his objectives in terms of the transformation of products to the supplier actor, the latter controls the availability of the products and the means, and transforms the products by using an operation. Then the supplier gives the results back to the customer and the latter evaluates the performances with his initial objectives. 3. The communication between the control and the operating processes The exchanges (or communication) between the operating process and the control one are realised through a message sending system, thanks to the control points. The control points are located on the product ¯ows and allow (see Fig. 3): · to act on these ¯ows by sending messages coming from the activities related to the control processes, e.g. launching of an operating activity, · to acquire the information as messages coming from indicators related to activities operating on these ¯ows. Therefore, with this control concept, it is possible to de®ne the dierent relationships (or communications) with the control process for each operating activity related to the product. The de®nition of the control points makes the de®nition of particular control characteristics in a modelling easier. For example, during the de®nition of a moulding operating activity, the de®nition of the particular monitoring of the moulding operating activity will be considered (management rules, information acquisition, etc.). The following sec-
Fig. 3. The control point concept.
tions deal particularly with the control process modelling. 4. The control approach 4.1. The dierent natures of control The control of a manufacturing process can be distinguished by its nature. There are, in this sense, two kinds of control related to the manufacturing processes: · the economical control, · the technical control. The activities related to the economical control are executed in order to de®ne the organisation of the operating processes according to the dierent data linked to the previsional management on the one hand, and the analysis of the operating process enactment data on the other hand. The driven actions have to de®ne or recon®gure the processes to be started, or to eventually modify the global objectives. The economical control partitions the means available with the aim of evaluating and improving the technical control. The technical control, which illustrates the traditional phases of planning and command, has to check that operating, as well as improving and recon®guring the production is satisfactory. It
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involves monitoring and starting, i.e. everything that has to be done in order to reach the objectives de®ned at a higher level (i.e. the realisation of the operating processes involved). The choices issued from the planning consist in changing the operating process activities locally without changing the global objectives (by using the data coming from the dierent considered indicators). 4.2. The control functions With regard to the customer/supplier relationships (see Fig. 1), the control process ®nalities can be divided into: · satisfying the external performance, · managing the internal performance of the company. With regard to this approach, or any approach based on processes [5], it is important to notice that this process approach does not provide a decisional framework. This framework is important in order to organise and enact the processes. So, it is important to associate a functional framework to the classical process approach. Nevertheless, we refer to several functional frameworks as for example the GIM approach [10]. This framework is useful for the de®nition of the dierent functions used at the dierent levels of the control, such as the scheduling, the launching, etc. In this study, the dierent functions considered are de®ned according to the distinction between internal and external performances. The functions related to the external performances. These functions are directly concerned with the customer needs and order treatments (internal or external customers), or related to the availability of the process resources (internal or external suppliers). Among these functions, we consider the following. · The management of the resources, with regard to supplier aspects. The function objectives will be to send information concerning the availability of resources (actors, products, technical data, manufacturing orders, means management, etc.) to the control activities. · The company logistics management, which is de®ned from the supplier point of view. These
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functions will allow to manage all the customer's needs (previsional orders, sales administration, methods (interaction with the customer), delivery, etc.). The functions related to the management of the internal performances. Concerning internal performances, we consider the following functions. · the scheduling and launching function (close to the production system), which propose a launching order of operating activities under several constraints (customers, resources, drifts on ¯ows, etc.). · the monitoring function, which is able to measure the evolution of the processes with regard to the objectives of each operating activity on the one hand, and the global objective of the process on the other hand. This set of functions is used by the control process in order to design, enact and control one or several operating processes. These functions can be called at any moment in the process enactment. The table given on Fig. 4 summarises each of these functions, with regard to the technical and the economical levels of control. According to this general description of the control approach, let us now apply our generic process approach to the description of the control process parameters. 5. The control process modelling The control activity de®nition is based on the de®nition of the generic activity. In the following section, we are going to describe the dierent components of the control activities, as seen before, i.e. products (information), means (multicriteria DSS and performance indicators), actors (production supervisor or interactive decision support systems), and ®nally the procedure to implement the proposed model. 5.1. The control activities products This set of data (products) is taken into account by the dierent functions of the two types of control processes (both economical and technical, see
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Fig. 4. The link between the functions and the level control levels.
Section 4) [11]. These data come from several departments of the company (see Fig. 5). 5.2. The control operations The proposed description of the control operations is non-exhaustive and depends on the company typology (with or without scheduling, just in time production, etc.). Nevertheless, we can distinguish: · the operations linked to the economical control, such as: ± previsional order handling, EDI, etc., ± manufacturing resource planning, ± manufacturing order description for the technical control type, ± control (monitoring) of the manufacturing order execution (operating processes), ± reacting to the drift vs. the ®nality of the process, ± searching for a solution in order to reach the ®nality (by ± creating new operating activities), etc. · the operations linked to the technical control, such as: ± scheduling of the manufacturing orders, ± searching for and evaluating an organisation in terms of resources, ± control of the real feasibility, ± launching of the operating activities,
± analysis of indicator data (drift), ± searching of corrective activities (without changing the global objective). 5.3. The control means The control process means are constituted by the process indicators on the one hand, and the multicriteria DSS on the other hand. The performance indicators. The performance indicators allow the observation and the reporting of what is happening during the operating process enactment. They are associated to the objectives assigned to the operating activities. In this approach, the dierent stages of performance assessment are integrated in three facets [12] (see Fig. 6): · the expression of the objective to realise, · the acquisition and comparison of the eected measure with the objective, · the appreciation of the acquired measure in accordance with the context and the know-how of the observer. While the evaluation facet elaborates the performance related to the objective, the appreciation facet is more appropriate for the control. Indeed, on the one hand, this facet proposes an estimation of the validity of the eected measures. When the validity is weak, a new assessment which really represents the result of the activity will be made, rather than an analysis of the operating activity.
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Fig. 5. The control products.
Fig. 6. The model of performance indicators.
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On the other hand, the appreciation of the evaluated performance gives some useful information which makes the analysis of the decisions to react to the drifts easier. Actually, the evaluated performance is appreciated with regard to (often by the means of sets of rules): · the context and the conditions of the execution of the operating activity, · the repercussions of the reached performance on the products to be delivered and the execution of the other operating activities; and consequently on the cost, the lead time and the quality of the realisation of the objective, · the trends of the evaluated performance. These facets are formalised into a performance indicator model, which is particularly dedicated to the control. Indeed, the conventional use of the performance indicators was limited to the veri®cation of the productivity of the resources or the assessment of the workmanship. The performance indicator as it is de®ned here is essentially a support system for reactive control, diagnosis and monitoring [12]. Moreover, the use of the indicators for a reactive control is expressed by the setting of a system of indicators, in coherence with the objectives and the performance drivers of the considered operating process. The key idea of the procedure used in this sense is the necessity of the deployment of the global objective of the processes on the dierent considered activities, in order to ensure a global and distributed view and also a traceability of the performance [12]. The DSS modules. The indicator system has to indicate how the objectives have been realised and what in the operating activities has caused the gap between the real performances and the expected ones. These pieces of information give a support to the search for piloting actions (often by the means of sets of rules). At this level, according to his know-how, the supervisor has to de®ne the set of alternatives which could be considered as reactions to the problem identi®ed. As regards the selected alternatives, we choose a multicriteria algorithm in order to take a particular context at the moment of the decision into account. This multicriteria approach [13,14] allows us to compare the dierent alternatives concerning con¯ictual criteria. Thus, the user will be able to choose
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Fig. 7. The multicriteria DSS.
a good compromise by changing weights, add new alternatives, etc. In our approach, the evaluation will be eected according to each criteria by successive simulations (see Fig. 7). So, for a given problem, dierent solutions will be selected, according
to the chosen weighting for a given context (cost reduction strategy, increase of quality, etc.). According to the two types of control, the multicriteria DSS will not, obviously have the same functionality for the dierent users (see Fig. 8).
Fig. 8. The control types and the associated DSS.
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For the economical type, the user will be able to simulate several strategies, by changing the weights, testing new alternatives, etc. in order to design optimised operating processes and control processes. For the technical control aspect, the lead time to react is shorter and the interaction between the DSS and the users is reduced. An example of the use of a DSS for the technical control is given in Section 6. 5.4. The control process actors The actors involved in a control process can be company pilots or interactive DSS. This parameter is not studied here. 5.5. The modelling procedure According to this description of the control process, we have now to describe the modelling procedure which allows to model the production processes easily and structurally. The modelling phase of the control and operating processes can be described through dierent steps gathered in two analyses. · The analysis of the physical ¯ows in the production system by using a user-friendly graphic representation [15]. This representation allows an easy modelling of deliverable ¯ows, the transformations on these ¯ows and the control points on these ¯ows. The latter represent the exchanges between the physical ¯ows and the information and decisions ¯ows. · The analysis of the control activities and the performance indicators associated to each control point vs. the objectives (monitoring activities and launching activities) at the technical and economical levels. 6. Industrial application 6.1. Context The application being considered concerns one metallurgic process. The Pechiney Rhenalu Com-
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pany produces several articles from aluminium and has a production system characterised by a lot of internal constraints as the capacity of thermal furnace, the constraints of succession of the dierent products, the minimal economical lot sizing, the big variety of required products, etc. The objective for this company is to give a coherent cost and delay to the customers with regard to the various constraints. Our collaboration with this company consists in modelling the production processes (operating and control processes). On each activity, the performance indicators will give by simulation a performance evaluation in terms of the realisation of the objectives related to the cost and the delay. By using the multicriteria DSS, the user is able to change the weighting on the costs or on the delays (they depend on the customers) and simulate the impact in terms of cost and delay. These simulations are done by using real time information coming from the databases of the production software (scheduler and monitoring). Thus, the proposed simulator allows the integration of a previsional order (by simulation) in a real context in order to give a coherent price and delay to the customer. 6.2. Modelling steps through an example During the ®rst step, the production system is described in terms of physical means, actor competence operations, products, etc. The second step concerns the description of the whole product ¯ow circulation, the transformation on the products, the stocking point and the control points. This step is very close to the consultancy tasks that evaluate the company potential. This representation (see Fig. 9) must allow us to describe any manufacturing order in terms of operations on ¯ows. The realisation of a manufacturing order will represent a particular path on this graph. The third step consists in describing the control strategy by using the same concepts and tools as the physical ¯ows (see Fig. 10). For this example, we will de®ne a strategy which consists in the treatment of several control activities like the order
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Fig. 9. Description of product ¯ow circulation.
treatments (fax, EDI), manufacturing order preparation, scheduling, resource availability control, means, actor and product reservation, launching and controlling of the operating activities, etc. The fourth step consists in specifying the control processes. We specify in detail the dierent control activities by describing the competence needed for the actors, the information needed and the deliverable information, the dierent rules used in the DSS (rules for product replacement, alternative operations, etc. default weighting). This step is very important ± especially for the speci®cation of the ``launch and control'' activities. The tool requires us to describe each action on the physical ¯ows in terms of associated components of the control activities (DSS rules, indicators, etc.). The Fig. 11 and the Fig. 12 (see on next page) represents the design steps of these control activities. We can see the general description of the activity (objective, actor, means, ¯ows of information) and the description of the means in terms of a multicriteria decision support system (substitution of products, means, actors, weighting) and the description of the indicators.
Fig. 10. Example of control strategy diagram.
The ®fth and last step will enact the control process, and therefore enact the operating process. During the execution of the operating activities, a control interface allows the actors to give information concerning the state of the dierent components of the operating activity. This information is then used by the multicriteria resolution problem module. This module will create and launch a new operating activity from this information and a set of rules and possible actions in order to react to a malfunctioning. Let us consider, for example, the operating activity ``laminating'' and the associated control point and control activity. During the operating activity execution, the L01 means (see Fig. 9) is out of order. This information is given to the control activity through the control point. By using the de®ned rules in the associated DSS, we obtain a set of alternatives, such as reparing the machine or replacing it, etc. The weighting of the multicriteria matrix (which handles the criteria, the alternatives, the
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Fig. 11. Launch and control activity design (DSS aspect).
Fig. 12. Launch and control activity design (indicators aspect).
dierent weights and the dierent evaluations of the alternatives with regard to the criteria) will depend on the context at the moment of the decision
making (see Fig. 13). Let us consider that the delay is the most important criterion vs. the customer satisfaction. Therefore, the weighting will be for
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Fig. 13. Multicriteria matrix.
example: 1:3:1. The cost, the delay and the quality are evaluated for each alternative by using the company databases (maintenance, quality, etc.). Once this matrix is de®ned, a multicriteria algorithm (Promethee II) is used to make a ranking of these alternatives. If the pilot has the time to make a decision concerning the process enactment, he will be able to change the criteria, the weighting, and also to evaluate other alternatives, etc. By using this matrix, the ranking will indicate the second alternative which is better than the others. So, the control activity will create a new operating activity with L03 means and will execute this new activity. 7. Conclusion According to this approach, the modelling of the control process is more structured and coherent than the classical simulation approach for control modelling. Based on both a process approach and a functional approach, it allows us to model both the operating processes and the control processes by using the same model. It allows the user to take the two following objectives into account: ± satisfying the internal and external customer/ supplier relationship, ± optimising the internal performance. This modelling approach of both the control processes and the relationships with the operating processes gives a real control structure. The modi®cation on the control processes, in order to simulate the impact of the control process on the operating processes is in this sense more visible and user-friendly than the classical simulation tools.
The multicriteria DSS allows the users to make a contextualised choice by taking into account opposite criteria (cost, delay, quality) in a particular context of decision (such as the reduction of costs and the increase of quality). The design stage has been realised by using OMT analysis concepts. The implementation of the process generic model is currently developed with Delphia Object Modeller, a SLIGOS (ATOS) product. For the indicators model, an implementation in C++ has to be done. The indicator software is currently being interfaced with the generic process model. Moreover, experiments are being made now at the Pechiney company in order to improve the control process, notably through a capitalisation of the operators' know-how, an improvement of the choice of the process indicators, etc. References [1] M. Bakalem, R. Dindeleux, G. Habchi, A. Haurat, Proposal for a simulation model integrating a hierarchical and multicriteria control model, Eurosim 95, Vienna, Austria, 1995. [2] G. Bel, J.B. Cavaille, Integration of simulation within production systems design: Advantages and dangers of the object-oriented language, in: Proceedings of CIM'90, Bordeaux, France, June 1990, pp. 597±603. [3] J. Bernad, in: Masson (Ed.), Approche Systemique de L'entreprise et de Son Informatisation, Paris, France, 1992. [4] L. Berrah, G. Mauris, A. Haurat, L. Foulloy, Fuzzy performance indicators for ski quality control, IEEE Instrumentation and Measurement Technology Conference, Brussels, Belgium, 4±6 June 1996, pp. 1275±1280. [5] E. Dindeleux, Proposition d'un systeme interactif d'aide a la conduite d'atelier, These de doctorat, Universite de Valenciennes et du Hainault Cambresis, France, 1992. [6] P.H. Feiler, W.S. Humphrey, Software process development and enactment, Technical report SEI 92-TR-4, Cernegie Mellon University, Pittsburgh, PA, 1992. [7] L. Fortuin, Performance indicators ± Why, where and how? Eur. J. Oper. Res. 34 (1988) 11±20. [8] R. Guetari, F. Piard, From the Speci®cation to the design of an industrial information system: The Olympios model, IEEE conference on System Man and Cybernetics, San antonio, TX, 24 October, 1994. [9] R.S. Kaplan, D.P. Norton, The balanced scorecard ± Measures that drive performances, Harvard Business Review, January±February (1992) 71±79. [10] K. Kosanke, M. Mollo, F. Naccari, C. Reyneri, Enterprise Engineering with CIMOSA ± Application at FIAT, in:
R. Dindeleux et al. / European Journal of Operational Research 109 (1998) 377±389 Integrated Manufacturing Systems Engineering, Chapman & Hall, London, 1995. [11] J.G. March, A.H. Simon (Eds.), Organizations, Wiley, New York, 1958. [12] B. Roy, D. Bouyssou (Eds.), Aide Multicritere a la Decision: Methodes et cas, Economica, France, 1993. [13] D. Trentesaux, R. Dindeleux, C. Tahon, A multicriteria decision support system for dynamic task allocation in a
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distributed production activity control structure, IMSE 94, 1994. [14] B. Vallespir, C. Merle, G. Doumeingts, GIM: A technicoeconomic methodology to design manufacturing systems, Control, Engineering Practice 1 (6) (1993) 1031±1038. [15] F. Vernadat, Enterprise Modelling and Integration ± Principles and Applications, Chapman & Hall, 1996.
A formal modelling of control processes Regis Dindeleux a
a,b,*
, Lamia Berrah
a,c
, Alain Haurat
a
Laboratoire de Logiciels pour la Productique (LLP-CESALP), 41, Avenue de la plaine, 74016 Annecy Cedex, France b ATOS 1 Avenue Newton, Clamart, France c IPI-ENSGI-INPG, 46, Avenue F elix Viallet, 38031 Grenoble Cedex 1, France Received 1 March 1997
Abstract This study concerns the performance management of manufacturing processes. Such processes are decomposed into two types: the operating processes and the control ones. While an operating process has to realise the deliverable products, a control one is designed to plan, control and supervise the execution of the activity of the operating processes. In this sense, one approach of control is discussed. The proposed model is based on the exchange relation concept between a customer and a supplier. The dierent control parameters are de®ned from this point of view, such as the activities assigned objectives and the performance indicators associated with them, the control activities and their functions, the control means... this for both the technical and economical levels of control. These ideas are applied to some problems encountered in manufacturing processes. In particular, aluminium metallurgy processes are considered. The performance of these processes are driven by many parameters such as the quality of the raw materials, the conformity of the tools used, the elements to assemble, the know-how of the operators. Ó 1998 Elsevier Science B.V. All rights reserved. Keywords: Control; Simulation; Formal process modelling; Multicriteria decision support systems performance indicators
1. Introduction ± Context Manufacturing companies have to deal with a strong competition between comparable manufactured products and exacting customers. Their survival depends, on a ®rst level, on an external performance which is measured by the customer
*
Corresponding author. Fax: 33 50 66 60 20; e-mail: [email protected]. 0377-2217/98/$19.00 Ó 1998 Elsevier Science B.V. All rights reserved. PII S 0 3 7 7 - 2 2 1 7 ( 9 8 ) 0 0 0 6 4 - 2
satisfaction. Then, this satisfaction is translated into an internal performance, essentially characterised by a quick reacting to changing demands, a continuous improvement of the quality and the delivery of the products, a high productivity of the resources, etc. leading thus to cost reductions. To satisfy the new requirements of competitiveness and performance, traditional management systems which are based on functional organisations and ®nancial control have quickly shown their limits. Companies nowadays turn towards a
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management by processes ± in contrast to a traditional functional one ± based on technical performance indicators which directly measure the process enactment [1,2]. Moreover, decision support systems which are able to react to any event, such as malfunctioning, urgent orders, etc. are needed. Moreover, simulation can be useful in the area of the decision support for the analysis, design and exploitation of manufacturing processes. This explains that simulation tools [3,4] currently proposed on the market are more and more eective and user-friendly. However, these tools essentially concern the modelling of the operating processes. These tools do not yet allow to model and simulate in a coherent and simple manner the control processes and their connections with the operating systems [4]. This lack also limits the analysis of the impact of the control driven on the performances of the operating processes. Indeed, often at the technical level, decisions must be taken with regard to more than one criterion. The problem is how to select the alternative which best satis®es all the criteria involved in the decision system. Multicriteria decision supports are very helpful in this context. However they must be handled by models which allow a simulation of what would happen for each considered alternative. This study focuses particularly on the modelling ± for the simulation ± of manufacturing processes. The enactment of a manufacturing process is faced with many kinds of malfunctioning, drifts and other uncertainties (e.g. machine failure), being the sources of non-performances, such as delays on the deliveries, losses with regard to global manufacturing capacities, intermediary inventories. Therefore, it is advisable to supervise and control the processes, by using a decision support system able to quickly detect, diagnose and then react to any malfunctioning. The detection step must be necessarily supported by performance indicators. These indicators allow the observation and the reporting of what is happening during the operating process enactment. Whenever one drift is detected by any performance indicator, supervisors can immediately react, rather than passively wait for the subsequent ®nancial evaluation. But often, for one detected malfunc-
tioning, more than one decision can be eventually taken with regard to more than one parameter and more than one constraint. This is the problematics of every control system. Moreover, knowing that a process can be seen as ``a set of partially ordered set of activities executed to realise a given ®nality of or within a company'' [5], its control naturally concerns the enactments of the activities. More precisely, each activity in a process is seen either as a supplier to the next one and or as a customer to the previous one [2,6]. From this point of view, two kinds of performances are distinguished for any activity [5] (see Fig. 1). · An external performance which illustrates the realisation of the objectives expressed by the customer (delivery, quality, quantity, etc.); this notion extends the customer satisfaction concept to all the activities of the company. · An internal performance, related to the execution of the dierent activities (productivity, etc.). In this context, the control problem can be summarised in the following points: · What are the external and internal objectives (i.e. the external and the internal performances to reach)? · How to realise these objectives? · How to control the enactment of the activities concerned by the assigned objectives (monitoring and action)? While the answer to the ®rst question is provided by strategic and economic analyses, the two others precisely concern the industrial (technical) control. In this context we propose a modelling approach of the manufacturing processes. This approach is based on a generic model of the process, which is
Fig. 1. External and internal performances of an industrial activity.
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speci®ed according to the kind of process (operating or control). The links between the operating process and the control one are done by the socalled control points, which handle both the information given by the performance indicators and the control actions. 2. The industrial process modelling The ®rst step in this study concerns the industrial (operating and control) process modelling. In accordance with the CIM concepts, each entity, in particular the process, can be seen through its functionally on the one hand, and its behaviour on the other hand. Moreover, the level of details is de®ned according to the visibility that the users have. The scope of functional modelling concerns the description of the dierent activities which may occur in a company. The behaviour modelling concerns the control ¯ow within a company. 2.1. Functional aspects of the process The functional aspect can be seen through the structure of a partially ordered set of activities considered in order to realise an objective linked with manufacturing. An activity [7,5] is the association of an objective (considered as a sub-objective of the process global objective this activity is part of), a set of products, an operation, an order, the means allocated to this operation and a couple of actors. The latter is the exchange relationship between two actors, where the ®rst actor realises the activity for the second one (see Fig. 2).
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The objective. The objective can be de®ned through three facets [8]: the data facet (What: products and quantities to deliver), the economical facet (How much: cost) and the lead time facet (When: deliver dates). Example 1. Let the objective be expressed by: delivering 20 aluminium coils, in a delay of a week, for 200 monetary units. The products. A product is seen through a set of properties. Each property is composed of an attribute and a domain of values associated to this attribute. A ¯ow function allows to associate quantities to vectors of articles (products + quantity article). Example 2. Concerning the objective previously described, the products are the coils, the aluminium sheets, etc. The operation and the orders. An operation transforms a set of input products into a set of output products. Every operation can therefore be interpreted as a transformation of unitary input product ¯ows into unitary output product ¯ows. Quantities associated to operations are de®ned by linear combinations of unitary ¯ows relative to articles (i.e. products + units) of the set of products. The order function allows to associate quantities of output products to the set of quanti®ed input products of an operation. Example 3. The operation which is necessary to produce aluminium coils is the laminating (cooling) one, by using orders such as: 10 tons of aluminium provide 10 sheets. The actors. An actor is an individual being within the company able to enact a certain number of operations using his competence and abilities. Example 4. The competence required to produce the coils is the laminating know-how. The means. Means can be physical, informational or economic resources. We de®ne a means with the help of a set of properties and services.
Fig. 2. The activity components.
Example 5. Laminating requires a ¯attening mill (service) whose velocity is 5 m/s.
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2.2. Behaviour aspect of the process From the behaviour point of view, the process enactment can be seen through an executed sequence of activities [5,9]. This sequence is realised through exchange relationships between customers and suppliers, who are in fact the actors of the activities. During its execution, the operating activity is ``communicating'' with the process control through messages. The availability of the product to be transformed, the means to use and the given actors are controlled. The customer actor gives his objectives in terms of the transformation of products to the supplier actor, the latter controls the availability of the products and the means, and transforms the products by using an operation. Then the supplier gives the results back to the customer and the latter evaluates the performances with his initial objectives. 3. The communication between the control and the operating processes The exchanges (or communication) between the operating process and the control one are realised through a message sending system, thanks to the control points. The control points are located on the product ¯ows and allow (see Fig. 3): · to act on these ¯ows by sending messages coming from the activities related to the control processes, e.g. launching of an operating activity, · to acquire the information as messages coming from indicators related to activities operating on these ¯ows. Therefore, with this control concept, it is possible to de®ne the dierent relationships (or communications) with the control process for each operating activity related to the product. The de®nition of the control points makes the de®nition of particular control characteristics in a modelling easier. For example, during the de®nition of a moulding operating activity, the de®nition of the particular monitoring of the moulding operating activity will be considered (management rules, information acquisition, etc.). The following sec-
Fig. 3. The control point concept.
tions deal particularly with the control process modelling. 4. The control approach 4.1. The dierent natures of control The control of a manufacturing process can be distinguished by its nature. There are, in this sense, two kinds of control related to the manufacturing processes: · the economical control, · the technical control. The activities related to the economical control are executed in order to de®ne the organisation of the operating processes according to the dierent data linked to the previsional management on the one hand, and the analysis of the operating process enactment data on the other hand. The driven actions have to de®ne or recon®gure the processes to be started, or to eventually modify the global objectives. The economical control partitions the means available with the aim of evaluating and improving the technical control. The technical control, which illustrates the traditional phases of planning and command, has to check that operating, as well as improving and recon®guring the production is satisfactory. It
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involves monitoring and starting, i.e. everything that has to be done in order to reach the objectives de®ned at a higher level (i.e. the realisation of the operating processes involved). The choices issued from the planning consist in changing the operating process activities locally without changing the global objectives (by using the data coming from the dierent considered indicators). 4.2. The control functions With regard to the customer/supplier relationships (see Fig. 1), the control process ®nalities can be divided into: · satisfying the external performance, · managing the internal performance of the company. With regard to this approach, or any approach based on processes [5], it is important to notice that this process approach does not provide a decisional framework. This framework is important in order to organise and enact the processes. So, it is important to associate a functional framework to the classical process approach. Nevertheless, we refer to several functional frameworks as for example the GIM approach [10]. This framework is useful for the de®nition of the dierent functions used at the dierent levels of the control, such as the scheduling, the launching, etc. In this study, the dierent functions considered are de®ned according to the distinction between internal and external performances. The functions related to the external performances. These functions are directly concerned with the customer needs and order treatments (internal or external customers), or related to the availability of the process resources (internal or external suppliers). Among these functions, we consider the following. · The management of the resources, with regard to supplier aspects. The function objectives will be to send information concerning the availability of resources (actors, products, technical data, manufacturing orders, means management, etc.) to the control activities. · The company logistics management, which is de®ned from the supplier point of view. These
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functions will allow to manage all the customer's needs (previsional orders, sales administration, methods (interaction with the customer), delivery, etc.). The functions related to the management of the internal performances. Concerning internal performances, we consider the following functions. · the scheduling and launching function (close to the production system), which propose a launching order of operating activities under several constraints (customers, resources, drifts on ¯ows, etc.). · the monitoring function, which is able to measure the evolution of the processes with regard to the objectives of each operating activity on the one hand, and the global objective of the process on the other hand. This set of functions is used by the control process in order to design, enact and control one or several operating processes. These functions can be called at any moment in the process enactment. The table given on Fig. 4 summarises each of these functions, with regard to the technical and the economical levels of control. According to this general description of the control approach, let us now apply our generic process approach to the description of the control process parameters. 5. The control process modelling The control activity de®nition is based on the de®nition of the generic activity. In the following section, we are going to describe the dierent components of the control activities, as seen before, i.e. products (information), means (multicriteria DSS and performance indicators), actors (production supervisor or interactive decision support systems), and ®nally the procedure to implement the proposed model. 5.1. The control activities products This set of data (products) is taken into account by the dierent functions of the two types of control processes (both economical and technical, see
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Fig. 4. The link between the functions and the level control levels.
Section 4) [11]. These data come from several departments of the company (see Fig. 5). 5.2. The control operations The proposed description of the control operations is non-exhaustive and depends on the company typology (with or without scheduling, just in time production, etc.). Nevertheless, we can distinguish: · the operations linked to the economical control, such as: ± previsional order handling, EDI, etc., ± manufacturing resource planning, ± manufacturing order description for the technical control type, ± control (monitoring) of the manufacturing order execution (operating processes), ± reacting to the drift vs. the ®nality of the process, ± searching for a solution in order to reach the ®nality (by ± creating new operating activities), etc. · the operations linked to the technical control, such as: ± scheduling of the manufacturing orders, ± searching for and evaluating an organisation in terms of resources, ± control of the real feasibility, ± launching of the operating activities,
± analysis of indicator data (drift), ± searching of corrective activities (without changing the global objective). 5.3. The control means The control process means are constituted by the process indicators on the one hand, and the multicriteria DSS on the other hand. The performance indicators. The performance indicators allow the observation and the reporting of what is happening during the operating process enactment. They are associated to the objectives assigned to the operating activities. In this approach, the dierent stages of performance assessment are integrated in three facets [12] (see Fig. 6): · the expression of the objective to realise, · the acquisition and comparison of the eected measure with the objective, · the appreciation of the acquired measure in accordance with the context and the know-how of the observer. While the evaluation facet elaborates the performance related to the objective, the appreciation facet is more appropriate for the control. Indeed, on the one hand, this facet proposes an estimation of the validity of the eected measures. When the validity is weak, a new assessment which really represents the result of the activity will be made, rather than an analysis of the operating activity.
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Fig. 5. The control products.
Fig. 6. The model of performance indicators.
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On the other hand, the appreciation of the evaluated performance gives some useful information which makes the analysis of the decisions to react to the drifts easier. Actually, the evaluated performance is appreciated with regard to (often by the means of sets of rules): · the context and the conditions of the execution of the operating activity, · the repercussions of the reached performance on the products to be delivered and the execution of the other operating activities; and consequently on the cost, the lead time and the quality of the realisation of the objective, · the trends of the evaluated performance. These facets are formalised into a performance indicator model, which is particularly dedicated to the control. Indeed, the conventional use of the performance indicators was limited to the veri®cation of the productivity of the resources or the assessment of the workmanship. The performance indicator as it is de®ned here is essentially a support system for reactive control, diagnosis and monitoring [12]. Moreover, the use of the indicators for a reactive control is expressed by the setting of a system of indicators, in coherence with the objectives and the performance drivers of the considered operating process. The key idea of the procedure used in this sense is the necessity of the deployment of the global objective of the processes on the dierent considered activities, in order to ensure a global and distributed view and also a traceability of the performance [12]. The DSS modules. The indicator system has to indicate how the objectives have been realised and what in the operating activities has caused the gap between the real performances and the expected ones. These pieces of information give a support to the search for piloting actions (often by the means of sets of rules). At this level, according to his know-how, the supervisor has to de®ne the set of alternatives which could be considered as reactions to the problem identi®ed. As regards the selected alternatives, we choose a multicriteria algorithm in order to take a particular context at the moment of the decision into account. This multicriteria approach [13,14] allows us to compare the dierent alternatives concerning con¯ictual criteria. Thus, the user will be able to choose
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Fig. 7. The multicriteria DSS.
a good compromise by changing weights, add new alternatives, etc. In our approach, the evaluation will be eected according to each criteria by successive simulations (see Fig. 7). So, for a given problem, dierent solutions will be selected, according
to the chosen weighting for a given context (cost reduction strategy, increase of quality, etc.). According to the two types of control, the multicriteria DSS will not, obviously have the same functionality for the dierent users (see Fig. 8).
Fig. 8. The control types and the associated DSS.
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For the economical type, the user will be able to simulate several strategies, by changing the weights, testing new alternatives, etc. in order to design optimised operating processes and control processes. For the technical control aspect, the lead time to react is shorter and the interaction between the DSS and the users is reduced. An example of the use of a DSS for the technical control is given in Section 6. 5.4. The control process actors The actors involved in a control process can be company pilots or interactive DSS. This parameter is not studied here. 5.5. The modelling procedure According to this description of the control process, we have now to describe the modelling procedure which allows to model the production processes easily and structurally. The modelling phase of the control and operating processes can be described through dierent steps gathered in two analyses. · The analysis of the physical ¯ows in the production system by using a user-friendly graphic representation [15]. This representation allows an easy modelling of deliverable ¯ows, the transformations on these ¯ows and the control points on these ¯ows. The latter represent the exchanges between the physical ¯ows and the information and decisions ¯ows. · The analysis of the control activities and the performance indicators associated to each control point vs. the objectives (monitoring activities and launching activities) at the technical and economical levels. 6. Industrial application 6.1. Context The application being considered concerns one metallurgic process. The Pechiney Rhenalu Com-
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pany produces several articles from aluminium and has a production system characterised by a lot of internal constraints as the capacity of thermal furnace, the constraints of succession of the dierent products, the minimal economical lot sizing, the big variety of required products, etc. The objective for this company is to give a coherent cost and delay to the customers with regard to the various constraints. Our collaboration with this company consists in modelling the production processes (operating and control processes). On each activity, the performance indicators will give by simulation a performance evaluation in terms of the realisation of the objectives related to the cost and the delay. By using the multicriteria DSS, the user is able to change the weighting on the costs or on the delays (they depend on the customers) and simulate the impact in terms of cost and delay. These simulations are done by using real time information coming from the databases of the production software (scheduler and monitoring). Thus, the proposed simulator allows the integration of a previsional order (by simulation) in a real context in order to give a coherent price and delay to the customer. 6.2. Modelling steps through an example During the ®rst step, the production system is described in terms of physical means, actor competence operations, products, etc. The second step concerns the description of the whole product ¯ow circulation, the transformation on the products, the stocking point and the control points. This step is very close to the consultancy tasks that evaluate the company potential. This representation (see Fig. 9) must allow us to describe any manufacturing order in terms of operations on ¯ows. The realisation of a manufacturing order will represent a particular path on this graph. The third step consists in describing the control strategy by using the same concepts and tools as the physical ¯ows (see Fig. 10). For this example, we will de®ne a strategy which consists in the treatment of several control activities like the order
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Fig. 9. Description of product ¯ow circulation.
treatments (fax, EDI), manufacturing order preparation, scheduling, resource availability control, means, actor and product reservation, launching and controlling of the operating activities, etc. The fourth step consists in specifying the control processes. We specify in detail the dierent control activities by describing the competence needed for the actors, the information needed and the deliverable information, the dierent rules used in the DSS (rules for product replacement, alternative operations, etc. default weighting). This step is very important ± especially for the speci®cation of the ``launch and control'' activities. The tool requires us to describe each action on the physical ¯ows in terms of associated components of the control activities (DSS rules, indicators, etc.). The Fig. 11 and the Fig. 12 (see on next page) represents the design steps of these control activities. We can see the general description of the activity (objective, actor, means, ¯ows of information) and the description of the means in terms of a multicriteria decision support system (substitution of products, means, actors, weighting) and the description of the indicators.
Fig. 10. Example of control strategy diagram.
The ®fth and last step will enact the control process, and therefore enact the operating process. During the execution of the operating activities, a control interface allows the actors to give information concerning the state of the dierent components of the operating activity. This information is then used by the multicriteria resolution problem module. This module will create and launch a new operating activity from this information and a set of rules and possible actions in order to react to a malfunctioning. Let us consider, for example, the operating activity ``laminating'' and the associated control point and control activity. During the operating activity execution, the L01 means (see Fig. 9) is out of order. This information is given to the control activity through the control point. By using the de®ned rules in the associated DSS, we obtain a set of alternatives, such as reparing the machine or replacing it, etc. The weighting of the multicriteria matrix (which handles the criteria, the alternatives, the
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Fig. 11. Launch and control activity design (DSS aspect).
Fig. 12. Launch and control activity design (indicators aspect).
dierent weights and the dierent evaluations of the alternatives with regard to the criteria) will depend on the context at the moment of the decision
making (see Fig. 13). Let us consider that the delay is the most important criterion vs. the customer satisfaction. Therefore, the weighting will be for
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Fig. 13. Multicriteria matrix.
example: 1:3:1. The cost, the delay and the quality are evaluated for each alternative by using the company databases (maintenance, quality, etc.). Once this matrix is de®ned, a multicriteria algorithm (Promethee II) is used to make a ranking of these alternatives. If the pilot has the time to make a decision concerning the process enactment, he will be able to change the criteria, the weighting, and also to evaluate other alternatives, etc. By using this matrix, the ranking will indicate the second alternative which is better than the others. So, the control activity will create a new operating activity with L03 means and will execute this new activity. 7. Conclusion According to this approach, the modelling of the control process is more structured and coherent than the classical simulation approach for control modelling. Based on both a process approach and a functional approach, it allows us to model both the operating processes and the control processes by using the same model. It allows the user to take the two following objectives into account: ± satisfying the internal and external customer/ supplier relationship, ± optimising the internal performance. This modelling approach of both the control processes and the relationships with the operating processes gives a real control structure. The modi®cation on the control processes, in order to simulate the impact of the control process on the operating processes is in this sense more visible and user-friendly than the classical simulation tools.
The multicriteria DSS allows the users to make a contextualised choice by taking into account opposite criteria (cost, delay, quality) in a particular context of decision (such as the reduction of costs and the increase of quality). The design stage has been realised by using OMT analysis concepts. The implementation of the process generic model is currently developed with Delphia Object Modeller, a SLIGOS (ATOS) product. For the indicators model, an implementation in C++ has to be done. The indicator software is currently being interfaced with the generic process model. Moreover, experiments are being made now at the Pechiney company in order to improve the control process, notably through a capitalisation of the operators' know-how, an improvement of the choice of the process indicators, etc. References [1] M. Bakalem, R. Dindeleux, G. Habchi, A. Haurat, Proposal for a simulation model integrating a hierarchical and multicriteria control model, Eurosim 95, Vienna, Austria, 1995. [2] G. Bel, J.B. Cavaille, Integration of simulation within production systems design: Advantages and dangers of the object-oriented language, in: Proceedings of CIM'90, Bordeaux, France, June 1990, pp. 597±603. [3] J. Bernad, in: Masson (Ed.), Approche Systemique de L'entreprise et de Son Informatisation, Paris, France, 1992. [4] L. Berrah, G. Mauris, A. Haurat, L. Foulloy, Fuzzy performance indicators for ski quality control, IEEE Instrumentation and Measurement Technology Conference, Brussels, Belgium, 4±6 June 1996, pp. 1275±1280. [5] E. Dindeleux, Proposition d'un systeme interactif d'aide a la conduite d'atelier, These de doctorat, Universite de Valenciennes et du Hainault Cambresis, France, 1992. [6] P.H. Feiler, W.S. Humphrey, Software process development and enactment, Technical report SEI 92-TR-4, Cernegie Mellon University, Pittsburgh, PA, 1992. [7] L. Fortuin, Performance indicators ± Why, where and how? Eur. J. Oper. Res. 34 (1988) 11±20. [8] R. Guetari, F. Piard, From the Speci®cation to the design of an industrial information system: The Olympios model, IEEE conference on System Man and Cybernetics, San antonio, TX, 24 October, 1994. [9] R.S. Kaplan, D.P. Norton, The balanced scorecard ± Measures that drive performances, Harvard Business Review, January±February (1992) 71±79. [10] K. Kosanke, M. Mollo, F. Naccari, C. Reyneri, Enterprise Engineering with CIMOSA ± Application at FIAT, in:
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distributed production activity control structure, IMSE 94, 1994. [14] B. Vallespir, C. Merle, G. Doumeingts, GIM: A technicoeconomic methodology to design manufacturing systems, Control, Engineering Practice 1 (6) (1993) 1031±1038. [15] F. Vernadat, Enterprise Modelling and Integration ± Principles and Applications, Chapman & Hall, 1996.