MAIN AUTOMATION CONTRACTOR (MAC): THE TECHNICAL ANALYSIS OF SUPPLIER BASED ON AHP (ANALYTIC HIERARCHY PROCESS) MULTI- CRITERIA METHODOLOGY

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1 ISSN MAIN AUTOMATION CONTRACTOR (MAC): THE TECHNICAL ANALYSIS OF SUPPLIER BASED ON AHP (ANALYTIC HIERARCHY PROCESS) MULTI- CRITERIA METHODOLOGY Carlos Eduardo Barateiro (UFF) João Carlos Correia Baptista Soares de Mello (UFF) José Rodrigues de Farias Filho (UFF) Luiz Antonio da Paz Campagnac (UFF) Resumo Em 1998 foi publicado no Construction Industry Institute na Universidade do Texas em Austin, um estudo sobre as vantagens de terse a compra antecipada de alguns itens estratégicos, mesmo antes da própria definição da empresa responsável peela construção do empreendimento. Estudos posteriores mostraram que a automação industrial, para o controle de processos de grandes plantas na área de petroquímica, deveria ser um desses itens estratégicos comprados antecipadamente e essa contratação deveria seguir o modelo denominado Main Automation Contractor (MAC), onde uma única empresa fica responsável por toda a automação do empreendimento. O objetivo deste artigo é apresentar, através da aplicação da técnica multicritério denominada Analytic Hierarchy Process (AHP), um modelo para a análise técnica das empresas interessadas em ser o MAC de um empreendimento petroquímico de forma a podermos maximizar os investimentos na construção - Capital Expenditure (CAPEX) e operação do projeto - Operational Expenditure (OPEX). Ou seja, trata-se de uma particular aplicação da solução do problema de seleção de fornecedor - Supplier Selection Problem (SSP). Para a construção desse modelo foi efetuado uma série de entrevistas com usuários finais (operadores das plantas), empresas contratistas, empresas de engenharia, integradores de sistema, fornecedores dos sistemas de automação e formadores de opinião que habitualmente se confrontam com esse problema.

2 Palavras-chaves: Main Automation Contractor, multicritério, automação 2

3 1. Introduction The financial crisis that erupted in September 2008 interrupted a period of growth in consumption and industrial production. Economically, the last months were very difficult not only for the countries, but also for the companies and many of the scheduled investments were canceled or postponed. We started a slow global recovery and Brazil, thanks to its solid macroeconomic base compared to other countries, presented production rates, in the third quarter of 2009, at the same levels before the crisis (ABIQUIM, 2009). At the first moment of the crisis all segments were affected, however, the Brazilian oil and gas market remained the majority of its investments based on Petrobras plans, the oil state company, to be one of the five largest integrated energy companies in the world by This plan, submitted in January 2009, shows values above USD 174 B to be invested in the period, in which we have over USD 150 B in exploration, production, refining and transportation (Figure 1). The investment plan presented by Petrobras shows a large portfolio of 530 projects in which 75% are in the opportunity or conceptual phase (Figure 2). It shows the great potential for the incorporation of new technologies and procedures that bring benefits for these investments. Figure 1: Petrobras Investment Plan Source: PETROBRAS, Investment Plan (2009) Figure 2: Petrobras Project Stage 3

4 Source: PETROBRAS, Investment Plan (2009) In almost all these projects we have the extensive use of automation systems that serve not only to increase production but also for the safety of the units. According to ALGAFFAS (2007), "the automation systems for process control represent between 3 to 5% of the plant value and the inadequate design or its poor implementation can increase this value from 10 to 25% - but the automation system can represent 100% of inactivity in the same plant if the project or its configuration is not appropriate. That is, automation has a major impact not only on the total investment cost - CAPEX (Capital Expenditure) for the implementation of the plant, but also on the cost of its own operation - OPEX (Operational Expenditure), after the beginning of its activities. With the incorporation of new technology, the automation shows its increasing importance in this scenario and the need of changing the way the organizations run their projects to get the benefits of these advances. Based on field research conducted by BARATEIRO (2011), it was found that there is still an excessive focus on technical specification of the projects and there are few who really understood that the new concept of digital plants must also change the way the enterprises are executed. At this moment, the traditional structures of project management are not prepared to maximize these gains. Vorster et al. (1998) published a study for the Construction Industry Institute at the University of Texas, Austin (USA), showing the importance of early strategic purchases before the definition of who will be responsible for the project construction (contractor). This study considered the hiring based on the model EPC (Engineering, Procurement and Construction) - building model in which a single company is responsible for engineering, procurement of materials and equipment, installation, commissioning and project start up. This study showed gains in terms of cost, time and resources to the project, mainly due to the uncertainty reduction in the initial project development. The industry has named this model PEpC (Procurement, Engineering, procurement and Construction) by the fact that the most important purchases (strategic) must be carried out even before the full definition of the EPC company - this concept is currently used in the oil and natural gas and a good example is the purchase of large compressors for the oil platforms after the completion of basic engineering. 4

5 Based on this concept, BARATEIRO (2011) showed that the industrial automation of petrochemical plants should be considered as a strategic item on the PEpC model. BARATEIRO (2011) also showed that the hiring of automation should follow the purchasing model in which a single company is responsible for the specification and supply of all instruments and final control elements, communication interfaces and control rooms, as well as the commissioning and start up of all automation systems. The industry has named this hiring model MAC (Main Automation Contractor) and it has been used in large oil refinery projects. In Brazil, we can mention as examples the use of this model in the Petrochemical Complex in Rio de Janeiro (COMPERJ) and the Refinery Abreu e Lima (RNEST). BARATEIRO (2011) presented a model for the choosing of the automation supplier that could maximize the investment on construction and operation project. In this model the choice of the best solution was based on the best proposal that is not necessarily the lowest price. To achieve this, BARATEIRO (2011) has proposed a bonus in the price based on the better technical analysis, that is, the better technical evaluation allows the supplier to have price compensation. For this, he has conducted an extensive field research with several market players getting the critical success factors for the supplier technical qualification. The objective of this work is to redefine the importance degree of these critical success factors for the MAC technical analysis obtained in the BARATEIRO s research, considering a new approach based on the use of multi-criteria techniques for the decision analysis. This article has been structured to present a brief introduction of BARATEIRO s research that subsidies the application of multi-criteria methodology. Then the problem was structured for a decision support, in order to obtain the criteria and sub-criteria weights. We have kept the value functions established in the BARATEIRO s study for pre-defined fictitious alternatives according to the criterion compliance degree. This model was revised with new scores for each criterion and sub-criterion evaluated without extra comparisons. In BARATEIRO s study the field research has used interviews with end users (plant operators), contractors (EPC companies), system integrators and automation suppliers that represent these segments. He used a total of twenty-five interviews with the distribution showed in the Figure 3. Figure 3: Distribution of Interviewees by Segment 5

6 Source: BARATEIRO (2011) 2. The Multiple Criteria Aspects For the purposes of this paper we considered the use of multi-criteria techniques to solve the supplier selection problem (SSP). The SSP is defined by SAEN (2007) as the process which suppliers are selected, analyzed and evaluated to be included in the supply chain. SONMEZ (2006) considers it a complex problem of decision because it contains multiple alternatives and multiple criteria - qualitative and quantitative, and it can be conflicting with each other, and also because of restrictions imposed on the purchasing process. It is a decision problem because it involves trade-offs among the various criteria that must be considered during the decision making process. SAEN (2007) comments that the correct selection of suppliers can strongly reduce the procurement cost, improving the company's competitiveness and reducing the problems of poor-quality and delivery time. HO, DEY and XU (2010) classified 78 articles published on international journals, during the period from 2000 to 2008, about the supplier selection criteria and approaches used for an efficient supply chain. They found that most of the decision makers used the following criteria to select the suppliers: quality, delivery, price/cost, capacity, services, management, technology, research and development, finance, flexibility, reputation, relationship, risk, safety and environment - and possibly other criteria varying according to the characteristics and needs of each business. However, the research carried out by the authors of the present paper, found that there is no specific literature about SSP focused on the technical selection of automation system suppliers. For a large petrochemical plant, BARATEIRO (2011) found more specific parameters: a) the supplier capability; b) experience and references; 6

7 c) project implementation plan; d) support local capacity; e) technology; f) portfolio of field instruments; g) portfolio of valves and final control elements; h) portfolio of flow meters; i) portfolio of analyzers; j) portfolio of asset management systems; k) portfolio of predictive maintenance systems; l) portfolio of custody transfer systems. We have considered the SSP techniques to build a model for technical analysis of MAC vendor based on the proposal of SILVA et al. (2008), who proposed a framework for the SSP supported in the studies of BOER et al. (1998, 2001, 2003) and SONMEZ (2006). This framework consists of the following steps: 1) definition of the problem, 2) formulation of decision criteria, 3) pre-qualification of potential suppliers, 4) final selection of suppliers, and 5) monitoring of selected suppliers. HO, DEY and XU (2010) presented the methods used for supplier selection: DEA (Data Envelopment Analysis), Mathematical Programming, AHP (Analytic Hierarchy Process), CBR (Case-Based Reasoning), ANP (Analytic Network Process), fuzzy theory, SMART (Simple Multi-Attribute Rating Technique), and GA (Genetic Algorithm). In this paper, the SSP will be addressed by the AHP method for the issue of choice (Pα) due to its flexibility and versatility, despite the criticism that has been done to this model facing the possibility of reversal order with the introduction of a new alternative it will be minimized with a well modeling phase of criteria and sub-criteria definition. This method was developed by SAATY (1980) and it is one of the first methods developed in the environment of discrete multi-criteria decisions. The AHP divides the problem into hierarchical levels, facilitating their understanding and evaluation, and determines through the synthesis of the values of decision makers, a global measure for each one of the alternatives, giving them priority at the end of the method (GOMEZ, 2004). In terms of modeling, to generate priorities, the decision problem needs to be decomposed in the following steps: 1) definition of the problem, 2) structuring of the hierarchy decision, 3) construction of arrays for pair-wise comparison, and 4) use of priorities obtained in the 7

8 comparisons to consider the priorities of the next lowest level (SAATY, 2008). The assignment of values in the construction of the comparison matrix was based on the SAATY fundamental scale and a group decision aggregation approach was used in individual trials (PENIWATI and SAATY, 2008). The method will not be explained here but it is worth mentioning that the priorities were obtained through the unit auto-vector corresponding to the greater auto-value. For more details see SAATY (1980), SILVA (2010) and BARBA-ROMERO and POMEROL (1997). 3. Methodology The objective of this paper is to have a model for technical evaluation of the MAC suppliers using the AHP multi-criteria analysis tool. For the solution of this problem we divided its resolution in three steps: a) Structuring the problem for the use of the AHP tool to obtain the weights of criteria and sub-criteria; b) Application of AHP tool to obtain the auto-vectors; c) Redefinition of the model of technical evaluation of MAC suppliers created by BARATEIRO (2011) in accordance with the weights obtained with the use of multi-criteria analysis tool. The Figure 4 presents the algorithm used to get the results of this article. Figure 4: Algorithm for redefinition of the model of MAC supplier technical analysis 8

9 To use the AHP tool, we considered five interviews - one for each segment chosen by BARATEIRO (2011): end users (plant operators), contractors (EPC companies), system integrators and automation system suppliers all working in the oil and natural gas area. 4. Results 4.1 AHP Problem Structuring Based on the study presented by BARATEIRO (2011), the Table 1 has the hierarchy structure of the problem of supplier s selection. The hierarchy begins with the overall goal "supplier selection". In the second level we have the criteria and sub-criteria in the third level, which are related to their respective criteria. Table 1: Structure Hierarchy This structure is different from the traditional AHP, as in the last level there are not alternatives. The evaluation is performed from the intensity levels assigned to each subcriteria related to each alternative, instead of evaluating the pair-wise comparisons of alternatives. All the sub-criteria are represented with the same evaluation categories: Excellent, Middle and Lower. In the BARATEIRO s study, we have the requirements of each 9

10 category that we are not reproducing in this article. The Tables 2, 3 and 4 reflects the obtained data of this initial study. This set of criteria and sub-criteria are meeting the conditions defined by axioms of ROY and BOYSSOU (1993): a) Exhaustiveness: all relevant aspects to characterize the problem and the most appropriate alternative were considered. The automation system for the large petrochemical plant is very complex as demonstrated by BARATEIRO (2011). All criteria and sub-criteria were taken from his work, which was based on interviews with players working with projects in this area. b) No redundancy: the criteria used are not in conflict and contain all the elements needed to do the right evaluation of the supplier. Despite the fact that we have twelve criteria considered and, sometimes, up to twelve sub-criteria in the same criterion, they are necessary and complementary as shown by BARATEIRO (2011). c) Cohesion: All criteria and sub-criteria have the objective of maximization. The analysis concluded that this aspect is necessary to maximize the investments in a petrochemical plant. Table 2: Criteria Definition of C1, C2, C3, C4 and C5 and its sub-criteria 10

11 Source: Adapted from BARATEIRO (2011) 11

12 Table 3: Criteria Definition of C6, C7, C8 and C9 and its sub-criteria Source: Adapted from BARATEIRO (2011) 12

13 Table 4: Criteria Definition of C10, C11 and C12 and its sub-criteria Source: Adapted from BARATEIRO (2011) 4.2 AHP Tool Application In this step we are using the decision matrix to obtain the importance values of criteria and sub-criteria. In this approach the decision-makers' judgments are aggregated into a single decision matrix by calculating the geometric average of the trials. This matrix is also called 13

14 the matrix of consensus and is used to obtain the final result of the priority vector. The decision matrix of the group is shown in the Table 5. Table 5: Evaluation Matrix for Main Objectives For the matrix defined in Table 5 the inconsistency degree was calculated at 8.27% and therefore considered acceptable according to criteria developed by SAATY (1980). The the application of criteria C1 to C12. From Table 6 to 17 we have the group decision matrix for the sub-criteria of each criterion C1 to C12. Table 6: Evaluation Matrix of Supplier s Capability For the matrix defined in Table 6 the inconsistency degree was calculated at 8.21% and therefore considered acceptable according to criteria developed by SAATY (1980). The 14

15 the application of sub-criteria C1-1 to C1-5. Table 7: Evaluation Matrix for Experience and References For the matrix defined in Table 7 the inconsistency degree was calculated at 5.70% and therefore considered acceptable according to criteria developed by SAATY (1980). The the application of sub-criteria C2-1 to C2-5. Table 8: Evaluation Matrix for Project Execution Plan For the matrix defined in Table 8 the inconsistency degree was calculated at 8.44% and therefore considered acceptable according to criteria developed by SAATY (1980). The 15

16 the application of sub-criteria C3-1 to C3-12. Table 9: Evaluation Matrix for Local Support For the matrix defined in Table 9 the inconsistency degree was calculated at 5.05% and therefore considered acceptable according to criteria developed by SAATY (1980). The the application of sub-criteria C4-1 to C4-9. Table 10: Evaluation Matrix for Technology 16

17 For the matrix defined in Table 10 the inconsistency degree was calculated at 5.71% and therefore considered acceptable according to criteria developed by SAATY (1980). The the application of sub-criteria C5-1 to C5-12. Table 11: Evaluation Matrix for Portfolio of Field Instruments For the matrix defined in Table 11 the inconsistency degree was calculated at 3.68% and therefore considered acceptable according to criteria developed by SAATY (1980). The the application of sub-criteria C6-1 to C6-11. Table 12: Evaluation Matrix for Portfolio of Flow Meters For the matrix defined in Table 12 the inconsistency degree was calculated at 3.81% and therefore considered acceptable according to criteria developed by SAATY (1980). The 17

18 the application of sub-criteria C7-1 to C7-9. Table 13: Evaluation Matrix for Portfolio of Analyzers For the matrix defined in Table 13 the inconsistency degree was calculated at 5.40% and therefore considered acceptable according to criteria developed by SAATY (1980). The the application of sub-criteria C8-1 to C8-9. Table 14: Evaluation Matrix for Portfolio of Asset Management 18

19 For the matrix defined in Table 14 the inconsistency degree was calculated at 6.53% and therefore considered acceptable according to criteria developed by SAATY (1980). The the application of sub-criteria C9-1 to C9-12. Table 15: Evaluation Matrix for Portfolio of Predictive Maintenance For the matrix defined in the Table 15 the inconsistency degree was calculated at 9.82% and therefore considered acceptable according to criteria developed by SAATY (1980). The the application of sub-criteria C10-1 to C

20 Table 16: Evaluation Matrix for Portfolio of Valves and Control Final Elements For the matrix defined in Table 16 the inconsistency degree was calculated at 3.87% and therefore considered acceptable according to criteria developed by SAATY (1980). The the application of sub-criteria C11-1 to C Table 17: Evaluation Matrix for Custody Transfer Portfolio For the matrix defined in Table 17 the inconsistency degree was calculated at 6.03% and therefore considered acceptable according to criteria developed by SAATY (1980). The the application of sub-criteria C12-1 to C12-8. Finally we have the group decision matrix regarding the intensity levels. When working with ratings, SAATY (2006) advises that the priority vectors be idealized, that is, the best category is assigned with the value 1. Table 18 reflects the results for the intensity levels. Table 18: Evaluation Matrix for Intensity Levels 20

21 For the matrix define in Table 18 the inconsistency degree was calculated at 1.00% and therefore considered acceptable according to criteria developed by SAATY (1991). The idealized values will correspond to the score obtained by the supplier in accordance with each sub-criteria evaluation. 4.3 Redefinition of Technical Analysis Model The MAC supplier technical analysis model uses the criteria and sub criteria obtained in the previous items based on the following structure: a) Weights for main objectives: values obtained from the auto-vector of Table 5 as retract in the Figure 5. Figure 5: Weights for Main Criteria b) Weights for each sub criterion: values obtained from the auto-vector of Table 6 to17. Figure 6 shows an example of weights for supplier s capability. Figure 6: Weights for Capability Criteria c) Supplier s score: Based on the idealized values of the intensity matrix obtained in Table 18, we can assign a score to the supplier in each sub criterion according to the technical evaluation performed as represented in Table 19. Table 19: Evaluation Score for each sub criterion 21

22 In this way, we can assign a score to the supplier for each criterion. Table 20 represents an example of this analysis for the item "Supplier s Capability." The degree of evaluation of each sub criterion was obtained in accordance with the recommendations contained in BARATEIRO s work (2011). Table 20: Example for Evaluation for Supplier Capability Criteria Source: Adapted from BARATEIRO (2011) By having all the criteria evaluated, we can get the final technical evaluation for the supplier. Table 21 shows an example of an evaluation performed for a supplier. Table 21: Example of Evaluation for all Criteria for MAC Vendor Source: Adapted from BARATEIRO (2011) 22

23 5. Conclusion Globalization and competition are demanding an increasing efficiency for the supply chain. Automation is an important item in terms of impacts on the CAPEX and OPEX and the supplier proper choice of this item is very relevant to the implementation of a large petrochemical project. In this type of project, only the MAC may represent a purchase order around of USD 200 M and this fact alone justifies a technical evaluation of suppliers based on a methodology of proven success. One way is to select suppliers through the Multi-Criteria Decision Analysis (MCDA) in which the AHP in one of the methods. The use of this methodology can reduce the number of trials required from the decision maker when the alternatives are numerous. Most of the results obtained in this article are aligned with the initial work produced by BARATEIRO (2011): the supplier's capability, experience and references, the project execution plan, the technology and the portfolio of asset management were the most important items in both studies. However, we found quite different values in the weights for the criteria and sub-criteria, which would be predictable and expected since an appropriate methodology to achieve these values was not used in BARATEIRO s work. We understand that the model proposed in this paper, for technical analysis of the MAC suppliers, can contribute for the choice of the best alternative that meets the technical requirements for such large petrochemical projects, reducing impartial judgments that may occur when an appropriate methodology is not used. References ABIQUIM (Brasil) (Ed.). Relatório de Acompanhamento Conjuntural: Resumo Outubro São Paulo, Oct p. (Monthly). Report. ALQAFFAS, Saleh A., Controlling automation contractors: Front end planning may take time, but will save you big time in the end. INTECH, Research Triangle Park (USA), p.01-08, Oct Monthly. Versão WEB Exclusive. BARATEIRO, Carlos E.;. O Impacto da Automação na Execução de Projetos do Tipo EPC (Engineering, Procurement and Construction): Como a Execução dos Empreendimentos 23

24 deve Considerar a Contratação da Automação das Plantas Industriais. Master Science Dissertation by Fluminense Federal University, Niteroi, Jan p. BARBA-ROMERO, S. e POMEROL, J.C. Decisiones Multicriterio: Fundamentos Teóricos e Utilización Práctica. Colección de Economia, Universidad de Alcalá, Spain, GOMES, L.F.A.M.; GONZALEZ-ARAYA, M.C. & CARIGNANO, C. (2004). Tomada de decisões em cenários complexos. Pioneira Thompson Learning, São Paulo. 168p. Ho, W., XU, X. e DEY, P. K. (2010), Multi-criteria decision making approaches for supplier evaluation and selection: A literature review. European Journal of Operational Research, 202, PETRÓLEO BRASILEIRO S.A. - PETROBRAS (Brasil) (Ed.). Plano de Negócios Rio de Janeiro, p. Presentation. SAEN, R. F. (2007), Suppliers selection in the presence of both cardinal and ordinal data. European Journal of Operational Research, 183, SAATY, T. L., The analytic hierarchy process. McGraw-Hill, New York, SAATY, T. L. (2006), Rank from comparisons and from ratings in the analytic hierarchy/network processes. European Journal of Operational Research, 168, SAATY, T. L. (2008), Decision making with the analytic hierarchy process. International Journal of Services Sciences, 1 (1), SAATY, T. L.; PENIWATI, P. (2008), Group Decision Making: Drawing out and Reconciling Differences, RWS Publications, Pittsburgh, 384p SILVA, A. C. S., NASCIMENTO, L. P. A. S. e BELDERRAINN, M. C. N. (2008), Supplier Selection Problem (SSP): estado-da-arte. XIV Congresso Latino-Ibero_Americano en Investigación de Operaciones (CLAIO), Cartagena, SONMEZ, M. (2006), A review and critique of supplier selection process and practices, Business School Occasional Papers Series, 1, VORSTER, Michael C.; MAGROGAN, Stephanie A.; MCNEIL, Blair W.. A Breakthrough Project Delivery System that Improves Performance by Reforming Owner, Contractor, Supplier Relationships: Under the Guidance of the of the Owner/Contractor/Supplier Relationships Research Team Number 130. First edition Austin: Construction Industry Institute, p. 24

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