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GLOBAL MODELLING OF AN AUTOMOTIVE INDUSTRY SUPPLY CHAIN APPLIED TO PRODUCTION MANAGEMENT DECISION-SUPPORT
IFAC MCPL 2007 The 4th International Federation of Automatic Control Conference on Management and Control of Production and Logistics September 27-30, Sibiu - Romania
GLOBAL MODELLING OF AN AUTOMOTIVE INDUSTRY SUPPLY CHAIN APPLIED TO PRODUCTION MANAGEMENT DECISION-SUPPORT Fabien Petitjean 1, 2, Patrick Charpentier , Jean-Yves Bron 1, Alexandre Villeminot 2 1
1
Centre de Recherche en Automatique de Nancy UMR 7039 CRAN – Faculté des Sciences BP 239 F-54506 Vandoeuvre-lès-Nancy, France [email protected] 2
PSA Peugeot-Citroën Site de Sochaux Case courrier SX3 S53.01 57 avenue du Général Leclerc F-25600 Sochaux, France [email protected]
Abstract: Automotive industry is nowadays characterised by a huge diversity of proposed models, a shorter and shorter product’s life cycle and an increasing concurrency between generalist builders. Satisfying customers implies improving Quality and reducing Costs and Lead-time. To stay economically competitive in such a context, it is compulsory to use the production system at the best. Optimising locally the production does not ensure a global performance. That is why we propose in this paper a global modelling of French car manufacturer PSA Peugeot Citroën’s Supply Chain. Such an approach allows highlighting and evaluating global impacts of local decision-making. Lastly, we will introduce a decision-support tool, dedicated to factory management. Copyright 2007 IFAC Keywords: Supply Chain Management, car industry, modelling, proactive simulation, decision-support.
1. INDUSTRIAL CONTEXT AND PROBLEMS
small and large productions (concept of “mass customization” (Anderson, 2004)) and allow a Justin-Time production’s deployment. On account of high costs when modifying infrastructures and physical flows inside the plant, several research works have been launched in order to optimise the production resources’ management and the components’ supplying.
1.1. Automotive industry characteristics. After a small-scale production beginning, car production has been a mass production during a long time, like many other industrial sectors. Rationalisation allowed to produce at high rates vehicles on assembly lines. Today, such a concept is not adequate anymore. Customers are looking for high personality models, which can be customized. Increase in concurrency lead companies to move towards an economy of variety, characterised by short product’s life-cycle. Feasible vehicles diversity has exploded according to the number of components offered to the customers. For economical reasons, these different models are built on the same assembly line, called “mixed model assembly line”. These units reduce the gap between
1.2. Research works linked to automotive production. Previous works have been classified: – Creation of a reference production list. This phase is a static and forecast problem to obtain the best vehicles sequence taking into account production shops constraints (body shop, paint shop and assembly shop) while smoothing components’ consumption (Delaval et al., 1995).
415
– Dynamic re-sequencing and rate giving during production (Bernier, 2000). The aim of this phase is to adapt the production sequence to disruptions of production flow called “Vehicles Flow” (like manufacturing reworks...) or of supply flows called “Components Flow” (components shortage, transport delayed…) (Joly, 2005). This dynamic work is done in real-time inside vehicles’ storages between the different factory shops (Muhl et al., 2003). – Assembly line balancing. According to a forecast production mix upon a determined period, assembly operations are assigned to assembly line stations. This phase is realised to respect industrial constraints (minimizing station’s number, smoothing workloads…) (Boutevin, 2003) (Lesert, 2006). – Synchronizing and tensing flows all over the components supplying. The objectives are to size the logistics accurately and to evaluate impacts of disruptions on and between the supply flows (Petitjean et al., 2004) (Villeminot, 2004). However, increasing the Supply Chain global performance does not mean optimising locally the functional domains previously described. They have to be combined to get a better cost-effectiveness, without disturbing the enterprise structure, so that, it is possible to consider numerous and complicated interactions within the Supply Chain.
Economically and qualitatively, the envisaged solution is not always the best. To manage the Supply Chain more efficiently, it is crucial to get a global perception of the system’s state. Also, we propose to evaluate interactions inside the automotive manufacturer’s Supply Chain, considered globally. It requires firstly modelling globally the Supply Chain in order to point out the mechanisms between distinctive perimeters.
2. PROPOSAL OF A SUPPLY CHAIN’S GLOBAL MODELLING METHODOLOGY
2.1. Enterprise audit based on “Organicube”. Decision process aims at analysing and modelling a situation in order to highlight elements leading to decision-making (Claver et al., 1997). All alternatives and their consequences have to be identified. In that sense, different models exist; they give either a general enterprise’s image or a precise vision by concentrating on a limited area. Then these models allow gathering information about enterprise to describe it textually or graphically. Different referent architectures (CIMOSA, GRAI, GERAM), reference models linked to interoperability (Zachman’s Framework, MDA, Safira) and to resource’s allocation (TEAF matrix, DoDAF model) have been examined. These modelling techniques allow formalising knowledge to operate it by a decision-support tool. Most of existing models have their own particular enterprise or studied system vision. A macro-model based on those, “Organicube” (Kowalski, 2006), has the advantage to describe the enterprise and its surrounding areas in different ways: quality, performance, human and material resources, production system etc. (Fig. 2). Moreover this concept proposes also to follow a thread, allowing a coherent and full audit.
1.3. Necessity of a more global vision on interactions inside the Supply Chain. Inside the factories, called Manufacturing Plants, decisions are made every day, among several boundaries. Besides their expertise, local decisional centres can base their judgement on existing tools resulting from previous works. These tools allow evaluating local impacts of hazards or of a decision concerning the Supply Chain Management. However, each action has impacts on surrounding areas; also it is difficult to value those. For instance (Fig. 1), solving a forecasted supply problem (1) by delaying the production of vehicles consuming these components (2) can cause other hazards. These new issues can be a vehicles’ storage stoppage (3), components shortages on other references over-consumed (4) or even an overworkload on assembly line stations (5) which can bring to vehicles’ reworks after the line…
Fig. 2. Organicube, structure and thread. Organicube allows a more global approach, which makes it more suited to decision’s and interaction’s analysis. That is why we have chosen to base on for a Supply Chain’s audit by PSA Peugeot Citroën.
Fig. 1. Production’s potential fall after a hazard.
416
After that, it is necessary to highlight and understand mechanisms between distinctive operational perimeters, in other words formalizing interactions from data collected during audit.
3. APPLICATION TO THE MODELLING OF A PRODUCTION POTENTIAL FALL
3.1. Supply Chain Management audit. 2.2. Pointing out decisional interactions aggregating cause-and-effect diagrams.
by
Considered perimeter, criteria analysed, collected data. We concentrate on decisional centres and on functional domains included in the perimeter from Manufacturing Plant (inside included) to its first level suppliers. Interviews are carried out inside the different domains implied in vehicles production, following the Organicube thread. From that, current Supply Chain Management’s practices are identified and actors, their role, possible hazards, possible corrections to solve those are highlighted.
It is possible to model the decision-makers thought process, or more generally actors with a certain judgement’s power, by causal diagrams (Holweg et al., 2005). A modular approach, known as “management’s interactive reticular approach”, allows treating complex problems step by step, after decomposing those. Resulting diagrams take then into account the decisions’ context (Gomez and Probst, 1987). In our study and more generally to globally model interactions inside a Supply Chain, we propose formalising local audits’ results by cause-and-effect sub-diagrams. After that, by way of aggregation, it is easy to highlight interactions’ loops, whose perception is not always so obvious. When loops are pointed out, it is necessary to precise performance indicators, procedures to be followed and concerned actors… Such information is modelled thanks to U.M.L. (Unified Modeling Language), which can also be a basis for a future computer implementation.
Actors implied in the management. The audit leads to identify these decision-makers and functional managers: – Plant Direction manages the factory and organises production planning; – Production Coordination plans and controls production flow inside the shops; – Line Balancers determine manpower to be set on assembly lines according to forecast production mix; – Inspection Manager takes on all kinds of vehicle’s reworks. It can be either on-line or off-line reworks (after assembly line); – Supply Manager follows the components storage’s levels, the orders and the transports; – Suppliers produce components in Just-in-Time, in accordance with negotiated flexibility threshold; – Transporter manages transports between Manufacturing Plant and suppliers. Supply Chain structure is today a distributed system (Fig. 3) (Thierry, 2003).
2.3. Supply Chain global modelling thanks to U.M.L. A U.M.L. model can be built on both highlighted interactions and enterprise audit. We have retained the following approach, based on U.M.L. language reference books (Roques, 2006) (Rumbaugh et al., 2006): – Use cases diagram, showing actors and main functions; – Partial class diagram, from the previous diagram, to synthesise relations between system’s constitutive entities; – Collaboration diagrams to point out exchanges between these entities; – Filling the class diagram and finalising the model. With such a validated model, it is possible evaluating interactions between decisional domains, either analytically or through simulation to quantify noncalculable impacts. We have applied this methodology to our study context and more particularly to production’s potential falls inside Manufacturing Plants. Production’s potential fall means supply shortages, temporary workload’s increase, etc. We also include in our model a global evaluation, today inexistent, to integrate inter-decisional impacts’ evaluation in the decision process linked to Supply Chain Management.
Fig. 3. Current structure: distributed system. Also, the audit allows us to list hazards disrupting system functioning. Hazards disrupting system. We distinguish the following hazards: – Supplier problem (process failure…); – Transport delayed (failure, climatic conditions…); – Storage shortage inside Manufacturing Plant; – Process failure on assembly line;
417
– Forecasted production mix’s variation; – Assembly line station overloaded (workload); – Exceptional event (strike…). Several solutions can solve or reduce these hazards.
– Incomplete vehicle: this indicator shows vehicles needing reworks. We insist on the fact than nonnominal assembling operations bring high quality fall risks, hardly quantifiable.
Applicable corrective actions. We number six solutions to solve a production potential’s fall: – Stopping vehicles during their fabrication inside inter-shop storages; stoppage is ordered by Production Coordination; – Adding manpower on stations; these actions are asked by Line Balancers. Moreover, in case of assembly constraints’ non-respect, polyvalent operators can help out a temporary overloaded operator; – On-line or off-line reworks on incomplete vehicles; they are ordered by Inspection Manager; – Line-stop or shop-stop, asked by Production Coordination. Lean Management advises to practice these stops in case of hazard (Ohno, 1998); – Whole-stop, ordered by Plant Direction; – Adding a special transport. Supply Manager can ask the transporter adding in emergency a supplementary components’ transport. However, each of these actions does not solve all hazard types. For instance, adding a special transport to solve a process failure on assembly line has no effect. We classify then these solutions according to their interest to solve a specific hazard (Fig. 4).
Audit phase results. Audit allows gathering information useful to model automotive Supply Chain. Managing both production and components flows is a complex operation in which many actors take part. They make decisions to solve hazards, but today’s decision-making is local and its global impacts on the whole Supply Chain are badly (or not) evaluated. The second phase of our methodology points out these interactions.
3.2. Inter-decisional impacts while Supply Chain Management decision-making. The aim of this phase is to formalize local audits results into cause-and-effect diagrams and then to aggregate those. We consider the Supply Chain Management and its impacts. So cause-and-effect sub-diagrams are built around hazards (causes) and corrective actions (effects). Moreover, other sub-diagrams are also built around actions (causes) and local impacts (effects). Aggregating those highlights interactions, which are examined and justified. For instance (Fig. 5) “a supplier problem could lead to incomplete vehicles. A stoppage to avoid those causes production sequence’s variations which can lead to components shortages on other references; finally concerned vehicles need off-line reworks”.
Fig. 4. Corrective actions for each hazard. Decision to apply a specific action is made basing on different performance indicators. Performance indicators analysed. The following performance indicators allow the highlighting of a potential fall’s risk. – Production sequence number; it is possible to verify that a vehicle has neither lead nor delay on forecasted production date; – Inter-shop vehicle storages filling level; when there is not place enough to sequence vehicles dynamically, flow production’s quality can lower; – Constraints’ non-respect; they happen in case of assembly line station overload; There is then an incomplete vehicle risk; – Components storage’s levels; of course, a vehicle can not be completed when components are missing, unlike exceeding storages cost a lot;
Fig. 5. Part of inter-decisional interaction diagram. In fact, while a vehicle is stopped in inter-shop storages, other vehicles get ahead over their assembly date. On one hand, components dedicated to the stopped vehicles still arrive in storages and their levels increase (Fig. 6). On the other hand, it can generate components shortages on other references over-consumed because of the new sequencing after stoppage; some vehicles will also be incomplete.
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4. TOWARDS A DECISION-SUPPORT TOOL.
4.1. Estimating decision impacts. We are interested in both production flow’s decisions (for instance an assembly line-stop) and connected ones (like supply domain, line balancing or reworks). When evaluating impacts of solution, we work on local perimeters corresponding to decisional operational domains. We distinguish two types of estimation: – Analytical evaluation; it is possible for some solutions to know their impacts directly. For instance, a special transport’s cost is well known; a rework’s cost can be determined by experts as it corresponds to a work time; – Evaluation through the way of simulation; in other cases, there exists no other equation to rule performance indicators’ variation. We do not have any other solution to emulate the Supply Chain’s functioning. For instance, it is impossible to “calculate” re-sequencing impacts. Likewise simulation is used to forecast components shortages or risks of assembly stations’ overload. Knowing how to evaluate decision’s consequences, it is then possible to compare several decisional alternatives to better forecast global impacts on the Supply Chain.
Fig. 6. Components shortage after a stoppage. As it is difficult for a local decisional centre to identify intuitively indirect action’s consequences, we propose to add a global evaluation, non-existent until now. Such an adding is compulsory in case of a global decision-support. That will allow production’s potential falls’ management at the best.
3.3. Proposing an integrated management. Comparatively to Fig. 3, we propose to integrate a global economical evaluation to coordinate different decisions (Fig. 7). In other worlds, without disturbing enterprise structure, that is the same than adding an actor (which could be emulated by a software tool).
4.2. Simulation platform architecture. We base on existing simulation tools emulating the different parts of Supply Chain. Besides that, other tools have been developed to answer our needs. Interfaces are then realised to link those different tools, automating data’s formatting and analysis. Finally, it will be an integrated platform emulating the whole manufacturing plant’s Supply Chain. Thanks to this software tool, it is therefore possible to evaluate impacts of decision-making (Fig. 8).
Fig. 7. Proposition: integrated system. We can tell apart: – Global Evaluation; different decision-making are evaluated in this “black box” by comparing their respective impacts, by means of a same economical indicator; – Decisional Centres; these actors are involved in decision-making and/or local impact’s evaluating; – Local Evaluations; they are decisional domains on which a Decisional Centre acts (evaluation/action). So we have built our data model in U.M.L. from audit and rules that we recommend in the methodology. After this model validation, it is the time to think about a decision-support tool.
Fig. 8. Dynamic sequencing and stoppage impacts. We want also to evaluate consecutive decisions.
4.3. Decision-support by proactive simulation. Estimating a “decision chain” impacts means to cumulate individual decisions’ impacts. It is like evaluating a first decision and a consecutive one from the resulting context (Fig. 9).
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PhD Thesis, Institut National Polytechnique de Grenoble, France. Boutevin C. (2003). Problème d’ordonnancement et d’affectation avec contraintes de ressources de type RCPSP et line balancing, PhD Thesis, Clermont-Ferrand, France. Claver J.-F., J. Gélimier and D. Pitt (1997). Gestion de flux en entreprises, modélisation et simulation. Editions Hermès, Paris, France. Delaval M., E. Castelain and J.-C. Gentina (1995). Dynamic resequencing of cars on assembly lines using simulated annealing, 28th International Symposium on Automotive Technology and Automation, Dedicated Conference on Lean/Agile Manufacturing in the Automotive Industries, Stuttgart, Germany, 169-175. Gomez P. and J.-B. Probst (1987). Pour une approche réticulaire interactive du management. Banque Populaire Suisse, Berne, Switzerland. Holweg M., S. M. Disney, P. Hines and M. M. Naim (2005). Towards responsive vehicle supply: a simulation-based investigation into automotive scheduling systems, Journal of Operations Management, Vol. 23, 507-530. Joly A. (2005). Etude de modes de fonctionnement réactifs et robustes aux aléas sur le flux de production d’une usine terminale automobile, PhD Thesis, Institut National Polytechnique de Grenoble, France. Kowalski Y. (2006). Aide à la décision par l’analyse sémantique des interactions dans l’organicube, modèle qualitatif d’audit pour les entreprises, PhD Thesis, Université of Fribourg, Switzerland. Lesert A. (2006). Evaluation de la flexibilité de l’atelier montage d’une usine terminale automobile, PhD Thesis, Grenoble, France. Muhl, E., P. Charpentier and F. Chaxel (2003). Optimization of physical flows in an automotive manufacturing plant: some experiments and issues. Engineering Applications of Artificial Intelligence, Vol. 16, 293-305. Ohno T. (1988). Toyota Production System – beyond large-scale production. Productivity Press, Portland, Oregon, USA. Petitjean F., A. Villeminot, J.-Y. Bron and P. Charpentier (2004). Petri nets simulation of components flows in car manufacturing. INCOM’04, Salvador - Bahia, Brésil. Rumbaugh J., I. Jacobson and G. Booch (2004). UML 2.0 guide de référence. Pearson Education - Campus Press. Roques P. (2006). UML 2 par la pratique. Editions Eyrolles, Paris, France. Thierry C., Gestion de chaînes logistiques – Modèles et mise en œuvre pour l’aide à la décision à moyen terme, mémoire d’Habilitation à Diriger les Recherches, Toulouse, France. Villeminot A. (2004). Modélisation et simulation de la logistique d’approvisionnement dans l’industrie automobile, PhD Thesis, Université Henri Poincaré, Nancy, France.
Fig. 9. Principle of decision chains’ evaluation. This principle is valid for both analytical evaluation and evaluation through simulation. From these different alternatives comparisons, it is possible to recommend the less disturbing decision chain for the whole Supply Chain (Fig. 10).
Fig. 10. Some decision impacts after one hazard. A demonstrator showing decision chains’ global evaluation is currently being developed. It could be a great decision-support tool to help decision-makers.
5. CONCLUSION AND PERSPECTIVES
In this article we present current results of works aiming at improving global performance of PSA Peugeot Citroën’s Supply Chain. A Supply Chain’s global modelling methodology has been conceived then applied, highlighting non-trivial inter-decision’s impacts from an enterprise audit. From a resulting global U.M.L. model, we have deduced proactive simulation platform architecture; then we have proposed integrated Supply Chain Management principles. Lastly, we introduce a decision-support tool, still being developed, which could allow decision-makers to make the best decision, by the way of a better anticipation of their global impacts of actions.
REFERENCES Anderson D. M. (2004). Mass customization, the Proactive Management of Variety. www.buildto-order-consulting.com/mc.htm. Bernier V. (2000). Sur une nouvelle politique de gestion de flux : le cadencement reséquençable,
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GLOBAL MODELLING OF AN AUTOMOTIVE INDUSTRY SUPPLY CHAIN APPLIED TO PRODUCTION MANAGEMENT DECISION-SUPPORT Fabien Petitjean 1, 2, Patrick Charpentier , Jean-Yves Bron 1, Alexandre Villeminot 2 1
1
Centre de Recherche en Automatique de Nancy UMR 7039 CRAN – Faculté des Sciences BP 239 F-54506 Vandoeuvre-lès-Nancy, France [email protected] 2
PSA Peugeot-Citroën Site de Sochaux Case courrier SX3 S53.01 57 avenue du Général Leclerc F-25600 Sochaux, France [email protected]
Abstract: Automotive industry is nowadays characterised by a huge diversity of proposed models, a shorter and shorter product’s life cycle and an increasing concurrency between generalist builders. Satisfying customers implies improving Quality and reducing Costs and Lead-time. To stay economically competitive in such a context, it is compulsory to use the production system at the best. Optimising locally the production does not ensure a global performance. That is why we propose in this paper a global modelling of French car manufacturer PSA Peugeot Citroën’s Supply Chain. Such an approach allows highlighting and evaluating global impacts of local decision-making. Lastly, we will introduce a decision-support tool, dedicated to factory management. Copyright 2007 IFAC Keywords: Supply Chain Management, car industry, modelling, proactive simulation, decision-support.
1. INDUSTRIAL CONTEXT AND PROBLEMS
small and large productions (concept of “mass customization” (Anderson, 2004)) and allow a Justin-Time production’s deployment. On account of high costs when modifying infrastructures and physical flows inside the plant, several research works have been launched in order to optimise the production resources’ management and the components’ supplying.
1.1. Automotive industry characteristics. After a small-scale production beginning, car production has been a mass production during a long time, like many other industrial sectors. Rationalisation allowed to produce at high rates vehicles on assembly lines. Today, such a concept is not adequate anymore. Customers are looking for high personality models, which can be customized. Increase in concurrency lead companies to move towards an economy of variety, characterised by short product’s life-cycle. Feasible vehicles diversity has exploded according to the number of components offered to the customers. For economical reasons, these different models are built on the same assembly line, called “mixed model assembly line”. These units reduce the gap between
1.2. Research works linked to automotive production. Previous works have been classified: – Creation of a reference production list. This phase is a static and forecast problem to obtain the best vehicles sequence taking into account production shops constraints (body shop, paint shop and assembly shop) while smoothing components’ consumption (Delaval et al., 1995).
415
– Dynamic re-sequencing and rate giving during production (Bernier, 2000). The aim of this phase is to adapt the production sequence to disruptions of production flow called “Vehicles Flow” (like manufacturing reworks...) or of supply flows called “Components Flow” (components shortage, transport delayed…) (Joly, 2005). This dynamic work is done in real-time inside vehicles’ storages between the different factory shops (Muhl et al., 2003). – Assembly line balancing. According to a forecast production mix upon a determined period, assembly operations are assigned to assembly line stations. This phase is realised to respect industrial constraints (minimizing station’s number, smoothing workloads…) (Boutevin, 2003) (Lesert, 2006). – Synchronizing and tensing flows all over the components supplying. The objectives are to size the logistics accurately and to evaluate impacts of disruptions on and between the supply flows (Petitjean et al., 2004) (Villeminot, 2004). However, increasing the Supply Chain global performance does not mean optimising locally the functional domains previously described. They have to be combined to get a better cost-effectiveness, without disturbing the enterprise structure, so that, it is possible to consider numerous and complicated interactions within the Supply Chain.
Economically and qualitatively, the envisaged solution is not always the best. To manage the Supply Chain more efficiently, it is crucial to get a global perception of the system’s state. Also, we propose to evaluate interactions inside the automotive manufacturer’s Supply Chain, considered globally. It requires firstly modelling globally the Supply Chain in order to point out the mechanisms between distinctive perimeters.
2. PROPOSAL OF A SUPPLY CHAIN’S GLOBAL MODELLING METHODOLOGY
2.1. Enterprise audit based on “Organicube”. Decision process aims at analysing and modelling a situation in order to highlight elements leading to decision-making (Claver et al., 1997). All alternatives and their consequences have to be identified. In that sense, different models exist; they give either a general enterprise’s image or a precise vision by concentrating on a limited area. Then these models allow gathering information about enterprise to describe it textually or graphically. Different referent architectures (CIMOSA, GRAI, GERAM), reference models linked to interoperability (Zachman’s Framework, MDA, Safira) and to resource’s allocation (TEAF matrix, DoDAF model) have been examined. These modelling techniques allow formalising knowledge to operate it by a decision-support tool. Most of existing models have their own particular enterprise or studied system vision. A macro-model based on those, “Organicube” (Kowalski, 2006), has the advantage to describe the enterprise and its surrounding areas in different ways: quality, performance, human and material resources, production system etc. (Fig. 2). Moreover this concept proposes also to follow a thread, allowing a coherent and full audit.
1.3. Necessity of a more global vision on interactions inside the Supply Chain. Inside the factories, called Manufacturing Plants, decisions are made every day, among several boundaries. Besides their expertise, local decisional centres can base their judgement on existing tools resulting from previous works. These tools allow evaluating local impacts of hazards or of a decision concerning the Supply Chain Management. However, each action has impacts on surrounding areas; also it is difficult to value those. For instance (Fig. 1), solving a forecasted supply problem (1) by delaying the production of vehicles consuming these components (2) can cause other hazards. These new issues can be a vehicles’ storage stoppage (3), components shortages on other references over-consumed (4) or even an overworkload on assembly line stations (5) which can bring to vehicles’ reworks after the line…
Fig. 2. Organicube, structure and thread. Organicube allows a more global approach, which makes it more suited to decision’s and interaction’s analysis. That is why we have chosen to base on for a Supply Chain’s audit by PSA Peugeot Citroën.
Fig. 1. Production’s potential fall after a hazard.
416
After that, it is necessary to highlight and understand mechanisms between distinctive operational perimeters, in other words formalizing interactions from data collected during audit.
3. APPLICATION TO THE MODELLING OF A PRODUCTION POTENTIAL FALL
3.1. Supply Chain Management audit. 2.2. Pointing out decisional interactions aggregating cause-and-effect diagrams.
by
Considered perimeter, criteria analysed, collected data. We concentrate on decisional centres and on functional domains included in the perimeter from Manufacturing Plant (inside included) to its first level suppliers. Interviews are carried out inside the different domains implied in vehicles production, following the Organicube thread. From that, current Supply Chain Management’s practices are identified and actors, their role, possible hazards, possible corrections to solve those are highlighted.
It is possible to model the decision-makers thought process, or more generally actors with a certain judgement’s power, by causal diagrams (Holweg et al., 2005). A modular approach, known as “management’s interactive reticular approach”, allows treating complex problems step by step, after decomposing those. Resulting diagrams take then into account the decisions’ context (Gomez and Probst, 1987). In our study and more generally to globally model interactions inside a Supply Chain, we propose formalising local audits’ results by cause-and-effect sub-diagrams. After that, by way of aggregation, it is easy to highlight interactions’ loops, whose perception is not always so obvious. When loops are pointed out, it is necessary to precise performance indicators, procedures to be followed and concerned actors… Such information is modelled thanks to U.M.L. (Unified Modeling Language), which can also be a basis for a future computer implementation.
Actors implied in the management. The audit leads to identify these decision-makers and functional managers: – Plant Direction manages the factory and organises production planning; – Production Coordination plans and controls production flow inside the shops; – Line Balancers determine manpower to be set on assembly lines according to forecast production mix; – Inspection Manager takes on all kinds of vehicle’s reworks. It can be either on-line or off-line reworks (after assembly line); – Supply Manager follows the components storage’s levels, the orders and the transports; – Suppliers produce components in Just-in-Time, in accordance with negotiated flexibility threshold; – Transporter manages transports between Manufacturing Plant and suppliers. Supply Chain structure is today a distributed system (Fig. 3) (Thierry, 2003).
2.3. Supply Chain global modelling thanks to U.M.L. A U.M.L. model can be built on both highlighted interactions and enterprise audit. We have retained the following approach, based on U.M.L. language reference books (Roques, 2006) (Rumbaugh et al., 2006): – Use cases diagram, showing actors and main functions; – Partial class diagram, from the previous diagram, to synthesise relations between system’s constitutive entities; – Collaboration diagrams to point out exchanges between these entities; – Filling the class diagram and finalising the model. With such a validated model, it is possible evaluating interactions between decisional domains, either analytically or through simulation to quantify noncalculable impacts. We have applied this methodology to our study context and more particularly to production’s potential falls inside Manufacturing Plants. Production’s potential fall means supply shortages, temporary workload’s increase, etc. We also include in our model a global evaluation, today inexistent, to integrate inter-decisional impacts’ evaluation in the decision process linked to Supply Chain Management.
Fig. 3. Current structure: distributed system. Also, the audit allows us to list hazards disrupting system functioning. Hazards disrupting system. We distinguish the following hazards: – Supplier problem (process failure…); – Transport delayed (failure, climatic conditions…); – Storage shortage inside Manufacturing Plant; – Process failure on assembly line;
417
– Forecasted production mix’s variation; – Assembly line station overloaded (workload); – Exceptional event (strike…). Several solutions can solve or reduce these hazards.
– Incomplete vehicle: this indicator shows vehicles needing reworks. We insist on the fact than nonnominal assembling operations bring high quality fall risks, hardly quantifiable.
Applicable corrective actions. We number six solutions to solve a production potential’s fall: – Stopping vehicles during their fabrication inside inter-shop storages; stoppage is ordered by Production Coordination; – Adding manpower on stations; these actions are asked by Line Balancers. Moreover, in case of assembly constraints’ non-respect, polyvalent operators can help out a temporary overloaded operator; – On-line or off-line reworks on incomplete vehicles; they are ordered by Inspection Manager; – Line-stop or shop-stop, asked by Production Coordination. Lean Management advises to practice these stops in case of hazard (Ohno, 1998); – Whole-stop, ordered by Plant Direction; – Adding a special transport. Supply Manager can ask the transporter adding in emergency a supplementary components’ transport. However, each of these actions does not solve all hazard types. For instance, adding a special transport to solve a process failure on assembly line has no effect. We classify then these solutions according to their interest to solve a specific hazard (Fig. 4).
Audit phase results. Audit allows gathering information useful to model automotive Supply Chain. Managing both production and components flows is a complex operation in which many actors take part. They make decisions to solve hazards, but today’s decision-making is local and its global impacts on the whole Supply Chain are badly (or not) evaluated. The second phase of our methodology points out these interactions.
3.2. Inter-decisional impacts while Supply Chain Management decision-making. The aim of this phase is to formalize local audits results into cause-and-effect diagrams and then to aggregate those. We consider the Supply Chain Management and its impacts. So cause-and-effect sub-diagrams are built around hazards (causes) and corrective actions (effects). Moreover, other sub-diagrams are also built around actions (causes) and local impacts (effects). Aggregating those highlights interactions, which are examined and justified. For instance (Fig. 5) “a supplier problem could lead to incomplete vehicles. A stoppage to avoid those causes production sequence’s variations which can lead to components shortages on other references; finally concerned vehicles need off-line reworks”.
Fig. 4. Corrective actions for each hazard. Decision to apply a specific action is made basing on different performance indicators. Performance indicators analysed. The following performance indicators allow the highlighting of a potential fall’s risk. – Production sequence number; it is possible to verify that a vehicle has neither lead nor delay on forecasted production date; – Inter-shop vehicle storages filling level; when there is not place enough to sequence vehicles dynamically, flow production’s quality can lower; – Constraints’ non-respect; they happen in case of assembly line station overload; There is then an incomplete vehicle risk; – Components storage’s levels; of course, a vehicle can not be completed when components are missing, unlike exceeding storages cost a lot;
Fig. 5. Part of inter-decisional interaction diagram. In fact, while a vehicle is stopped in inter-shop storages, other vehicles get ahead over their assembly date. On one hand, components dedicated to the stopped vehicles still arrive in storages and their levels increase (Fig. 6). On the other hand, it can generate components shortages on other references over-consumed because of the new sequencing after stoppage; some vehicles will also be incomplete.
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4. TOWARDS A DECISION-SUPPORT TOOL.
4.1. Estimating decision impacts. We are interested in both production flow’s decisions (for instance an assembly line-stop) and connected ones (like supply domain, line balancing or reworks). When evaluating impacts of solution, we work on local perimeters corresponding to decisional operational domains. We distinguish two types of estimation: – Analytical evaluation; it is possible for some solutions to know their impacts directly. For instance, a special transport’s cost is well known; a rework’s cost can be determined by experts as it corresponds to a work time; – Evaluation through the way of simulation; in other cases, there exists no other equation to rule performance indicators’ variation. We do not have any other solution to emulate the Supply Chain’s functioning. For instance, it is impossible to “calculate” re-sequencing impacts. Likewise simulation is used to forecast components shortages or risks of assembly stations’ overload. Knowing how to evaluate decision’s consequences, it is then possible to compare several decisional alternatives to better forecast global impacts on the Supply Chain.
Fig. 6. Components shortage after a stoppage. As it is difficult for a local decisional centre to identify intuitively indirect action’s consequences, we propose to add a global evaluation, non-existent until now. Such an adding is compulsory in case of a global decision-support. That will allow production’s potential falls’ management at the best.
3.3. Proposing an integrated management. Comparatively to Fig. 3, we propose to integrate a global economical evaluation to coordinate different decisions (Fig. 7). In other worlds, without disturbing enterprise structure, that is the same than adding an actor (which could be emulated by a software tool).
4.2. Simulation platform architecture. We base on existing simulation tools emulating the different parts of Supply Chain. Besides that, other tools have been developed to answer our needs. Interfaces are then realised to link those different tools, automating data’s formatting and analysis. Finally, it will be an integrated platform emulating the whole manufacturing plant’s Supply Chain. Thanks to this software tool, it is therefore possible to evaluate impacts of decision-making (Fig. 8).
Fig. 7. Proposition: integrated system. We can tell apart: – Global Evaluation; different decision-making are evaluated in this “black box” by comparing their respective impacts, by means of a same economical indicator; – Decisional Centres; these actors are involved in decision-making and/or local impact’s evaluating; – Local Evaluations; they are decisional domains on which a Decisional Centre acts (evaluation/action). So we have built our data model in U.M.L. from audit and rules that we recommend in the methodology. After this model validation, it is the time to think about a decision-support tool.
Fig. 8. Dynamic sequencing and stoppage impacts. We want also to evaluate consecutive decisions.
4.3. Decision-support by proactive simulation. Estimating a “decision chain” impacts means to cumulate individual decisions’ impacts. It is like evaluating a first decision and a consecutive one from the resulting context (Fig. 9).
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Fig. 9. Principle of decision chains’ evaluation. This principle is valid for both analytical evaluation and evaluation through simulation. From these different alternatives comparisons, it is possible to recommend the less disturbing decision chain for the whole Supply Chain (Fig. 10).
Fig. 10. Some decision impacts after one hazard. A demonstrator showing decision chains’ global evaluation is currently being developed. It could be a great decision-support tool to help decision-makers.
5. CONCLUSION AND PERSPECTIVES
In this article we present current results of works aiming at improving global performance of PSA Peugeot Citroën’s Supply Chain. A Supply Chain’s global modelling methodology has been conceived then applied, highlighting non-trivial inter-decision’s impacts from an enterprise audit. From a resulting global U.M.L. model, we have deduced proactive simulation platform architecture; then we have proposed integrated Supply Chain Management principles. Lastly, we introduce a decision-support tool, still being developed, which could allow decision-makers to make the best decision, by the way of a better anticipation of their global impacts of actions.
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