Implementation and Validation of Supplier Selection Model for Shaper Machine Arm by Fuzzy Inference Decision Support System
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This document details the implementation of a supplier selection model using a fuzzy inference decision support system for radial drilling beds. The study emphasizes the importance of expert opinions and the conversion of these views into fuzzy terms, utilizing an 8-rule base model within MATLAB. Key findings suggest that considering multiple criteria such as delivery, quality, and price can enhance vendor selection efficiency through a three-dimensional surface view of potential vendors.
![IJSRD - International Journal for Scientific Research & Development| Vol. 3, Issue 10, 2015 | ISSN (online): 2321-0613
All rights reserved by www.ijsrd.com 750
Implementation and Validation of Supplier Selection Model for Shaper
Machine Arm by Fuzzy Inference Decision Support System
Prof. Nital P. Nirmal
Assistant Professor
Department of Production Engineering
Shantilal Shah Engineering College, Bhavnagar, Gujarat
Abstract— Supplier selection is the process through which
firms identify, evaluate, and contract with suppliers. The
supplier selection process deploys a tremendous amount of a
firm’s financial resources. In return, manufacturing
industries expect important benefits from contracting with
suppliers offering high value. Like many complex supply
chain problems, vendor selection problems are not so well
defined which can be handed over completely to computers,
whereas many human characteristics are also essential to the
issues. In this paper attention is given to the fuzzy System
helps Vendor Selection Problem (VSP) for Radial Drilling
Bed (RDB). It required expert’s view, conversion it into
fuzzy term, making 8 rule base Model with implementing
Fuzzy System using MATLAB. At ending point,
conclusions and likely areas of Fuzzy in selecting vendors
are present.
Key words: Vendor Selection, Fuzzy Inference System,
Decision Support System
I. INTRODUCTION
The vendor selection process has undergone significant
changes during the past twenty years. These include
increased quality guidelines, improved computer
communications, and increased technical capabilities,
essential changes in the vendor selection process. Here, the
tabular form of literature survey on VSP.
Approach Research work
Linear Weighting
Method
Vendor selection decision is the most
common way of rating different vendors on
the performance criteria for their quota
allocations [1].
Selection Criteria
Proposed multiple criteria vendor service
factor ratings and an overall vendor
performance index reference number, as in
[2].
LP (Linear
Programming
Method)
Formulated the vendor selection decision as a
LP problem to minimize the total purchasing
and storage costs [3, 4, 5, 6].
LP Model
A single item LP model to minimize the
aggregate price under constraints of quality;
service level and lead-time [7].
MIP (Mix Integer
Programming)
MIP approach with the objective of
minimizing purchasing, inventory and
transportation related costs without any
specific mathematical formulation and
demonstrated it through selecting the vendors
[8].
GP ( Goal
Programming)
Also proposed the use of GP for price, quality
and delivery objectives [9].
DSS
By integrating the analytical hierarchy
process with linear programming [10].
Five-stage model for vendor evaluation [11].
VSP
Fuzzy mixed integer goal programming [12].
Presented a data envelopment analysis
method for a VSP with multiple objectives
[13].
Used the analytical hierarchical process to
generate weights for VSP reference number,
as in [14], [15].
Table 1: Literature survey on VSP
II. FUZZY INFERENCE DECISION SUPPORT SYSTEM
It requires conversion of expert’s view, Fuzzification, Rule
Construction, Defuzzification in at the end surface views for
DSS [16].
A. Fuzzy ranges (Converted through Experts view)
Name
of the
Vendo
r
Quality of Material Delivery of Material Price
Expert-
1
Expert-
2
Expert-
3
AV
G
Exp-
1
E-2 E-3
AV
G
AV
G
V-1 (0.9-1) (0.7-0.9)(0.3-0.8)
(0.3-
1)
(0.9-
1)
(0.7
-
0.9)
(0.3
-
0.8)
(0.3-
1)
(0.3-
1)
V-2 (0.2-0.7)(0.1-0.3) (0-0.3)
(0-
0.7)
(0.2-
0.7)
(0.1
-
0.3)
(0-
0.3)
(0-
0.7)
(0-
0.7)
Table 2: Fuzzy conversion of experts view for RDB
B. Fuzzification and the Membership functions
Figure 1 and 2 show the Fuzzification of QOM and Net-
Rating respectively. This same procedure is repeated for
other two inputs variable “DOM and Price”.
Fig. 1: Fuzzification of QOM (RDB)](https://image.slidesharecdn.com/ijsrdv3i100537-160128085954/85/Implementation-and-Validation-of-Supplier-Selection-Model-for-Shaper-Machine-Arm-by-Fuzzy-Inference-Decision-Support-System-1-320.jpg)
![Implementation and Validation of Supplier Selection Model for Shaper Machine Arm by Fuzzy Inference Decision Support System
(IJSRD/Vol. 3/Issue 10/2015/163)
All rights reserved by www.ijsrd.com 751
Fig. 2: Fuzzification of Net- rating (RDB)
C. The Rule viewer
As shown in Figure 3, the Rule viewer displays the fuzzy
process. Each row of plots corresponds to one rule and each
Bed of plots corresponds to either an input variable (Yellow)
or an output variable (Blue). Here, putting the input (0.5 0.5
0.2).
Fig. 3: Rule Viewer for RDB -Input [0.5 0.5 0.2]
D. The Surface viewe
The surface view of DOM v/s QOM v/s Net- rating (Price=
0.8), QOM v/s Price v/s Net- rating (DOM= 0.5) and DOM
v/s Price v/s Net- rating (QOM= 0.5) are in figure. 4, 5, 6
respectively.
Fig. 4: 3D - Surface for DOM v/s QOM v/s Net- rating with Price = 0.8 (RDB)](https://image.slidesharecdn.com/ijsrdv3i100537-160128085954/85/Implementation-and-Validation-of-Supplier-Selection-Model-for-Shaper-Machine-Arm-by-Fuzzy-Inference-Decision-Support-System-2-320.jpg)
![Implementation and Validation of Supplier Selection Model for Shaper Machine Arm by Fuzzy Inference Decision Support System
(IJSRD/Vol. 3/Issue 10/2015/163)
All rights reserved by www.ijsrd.com 752
Fig. 5: 3D - Surface for QOM v/s Price v/s Net- rating (RDB) with DOM= 0.5
Fig. 6: 3D- Surface for DOM v/s Price v/s Net- rating (RDB) with QOM = 0.5
III. CONCLUSION & DISCUSSION
Based on the present study following conclusions are made.
1) For selecting the potential vendor one can consider at
least three selection criteria Delivery of Material,
Quality of Material, Price as input variables to a Fuzzy
Inference System (FIS).
2) Experts’ view plays an important role hence they must
be considered before Fuzzification in FIS modelling.
Many factors as suggested by experts may be
considered in arriving at a final decision for a particular
vendor.
3) Fuzzy Inference System may be easily incorporated in
decision making by involving input variables,
membership function to obtain subsequent Net-rating
after Diffuzzification.
4) Fuzzy Inference System offers 3D-Surface view, which
distinguishes potential vendor from others thus, may be
incorporated in selection mechanism.
REFERENCES
[1] Wind, Y. and Robinson, P. The determinants of vendor
selection: The evaluation function approach, Journal of
Purchasing and Materials Management, Fall, 29-41,
1968.
[2] Monozka, R. and Trecha, S. Cost-based supplier
performance evaluation, Journal of Purchasing and
Materials Management, Spring, 2-7, 1988.
[3] Moore, D. and Fearon, H. Computer-assisted decision-
making in purchasing, Journal of Purchasing, 9(4), 5-
25, 1972.
[4] Oliveria, R. and Lourenco, J. A Multi-criteria model
for assigning new orders to service suppliers, European
Journal of Operational Research, 139, 390-399, 2002.
[5] Sharma, D., Benton, W. and Srivastava, R.
Competitive strategy and purchasing decisions, In:
Proceedings of the 1989 International Conference of
the Decision Sciences Institute, 1088-1090, 1989.](https://image.slidesharecdn.com/ijsrdv3i100537-160128085954/85/Implementation-and-Validation-of-Supplier-Selection-Model-for-Shaper-Machine-Arm-by-Fuzzy-Inference-Decision-Support-System-3-320.jpg)
![Implementation and Validation of Supplier Selection Model for Shaper Machine Arm by Fuzzy Inference Decision Support System
(IJSRD/Vol. 3/Issue 10/2015/163)
All rights reserved by www.ijsrd.com 753
[6] Anthony, T. and Buffa, F. Strategic purchase
scheduling, Journal of Purchasing and Materials
Management, Fall, 27-31, 1977.
[7] Pan, A. Allocation of order quantity among suppliers,
Journal of Purchasing and Materials Management, Fall,
36-39, 1989.
[8] Bendor, P., Brown, R., Issac, M. and Shapiro, J.
Improving purchasing productivity at IBM with a
normative decision support system, Interfaces, 15, 106-
115, 1985.
[9] Buffa, F. and Jackson, W. A goal programming model
for purchase planning Journal of Purchasing and
Materials Management, Fall, 27—34, 1983.
[10] Ghodsypour, S. and O'Brien, C. Decision support
system for supplier selection using an integrated
analytic hierarchy process and linear programming,
International Journal of Production Economics, 56-57,
199-212, 1998.
[11] Lau, H., Pang, W. and Wong, W. Methodology for
monitoring supply chain performance: A fuzzy logic
approach, Journal of Logistics Information
Management 15(4), 271-281, 2002.
[12] Kumar, M., Vrat, P. and Shankar, R. A fuzzy goal
programming approach for vendor selection problem in
a supply chain, 70-72, 2003.
[13] Weber, C., Current, J. and Desai, A. An optimization
approaches to determining the number of vendors to
employ, Supply Chain Management: an International
Journal, 2(5), 90—98, 2000.
[14] Narsimhan, R. An analytical approach to supplier
selection, Journal of Purchasing and Materials
Management, Winter, 27-32, 1983.
[15] Handfield, R., Walton, S., Sroufe, R. and Melnyk, S.
Applying environmental criteria to supplier
assessment: A Study in the Application of the
Analytical Hierarchy Process, European Journal of
Operational Research, 141, 70-87, 2002.
[16] N. Nirmal, H. Nirmal and R. Nirmal, “The Role of
Fuzzy for Selecting the Industrial Vendors: Framework
for Development”, Proceeding of National Conference
Emerging Trends in Mechanical Engineering pp 140-
146, 2011.](https://image.slidesharecdn.com/ijsrdv3i100537-160128085954/85/Implementation-and-Validation-of-Supplier-Selection-Model-for-Shaper-Machine-Arm-by-Fuzzy-Inference-Decision-Support-System-4-320.jpg)
Implementation and Validation of Supplier Selection Model for Shaper Machine Arm by Fuzzy Inference Decision Support System
- 1. IJSRD - International Journal for Scientific Research & Development| Vol. 3, Issue 10, 2015 | ISSN (online): 2321-0613 All rights reserved by www.ijsrd.com 750 Implementation and Validation of Supplier Selection Model for Shaper Machine Arm by Fuzzy Inference Decision Support System Prof. Nital P. Nirmal Assistant Professor Department of Production Engineering Shantilal Shah Engineering College, Bhavnagar, Gujarat Abstract— Supplier selection is the process through which firms identify, evaluate, and contract with suppliers. The supplier selection process deploys a tremendous amount of a firm’s financial resources. In return, manufacturing industries expect important benefits from contracting with suppliers offering high value. Like many complex supply chain problems, vendor selection problems are not so well defined which can be handed over completely to computers, whereas many human characteristics are also essential to the issues. In this paper attention is given to the fuzzy System helps Vendor Selection Problem (VSP) for Radial Drilling Bed (RDB). It required expert’s view, conversion it into fuzzy term, making 8 rule base Model with implementing Fuzzy System using MATLAB. At ending point, conclusions and likely areas of Fuzzy in selecting vendors are present. Key words: Vendor Selection, Fuzzy Inference System, Decision Support System I. INTRODUCTION The vendor selection process has undergone significant changes during the past twenty years. These include increased quality guidelines, improved computer communications, and increased technical capabilities, essential changes in the vendor selection process. Here, the tabular form of literature survey on VSP. Approach Research work Linear Weighting Method Vendor selection decision is the most common way of rating different vendors on the performance criteria for their quota allocations [1]. Selection Criteria Proposed multiple criteria vendor service factor ratings and an overall vendor performance index reference number, as in [2]. LP (Linear Programming Method) Formulated the vendor selection decision as a LP problem to minimize the total purchasing and storage costs [3, 4, 5, 6]. LP Model A single item LP model to minimize the aggregate price under constraints of quality; service level and lead-time [7]. MIP (Mix Integer Programming) MIP approach with the objective of minimizing purchasing, inventory and transportation related costs without any specific mathematical formulation and demonstrated it through selecting the vendors [8]. GP ( Goal Programming) Also proposed the use of GP for price, quality and delivery objectives [9]. DSS By integrating the analytical hierarchy process with linear programming [10]. Five-stage model for vendor evaluation [11]. VSP Fuzzy mixed integer goal programming [12]. Presented a data envelopment analysis method for a VSP with multiple objectives [13]. Used the analytical hierarchical process to generate weights for VSP reference number, as in [14], [15]. Table 1: Literature survey on VSP II. FUZZY INFERENCE DECISION SUPPORT SYSTEM It requires conversion of expert’s view, Fuzzification, Rule Construction, Defuzzification in at the end surface views for DSS [16]. A. Fuzzy ranges (Converted through Experts view) Name of the Vendo r Quality of Material Delivery of Material Price Expert- 1 Expert- 2 Expert- 3 AV G Exp- 1 E-2 E-3 AV G AV G V-1 (0.9-1) (0.7-0.9)(0.3-0.8) (0.3- 1) (0.9- 1) (0.7 - 0.9) (0.3 - 0.8) (0.3- 1) (0.3- 1) V-2 (0.2-0.7)(0.1-0.3) (0-0.3) (0- 0.7) (0.2- 0.7) (0.1 - 0.3) (0- 0.3) (0- 0.7) (0- 0.7) Table 2: Fuzzy conversion of experts view for RDB B. Fuzzification and the Membership functions Figure 1 and 2 show the Fuzzification of QOM and Net- Rating respectively. This same procedure is repeated for other two inputs variable “DOM and Price”. Fig. 1: Fuzzification of QOM (RDB)
- 2. Implementation and Validation of Supplier Selection Model for Shaper Machine Arm by Fuzzy Inference Decision Support System (IJSRD/Vol. 3/Issue 10/2015/163) All rights reserved by www.ijsrd.com 751 Fig. 2: Fuzzification of Net- rating (RDB) C. The Rule viewer As shown in Figure 3, the Rule viewer displays the fuzzy process. Each row of plots corresponds to one rule and each Bed of plots corresponds to either an input variable (Yellow) or an output variable (Blue). Here, putting the input (0.5 0.5 0.2). Fig. 3: Rule Viewer for RDB -Input [0.5 0.5 0.2] D. The Surface viewe The surface view of DOM v/s QOM v/s Net- rating (Price= 0.8), QOM v/s Price v/s Net- rating (DOM= 0.5) and DOM v/s Price v/s Net- rating (QOM= 0.5) are in figure. 4, 5, 6 respectively. Fig. 4: 3D - Surface for DOM v/s QOM v/s Net- rating with Price = 0.8 (RDB)
- 3. Implementation and Validation of Supplier Selection Model for Shaper Machine Arm by Fuzzy Inference Decision Support System (IJSRD/Vol. 3/Issue 10/2015/163) All rights reserved by www.ijsrd.com 752 Fig. 5: 3D - Surface for QOM v/s Price v/s Net- rating (RDB) with DOM= 0.5 Fig. 6: 3D- Surface for DOM v/s Price v/s Net- rating (RDB) with QOM = 0.5 III. CONCLUSION & DISCUSSION Based on the present study following conclusions are made. 1) For selecting the potential vendor one can consider at least three selection criteria Delivery of Material, Quality of Material, Price as input variables to a Fuzzy Inference System (FIS). 2) Experts’ view plays an important role hence they must be considered before Fuzzification in FIS modelling. Many factors as suggested by experts may be considered in arriving at a final decision for a particular vendor. 3) Fuzzy Inference System may be easily incorporated in decision making by involving input variables, membership function to obtain subsequent Net-rating after Diffuzzification. 4) Fuzzy Inference System offers 3D-Surface view, which distinguishes potential vendor from others thus, may be incorporated in selection mechanism. REFERENCES [1] Wind, Y. and Robinson, P. The determinants of vendor selection: The evaluation function approach, Journal of Purchasing and Materials Management, Fall, 29-41, 1968. [2] Monozka, R. and Trecha, S. Cost-based supplier performance evaluation, Journal of Purchasing and Materials Management, Spring, 2-7, 1988. [3] Moore, D. and Fearon, H. Computer-assisted decision- making in purchasing, Journal of Purchasing, 9(4), 5- 25, 1972. [4] Oliveria, R. and Lourenco, J. A Multi-criteria model for assigning new orders to service suppliers, European Journal of Operational Research, 139, 390-399, 2002. [5] Sharma, D., Benton, W. and Srivastava, R. Competitive strategy and purchasing decisions, In: Proceedings of the 1989 International Conference of the Decision Sciences Institute, 1088-1090, 1989.
- 4. Implementation and Validation of Supplier Selection Model for Shaper Machine Arm by Fuzzy Inference Decision Support System (IJSRD/Vol. 3/Issue 10/2015/163) All rights reserved by www.ijsrd.com 753 [6] Anthony, T. and Buffa, F. Strategic purchase scheduling, Journal of Purchasing and Materials Management, Fall, 27-31, 1977. [7] Pan, A. Allocation of order quantity among suppliers, Journal of Purchasing and Materials Management, Fall, 36-39, 1989. [8] Bendor, P., Brown, R., Issac, M. and Shapiro, J. Improving purchasing productivity at IBM with a normative decision support system, Interfaces, 15, 106- 115, 1985. [9] Buffa, F. and Jackson, W. A goal programming model for purchase planning Journal of Purchasing and Materials Management, Fall, 27—34, 1983. [10] Ghodsypour, S. and O'Brien, C. Decision support system for supplier selection using an integrated analytic hierarchy process and linear programming, International Journal of Production Economics, 56-57, 199-212, 1998. [11] Lau, H., Pang, W. and Wong, W. Methodology for monitoring supply chain performance: A fuzzy logic approach, Journal of Logistics Information Management 15(4), 271-281, 2002. [12] Kumar, M., Vrat, P. and Shankar, R. A fuzzy goal programming approach for vendor selection problem in a supply chain, 70-72, 2003. [13] Weber, C., Current, J. and Desai, A. An optimization approaches to determining the number of vendors to employ, Supply Chain Management: an International Journal, 2(5), 90—98, 2000. [14] Narsimhan, R. An analytical approach to supplier selection, Journal of Purchasing and Materials Management, Winter, 27-32, 1983. [15] Handfield, R., Walton, S., Sroufe, R. and Melnyk, S. Applying environmental criteria to supplier assessment: A Study in the Application of the Analytical Hierarchy Process, European Journal of Operational Research, 141, 70-87, 2002. [16] N. Nirmal, H. Nirmal and R. Nirmal, “The Role of Fuzzy for Selecting the Industrial Vendors: Framework for Development”, Proceeding of National Conference Emerging Trends in Mechanical Engineering pp 140- 146, 2011.