enterprises business processes of established Extended. Enterprise for Supply Chain, focusing in the modeling of collaborative decisional model. The study has ...
SUPPLY CHAIN MANAGEMENT. MODELLING COLLABORATIVE DECISION. Francisco Lario Esteban Angel Ortiz Bas Raul Poler Escoto David Perez Perales Centro de lnvestigacibn en Gestion e lngenieria de Produccih (CIGIP) Universidad Politicnica de Valencia. Camino de Vera sin 46022 Valencia. SPAIN Processes (BP) in which palticipate some companies (Entities), which allows developing effective management strategies. In this context the use of Enterprise Engineering and Integration (EEI) proposals can be useful to generate collaborative and integrated Business Processes. To develop this kind of projects we use the (E-GIP Methodology [4].IEGIP (Fig. I) establishes three levels in order to generate adequate mechanisms of coordination and cooperation: Business Level (Concept and Definition phases)
Abstract.-. This paper shows how Enterprise Engineering and Integration (EEI) c m help in the improvement of inter
enterprises business processes of established Extended Enterprise for Supply Chain, focusing in the modeling of collaborative decisional model. The study has been developed in the Automotive Sector, identifying the relationships between an automotive manufseturer (OEM, Original Equipment Manufaetnrer) and its first-tier suppliers. T o develop the study We have considered well-know EEI Architectures such as ClMOSA and GRAl and particularly IE-GIP Methodology (an extension of CIMOSA) and DAROMS Methodology (an extension of GRAI).
Engineering Level (Engineering phase) Execution and Operational Level. (Construction and Operation phases)
I. INTRODUCTION We define an Extended.Enterprise “as a dominant company wit,l which extends its boundaries building in its supply chain, improving its to achieve a sustainable colnpetitiveadvantage,, 111. . . As it may be observed in the tlrevious definition when an Extended Enterprise is created, a dominant company extends its limits crealing “palttiership” with other companies. Partnership implies a relationship between the companies which goes beyond transactions of materials, data and money. I t is necessary to establish inter-company processes which are able to add other aspects to the previous ones, like knowledge, agile and collaborative decisionmaking systems and integration between the different companies systems. In this paper, an analysis of an Enterprise Network has been developed [Z], specifically an Extended Enterprise for the Supply Chain Management, using concepts as Enterprise Engineering and Integration, in a Automotive Sector context, and identifying the relationships between an automotive manufacturer (OEM, Original Equipment Manufacturer) and its first-tier suppliers. In the paper we present the methodology used to develop the study and the analysis and redesign of the decisional aspects: The main goal of the redesign has been to increase collaborative decisions. To developed this work we have used IE-GIP Methodology [3] [4] and DAROMS [ 5 ] [6]proposals.
In this project we have applied the IE-GIP Methodology to develop new collaborative Production Planning and Sequencing Business Process in the Automotive Sector, identifying the relationships between an automotive manufacturer and its first-tier suppliers’
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II SUPPLY CHAIN MANAGEMENT, ENTERPRISE MODELLING AND ENTERPRISE INTEGRATION
In the highly dynamic and competitive current environment, it is necessary to improve Supply Chain (SC) communication, coordination and cooperation The SC establishes the common business relationship between supplier-manufacturer using inter-enterprise Business
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Fig I,- E-GIP Melhodology
In the next section we will focus in the engineering phase using GRAl and DGRAl as modelling techniques and DGRAI 2.0 as modelling tool.
Within the PPS process, in the OEM and its suppliers, various types of suppliers may be considered, according to the treatment they receive regarding to available information and types of decisions affecting them and even the impact of their own decisions over the OEM. In this study we have identified two types of suppliers: JIT and Sequenced. In the GRAl Grid those types have been analysed as two different functions due to the different decision processes.
111 DECISIONAL VIEW IN THE MODELLING SUPPLY CHAIN A. lntroducfion
GRAI Method [7] and the DGRAl 2.0 Tool were adapted the for the analysis of the Automotive Sector Decisional System, the decision scope of the analyzed Spanish factory in relation to others European Decision Centres of the company was clarified and the existing relationship between the Decisional Subsystem and its relationships with different typology suppliers (JIT and Synchronous and Sequenced) was pointed out. Among the different models created using the Methodology, in this paper we will focus in the Decisional Model. This is a very critical model due to the new decisional requirements needed in a collaborative context.
The different steps (sub-steps of 1E-GIP Methodology) that have been necessary for the achievement of tlie described objectives were: 1.Working Team set up, made of personnel of the OEM Material Planning and Logistics (MP&L) area and the ClGlP personnel joining this project, defining the working way in accordance with the GRAI Methodology. 2. Information collecting (interviews and data collecting). 3. Analysis of the current situation and objectives definition. 4. GRAI Method Adaptation to the case study particularities. 5.Definition of the GRAl grid (functions and decisionmaking Horizon/Periods and decision centres). 6.GRAl grid Validation. 7.GRAl Networks Definition (only for the more interesting cells (DC), that means, definition of execution and decision activities, information used and human resources involved.
8.Application The objectives of this analysis have been: Performing an application of the GRAI Methodology to an specific company of the Automotive Sector, being modelled the Production Planning and Sequencing Process considering suppliers interactions Adapting the GRAI Method to the characteristics of the analyzed sectorlproblem. Clarifying the decision scope of an assembly Plant and its suppliers in relation to others OEM European Decisions Centres. Stressing the existing relationship between the Decisional Subsystem o f the analyzed Plant and its relationship with different suppliers’ typology (JIT and Synchronous1Sequenced) Identifying the current problems. in the interenterprise decisional flows Generate a !new model to improve inter-enterprise decisional flows
D.AS-IS ond TO-BE Decisional Models
In the case of the analyzed Plant its Decisional System is limited by the fact that the main Planning and Management Systems of the company in Europe are corporative and centralized in Germany. It is there where most of the medium and long term decisions are taken; the decision scope of the Spanish Plant only affects to shorter horizons and Periods and to functions exclusively related with operational activities (vehicle sequencing, retention in case of problems, modules, sets and components deliveries...). In this paper the global analysis of all company Decisional Centre in a detailed level will be exposed within the GRAl grid (Fig. 2). Additionally, it is developed tlie detailed analysis of those Decisional Centre in which the decisional network is really relevant for the Spanish factory, reaching the level of Decisional Networks.
Figure 2 shows the AS-IS GRAl grid, identifying the Decisional Centres, it is also described one of the associated Decisional Networks (Fig. 3). The grid shows the Decisions and Information Flow as well as the responsibilities of all the Company departments, analyzing in which degree they take part in the decisions. In accordance with GRAl Methodology, in the columns there have been included the performed functions and in the rows de decisions horizons and revision periods. The first and the last column contain the information, either extemal or intemal, that are used. The thick outline in the arrows means the decisional flow and the fine one the informational flow. Each column is identified with acronyms and each horizon with a number so that the cells are finally identified combining both identifications. The results shows that the decisions-making process and the medium and long term planning corresponds to OEM Europe, being from the ten days horizon when Spanish OEM has the capability to influence in the decisional subsystem. The typology of Spanish OEM factory is the typical of the automotive assembly plants. Once the bodies have been assembled, these are carried to the Paint Plant and then to the Assembly Plant where all the components integrating every sequenced vehicle are finally assembled. A particularity of this factory appears between the Paint and the Assembly Plant, where an intelligent warehouse (ASRS) with a capacity for 400 bodies exists.
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Fig. 2.- AS-IS GKAl Grid
Support to the decision to retain or not (Decisional Activity 7) and to other activities also analyzed.
This warehouse stores the painted bodies coming from the Paint Plant (that in theory already come according to the called Predicted Sequence or PS, established in PP&C40 -see figure 2-) and take out (according with the Predicted Sequence) bodies towards the Assembly Plant. The ILVS (In-Line Vehicle System) is the soAware that control the ASIRS’s output. The ILVS working rule (which mns the ASIRS) is simple: “to try to always extract from the ASiRS the oldest body among the required type according to the Predicted Sequence”. When special circumstances occur, like the shortage of a determined piece supplied by a supplier (external or internal), and this piece is assembled in certain types of vehicles, it is necessary to decide how to manage this shortage. The decision between two main options is taken in the Decisional Activity 7 (see Fig. 3, up-right) among the Plant, the Logistics and the Production Managers, on the basis of several Informational Supports which are the result of others previous Execution Activities which are also shown in Fig. 3. From the PP&C60 cell (starting from the left in Fig.3), when in SMJlT60 or SMSEQ60 (not exposed by its extreme simplicity) the commented shortage takes place, the first action is to activate the Execution Activity 3: “to calculate the pieces and sub-assemblies affected by the shortage”, whose result simultaneously activates two Execution Activities, the 4: “to calculate the number of affected sequences” and the 5: “to verify if the affected pieces are recoverable”; whose results will be used as
Each Decisional and Execution Activity needs different Informational Supports in each case and in addition it also needs the intervention of specific Human Resources such as decision-makers or executors. All of them were analyzed and there have been shown the corresponding ones in the Fig.3 which models all the decisions result from the Decisional Networks included in the DC: PP&C60, RM60 and PP&C70. The relationship between these three DC is summarized as follows:
I f the decision in the DC PP&C60 is to retain (Support 12A in the corresponding Networks) it is activated a later decision in the DC PP&C70 about the way in which it must perform the feedback to the stable “Predicted Sequence” through a transitory sequence (Decisional Activity SA). If the final decision is not to retain (Support 12B), this leads to a decision (Decisional Activity SE) en the DC RM60 about how must be performed that piece assembly when it is already available, which means normally an additional cost caused by its later assembly in the vehicles.
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Fig. 3.- Associated Decisional Network
As main conclusions of the previous analysis we can pointed out: The difficulty to define decision-making models in multinational companies with different Business Unit and with far locations, stressing the difficulty of getting the necessary information The current process to go back to the Predicted Sequence when vehicle retention is over is very poor. It has been detected the lack of a Automated DecisionMaking System in the analyzed Decision Centres (DC), this system should analyse all the cases of pieces shortages and taking the best decision, evaluating the internal and external (supply chain) impact. The previous conclusion leads to a decision system with a high variability depending of the decision maker. The current system don't provide enough information to the decision maker, then the decision normally depends of factor as experience, current situation, suppliers involved, the real problem is a lack of a well defined decision process. From the inter-enterprise point of view the current decisioiial flow doesn't consider aspects from suppliers
provides functionalities to solve the previous problems and the most important, provide functionalities to involve in the decisional flow people and systems not only from the OEM but from suppliers as well. As it is pointed out in cells E120/E130iEI40 information and decisions coming from suppliers (External Infoimation), it is taking into account in the OEM systems. Additionally, decision from JIT (SNJIT30) .and Sequenced suppliers (SNSEQ30) is considered. This new scenario implies a big change in the automotive sector when usually the OEM decides and suppliers try to fit these decisions.
Then we have decided to create a new system called OPS (Fig.4). From a decisional point of view this new system
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Strategic Management of the Manufacturing Value Chain. (U.S. Bititci and A.S Carrie ed.), Kluwer.
IV CONCLUSIONS The Production Planning and Sequencing Processes of a Supply Chain formed by an OEM and some first-tier suppliers have been modelling.
[3]
From the point of view of the decisional aspects affecting to the Automotive Supply Chain the main conclusions are:
[4]
It has been verified that GRAI Methodology can be integrated into IE-GIP Methodology, to develop the AS-IS analysis and the TO-BE. definition for the decisional view It has been verified GRAI Methodology validity for the analysis of the Decisional Subsystem in multinational companies with the quoted characteristics. .A new model (called OPS) has been built to increase inter-enterprise collaborative decision.
IS]
[6]
V. REFERENCES
[I]
Ortiz, A. and M. Hawa(2002). Extended Enterprise for Supply Chain Management. Why, When and A reference model for How to apply it. collaborative planning in the Automotive Sector
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Moller, Riis and Hansen. (1998). Interorganizational network classification. A Framework for studying industrial networks.
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