VENDOR SELECTION FOR LONG-TERM PARTNERSHIP -EMPLOYING CRITIC AND TOPSIS METHODS

1. Doctoral Student-Engineering Systems Management, American University of Sharjah. 2. StudentShaw Academy, India. ...................................................................................................................... Manuscript Info Abstract ......................... ........................................................................ Manuscript History Received: 08 March 2020 Final Accepted: 10 April 2020 Published: May 2020


ISSN: 2320-5407
Int. J. Adv. Res. 8(05), 220-229 221 This research concentrates on employing CRITIC and TOPSIS together for identifying a potential vendor for long term partnership and collaboration using the data on key attributes related to Battery Suppliers that serve a firm (XYZ) involved in Oil and Gas Offshore Projects.
The next sections within this report, would elaborate on defining the Aim & Objectives for addressing the identified problem; exploring the literature on key performance criterion for supplier selection; detailing specific procedures and techniques that are used for identifying a potential vendor; pursuing data analysis employing CRITIC and TOPSIS together; stating the results and conclusions based on the data analysis pursued and finally listing the limitations of the study and ethical norms followed for pursuing the study

Research Aim:
The study is aimed at selecting a potential vendor for long term collaboration and partnership.

Research Objective:-
The study is pursued to fulfil an objective of "Using an Industrial case to demonstrate the application of comprehensive, systematic and formal quantitative methods for vendor selection" and to employ "Multi-Criteria Decision-Making Methods (MCDM) such as CRTIC & TOPSIS for selecting a potential vendor for long term partnership, collaborative growth and development".

Literature Review:-
The section aims to examine the literature related to Supplier Selection Criterion, Multi-Criteria Decision-Making Methods and the Importance of Batteries for Oil and Gas Offshore Applications.

Supplier Selection Criterion:
According to [8], Supplier Selection Process involves Identification, Evaluation and Final Selection of Suppliers and it is one of the critical processes-as it deploys a lot of firms resources and plays a crucial role in the success of the firms operations, hence identification of critical parameters or criterion for selection is highly important for reducing purchase risks, maximizing the overall purchase value and developing long term relationships for collaborative growth. Further [9, 10 & 11], highlighted the importance of the ability of the supplier to consistently meet the "Quality"requirements for material, dimensions, design, durability, variety and also the ability of the supplier to continuously improve quality through implementation of Quality Systems for control, assessment and continuous improvement. Further [12], highlighted the importance of "Technical Expertise and Experience" as an important criterion for Supplier Selection for Technology Oriented Projects.
In addition, [10 & 12], underlined the importance of "Price Factor" for supplier selectionthat includes product unit prices and additional costs for warranty, installation, delivery etc. Likewise [10, 11 & 13], emphasized on the importance of delivery times for on-time project completions and inclusion of "Delivery Factor" for supplier selection.

Multi-Criteria Decision-Making Methods:
According to [14], Multi-Criteria Decision-Making (MCDM) is one of the fastest growing problem areas across many disciplines, as it involves evaluation of set of alternatives based on a number of conflicting criterion, further the authors classified MCDM into Multiple Attribute Decision Making (MADM)-involving selection of a best alternative from a set of pre-specified alternatives and Multiple Objective Decision Making (MODM)-involving the design of multiple alternatives to optimize the multiple objectives specified by the decision maker. Likewise [15], conducted an in-depth literature review-to list out the major MCDM methods applied within the Supply Chain Domain and identified that Analytical Hierarchic Process (AHP), Analytical Network Process (ANP), Fuzzy Sets, TOPSIS and Hybrid methods were most commonly used for solving problems related to Supplier Selection, Manufacturing, Warehousing and Logistics within the supply chains.
In addition [16], stated that AHP can be applied to supplier selection problems-as itaids the decision maker to provide judgments about the relative importance of each criterion and then specify a preference for each decision alternative using each criterion in order to finally provide a prioritized ranking of the decision alternatives based on the overall preferences specified for the alternatives. Likewise [17], illustrated the usage of ANP within a 222 Hospitality Industry -which is a general form of AHP where dependence among alternatives and criteria are considered for pairwise comparisons in order to convene the final supplier selection. Further [18], demonstrated the use of Fuzzy logic-that enables emulation of human reasoning for making decisions based on imprecise data. Additionally, the authors also illustrated that the method also enables calculation of a fuzzy suitability index for the efficient vendor alternatives-to help rank the fuzzy indices for best supplier alternative selection.
Likewise [19], employed TOPSIS for supplier selection where in the best alternative is selected based on the shortest geometric distance from the positive ideal solution and negative ideal solution. Similarly [20], developed a hybrid model wherein the authors -formulated criterion for the proposed model, performed AHP computations and defined a Fuzzy TOPSIS logic for evaluating and ranking of supplier alternatives.

Importance of Batteries for Oil and Gas Offshore Applications:
According to [21], Batteries are Electro Chemical Devices that store and release energy through conversion of Chemical Energy to Electricity. Further the author, defined a Primary Battery as the one that cannot be recharged and Secondary Batteries as the ones that can be recharged. In addition, the authors also projected the use of Secondary batteries as a reliable backup power source that guarantees the safe operation of critical equipment within Offshore Applications. According to the author, the Batteries can be used for multiple offshore applications -such as Process controls, UPS, Turbine Operations, Emergency Lighting, Safety Systems and Switchgear Operations.

Research Methodology:-
Supplier Specific data on "Price per AH", "Years of Experience-portraying the Technical Expertise", "Delivery Lead Times", "Transportation Charges" and "Quality Ratings" was collected for an Offshore Emergency Lighting Application with a Total Load Requirement of 18 Amps, Back up Time Requirement of 180 min and a Nominal System Voltage Requirement of 110 V. Data was collected for a total of five suppliers belonging to local and international territories. A hybrid method that combined the CRITIC and TOPSIS methods was used for selecting the best Supplier.
According to [22], the CRITIC method is used to determine the objective weights for the selection criterion based on the quantification of two fundamental notions of Multi-Criteria Decision-Making i.e., the contrast intensity and the conflicting character of the evaluation criteria. The CRITIC process starts by defining the decision matrix with "m" feasible alternatives A i (i=1,2, ….m) and "n" evaluation criterion C j (j= 1,2, …. n).
Step 1: A decision matrix with the performance values for different alternative for each of the criterion is developed.
represents the performance value of the i th alternative for the j th criterion. Then Best -max ( )and Worst-min ( ) performance values are evaluated for each criterion.
Step 2: The decision matrix is Normalized by evaluating ′ for each of the using Based on the Normalized Matrix the Standard Deviation σ j values for each of the Criterion are calculated.
Step 3: A Symmetric matrix of nxn, with elements r jk -linear correlation coefficients between vectors X j and X k is defined.
Step 4: A Measure of conflict created by criterion j, with respect to decision situation defined by the rest of the criteria is calculated using Step 5: Quantity of Information in relation to each Criterion is calculated using c j = σ j * (1 − ) =1

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Step 6: Finally, Objective weights for each Criterion are determined using The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is one of the classical Multi-Criteria Decision-Making Methods developed by [23]. The method identifies the best solution by calculating the shortest Geometric/ Euclidean distance from the positive ideal solution and negative ideal solution. The algorithm below (Fig 4.1), demonstrates the basic steps to be followed in order to define the preference order for the alternatives. A Hybrid method was employed for the final data analysis, wherein the objective weights defined by the CRITIC Method, were used for formulating the Weighted Matrix within the TOPSIS method.
Data Analysis: Critic Method: Step 1: Creation of the decision matrix with the performance values for different alternatives for each of the criterion (Table 1). Step 2: Defining the Normalized Decision Matrix (Table 2) Step 3: Defining a Symmetric matrix of nxn, with elements r jk -linear correlation coefficients between vectors X j and X k ( Table 3). Step 4: Calculating the Measure of conflict created by criterion j, with respect to decision situation defined by the rest of the criteria (Table 4) 225 Table 4:-Measure of Conflict.
Step 5: Calculating the Quantity of Information in relation to each Criterion (Table 5) Table 5:-Quantity of Information-Criterion.
Step 6: Finally, calculating the Objective weights for each Criterion (  Step 1: Creation of the decision matrix with performance values for different alternatives for each of the criterion ( Table 7).   (Table 9 and 10)  Step 4:-Calculating the Euclidean Distance from the Ideal Best and the Ideal Worst (Table 11).  Step 5:-Finally, Calculate the Performance Score Matrix (Table 12).

Results and Conclusion: -
Based on the analysis using the Hybrid Method for identifying a potential supplier for long term partnership using Supplier Specific data on "Price per AH", "Years of Experience-portraying the Technical Expertise", "Delivery Lead Times", "Transportation Charges" and "Quality Ratings" collected for an Offshore Emergency Lighting Application with a Total Load Requirement of 18 Amps, Back up Time Requirement of 180 min and a Nominal System Voltage Requirement of 110 V. The Supplier-5 received the highest performance score based on the TOPSIS method-which can be attributed to the highest experience and highest quality rating values for the supplier, as these criteria received the highest objective weights based on the CRITIC Method. Therefore, it can be concluded that a combination of CRITIC and TOPSIS methods, can provide an ideal solution for Multi-Criterion Decision-Making problems.

Limitations:
The focus of the study is limited to a firm (XYZ) involved in Oil and Gas Offshore Projects that works in collaboration with a Battery Supplier, however the methods used can be adopted to other firms in other geographical locations by feeding the data about the relevant suppliers.
The focus of the data analysis was limited the use of Batteries for one of the offshore applications (Emergency Lighting), however the analysis can be extended to other offshore applications such as Process controls, UPS,

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Turbine Operations, Safety Systems and Switchgear Operations. Finally, the data collected is assumed to be reliable, as it is acquired from the reports provided by the firm (XYZ) involved in Oil and Gas Offshore Projects.

Ethics:
As the study employs real-time data for analysis, the expectations of the study were clearly communicated to the Industry fraternity providing the data for analysis. The pertinent data management and storage protocols were followed to ensure the integrity of the data provided.
The rights, privacy and safety of people and firms involved either directly or indirectly were ensured and no individual was forced to participate, and the participation was voluntary convened through the gatekeepers within the organizations involved [25].