Supply chain performance evaluation through AHM and Membership degree transformation

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ScienceDirect Materials Today: Proceedings 4 (2017) 7848–7858

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ICAAMM-2016

Supply chain performance evaluation through AHM and Membership degree transformation S.Hemalathaa, K.Narayana Raob, G.Rambabuc, K.Venkatasubbaiahd a

Department of Mechanical Engg, Lendi Institute of Engg and Tech, Vizianagaram- 535 005,India b Government Model Residential Polytechnic, Paderu, Visakhapatnam -531024,India c Department of Mechanical Engg, Andhra University, Visakhapatnam-530 007,India d Department of Mechanical Engg, Andhra University, Visakhapatnam-530 007,India

Abstract In today’s competitive business environment, evaluation of supply chain is also a principal functionality in any kind of business organization. It is due to the uncertainty within the business environment. In this paper, hybrid methodology of Attribute Hierarchy Model (AHM) and Membership Degree Transformation- M (1, 2, 3) is proposed for evaluation of supply chain performance. This planned methodology is not only useful to judge the complete performance of the supply chain but also to identify the critical criteria upon which the organizations need to concentrate to improve the performance of their supply chains. © 2017 Elsevier Ltd. All rights reserved. Selection and Peer-review under responsibility of the Committee Members of International Conference on Advancements in Aeromechanical Materials for Manufacturing (ICAAMM-2016). Keywords: Supply chain performance, Attribute Hierarchy Model, Membership Degree transformation;

1. Introduction It is vital to identify the right performance measures and measurement system due to the reasons namely :1) To set up strategic objectives 2)To evaluate the organizational supply chain performance 3) To manage the future of business goal and activities effectively. * S.Hemalatha. Tel.: 9490707440. E-mail address: [email protected]

2214-7853 © 2017 Elsevier Ltd. All rights reserved. Selection and Peer-review under responsibility of the Committee Members of International Conference on Advancements in Aeromechanical Materials for Manufacturing (ICAAMM-2016).

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Petroleum products marketing company supply chain The Supply Chain of Petroleum Products Marketing Company mainly consists of six departments namely Demand Forecasting, Distribution Planning, Storing, Manufacturing, Feeding and Purchasing departments. The Revised Supply Chain of a petroleum company is as shown in Fig.1 given by Mohapatra el at (2010).

Fig.1. Revised petroleum supply chain

2. Literature: M Beamon: This paper presented supply chain performance measures and framework for measurement system. A Gunasekaran, C Patel, E Tirtiroglu: This paper measured supply chain performance and considered the measures namely suppliers delivery performance, customer service, inventory and logistic costs and also related these measures to customer satisfaction. Charan Parikshit,Baisya Rajat KShankar Ravi: This paper deals with selection of supply chain performance measurement system (SCPMS), calculated fuzzy positive-ideal solution (FPIS) and fuzzy negative-ideal solution (FNIS) and adopted TOPSIS technique for evaluation. Xia, L.X.X., Bin Ma, Lim, R.: This paper proposed AHP method to measure the supply chain performance. Nakhai Kamalabadi, Bayat, Ahmadi, Ebrahimi ,M. Safari Kahreh: This paper explained a new approach FMADM (Fuzzy Multi Attribute Decision Making) method for the measurement of supply chain performance and extracted the performance metrics based-on balanced scorecard (BSC) model. Hua Jiang , Hebei, Handan ; Junhu Ruan: This paper adopted SCOR model to analyze the factors and applied Attribute Hierarchical Model (AHM) for the measurement of supply chain performance. Ranjan Kumar Mohapatra , R. P. Mohanty , R.S. Dhalla: This paper adopted AHP approach for evaluation of supply chain performance in petroleum industry. Jing Yang, Hua Jiang: This paper constructed index system based on SCOR model and evaluated overall supply chain performance by using AHM and M (1,2,3) transformation. Alaalan W.Mackelprang, Jessika L.: This paper used Meta analysis to examine the relation among strategic supply chain integration and performance of supply chain and stated that integration should not be universally viewed as improving performance. Khan Rai Waqas Azfar, , Nawar Khan, Hamza Farooq Gabrie: This paper presented set of characteristics to measure supply chain performance and structure of the supply chain. S.Hemalatha, K. Ram Babu, K.Narayana Rao, K.Venkatasubbaiah: This paper proposed a hybrid methodology of Fuzzy positive Ideal rating /Fuzzy Negative Ideal rating and Membership Degree Transformation- M (1, 2, 3) for evaluation of supplier’s performance. From the review of literature it is observed that there is limited research in evaluation of supply chains using hybrid methodologies. In lieu of this, a hybrid methodology is proposed for evaluation of supply chain performance and illustrated by considering the case study of supply chain of a petroleum company existed in the literature (Mahapatra, 2010). In the proposed methodology, Attribute Hierarchy Model (AHM) is adopted to determine the relative weights connected with criteria/sub-criteria. Then, membership transformation method – M (1, 2, 3) is used to determine the overall performance of a supply chain. Proposed methodology is explained in section three. A case

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study is presented in section four. Results in addition to discussions are made in section five. In section six, the conclusions and future scope is presented. 3. Methodology Step 1: Establish Evaluation Index System of Supply Chain Performance To evaluate the performance of supply chain, initially the business organization needs to identify criteria on which the supply chain performance depends as well as the relative importance inclined to them. Step 2: Determine importance weights of the criteria/sub criteria Attribute Hierarchical Model (AHM) is used to calculate the relative weights of the criteria and sub criteria discussed by Xiao el at (2013). Attribute hierarchical model (AHM) is usually a method of unstructured decision making and is originated from analytic hierarchy process (AHP). Compared with the AHP, which based on the weight of the model, the biggest advantage from the game-based type of AHM will be the matrix regarding comparison between every a couple of indexes does not exist (consistency check difficulty), so that there is no need for a substantial amount calculation. Step 2.1: Formulate pair wise comparison matrix Pair wise comparison matrix can be prepared basing on the relative significance about the criteria over a size involving 1-5. (VeryDisatisfied-1; Disatisfied-2; General-3; Satisfied-4; VerySatisfied-5). The values introduced throughout pair wise comparison matrices would be the aggregated values of the all of the viewpoints. To the questionnaires make sure you refer Appendices. Step 2.2: Formulate Attribute matrix Attribute matrix A = (µ ij) nxn equation

μ ij

 β k β k +1  1  =  βk +1   0 .5   0

is formulated from pair wise comparison matrix using the following conversion

a ij = k 1 k = 1, i ≠ j

a ij = a ij

a i j = 1, i = j

Step 2.3: Relative weights of the Attributes Relative attribute weight is determined from the following relation

Wc j =

J 2 *  μij J * ( J − 1) i =1

j = 1, 2....J

Step 3: Membership Transformation through “Effective, Comparison and Composition” Membership transformation method – M (1,2,3) proposed by Hemalatha (2015) is adopted to find out the evaluation matrix of the alternative.

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Step 3.1: Determine Evaluation Membership

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μ jk (Q )

Percentage of satisfaction among the domain experts under each class is considered as evaluation matrix of each criterion.

μ jk (Q ) =membership of jth sub-criteria of the criteria group ‘Q’ belonging to the kth fuzzy membership class. Step 3.2: Determine Distinguishable Weights ( α j (Q ) ) Distinguishable weight represents the normalized and quantized value obtained from the following relation. m αj (Q) =vj (Q)/ j=1vj (Q) ( j =1..m) p

Where vj (Q) =1−(1/log( p))*Hj (Q) , H j (Q) = −μjk (Q)*log μjk (Q) k =1

v j (Q ) = weight of the jth sub criteria of the evaluation criteria object ‘Q’ obtained from uncertainty in the payoff information of the sub criteria

H j (Q ) = Measure of uncertainty in the payoff information of the jth sub criteria of the evaluation criteria object ‘Q’ Step 3.3: Determine Comparable sum Vector M k (Q) Comparable value of the sub criteria under the given criteria is determined from the following relation

Mk (Q) = j=1βj (Q)*αj (Q)*μjk (Q) m

β j (Q ) = Importance Weight Vector of sub-criteria Step 3.4: Determine Membership Vector

μk (Q)

Membership vector of the object ‘Q’ belonging to class ‘k’ is determined from the following relation. p

μk (Q) = Mk (Q)/ Mk (Q) k =1

Step 3.5: Determine Evaluation Matrix of the alternative U(S) Membership matrix of all the criteria of the object ‘Q’ is determined and evaluation matrix is formed as shown below.

U(S) =

μ  μ μ  μ  ..   ..

( C 1)   (C 2 )  (C 3)   (C 4 )    

Step 4: Determine Final membership Vector

μ (S )

Once the weights of the each criterion and evaluation matrix of the goal are known the process is repeated from the steps 3.1 to 3.5 to get the final membership vector of the goal.

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Step 5: Determine the grade of overall Performance (KO) Overall performance of the alternative is determined by applying confidence recognition rule (Confidence degree:

λ >0.7) k

KO = min {k|  μ k ( S ) ≥ λ } k =1

4.

Case study

The petroleum marketing company plays an important role in the economic development of the country. Performance of petroleum supply chain has become very significant. In this paper, petroleum supply chain performance evaluation using proposed methodology is illustrated. The metrics for evaluation are arranged into three layers namely, Goal, Criteria layer and sub-criteria layer as shown in fig.2. Goal

Evaluation of Supply Chain

Criteria

CP

Sub-Criteria

PQ PSL CS RES MR

FP

AB TC OC INV CS

IBP

TM WR AC UR SH VIS

ILP

AUT LG SI

Fig. 2.Hierarchy of Evaluation Index System of Supply chain performance

Evaluation of supply chain performance is considered as goal. Supply chain evaluation criteria namely, Customer Perspective (CP), Financial Perspective (FP), Internal Business Perspective (IBP) and Innovation & Learning Perspective (ILP) are considered at criterion level. Sub-criteria under each criterion are given below. Sub-criteria under Customer Perspective (CP): Product Quality (PQ), Product Service Level (PSL), Customer Satisfaction (CS), Responsiveness (RES), Market reach (MR); Sub-criteria under Financial Perspective (FP): Adherence to Budget (AB), Transportation Costs (TC), Operating Costs (OC), Inventory (INV), Cost Savings (CS); Sub-criteria under Internal Business Perspective (IBP): Timeliness (TM), Waster Reduction (WR), Accuracy (AC), Utilization of Resources (UR), Shipment (SH), Visibility (VIS); Sub-criteria under Innovation & Learning Perspective (ILP): Automation (AUT), Learning and Growth (LG), Suggestions Implemented (SI); Necessary data on the relative importance of criteria/sub-criteria gathered from discussions with the employees of a petroleum industry. These industries need to improve their supply chain performance by concentrating issues to face with the uncertainty within the business environment.

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4.1 Relative weights of the criteria/sub-criteria Relative weights of each criteria and sub-criteria are determined by using Attribute Hierarchy Model (AHM) discussed by Xiao et al(2013). The data used in this paper is collected from the employees of petroleum industry in evaluating the supply chain performance of petroleum industry. Relative weights of criteria and sub-criteria are shown in below tables. Table.1 Pair

wise comparisons and Relative weights of criteria:

CP

FP

BP

ILP

CP

FP

BP

ILP

WEIGHTS

CP

1.000

1.458

1.224

1.480

FP

0.686

1.000

0.950

1.017

CP

0.000

0.745

0.710

0.747

0.367015

FP

0.225

0.000

0.670

0.670

0.207966

BP

0.817

1.053

1.000

ILP

0.676

0.983

0.701

1.426

BP

0.290

0.678

0.000

0.740

0.284731

1.000

ILP

0.253

0.330

0.260

0.000

0.140288

From Table.1 it is observed that highest relative weight is obtained with Customer Perspective followed by Business Perspective, Financial Perspective and Innovation and Learning Perspective. Table.2 Pair

wise comparisons and Relative weights of sub criteria under Customer Perspective:

PQ

PSL

CS

RES

MR

PQ

PSL

CS

RES

MR

WEIGHTS

PQ

1.000

1.872

1.186

1.504

1.368

PQ

0.000

0.789

0.703

0.750

0.732

0.297548

PSL

0.534

1.000

1.021

1.084

1.452

PSL

0.211

0.000

0.671

0.684

0.744

0.231026

CS

0.843

0.979

1.000

1.276

1.598

CS

0.297

0.329

0.000

0.718

0.762

0.210544

RES

0.665

0.923

0.784

1.000

1.483

RES

0.250

0.316

0.282

0.000

0.748

0.159455

MR

0.731

0.689

0.626

0.674

1.000

MR

0.268

0.256

0.238

0.252

0.000

0.101428

From Table.2 it is observed that under Customer Perspective, Product Quality has highest relative weight followed by Product Service Level, Customer Satisfaction, Responsiveness and Market reach.

Table.3 Pair

wise comparisons and Relative weights of sub criteria under Financial Perspective:

AB

TC

OC

INV

CS

AB

1.000

0.771

0.608

0.688

0.675

AB

TC

OC

INV

CS

WEIGHTS

AB

0.000

0.278

0.233

0.256

0.252

0.101966

TC

1.297

1.000

1.216

1.423

0.888

TC

0.722

0.000

0.709

0.740

0.307

0.247785

OC

1.645

0.822

1.000

1.021

0.775

OC

0.767

0.291

0.000

0.671

0.279

0.200879

INV

1.453

0.703

0.979

1.000

1.194

INV

0.744

0.260

0.329

0.000

0.705

0.203763

CS

1.481

1.126

1.290

0.838

1.000

CS

0.748

0.693

0.721

0.295

0.000

0.245606

From Table.3 it is observed that under Financial Perspective, highest relative weight is obtained with Transportation Costs followed by Cost Savings, Inventory, Operating Costs and Adherence to Budget.

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Table.4 Pair

wise comparisons and Relative weights of sub criteria under Business Perspective:

TM

WR

AC

UR

SH

VIS

TM

WR

AC

UR

SH

VIS

WEIGHTS

TM

1.000

2.329

1.638

1.538

1.785

1.731

TM

0.000

0.823

0.766

0.755

0.781

0.776

0.260075

WR

0.429

1.000

0.706

0.725

1.025

1.088

WR

0.177

0.000

0.261

0.266

0.672

0.685

0.137398

AC

0.611

1.416

1.000

0.969

1.337

1.209

AC

0.234

0.739

0.000

0.326

0.728

0.707

0.182306

UR

0.650

1.379

1.032

1.000

1.752

1.511

UR

0.245

0.734

0.674

0.000

0.778

0.751

0.212150

SH

0.560

0.976

0.748

0.571

1.000

0.919

SH

0.219

0.328

0.272

0.222

0.000

0.315

0.090382

VI

0.578

0.919

0.827

0.662

1.088

1.000

VI

0.224

0.315

0.293

0.249

0.685

0.000

0.117690

From Table.4 it is observed that under Business Perspective, highest relative weight is obtained with Timeliness followed by Utilization of Resources, Accuracy, Waster Reduction, Visibility and Shipment. Table.5 Pair

wise comparisons and Relative weights of sub criteria under Innovation and Learning Perspective: AUT

LG

SI

AUT

1.000

1.040

1.357

LG

0.962

1.000

1.379

SI

0.737

0.725

1.000

AUT

LG

SI

WEIGHTS

AUT

0.000

0.675

0.731

0.468691

LG

0.325

0.000

0.734

0.352859

SI

0.269

0.266

0.000

0.178450

From Table.5 it is observed that under Innovation and Learning Perspective, highest relative weight is obtained with Automation followed by Learning Growth and Suggestions Implemented. The relative weights of all criteria and sub criteria are as shown in Fig.3.

Fig.3 Relative weights of the criteria/sub-criteria

4.2 Evaluation Membership Data for supply chain performance sub-criteria is obtained from 50 employees of Petroleum Industry. No of employees responded regarding the satisfaction levels in five classes (Very Satisfied-VS, Satisfied-SA, General-GE, Dissatisfied-DS, and Very Dissatisfied-VD) and the membership values are shown in Table.6. The final membership vectors for each criteria are determined by using Membership degree Transformation M (1,2,3).

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Table 6.Evaluation Responses and Memberships Criteria

Sub-Criteria PQ PSL CS RES MR AB TC OC INV CS TM WR AC UR SH VIS AUT LG SI

CP

FP

IBP

ILP

Evaluation Responses SA GE DS 12 10 7 9 8 14 11 12 7 13 9 11 10 11 13 10 7 13 9 8 7 15 5 11 8 15 9 14 13 8 13 7 7 7 11 12 14 17 6 12 8 6 13 10 5 9 20 8 12 8 6 13 10 5 14 17 6

VS 16 12 14 11 7 8 12 11 11 9 17 5 9 18 15 7 18 15 9

VD 5 7 6 6 9 12 14 8 7 6 6 15 4 6 7 6 6 7 4

VS 0.3200 0.2400 0.2800 0.2200 0.1400 0.1600 0.2400 0.2200 0.2200 0.1800 0.3400 0.1000 0.1800 0.3600 0.3000 0.1400 0.3600 0.3000 0.1800

Evaluation Memberships SA GE DS 0.2400 0.2000 0.1400 0.1800 0.1600 0.2800 0.2200 0.2400 0.1400 0.2600 0.1800 0.2200 0.2000 0.2200 0.2600 0.2000 0.1400 0.2600 0.1800 0.1600 0.1400 0.3000 0.1000 0.2200 0.1600 0.3000 0.1800 0.2800 0.2600 0.1600 0.2600 0.1400 0.1400 0.1400 0.2200 0.2400 0.2800 0.3400 0.1200 0.2400 0.1600 0.1200 0.2600 0.2000 0.1000 0.1800 0.4000 0.1600 0.2400 0.1600 0.1200 0.2600 0.2000 0.1000 0.2800 0.3400 0.1200

VD 0.1000 0.1400 0.1200 0.1200 0.1800 0.2400 0.2800 0.1600 0.1400 0.1200 0.1200 0.3000 0.0800 0.1200 0.1400 0.1200 0.1200 0.1400 0.0800

4.3 Evaluation matrix Evaluation Matrix is determined as discussed in step 3 of methodology section. Evaluation matrix of supply chain performance is shown in table 7. Table.7 Evaluation matrix VS

S

G

DS

VD

P

24.00%

22.00%

20.00%

20.80%

13.20%

FP

20.40%

22.40%

19.20%

19.20%

18.80%

BP

23.67%

22.67%

24.33%

14.67%

14.67%

ILP

30.15%

25.48%

21.31%

11.46%

11.60%

Table.8 Final membership values Satisfactio n Level

VS

S

G

DS

VD

Mew(C1)

0.264 0

0.236 5

0.218 0

0.150 9

0.130 7

From Table.7 it is understood that criteria Innovation & Learning Perspective has highest percentage 24% in very satisfied level, under very dissatisfied level criteria Financial Perspective has highest percentage of 18.80%. 4.4 Final membership Vector Final membership vector of the supply chain performance is determined as discussed in step 4 of the methodology section. The Final membership vector of the supply chain performance is shown in Table.8. 4.5 Grade of Overall Performance of the supply chain From the final membership vector, it is observed that the overall performance of the supply chain belongs to the ‘General’ level with the confidence level of 71.85% (26.40%+23.65%+21.80%).

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5. Results and discussions From the comparison judgement matrix it is understood that Customer Perspective (CP) showing high weight (0.367). Evaluation membership of supply chain performance is shown in Fig.4. From the figure, it is understood that Innovation and Learning Perspective (ILP) of the supply chain is showing relatively high confidence level of performances of 30.15% in ‘Very Satisfied’ level.

Fig.4. Evaluation memberships of supply chain performance criteria

From the results of the final membership values, it can be judged that the performance of the supply chain is considered as ‘General’ level as the confidence level of 71.85% (26.40%+23.65%+21.80%) is more than the minimum confidence level of 70%. Overall confidence level with ‘Very Satisfied’ is only 26.40% indicates that the supply chain should enhance the performance from every criteria.

6. Conclusions This proposed methodology is a hybrid technique that put together Attribute Hierarchy Model (AHM) along with Membership transformation method – M (1,2,3) to assess the supply chain performance. The suggested methodology pays to not merely to judge the general performance of the supply chain but to learn which criteria/sub-criteria has to be increased. The proposed hybrid method is useful to evaluate the supply chain performance. The methodology can be extended for the lean, agile and leagile strategy based supply chain evaluation. The suggested work may be applied to other areas of decision making evaluation.

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Annexure- 1 Questionnaire PART 1: Personal Profile A. Indicate your Department

B.

Indicate your Position

Production

Manager

Quality

Supervisor

Logistics

Engineer

Maintenance

Technician

Purchasing

Inspector/Auditor

Customer Centre

Operator

Engineering

Administration

PART 2: Satisfaction of BSC Perspectives 1

Customer Perspective:

1.1 1.2 1.3 1.4 1.5 2

Financial Perspective:

2.1 2.2 2.3 2.4 2.5

3.1

Internal Business Perspective: Timeliness

3.2

Waste Reduction

General

Dissatisfied

Very Dissatisfied

Very satisfied

Satisfied

General

Dissatisfied

Very Dissatisfied

Very satisfied

Satisfied

General

Dissatisfied

Very Dissatisfied

Very satisfied

Satisfied

General

Dissatisfied

Very Dissatisfied

Accuracy

3.3

Utilization of Resources

3.5

Shipment

3.6

Visibility

4.1 4.2

Innovation & Learning Perspective: Automation Learning & Growth

4.3

Suggestions Implemented

4

Satisfied

Adherence to Budget Transportation Costs Operating Costs Inventory Cost Savings

3

3.4

Very satisfied

Product Quality Product Service Customer Satisfaction Responsiveness Market Reach

References [1] A Gunasekaran, C Patel, E Tirtiroglu (2001)” Performance measures and metrics in a supply chain environment” International Journal of Operations & Production Management, Vol. 21, Iss. ½. [2] Alaalan W.Mackelprang, Jessika L. (2014) “The relationship between strategic supply chain integration and performance: A Meta-Analytic evaluation and implications for supply chain management research” Journal of business logistics Vol.35, Iss.1. [3] Charan Parikshit,Baisya Rajat KShankar Ravi(2005),”Selection of supply chain performance measurement system using fuzzy approach”

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[4] Hua Jiang , Hebei, Handan ; Junhu Ruan 2008,” Analysis of influencing factors on performance measurement of the supply chain based on SCOR-model and AHM” Proceedings of Service Operations and Logistics, and Informatics IEEE International Conference on (Volume:2 ) Page(s):2141 – 2146 [5] Jing Yang, Hua Jiang (2012),”Fuzzy evaluation on supply chains overall performance based on AHM and M(1,2,3)” Journal of Software, Vol .7,No 12. [6] Khan Rai Waqas Azfar, , Nawar Khan, Hamza Farooq Gabrie (2014),”Performance Measurement: A Conceptual Framework for Supply Chain Practices” 10th International Strategic Management Conference proceedings Volume 150, Pages 803–812. [7] M Beamon (1999), ”Measuring supply chain performance” International Journal of Operations & Production Management, Vol. 19 no. 3. [8] Nakhai Kamalabadi, Bayat, Ahmadi, Ebrahimi ,M. Safari Kahreh (2008),”Presentation a New Algorithm for Performance Measurement of Supply Chain by Using FMADM Approach I I”,World applied science journal Vol.5,no.5. [9] Ranjan Kumar Mohapatra , R. P. Mohanty , R.S. Dhalla (2010),” Reengineering of Logistics Value Chain of a Petroleum Products Marketing Company – Formulation of a Performance Measurement System” International Journal of Logistics and Transportation Research – Vol.1, Iss.1. [10]S.Hemalatha, K. Ram Babu, K.Narayana Rao, K.Venkatasubbaiah(2015),” supply chain strategy based supplier evaluation-An integrated framework “International Journal of Managing values and supply chains Vol.6,Iss.2. [11] Siddharth Varma,Subhas Wadhwa & SG Deshmukh (2008),”Evaluating petroleum supply chain-Application of Analytical hierarchy process to balance score card” Asia Pasifasific Journal of Marketing&Logistics,Vol.21,Iss.1. [12]Trkman, Peter, Stemeberger M, Faklic Furji & Goznik Ales (2006),”Process approach to supply chain integration” Supply chain management: An international journal,Vol.12, No.2. [13] Xia, L.X.X., Bin Ma, Lim, R. (2007),”AHP based supply chain performance measurement system” Emerging Technologies and Factory Automation, 2007. ETFA. IEEE Conference on Page(s):1308 - 1315.