- Email: [email protected]
Combinatorial optimization of petrochemical plants by asset integrity management indicators
Process Safety and Environmental Protection 127 (2019) 321–328
Contents lists available at ScienceDirect
Process Safety and Environmental Protection journal homepage: www.elsevier.com/locate/psep
Combinatorial optimization of petrochemical plants by asset integrity management indicators Mohammad Sheikhalishahi a,b,∗ , Maryam Karimi a , Raha Raghebi c a b c
School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran Institute for Trade Studies and Research, Tehran, Iran Faculty of Economic, management and social sciences, Shiraz University, Shiraz, Iran
a r t i c l e
i n f o
Article history: Received 3 January 2019 Accepted 10 May 2019 Available online 21 May 2019 Keywords: Asset management Combinatorial Optimization Analytic network process (ANP) Data envelopment analysis (DEA) Petrochemical plants
a b s t r a c t This paper proposes an integrated approach to evaluate and optimize performance of petrochemical plants based on asset integrity management criteria. The proposed approach consists of analytic network process (ANP) and data envelopment analysis (DEA). Asset management criteria are identified by surveying related standards and literature and categorized into three levels. First level criteria which are major criteria are based on plan-do-check-act (PDCA) cycle, second level criteria are sub sets of the first level criteria and third level criteria are quantitative and qualitative indicators derived from the literature. ANP is used to calculate relative importance of the second level criteria which enables decision makers to calculate the final score of each decision-making unit. Constant return to scale (CRS) and variable return to scale (VRS) DEA models in both input and output oriented forms are applied and the preferred model for measuring DMUs’ efficiency is selected via normality test. The proposed approach is applied to a set of real time petrochemical plants. Sensitivity analysis is performed to find significant factors affecting DMUs’ efficiency scores. The proposed approach would improve overall performance and could reduce operational loss, safety issues and incidents. © 2019 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.
1. Introduction There are different approaches for energy consumption management and efficiency improvement (Kharbach, 2016; Kermeli et al., 2014); asset management is known as an efficient approach to increase energy consumption efficiency and improve performance and safety of chemical plants (May, 2013). As the demand of standard instructions for managing their assets has been increased, the Institute of Asset Management (IAM), in collaboration with the British Standards Institution (BSI), and in consultation with organizations and individuals, developed a document called Publicly Available Specification (PAS). The first document was published in 2004 (PAS 55:2004) and in 2008 was updated to the new version (PAS 55:2008). This document contains principals to manage physical assets and guidelines for applications of these principals. In other word, PAS answers the question of what has to be done in an organization to successfully manage assets, and guides the organization in this path. The principals presented in PAS are applicable in any organization, regardless of their size and sector. As the asset
∗ Corresponding author. E-mail address: [email protected] (M. Sheikhalishahi).
management is critical for any kind of asset intensive business to become successful, the International Standards Organization (ISO) accepted PAS 55 as the basis to develop new series of standards. Three standards on asset management was developed by ISO in 2014 containing ISO 55,000:2014, Asset Management-Overview, principles and terminology, ISO 55,001:2014, Asset ManagementManagement Systems-Requirements and ISO 55,002:2014, Asset Management-Management Systems-Guidelines for the application of ISO 55,001. According to the PAS definition, asset management is defined as: “systematic and coordinated activities and practices through which an organization optimally and sustainably manages its assets and asset systems, their associated performance, risks and expenditures over their life cycles for the purpose of achieving its organizational strategic plan.” In this paper, an integrated approach is presented for evaluating performance of chemical plants taking into account asset management criteria. The remainder of this paper has been organized as follows: in section 2 a literature review of asset management as well as DEA and ANP methods are presented. Section 3 presents details of the proposed approach including ANP and DEA models and related inputs and outputs are presented. In Section 4 the proposed approach is applied on a real case study and the results of
https://doi.org/10.1016/j.psep.2019.05.017 0957-5820/© 2019 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.
322
M. Sheikhalishahi et al. / Process Safety and Environmental Protection 127 (2019) 321–328
Table 1 list of asset management indicators. First level criteria AM policy, strategy, objectives and plans
Second level criteria
Third level criteria (Reference) – Output (O)-Input (I)
AM policy, strategy and objective (c1)
Specific (PAS)-O Measurable (PAS)-O Achievable (PAS)-O Realistic (PAS)-O Time scaled (PAS)-O Number of incidents happened in the establishment not considered or badly planned by the emergency plan (Basso et al., 2004)-O hours of training specific to the planning for emergencies (Basso et al., 2004)-I Are roles responsibilities and authorities defined, documented and communicated to the relevant individuals? (PAS)-O Percentage of outsourcing (PAS)-I Investment in education Cao et al. (2015)-O Hours of training-O Training effectiveness -O Number of meetings (with employee, stakeholders, . . .), (Rahim, 2010)-O Number of standards/ documents (PAS)-O Access (Palmius)-O Quality (Palmius)-O Durability (Palmius)-O Reduction in levels of threat? (PAS)-O Regulatory/legal compliance (Cao et al., 2015) Amount of compensation paid -I Number of incidents due to wrong management of change (Basso et al., 2004)-I Do they include capital expenditure, financing & operational costs in the decision- making process? (PAS)-O Availability (Parida)-O Speed (Parida)-O Quality (Parida)-O Number of leading and lagging indicators for performance evaluation (PAS)-O Accident rate (Thomas et al., 2005)-I Incident rate (Thomas et al., 2005)-I Nonconformity rate (Thomas et al., 2005)-I Audits score (PAS)-O Number of audits (Basso et al., 2004)-O Investment in R&D Number of completed technical improvement projects (Cao et al., 2015)-O Number of records (PAS)-O Number of reviews of the major accident prevention policy (Basso et al., 2004)-O
Asset management plan (c2) Contingency planning (c3) Structure, authority & responsibilities (c4) Outsourcing of AM Activities (c5) Training, awareness & competence (c6)
AM enablers and controls
Communication, participation & consultation (c7) AM system documentation (c8) Information management (c9)
Risk management (c10) Legal and other requirements (c11) Management of change (c12) Life cycle activities (c13)
Implementation of AM plan(s)
Tools, facilities & equipment (c14)
Performance & condition monitoring (c15) Performance assessment and improvement
Investigation of asset-related failures, incidents and nonconformities (c16) Evaluation of compliance(c17) Audit (c18) Improvement action (c19) Records(c20) Management review(c21)
Management review
Table 2 Pairwise comparisons of first level criteria with respect to AM policy, strategy, objectives & plans. C1
Ca
Cb
Cc
Cd
Ce
Ca Cb Cc Cd Ce
1 1/3 1 1/2 1/4
3 1 3 3 2
1 1/3 1 1/2 1/4
2 1/3 2 1 1/3
4 1/2 4 3 1
The results of applying ANP is shown in Table 3. This table contains relative weight of each second level’s criteria.
ANP, DEA and sensitivity analysis are presented. Section 6 is dedicated to the conclusion.
Table 3 Relative importance of second level criteria obtained by ANP. Criteria
Relative weight
Criteria
Relative weight
c1 c2 c3 c4 c5 c6 c7 c8 c9 c10 c11
0.161 0.100 0.048 0.022 0.005 0.011 0.005 0.004 0.006 0.009 0.020
c12 c13 c14 c15 c16 c17 c18 c19 c20 c21
0.005 0.090 0.219 0.050 0.033 0.010 0.012 0.083 0.017 0.090
The ANP results shows that the most important factor in meeting the objective is c14 which is “Tools, facilities and equipment” with relative weight of 0.219 and the least important factor is AM system documentation with relative weight of 0.004.
2. Literature review Several studies have been done on different aspects of asset management, some of them proposed a framework for decision making in the area of asset management. Jooste and Vlok (2015) summarized 46 success factors for asset management service. A decision-making model is presented to enable asset owners and service providers to prioritize and select among these factors to improve asset management service. To incorporate sustainability into asset management, Niekamp et al. (2015) developed a multi criteria decision analysis framework that considers sustain-
ability criteria in addition to life cycle cost and environmental impact. Ujjwal et al. (2012) developed a risk-based decision making approach to determine the optimum time of maintenance actions such as repair or replacement in an asset integrity management system. This algorithm not only considers the probability of risk, but also incorporates the consequences of risk in financial term to calculate NPV of maintenance action for an asset. Burnett and Vlok (2014) presented a simple methodology for decision making in the area of physical asset management. Decisions making process con-
M. Sheikhalishahi et al. / Process Safety and Environmental Protection 127 (2019) 321–328
323
Fig. 1. Structure of the proposed method.
Fig. 2. DEA weights for level 1 criteria.
sists of various options including prioritizing assets and their failure mode for implementing maintenance, and selecting suitable maintenance technique for each asset by using MADM methods. Trappey et al. (2015) developed an asset management system for power transformers. They used PCA and BP-ANN algorithms to predict potential failures of power transformers, which enables maintenance managers to find suitable maintenance strategy based on failure prediction information. Various asset management criteria are presented in the literature. Rahim et al. (2010) presented a model called 5C, consists of 5 main criteria of asset integrity including Competence, Compliance,
Communication, Collaboration, Control. The model is used to evaluate current integrity status of the assets, identify the existing gaps and finally proposing required improvements based on organization’s requirements. Bam and Vlok (2014) presented an approach called multi-variate asset management assessment topography which is based on PAS-55. The approach can be used to determine current asset management system performance including weaknesses and strengths that prioritizes the asset management system areas. Also, a Pareto set of areas for investment and allocating resources is presented to maximize the gained financial benefit to investment ratio.
324
M. Sheikhalishahi et al. / Process Safety and Environmental Protection 127 (2019) 321–328
Table 4 DEA results for first level criteria. DMU
A1 A2 A3 A4 A5 A6 A7 A8 A9 A10 A11 A12 A13 A14 A15 A16 A17 A18 A19 A20 A21 A22 A23 A24 A25 A26 A27 A28 A29 A30 A31 A32 A33 A34 A35 A36 A37 A38 A39 A40 A41 A42 A43 A44 A45 A46 A47 A48 A49 A50
VRS input
VRS output
CRS input
CRS output
Efficiency
Rank
Efficiency
Rank
Efficiency
Rank
Efficiency
Rank
1.2369 1.0198 1.0000 1.0000 1.0000 1.0000 1.0000 1.0248 1.0184 1.0000 1.0000 1.0244 1.0000 1.0527 1.0000 1.0321 1.0000 1.0395 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0689 1.0072 1.0000 1.0756 1.0000 1.0000 1.0000 1.0000 1.0286 1.0328 1.0312 1.0000 1.0054 1.0062 1.0000 1.0000 1.0000 1.0000 1.0000
1 12 34 42 47 49 50 10 13 18 19 11 20 4 21 7 22 5 23 24 25 26 27 28 29 30 31 32 33 35 36 3 14 37 2 38 39 40 41 9 6 8 43 16 15 17 44 45 46 48
1.2369 1.0198 1.0000 1.0000 1.0000 1.0000 1.0000 1.0248 1.0184 1.0000 1.0000 1.0244 1.0000 1.0527 1.0000 1.0321 1.0000 1.0395 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0689 1.0072 1.0000 1.0756 1.0000 1.0000 1.0000 1.0000 1.0286 1.0328 1.0312 1.0000 1.0054 1.0062 1.0000 1.0000 1.0000 1.0000 1.0000
1 12 34 42 47 49 50 10 13 18 19 11 20 4 21 7 22 5 23 24 25 26 27 28 29 30 31 32 33 35 36 3 14 37 2 38 39 40 41 9 6 8 43 16 15 17 44 45 46 48
1.1343 1.0106 0.9776 0.9483 0.9569 0.9901 0.9505 1.0130 1.0095 0.9588 0.9948 1.0138 0.9993 1.0275 0.9905 1.0181 0.9463 1.0213 0.9787 0.9402 0.9788 0.9690 0.9643 0.9350 0.9518 0.9343 0.9608 0.9521 0.9448 0.8955 0.9738 1.0360 1.0039 0.9622 1.0425 0.9732 0.9350 0.9737 0.9613 1.0153 1.0177 1.0159 0.9688 1.0028 1.0034 1.0000 0.9863 0.9853 0.9360 0.9510
1 12 26 42 37 21 41 11 13 36 19 10 18 4 20 6 43 5 25 45 24 30 32 48 39 49 35 38 44 50 27 3 14 33 2 29 47 28 34 9 7 8 31 16 15 17 22 23 46 40
1.1343 1.0106 0.9776 0.9483 0.9569 0.9901 0.9505 1.0130 1.0095 0.9588 0.9948 1.0138 0.9993 1.0275 0.9905 1.0181 0.9463 1.0213 0.9787 0.9402 0.9788 0.9690 0.9643 0.9350 0.9518 0.9343 0.9608 0.9521 0.9448 0.8955 0.9738 1.0360 1.0039 0.9622 1.0425 0.9732 0.9350 0.9737 0.9613 1.0153 1.0177 1.0159 0.9688 1.0028 1.0034 1.0000 0.9863 0.9853 0.9360 0.9510
1 12 26 42 37 21 41 11 13 36 19 10 18 4 20 6 43 5 25 45 24 30 32 48 39 49 35 38 44 50 27 3 14 33 2 29 47 28 34 9 7 8 31 16 15 17 22 23 46 40
There are several studies on productivity and efficiency assessment of industrial sectors using data envelopment analysis (DEA) method. Here, some of the relevant studies evaluating performance of petrochemical plants and power plants are presented. Assaf et al. (2015) measured performance of 23 maintenance units in a petrochemical plant. They used maintenance indicators as DEA inputs and outputs. Azadeh et al. (2014) applied DEA to evaluate a petrochemical plant’s performance based on resilience engineering factors. Sarica and Or (2007) evaluated the performance of 65 power plants in Turkey using DEA based on operational and investment indexes. Barros and Peypoch (2008) used DEA to compare and determine the rank of thermos-electric power plants in Portugal based on their technical efficiency in a two-stage procedure. Liu et al. (2010) conducted efficiency analysis of thermal power plants in Taiwan and identified the most efficient plant among them. Sozen et al. (2012) used DEA and window analysis for efficiency assessment of hydro-power plants in Turkey. They employed two different models to analyze power plants based on 2 different
outputs, production (first model output), and unit cost of energy generation (second model output). Sueyoshi and Goto (2013a) applied DEA analysis on Pennsylvania–New Jersey–Maryland and California fossil fuel power plants to compare them considering their operational and environmental performance. Sueyoshi et al. (2013b) applied DEA window analysis on U.S. coal-fired power plants during 1995–2007 to assess their performance considering environmental assessment. Khalili-Damghani et al. (2015) developed a DEA model to assess the performance of 17 combined cycle power plants. They calculated the efficiency score for each power plant as an interval number based on interval uncertain inputs and undesirable outputs. ANP is also used in the literature for performance measurement in different sectors. Atmaca and Burak Basar (2012) evaluated Turkish power plants’ performance considering different aspects (criteria) such as technology and sustainability, economic aspect, socio-economic impacts and life quality using ANP and pairwise comparisons. Van Horenbeek and Pintelon (2014)
M. Sheikhalishahi et al. / Process Safety and Environmental Protection 127 (2019) 321–328
325
Table 5 DEA results for second level criteria. DMU
A1 A2 A3 A4 A5 A6 A7 A8 A9 A10 A11 A12 A13 A14 A15 A16 A17 A18 A19 A20 A21 A22 A23 A24 A25 A26 A27 A28 A29 A30 A31 A32 A33 A34 A35 A36 A37 A38 A39 A40 A41 A42 A43 A44 A45 A46 A47 A48 A49 A50
VRS input
VRS output
CRS input
CRS output
Efficiency
Rank
Efficiency
Rank
Efficiency
Rank
Efficiency
Rank
1.1376 1.2541 1.1172 1.0663 1.1232 1.0668 1.0333 1.1754 1.0888 1.0594 1.0969 1.1656 1.0724 1.2330 1.0703 1.0987 1.0986 1.1662 1.1986 1.1287 1.0922 1.0749 1.1463 1.0108 1.0732 1.0692 1.0469 1.0572 1.1162 1.0607 1.0287 1.2506 1.0898 1.2026 1.1832 1.1053 1.1255 1.1181 1.1586 1.1829 1.1098 1.2364 1.1418 1.1554 1.2094 1.0993 1.1624 1.1918 1.0396 1.1421
20 1 25 42 23 41 48 11 35 44 32 13 38 4 39 30 31 12 7 21 33 36 17 50 37 40 46 45 26 43 49 2 34 6 9 28 22 24 15 10 27 3 19 16 5 29 14 8 47 18
1.1376 1.2541 1.1172 1.0663 1.1232 1.0668 1.0333 1.1754 1.0888 1.0594 1.0969 1.1656 1.0724 1.2330 1.0703 1.0987 1.0986 1.1662 1.1986 1.1287 1.0922 1.0749 1.1463 1.0108 1.0732 1.0692 1.0469 1.0572 1.1162 1.0607 1.0287 1.2506 1.0898 1.2026 1.1832 1.1053 1.1255 1.1181 1.1586 1.1829 1.1098 1.2364 1.1418 1.1554 1.2094 1.0993 1.1624 1.1918 1.0396 1.1421
20 1 25 42 23 41 48 11 35 44 32 13 38 4 39 30 31 12 7 21 33 36 17 50 37 40 46 45 26 43 49 2 34 6 9 28 22 24 15 10 27 3 19 16 5 29 14 8 47 18
1.0741 1.1569 1.0668 1.0389 1.0468 1.0404 1.0276 1.1071 1.0536 1.0150 1.0612 1.0678 1.0537 1.1399 1.0262 1.0640 1.0390 1.0864 1.1329 1.1009 1.0869 1.0251 1.1065 1.0043 1.0205 1.0193 1.0469 1.0053 1.1128 1.0303 1.0223 1.0984 1.0358 1.1923 1.0982 1.0958 1.0854 1.0615 1.1028 1.1201 1.0866 1.1586 1.0543 1.0910 1.1693 1.0678 1.0975 1.0991 1.0149 1.0682
23 4 27 38 35 36 41 9 33 47 30 26 32 5 42 28 37 21 6 12 19 43 10 50 45 46 34 49 8 40 44 14 39 1 15 17 22 29 11 7 20 3 31 18 2 25 16 13 48 24
1.0741 1.1569 1.0668 1.0389 1.0468 1.0404 1.0276 1.1071 1.0536 1.0150 1.0612 1.0678 1.0537 1.1399 1.0262 1.0640 1.0390 1.0864 1.1329 1.1009 1.0869 1.0251 1.1065 1.0043 1.0205 1.0193 1.0469 1.0053 1.1128 1.0303 1.0223 1.0984 1.0358 1.1923 1.0982 1.0958 1.0854 1.0615 1.1028 1.1201 1.0866 1.1586 1.0543 1.0910 1.1693 1.0678 1.0975 1.0991 1.0149 1.0682
23 4 27 38 35 36 41 9 33 47 30 26 32 5 42 28 37 21 6 12 19 43 10 50 45 46 34 49 8 40 44 14 39 1 15 17 22 29 11 7 20 3 31 18 2 25 16 13 48 24
developed a framework for maintenance performance measurement. They proposed a methodology to determine organization’s specific maintenance objectives using ANP, and consequently corresponding maintenance performance indicators within the maintenance performance measurement framework are selected. Hefny et al. (2013) proposed a fuzzy ANP model to rank different electrical power generation scenarios considering major criteria (operation risk, economic risk, health risk, source risk and costs). After surveying asset management literature review, it is concluded that most of previous studies introduced a framework for decision making on asset management activities and some of them introduced asset management criteria focusing on special issues. This provides an opportunity to present a comprehensive view of asset management criteria and identify quantitative and qualitative indicators for each asset management criteria introduced by related standards and literature. Also, an integrated approach is presented
to use the indicators for evaluating and optimizing decision making units. 3. Methodology In this section after giving an overview of the proposed approach, as shown in Fig. 1, basic definition of analytic network process and data envelopment analysis are presented. The proposed approach is explained in two steps. In the first step, after identification of asset management indicators based on the literature and expert’s opinion, the required data is gathered. Then ANP network with pairwise comparisons are applied to find the relative importance of each indicator. The second step is dedicated to DEA approach including determining DEA inputs and outputs, calculating each DMU’s score with respect to AM indicators, calculating efficiency of DMUs for first level criteria and finally conducting sensitivity analysis using the preferred DEA model. The detailed structure of the proposed approach is depicted in Fig. 1.
326
M. Sheikhalishahi et al. / Process Safety and Environmental Protection 127 (2019) 321–328
Table 6 Spearman Correlations by omitting each factor for level 1 indicators. Omitted factor AM policy, strategy, objectives & plans 0.904
Spearman Correlation
AM enablers & controls
Implementation of AM plan(s)
Performance assessment & improvement
Management review
0.880
0.906
0.825
0.779
3.1. Analytic network process (ANP)
model however it has an extra constraint as follows (Banker et al., 1984; Emmanuel, 2001):
Analytic network process is a multi-attribute decision making technique that presents a framework for solving complex decision problems. As it converts the problem into several simple sub problems, ANP enables decision maker to understand and solve the problem in an easier way. ANP gives the problem a special structure and defines the problem three kind of clusters: goal cluster at the top level, criteria clusters at the second level and alternative clusters at the third level. ANP is capable of taking into account the relationships between and inside the clusters in the other word it is capable of considering any relationship in any direction. 3.2. Data envelopment analysis (DEA) DEA is a mathematical model for evaluating several similar alternatives known as decision making units (DMU) based on various input and output variables. DEA calculates efficiency score of each DMU and rank them from the highest to the lowest efficiency score. Assume we have n DMUs (j = 1,. . ., n), m inputs and s outputs. Xij is the amount of input variable i that DMU j consumes to produce Yrj unit of output r. Two basic models of DEA including constant return to scale and variable return to scale models are briefly explained below: The constant return to scale (CCR) model was first introduced by Charnes et al. (1978). The input and output oriented forms of CCR model could be presented as follows (Cooper et al., 2000; Emmanuel, 2001): Input- oriented CRS model: m
min − ε(
si− +
s.t :
xij j + si− = xio
i = 1, ..., m
j=1
n
sr+ )
r=1
i=1
n
s
yrj j − sr+ = yro
j=1
j , si− , sr+ ≥ 0
(1)
r = 1, ..., s
∀j, i, r
&
m
si− +
s.t :
sr+ )
xij j + si− = xio
i = 1, ..., m
j=1
n j=1
yrj j − sr+ = yro
j , si− , sr+
≥0
∀j, i, r
(2)
r = 1, ..., s &
(3)
3.3. Factor identification Firstly, asset management indicators introduced by PAS are extracted. As shown in Table 1, the indicators are divided into three levels. The first level is based on PDCA cycle of Deming. In order to aggregate asset management system with other related management systems, PAS presents the requirements and structure of asset management system within the PDCA framework in which the indicators are categorized in four major classes including Plan, Do, Check and Act. The second level consists of elements which are subsets of the first level indicators. For each indicator of the second level, several quantitative and qualitative indicators are found from literature and experts’ opinion. Finally, 35 indicators are presented in the third level. 3.4. Factors relative importance using ANP After identification of AM factors, a multi attribute decision making technique, The Analytic network process (ANP), is applied to determine relationships between factors based on experts opinion. By considering experts’ opinion ANP network is constructed. Pair wise comparison matrix is developed based on ANP network and 5 experts with years of experience of working in petrochemical plants as a manager or consultant, filled out each matrix by comparing each factor to another with respect to the upper level factor. SuperDecisions software was employed to check pair wise comparison consistency and finally determine the relative importance of each factor.
In this step, DEA is employed to evaluate relative performance of DMUs. For this purpose, AM indicators are divided into two groups: input and output variables. There are 4 inputs and 17 outputs which are represented in Table 1. After relative importance of each level 1 indicator is obtained from previous step, each DMU ‘s efficiency score is calculated. DMU i score in a level 1 factor (Sfli ) is calculated based on its score in level
r=1
i=1
n
s
j = 1
j=1
3.5. Performance assessment using DEA
free
Output-oriented CRS model: max + ε(
n
free
In the proposed model si− , sr+ are slack variables for input and output constraints, respectively, and ε is a very small positive number. The Variable return to scale (VRS) model is similar to the CRS
2 factors (SCsli ) and level 2 relative weights (wsl ) using following formula: Sfli =
wsl SCsli
fl = 1, ..., 5
i = 1, ..., 50
(3)
sl⊂fl
Equal weights are considered for third level indicator’s in which sum of weights equals to 1. The scores are applied as DEA criteria and the efficiency scores of DMUs are calculated.
M. Sheikhalishahi et al. / Process Safety and Environmental Protection 127 (2019) 321–328
327
Fig. 3. DEA weights for level 2 criteria.
4. Case study Performance evaluation of petrochemical plants from different, aspects especially from asset management aspect, is an important issue. In this study 50 petrochemical plants are evaluated based on their performance in asset management indicators. As mentioned before, asset management indicators are considered as outputs and inputs of DEA models. 4.1. ANP results Pairwise comparisons between indicators with respect to the upper level factors are done based on experts’ judgment. There are 10 pairwise comparisons matrixes that one of them is shown in Table 2. Table 2 shows pairwise comparisons between clusters with respect to AM policy, strategy, objectives & plans. After entering data in Super Decision software, all inconsistency indexes were checked to be less than 0.1. 4.2. DEA results The results of applying DEA models to the first level criteria and the second level criteria are given in Tables 4 and 5, respectively. The preferred DEA model is selected based on normality test (Azadeh et al., 2011). Thus, CRS output oriented model is selected as the preferred model for performance evaluation of first level criteria and VCR output oriented model is selected as the preferred model for performance evaluation of second level criteria. As mentioned before, each DMU’s score in the first level is calculated based on its score in the second level criteria and second level criteria’ weights obtained by pairwise comparisons of ANP. 4.3. Sensitivity analysis In order to find the impact of each factor, the preferred DEA model is repeated in which in each experiment one factor has been omitted and efficiency scores are calculated. Then, spearman correlation between the original dataset and new dataset is calculated. The calculated spearman correlations for level 1 indicators and level 2 indicators are shown in Tables 6 and 7, respectively. Lower spearman correlation implies that the factor’s effect is more significant. According to the results of Table 6, among level 1 factors, “Management review” has the most impact on petrochemical plants performance while “Implementation of AM plan(s)” has the least impact. According to the results of Table 7, among level 2 fac-
Table 7 Spearman correlation by omitting each factor for level 2 indicators. Omitted factor
Spearman Correlation
Omitted factor
Spearman Correlation
c1 c2 c3 c4 c5 c6 c7 c8 c9 c10 c11
0.992 0.998 0.999 0.949 0.927 0.997 0.899 0.993 0.996 0.979 0.928
c12 c13 c14 c15 c16 c17 c18 c19 c20 c21
0.948 0.965 0.996 0.992 1.000 0.968 0.93 0.999 0.934 0.941
tors, “c7: Communication, participation & consultation” is the most significant factor affecting petrochemical plants performance. For each criterion in level 1 and level 2, the weights obtained from DEA are shown in Figs. 2 and 3, respectively. Fig. 2 indicates that “Implementation of AM plan(s)” has the greatest weight. Also, Fig. 3 shows that “Communication, participation & consultation” has the greatest weight with the value of 0.115 in the second level.
5. Conclusions In this paper an integrated ANP- DEA approach is proposed to evaluate and rank petrochemical plants based on asset management criteria. Asset management criteria are collected from related literature and categorized in three levels. ANP is used to aggregate various factors using experts’ judgment. The factors are then considered as inputs and outputs of DEA. Different DEA models are applied and the preferred model is selected via normality test. 50 petrochemical plants are applied as a case study to show the applicability of the proposed approach. Sensitivity analysis is done to identify the most important factors affecting petrochemical plants’ performance from asset management viewpoint. The results of the sensitivity analysis showed that “Communication, participation & consultation” and “management review” are the most influential factors among 21 criteria of level 2 and 5 criteria of level 1, respectively. The proposed approach would help the decision makers to analysis their industrial unit according to asset management criteria to find the strengths and weaknesses of the system.
328
M. Sheikhalishahi et al. / Process Safety and Environmental Protection 127 (2019) 321–328
References Assaf, S.A., Hadidi, L.A., Hassanain, M.A., Rezq, M.F., 2015. Performance evaluation and benchmarking for maintenance decision making units at petrochemical corporation using a DEA model. Int. J. Adv. Manuf. Technol. 76 (9-12), 1957–1967. Atmaca, E., Basar, H.B., 2012. Evaluation of power plants in Turkey using analytic network process (ANP). Energy 44 (1), 555–563. Azadeh, A., Sheikhalishahi, M., Asadzadeh, S.M., 2011. A flexible neural networkfuzzy data envelopment analysis approach for location optimization of solar plants with uncertainty and complexity. Renew. Energ. 36 (12), 3394–3401. Azadeh, A., Salehi, V., Ashjari, B., Saberi, M., 2014. Performance evaluation of integrated resilience engineering factors by data envelopment analysis: The case of a petrochemical plant. Process Saf. Environ. Prot. 92 (3), 231–241. Bam, W.G., Vlok, P.J., 2014. Optimising invesment in asset management using the multivariate asset management assessment topography. South Afr. J. Ind. Eng. 25 (2), 29–38. Banker, R.D., et al., 1984. Some models for estimating technical and scale inefficiencies in data envelopment analysis. Manag. Sci. 30, 1078–1092. Barros, C.P., Peypoch, N., 2008. Technical efficiency of thermoelectric power plants. Energy Econ. 30 (6), 3118–3127. Basso, B., Carpegna, C., Dibitonto, C., Gaido, G., Robotto, A., Zonato, C., 2004. Reviewing the safety management system by incident investigation and performance indicators. J. Loss Prev. Process Ind. 17 (3), 225–231. Burnett, S., Vlok, P.J., 2014. A simplified numerical decision-making methodology for physical asset management decisions. South Afr. J. Ind. Eng. 25 (1), 162–175. Cao, Y., Zhao, K., Yang, J., Xiong, W., 2015. Constructing the integrated strategic performance indicator system for manufacturing companies. Int. J. Prod. Res., 1–15. Cooper, W.W., et al., 2000. Data Envelopment Analysis: A Comprehensive Tex with Models, Applications, References, and DEA-Solver Software. Kluwer Academic Publishers, Norwell, Massachusetts. Emmanuel, T., 2001. Introduction to the Theory and Application of Data Envelopment Analysis: A Foundation Text with Integrated Software. Kluwer Academic Publishers, Norwell, Massachusetts.
Hefny, H.A., Elsayed, H.M., Aly, H.F., 2013. Fuzzy multi-criteria decision making model for different scenarios of electrical power generation in Egypt. Egypt Informatics J. 14 (2), 125–133. Jooste, J.L., Vlok, P.J., 2015. A decision support model to determine the critical success factors of asset management services. South Afr. J. Ind. Eng. 26 (1), 27–43. Kermeli, K., Graus, W.H.J., Worrell, E., 2014. Energy efficiency improvement potentials and a low energy demand scenario for the global industrial sector. Energy Efficiency 7 (6), 987–1011. Kharbach, M., 2016. Fuel consumption efficiency for electricity and water production in Abu Dhabi. Energy Strategy Rev. 13–14, 109–114. Liu, C.H., Lin, S.J., Lewis, C., 2010. Evaluation of thermal power plant operational performance in Taiwan by data envelopment analysis. Energy Policy 38 (2), 1049–1058. May, I.L., 2013. Structural integrity and petrochemical industry. Energy Mater. 3 (4), 208–219. Niekamp, S., Bharadwaj, U.R., Sadhukhan, J., Chryssanthopoulos, M.K., 2015. A multicriteria decision support framework for sustainable asset management and challenges in its application. J. Ind. Prod. Eng. 32 (1), 23–36. Rahim, Y., Refsdal, I., Kenett, R.S., 2010. The 5C model: A new approach to asset integrity management. Int. J. Press. Vessels Pip. 87 (2), 88–93. Sueyoshi, T., Goto, M., 2013a. A comparative study among fossil fuel power plants in PJM and California ISO by DEA environmental assessment. Energy Econ. 40, 130–145. Sueyoshi, T., Goto, M., Sugiyama, M., 2013b. DEA window analysis for environmental assessment in a dynamic time shift: performance assessment of US coal-fired power plants. Energy Econ. 40, 845–857. Trappey, A.J., Trappey, C.V., Ma, L., Chang, J.C., 2015. Intelligent engineering asset management system for power transformer maintenance decision supports under various operating conditions. Comput. Ind. Eng. 84, 3–11. Van Horenbeek, A., Pintelon, L., 2014. Development of a maintenance performance measurement framework—using the analytic network process (ANP) for maintenance performance indicator selection. Omega 42 (1), 33–46.
Contents lists available at ScienceDirect
Process Safety and Environmental Protection journal homepage: www.elsevier.com/locate/psep
Combinatorial optimization of petrochemical plants by asset integrity management indicators Mohammad Sheikhalishahi a,b,∗ , Maryam Karimi a , Raha Raghebi c a b c
School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran Institute for Trade Studies and Research, Tehran, Iran Faculty of Economic, management and social sciences, Shiraz University, Shiraz, Iran
a r t i c l e
i n f o
Article history: Received 3 January 2019 Accepted 10 May 2019 Available online 21 May 2019 Keywords: Asset management Combinatorial Optimization Analytic network process (ANP) Data envelopment analysis (DEA) Petrochemical plants
a b s t r a c t This paper proposes an integrated approach to evaluate and optimize performance of petrochemical plants based on asset integrity management criteria. The proposed approach consists of analytic network process (ANP) and data envelopment analysis (DEA). Asset management criteria are identified by surveying related standards and literature and categorized into three levels. First level criteria which are major criteria are based on plan-do-check-act (PDCA) cycle, second level criteria are sub sets of the first level criteria and third level criteria are quantitative and qualitative indicators derived from the literature. ANP is used to calculate relative importance of the second level criteria which enables decision makers to calculate the final score of each decision-making unit. Constant return to scale (CRS) and variable return to scale (VRS) DEA models in both input and output oriented forms are applied and the preferred model for measuring DMUs’ efficiency is selected via normality test. The proposed approach is applied to a set of real time petrochemical plants. Sensitivity analysis is performed to find significant factors affecting DMUs’ efficiency scores. The proposed approach would improve overall performance and could reduce operational loss, safety issues and incidents. © 2019 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.
1. Introduction There are different approaches for energy consumption management and efficiency improvement (Kharbach, 2016; Kermeli et al., 2014); asset management is known as an efficient approach to increase energy consumption efficiency and improve performance and safety of chemical plants (May, 2013). As the demand of standard instructions for managing their assets has been increased, the Institute of Asset Management (IAM), in collaboration with the British Standards Institution (BSI), and in consultation with organizations and individuals, developed a document called Publicly Available Specification (PAS). The first document was published in 2004 (PAS 55:2004) and in 2008 was updated to the new version (PAS 55:2008). This document contains principals to manage physical assets and guidelines for applications of these principals. In other word, PAS answers the question of what has to be done in an organization to successfully manage assets, and guides the organization in this path. The principals presented in PAS are applicable in any organization, regardless of their size and sector. As the asset
∗ Corresponding author. E-mail address: [email protected] (M. Sheikhalishahi).
management is critical for any kind of asset intensive business to become successful, the International Standards Organization (ISO) accepted PAS 55 as the basis to develop new series of standards. Three standards on asset management was developed by ISO in 2014 containing ISO 55,000:2014, Asset Management-Overview, principles and terminology, ISO 55,001:2014, Asset ManagementManagement Systems-Requirements and ISO 55,002:2014, Asset Management-Management Systems-Guidelines for the application of ISO 55,001. According to the PAS definition, asset management is defined as: “systematic and coordinated activities and practices through which an organization optimally and sustainably manages its assets and asset systems, their associated performance, risks and expenditures over their life cycles for the purpose of achieving its organizational strategic plan.” In this paper, an integrated approach is presented for evaluating performance of chemical plants taking into account asset management criteria. The remainder of this paper has been organized as follows: in section 2 a literature review of asset management as well as DEA and ANP methods are presented. Section 3 presents details of the proposed approach including ANP and DEA models and related inputs and outputs are presented. In Section 4 the proposed approach is applied on a real case study and the results of
https://doi.org/10.1016/j.psep.2019.05.017 0957-5820/© 2019 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.
322
M. Sheikhalishahi et al. / Process Safety and Environmental Protection 127 (2019) 321–328
Table 1 list of asset management indicators. First level criteria AM policy, strategy, objectives and plans
Second level criteria
Third level criteria (Reference) – Output (O)-Input (I)
AM policy, strategy and objective (c1)
Specific (PAS)-O Measurable (PAS)-O Achievable (PAS)-O Realistic (PAS)-O Time scaled (PAS)-O Number of incidents happened in the establishment not considered or badly planned by the emergency plan (Basso et al., 2004)-O hours of training specific to the planning for emergencies (Basso et al., 2004)-I Are roles responsibilities and authorities defined, documented and communicated to the relevant individuals? (PAS)-O Percentage of outsourcing (PAS)-I Investment in education Cao et al. (2015)-O Hours of training-O Training effectiveness -O Number of meetings (with employee, stakeholders, . . .), (Rahim, 2010)-O Number of standards/ documents (PAS)-O Access (Palmius)-O Quality (Palmius)-O Durability (Palmius)-O Reduction in levels of threat? (PAS)-O Regulatory/legal compliance (Cao et al., 2015) Amount of compensation paid -I Number of incidents due to wrong management of change (Basso et al., 2004)-I Do they include capital expenditure, financing & operational costs in the decision- making process? (PAS)-O Availability (Parida)-O Speed (Parida)-O Quality (Parida)-O Number of leading and lagging indicators for performance evaluation (PAS)-O Accident rate (Thomas et al., 2005)-I Incident rate (Thomas et al., 2005)-I Nonconformity rate (Thomas et al., 2005)-I Audits score (PAS)-O Number of audits (Basso et al., 2004)-O Investment in R&D Number of completed technical improvement projects (Cao et al., 2015)-O Number of records (PAS)-O Number of reviews of the major accident prevention policy (Basso et al., 2004)-O
Asset management plan (c2) Contingency planning (c3) Structure, authority & responsibilities (c4) Outsourcing of AM Activities (c5) Training, awareness & competence (c6)
AM enablers and controls
Communication, participation & consultation (c7) AM system documentation (c8) Information management (c9)
Risk management (c10) Legal and other requirements (c11) Management of change (c12) Life cycle activities (c13)
Implementation of AM plan(s)
Tools, facilities & equipment (c14)
Performance & condition monitoring (c15) Performance assessment and improvement
Investigation of asset-related failures, incidents and nonconformities (c16) Evaluation of compliance(c17) Audit (c18) Improvement action (c19) Records(c20) Management review(c21)
Management review
Table 2 Pairwise comparisons of first level criteria with respect to AM policy, strategy, objectives & plans. C1
Ca
Cb
Cc
Cd
Ce
Ca Cb Cc Cd Ce
1 1/3 1 1/2 1/4
3 1 3 3 2
1 1/3 1 1/2 1/4
2 1/3 2 1 1/3
4 1/2 4 3 1
The results of applying ANP is shown in Table 3. This table contains relative weight of each second level’s criteria.
ANP, DEA and sensitivity analysis are presented. Section 6 is dedicated to the conclusion.
Table 3 Relative importance of second level criteria obtained by ANP. Criteria
Relative weight
Criteria
Relative weight
c1 c2 c3 c4 c5 c6 c7 c8 c9 c10 c11
0.161 0.100 0.048 0.022 0.005 0.011 0.005 0.004 0.006 0.009 0.020
c12 c13 c14 c15 c16 c17 c18 c19 c20 c21
0.005 0.090 0.219 0.050 0.033 0.010 0.012 0.083 0.017 0.090
The ANP results shows that the most important factor in meeting the objective is c14 which is “Tools, facilities and equipment” with relative weight of 0.219 and the least important factor is AM system documentation with relative weight of 0.004.
2. Literature review Several studies have been done on different aspects of asset management, some of them proposed a framework for decision making in the area of asset management. Jooste and Vlok (2015) summarized 46 success factors for asset management service. A decision-making model is presented to enable asset owners and service providers to prioritize and select among these factors to improve asset management service. To incorporate sustainability into asset management, Niekamp et al. (2015) developed a multi criteria decision analysis framework that considers sustain-
ability criteria in addition to life cycle cost and environmental impact. Ujjwal et al. (2012) developed a risk-based decision making approach to determine the optimum time of maintenance actions such as repair or replacement in an asset integrity management system. This algorithm not only considers the probability of risk, but also incorporates the consequences of risk in financial term to calculate NPV of maintenance action for an asset. Burnett and Vlok (2014) presented a simple methodology for decision making in the area of physical asset management. Decisions making process con-
M. Sheikhalishahi et al. / Process Safety and Environmental Protection 127 (2019) 321–328
323
Fig. 1. Structure of the proposed method.
Fig. 2. DEA weights for level 1 criteria.
sists of various options including prioritizing assets and their failure mode for implementing maintenance, and selecting suitable maintenance technique for each asset by using MADM methods. Trappey et al. (2015) developed an asset management system for power transformers. They used PCA and BP-ANN algorithms to predict potential failures of power transformers, which enables maintenance managers to find suitable maintenance strategy based on failure prediction information. Various asset management criteria are presented in the literature. Rahim et al. (2010) presented a model called 5C, consists of 5 main criteria of asset integrity including Competence, Compliance,
Communication, Collaboration, Control. The model is used to evaluate current integrity status of the assets, identify the existing gaps and finally proposing required improvements based on organization’s requirements. Bam and Vlok (2014) presented an approach called multi-variate asset management assessment topography which is based on PAS-55. The approach can be used to determine current asset management system performance including weaknesses and strengths that prioritizes the asset management system areas. Also, a Pareto set of areas for investment and allocating resources is presented to maximize the gained financial benefit to investment ratio.
324
M. Sheikhalishahi et al. / Process Safety and Environmental Protection 127 (2019) 321–328
Table 4 DEA results for first level criteria. DMU
A1 A2 A3 A4 A5 A6 A7 A8 A9 A10 A11 A12 A13 A14 A15 A16 A17 A18 A19 A20 A21 A22 A23 A24 A25 A26 A27 A28 A29 A30 A31 A32 A33 A34 A35 A36 A37 A38 A39 A40 A41 A42 A43 A44 A45 A46 A47 A48 A49 A50
VRS input
VRS output
CRS input
CRS output
Efficiency
Rank
Efficiency
Rank
Efficiency
Rank
Efficiency
Rank
1.2369 1.0198 1.0000 1.0000 1.0000 1.0000 1.0000 1.0248 1.0184 1.0000 1.0000 1.0244 1.0000 1.0527 1.0000 1.0321 1.0000 1.0395 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0689 1.0072 1.0000 1.0756 1.0000 1.0000 1.0000 1.0000 1.0286 1.0328 1.0312 1.0000 1.0054 1.0062 1.0000 1.0000 1.0000 1.0000 1.0000
1 12 34 42 47 49 50 10 13 18 19 11 20 4 21 7 22 5 23 24 25 26 27 28 29 30 31 32 33 35 36 3 14 37 2 38 39 40 41 9 6 8 43 16 15 17 44 45 46 48
1.2369 1.0198 1.0000 1.0000 1.0000 1.0000 1.0000 1.0248 1.0184 1.0000 1.0000 1.0244 1.0000 1.0527 1.0000 1.0321 1.0000 1.0395 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0689 1.0072 1.0000 1.0756 1.0000 1.0000 1.0000 1.0000 1.0286 1.0328 1.0312 1.0000 1.0054 1.0062 1.0000 1.0000 1.0000 1.0000 1.0000
1 12 34 42 47 49 50 10 13 18 19 11 20 4 21 7 22 5 23 24 25 26 27 28 29 30 31 32 33 35 36 3 14 37 2 38 39 40 41 9 6 8 43 16 15 17 44 45 46 48
1.1343 1.0106 0.9776 0.9483 0.9569 0.9901 0.9505 1.0130 1.0095 0.9588 0.9948 1.0138 0.9993 1.0275 0.9905 1.0181 0.9463 1.0213 0.9787 0.9402 0.9788 0.9690 0.9643 0.9350 0.9518 0.9343 0.9608 0.9521 0.9448 0.8955 0.9738 1.0360 1.0039 0.9622 1.0425 0.9732 0.9350 0.9737 0.9613 1.0153 1.0177 1.0159 0.9688 1.0028 1.0034 1.0000 0.9863 0.9853 0.9360 0.9510
1 12 26 42 37 21 41 11 13 36 19 10 18 4 20 6 43 5 25 45 24 30 32 48 39 49 35 38 44 50 27 3 14 33 2 29 47 28 34 9 7 8 31 16 15 17 22 23 46 40
1.1343 1.0106 0.9776 0.9483 0.9569 0.9901 0.9505 1.0130 1.0095 0.9588 0.9948 1.0138 0.9993 1.0275 0.9905 1.0181 0.9463 1.0213 0.9787 0.9402 0.9788 0.9690 0.9643 0.9350 0.9518 0.9343 0.9608 0.9521 0.9448 0.8955 0.9738 1.0360 1.0039 0.9622 1.0425 0.9732 0.9350 0.9737 0.9613 1.0153 1.0177 1.0159 0.9688 1.0028 1.0034 1.0000 0.9863 0.9853 0.9360 0.9510
1 12 26 42 37 21 41 11 13 36 19 10 18 4 20 6 43 5 25 45 24 30 32 48 39 49 35 38 44 50 27 3 14 33 2 29 47 28 34 9 7 8 31 16 15 17 22 23 46 40
There are several studies on productivity and efficiency assessment of industrial sectors using data envelopment analysis (DEA) method. Here, some of the relevant studies evaluating performance of petrochemical plants and power plants are presented. Assaf et al. (2015) measured performance of 23 maintenance units in a petrochemical plant. They used maintenance indicators as DEA inputs and outputs. Azadeh et al. (2014) applied DEA to evaluate a petrochemical plant’s performance based on resilience engineering factors. Sarica and Or (2007) evaluated the performance of 65 power plants in Turkey using DEA based on operational and investment indexes. Barros and Peypoch (2008) used DEA to compare and determine the rank of thermos-electric power plants in Portugal based on their technical efficiency in a two-stage procedure. Liu et al. (2010) conducted efficiency analysis of thermal power plants in Taiwan and identified the most efficient plant among them. Sozen et al. (2012) used DEA and window analysis for efficiency assessment of hydro-power plants in Turkey. They employed two different models to analyze power plants based on 2 different
outputs, production (first model output), and unit cost of energy generation (second model output). Sueyoshi and Goto (2013a) applied DEA analysis on Pennsylvania–New Jersey–Maryland and California fossil fuel power plants to compare them considering their operational and environmental performance. Sueyoshi et al. (2013b) applied DEA window analysis on U.S. coal-fired power plants during 1995–2007 to assess their performance considering environmental assessment. Khalili-Damghani et al. (2015) developed a DEA model to assess the performance of 17 combined cycle power plants. They calculated the efficiency score for each power plant as an interval number based on interval uncertain inputs and undesirable outputs. ANP is also used in the literature for performance measurement in different sectors. Atmaca and Burak Basar (2012) evaluated Turkish power plants’ performance considering different aspects (criteria) such as technology and sustainability, economic aspect, socio-economic impacts and life quality using ANP and pairwise comparisons. Van Horenbeek and Pintelon (2014)
M. Sheikhalishahi et al. / Process Safety and Environmental Protection 127 (2019) 321–328
325
Table 5 DEA results for second level criteria. DMU
A1 A2 A3 A4 A5 A6 A7 A8 A9 A10 A11 A12 A13 A14 A15 A16 A17 A18 A19 A20 A21 A22 A23 A24 A25 A26 A27 A28 A29 A30 A31 A32 A33 A34 A35 A36 A37 A38 A39 A40 A41 A42 A43 A44 A45 A46 A47 A48 A49 A50
VRS input
VRS output
CRS input
CRS output
Efficiency
Rank
Efficiency
Rank
Efficiency
Rank
Efficiency
Rank
1.1376 1.2541 1.1172 1.0663 1.1232 1.0668 1.0333 1.1754 1.0888 1.0594 1.0969 1.1656 1.0724 1.2330 1.0703 1.0987 1.0986 1.1662 1.1986 1.1287 1.0922 1.0749 1.1463 1.0108 1.0732 1.0692 1.0469 1.0572 1.1162 1.0607 1.0287 1.2506 1.0898 1.2026 1.1832 1.1053 1.1255 1.1181 1.1586 1.1829 1.1098 1.2364 1.1418 1.1554 1.2094 1.0993 1.1624 1.1918 1.0396 1.1421
20 1 25 42 23 41 48 11 35 44 32 13 38 4 39 30 31 12 7 21 33 36 17 50 37 40 46 45 26 43 49 2 34 6 9 28 22 24 15 10 27 3 19 16 5 29 14 8 47 18
1.1376 1.2541 1.1172 1.0663 1.1232 1.0668 1.0333 1.1754 1.0888 1.0594 1.0969 1.1656 1.0724 1.2330 1.0703 1.0987 1.0986 1.1662 1.1986 1.1287 1.0922 1.0749 1.1463 1.0108 1.0732 1.0692 1.0469 1.0572 1.1162 1.0607 1.0287 1.2506 1.0898 1.2026 1.1832 1.1053 1.1255 1.1181 1.1586 1.1829 1.1098 1.2364 1.1418 1.1554 1.2094 1.0993 1.1624 1.1918 1.0396 1.1421
20 1 25 42 23 41 48 11 35 44 32 13 38 4 39 30 31 12 7 21 33 36 17 50 37 40 46 45 26 43 49 2 34 6 9 28 22 24 15 10 27 3 19 16 5 29 14 8 47 18
1.0741 1.1569 1.0668 1.0389 1.0468 1.0404 1.0276 1.1071 1.0536 1.0150 1.0612 1.0678 1.0537 1.1399 1.0262 1.0640 1.0390 1.0864 1.1329 1.1009 1.0869 1.0251 1.1065 1.0043 1.0205 1.0193 1.0469 1.0053 1.1128 1.0303 1.0223 1.0984 1.0358 1.1923 1.0982 1.0958 1.0854 1.0615 1.1028 1.1201 1.0866 1.1586 1.0543 1.0910 1.1693 1.0678 1.0975 1.0991 1.0149 1.0682
23 4 27 38 35 36 41 9 33 47 30 26 32 5 42 28 37 21 6 12 19 43 10 50 45 46 34 49 8 40 44 14 39 1 15 17 22 29 11 7 20 3 31 18 2 25 16 13 48 24
1.0741 1.1569 1.0668 1.0389 1.0468 1.0404 1.0276 1.1071 1.0536 1.0150 1.0612 1.0678 1.0537 1.1399 1.0262 1.0640 1.0390 1.0864 1.1329 1.1009 1.0869 1.0251 1.1065 1.0043 1.0205 1.0193 1.0469 1.0053 1.1128 1.0303 1.0223 1.0984 1.0358 1.1923 1.0982 1.0958 1.0854 1.0615 1.1028 1.1201 1.0866 1.1586 1.0543 1.0910 1.1693 1.0678 1.0975 1.0991 1.0149 1.0682
23 4 27 38 35 36 41 9 33 47 30 26 32 5 42 28 37 21 6 12 19 43 10 50 45 46 34 49 8 40 44 14 39 1 15 17 22 29 11 7 20 3 31 18 2 25 16 13 48 24
developed a framework for maintenance performance measurement. They proposed a methodology to determine organization’s specific maintenance objectives using ANP, and consequently corresponding maintenance performance indicators within the maintenance performance measurement framework are selected. Hefny et al. (2013) proposed a fuzzy ANP model to rank different electrical power generation scenarios considering major criteria (operation risk, economic risk, health risk, source risk and costs). After surveying asset management literature review, it is concluded that most of previous studies introduced a framework for decision making on asset management activities and some of them introduced asset management criteria focusing on special issues. This provides an opportunity to present a comprehensive view of asset management criteria and identify quantitative and qualitative indicators for each asset management criteria introduced by related standards and literature. Also, an integrated approach is presented
to use the indicators for evaluating and optimizing decision making units. 3. Methodology In this section after giving an overview of the proposed approach, as shown in Fig. 1, basic definition of analytic network process and data envelopment analysis are presented. The proposed approach is explained in two steps. In the first step, after identification of asset management indicators based on the literature and expert’s opinion, the required data is gathered. Then ANP network with pairwise comparisons are applied to find the relative importance of each indicator. The second step is dedicated to DEA approach including determining DEA inputs and outputs, calculating each DMU’s score with respect to AM indicators, calculating efficiency of DMUs for first level criteria and finally conducting sensitivity analysis using the preferred DEA model. The detailed structure of the proposed approach is depicted in Fig. 1.
326
M. Sheikhalishahi et al. / Process Safety and Environmental Protection 127 (2019) 321–328
Table 6 Spearman Correlations by omitting each factor for level 1 indicators. Omitted factor AM policy, strategy, objectives & plans 0.904
Spearman Correlation
AM enablers & controls
Implementation of AM plan(s)
Performance assessment & improvement
Management review
0.880
0.906
0.825
0.779
3.1. Analytic network process (ANP)
model however it has an extra constraint as follows (Banker et al., 1984; Emmanuel, 2001):
Analytic network process is a multi-attribute decision making technique that presents a framework for solving complex decision problems. As it converts the problem into several simple sub problems, ANP enables decision maker to understand and solve the problem in an easier way. ANP gives the problem a special structure and defines the problem three kind of clusters: goal cluster at the top level, criteria clusters at the second level and alternative clusters at the third level. ANP is capable of taking into account the relationships between and inside the clusters in the other word it is capable of considering any relationship in any direction. 3.2. Data envelopment analysis (DEA) DEA is a mathematical model for evaluating several similar alternatives known as decision making units (DMU) based on various input and output variables. DEA calculates efficiency score of each DMU and rank them from the highest to the lowest efficiency score. Assume we have n DMUs (j = 1,. . ., n), m inputs and s outputs. Xij is the amount of input variable i that DMU j consumes to produce Yrj unit of output r. Two basic models of DEA including constant return to scale and variable return to scale models are briefly explained below: The constant return to scale (CCR) model was first introduced by Charnes et al. (1978). The input and output oriented forms of CCR model could be presented as follows (Cooper et al., 2000; Emmanuel, 2001): Input- oriented CRS model: m
min − ε(
si− +
s.t :
xij j + si− = xio
i = 1, ..., m
j=1
n
sr+ )
r=1
i=1
n
s
yrj j − sr+ = yro
j=1
j , si− , sr+ ≥ 0
(1)
r = 1, ..., s
∀j, i, r
&
m
si− +
s.t :
sr+ )
xij j + si− = xio
i = 1, ..., m
j=1
n j=1
yrj j − sr+ = yro
j , si− , sr+
≥0
∀j, i, r
(2)
r = 1, ..., s &
(3)
3.3. Factor identification Firstly, asset management indicators introduced by PAS are extracted. As shown in Table 1, the indicators are divided into three levels. The first level is based on PDCA cycle of Deming. In order to aggregate asset management system with other related management systems, PAS presents the requirements and structure of asset management system within the PDCA framework in which the indicators are categorized in four major classes including Plan, Do, Check and Act. The second level consists of elements which are subsets of the first level indicators. For each indicator of the second level, several quantitative and qualitative indicators are found from literature and experts’ opinion. Finally, 35 indicators are presented in the third level. 3.4. Factors relative importance using ANP After identification of AM factors, a multi attribute decision making technique, The Analytic network process (ANP), is applied to determine relationships between factors based on experts opinion. By considering experts’ opinion ANP network is constructed. Pair wise comparison matrix is developed based on ANP network and 5 experts with years of experience of working in petrochemical plants as a manager or consultant, filled out each matrix by comparing each factor to another with respect to the upper level factor. SuperDecisions software was employed to check pair wise comparison consistency and finally determine the relative importance of each factor.
In this step, DEA is employed to evaluate relative performance of DMUs. For this purpose, AM indicators are divided into two groups: input and output variables. There are 4 inputs and 17 outputs which are represented in Table 1. After relative importance of each level 1 indicator is obtained from previous step, each DMU ‘s efficiency score is calculated. DMU i score in a level 1 factor (Sfli ) is calculated based on its score in level
r=1
i=1
n
s
j = 1
j=1
3.5. Performance assessment using DEA
free
Output-oriented CRS model: max + ε(
n
free
In the proposed model si− , sr+ are slack variables for input and output constraints, respectively, and ε is a very small positive number. The Variable return to scale (VRS) model is similar to the CRS
2 factors (SCsli ) and level 2 relative weights (wsl ) using following formula: Sfli =
wsl SCsli
fl = 1, ..., 5
i = 1, ..., 50
(3)
sl⊂fl
Equal weights are considered for third level indicator’s in which sum of weights equals to 1. The scores are applied as DEA criteria and the efficiency scores of DMUs are calculated.
M. Sheikhalishahi et al. / Process Safety and Environmental Protection 127 (2019) 321–328
327
Fig. 3. DEA weights for level 2 criteria.
4. Case study Performance evaluation of petrochemical plants from different, aspects especially from asset management aspect, is an important issue. In this study 50 petrochemical plants are evaluated based on their performance in asset management indicators. As mentioned before, asset management indicators are considered as outputs and inputs of DEA models. 4.1. ANP results Pairwise comparisons between indicators with respect to the upper level factors are done based on experts’ judgment. There are 10 pairwise comparisons matrixes that one of them is shown in Table 2. Table 2 shows pairwise comparisons between clusters with respect to AM policy, strategy, objectives & plans. After entering data in Super Decision software, all inconsistency indexes were checked to be less than 0.1. 4.2. DEA results The results of applying DEA models to the first level criteria and the second level criteria are given in Tables 4 and 5, respectively. The preferred DEA model is selected based on normality test (Azadeh et al., 2011). Thus, CRS output oriented model is selected as the preferred model for performance evaluation of first level criteria and VCR output oriented model is selected as the preferred model for performance evaluation of second level criteria. As mentioned before, each DMU’s score in the first level is calculated based on its score in the second level criteria and second level criteria’ weights obtained by pairwise comparisons of ANP. 4.3. Sensitivity analysis In order to find the impact of each factor, the preferred DEA model is repeated in which in each experiment one factor has been omitted and efficiency scores are calculated. Then, spearman correlation between the original dataset and new dataset is calculated. The calculated spearman correlations for level 1 indicators and level 2 indicators are shown in Tables 6 and 7, respectively. Lower spearman correlation implies that the factor’s effect is more significant. According to the results of Table 6, among level 1 factors, “Management review” has the most impact on petrochemical plants performance while “Implementation of AM plan(s)” has the least impact. According to the results of Table 7, among level 2 fac-
Table 7 Spearman correlation by omitting each factor for level 2 indicators. Omitted factor
Spearman Correlation
Omitted factor
Spearman Correlation
c1 c2 c3 c4 c5 c6 c7 c8 c9 c10 c11
0.992 0.998 0.999 0.949 0.927 0.997 0.899 0.993 0.996 0.979 0.928
c12 c13 c14 c15 c16 c17 c18 c19 c20 c21
0.948 0.965 0.996 0.992 1.000 0.968 0.93 0.999 0.934 0.941
tors, “c7: Communication, participation & consultation” is the most significant factor affecting petrochemical plants performance. For each criterion in level 1 and level 2, the weights obtained from DEA are shown in Figs. 2 and 3, respectively. Fig. 2 indicates that “Implementation of AM plan(s)” has the greatest weight. Also, Fig. 3 shows that “Communication, participation & consultation” has the greatest weight with the value of 0.115 in the second level.
5. Conclusions In this paper an integrated ANP- DEA approach is proposed to evaluate and rank petrochemical plants based on asset management criteria. Asset management criteria are collected from related literature and categorized in three levels. ANP is used to aggregate various factors using experts’ judgment. The factors are then considered as inputs and outputs of DEA. Different DEA models are applied and the preferred model is selected via normality test. 50 petrochemical plants are applied as a case study to show the applicability of the proposed approach. Sensitivity analysis is done to identify the most important factors affecting petrochemical plants’ performance from asset management viewpoint. The results of the sensitivity analysis showed that “Communication, participation & consultation” and “management review” are the most influential factors among 21 criteria of level 2 and 5 criteria of level 1, respectively. The proposed approach would help the decision makers to analysis their industrial unit according to asset management criteria to find the strengths and weaknesses of the system.
328
M. Sheikhalishahi et al. / Process Safety and Environmental Protection 127 (2019) 321–328
References Assaf, S.A., Hadidi, L.A., Hassanain, M.A., Rezq, M.F., 2015. Performance evaluation and benchmarking for maintenance decision making units at petrochemical corporation using a DEA model. Int. J. Adv. Manuf. Technol. 76 (9-12), 1957–1967. Atmaca, E., Basar, H.B., 2012. Evaluation of power plants in Turkey using analytic network process (ANP). Energy 44 (1), 555–563. Azadeh, A., Sheikhalishahi, M., Asadzadeh, S.M., 2011. A flexible neural networkfuzzy data envelopment analysis approach for location optimization of solar plants with uncertainty and complexity. Renew. Energ. 36 (12), 3394–3401. Azadeh, A., Salehi, V., Ashjari, B., Saberi, M., 2014. Performance evaluation of integrated resilience engineering factors by data envelopment analysis: The case of a petrochemical plant. Process Saf. Environ. Prot. 92 (3), 231–241. Bam, W.G., Vlok, P.J., 2014. Optimising invesment in asset management using the multivariate asset management assessment topography. South Afr. J. Ind. Eng. 25 (2), 29–38. Banker, R.D., et al., 1984. Some models for estimating technical and scale inefficiencies in data envelopment analysis. Manag. Sci. 30, 1078–1092. Barros, C.P., Peypoch, N., 2008. Technical efficiency of thermoelectric power plants. Energy Econ. 30 (6), 3118–3127. Basso, B., Carpegna, C., Dibitonto, C., Gaido, G., Robotto, A., Zonato, C., 2004. Reviewing the safety management system by incident investigation and performance indicators. J. Loss Prev. Process Ind. 17 (3), 225–231. Burnett, S., Vlok, P.J., 2014. A simplified numerical decision-making methodology for physical asset management decisions. South Afr. J. Ind. Eng. 25 (1), 162–175. Cao, Y., Zhao, K., Yang, J., Xiong, W., 2015. Constructing the integrated strategic performance indicator system for manufacturing companies. Int. J. Prod. Res., 1–15. Cooper, W.W., et al., 2000. Data Envelopment Analysis: A Comprehensive Tex with Models, Applications, References, and DEA-Solver Software. Kluwer Academic Publishers, Norwell, Massachusetts. Emmanuel, T., 2001. Introduction to the Theory and Application of Data Envelopment Analysis: A Foundation Text with Integrated Software. Kluwer Academic Publishers, Norwell, Massachusetts.
Hefny, H.A., Elsayed, H.M., Aly, H.F., 2013. Fuzzy multi-criteria decision making model for different scenarios of electrical power generation in Egypt. Egypt Informatics J. 14 (2), 125–133. Jooste, J.L., Vlok, P.J., 2015. A decision support model to determine the critical success factors of asset management services. South Afr. J. Ind. Eng. 26 (1), 27–43. Kermeli, K., Graus, W.H.J., Worrell, E., 2014. Energy efficiency improvement potentials and a low energy demand scenario for the global industrial sector. Energy Efficiency 7 (6), 987–1011. Kharbach, M., 2016. Fuel consumption efficiency for electricity and water production in Abu Dhabi. Energy Strategy Rev. 13–14, 109–114. Liu, C.H., Lin, S.J., Lewis, C., 2010. Evaluation of thermal power plant operational performance in Taiwan by data envelopment analysis. Energy Policy 38 (2), 1049–1058. May, I.L., 2013. Structural integrity and petrochemical industry. Energy Mater. 3 (4), 208–219. Niekamp, S., Bharadwaj, U.R., Sadhukhan, J., Chryssanthopoulos, M.K., 2015. A multicriteria decision support framework for sustainable asset management and challenges in its application. J. Ind. Prod. Eng. 32 (1), 23–36. Rahim, Y., Refsdal, I., Kenett, R.S., 2010. The 5C model: A new approach to asset integrity management. Int. J. Press. Vessels Pip. 87 (2), 88–93. Sueyoshi, T., Goto, M., 2013a. A comparative study among fossil fuel power plants in PJM and California ISO by DEA environmental assessment. Energy Econ. 40, 130–145. Sueyoshi, T., Goto, M., Sugiyama, M., 2013b. DEA window analysis for environmental assessment in a dynamic time shift: performance assessment of US coal-fired power plants. Energy Econ. 40, 845–857. Trappey, A.J., Trappey, C.V., Ma, L., Chang, J.C., 2015. Intelligent engineering asset management system for power transformer maintenance decision supports under various operating conditions. Comput. Ind. Eng. 84, 3–11. Van Horenbeek, A., Pintelon, L., 2014. Development of a maintenance performance measurement framework—using the analytic network process (ANP) for maintenance performance indicator selection. Omega 42 (1), 33–46.