Nowadays, the demand of third-party logistics provider becomes an increasingly important issue for companies to improve their customer service and to decrease logistics costs. This paper presents an integrated fuzzy approach for the evaluation and selection of 3rd party logistics service providers. This method consists of two techniques: (1) use fuzzy analytic hierarchy process to identify weights of evaluation criteria; (2) apply fuzzy technique for order preference by similarity to ideal solution (TOPSIS) method to evaluate and sequence alternatives and to make the final selection. Finally, an actual industrial application is performed in logistics department of a tire manufacturing company. For this, first, eight logistics supplier selection criteria were determined, and then the best alternative among seven logistics service provider companies was selected by the proposed method.
Logistics plays an important role in integrating the supply chain of industries. Because the market becomes more global, logistics is now seen as an important area where industries can decrease costs and improve their customer service quality [
Nowadays, many companies are searching to outsource their logistics operations to what they call as Third Party Logistics Service Providers (LSPs) to introduce products and service innovations quickly to their markets [
Outsourcing means that an organization hires an outside organization to provide a good or service that it traditionally had provided itself, because this third party is an “expert” in efficiently providing this good or service, while the organization itself may not be [
Because of development of supply chain partnerships, cost reduction, restructuring of the company, success of the firms using contract logistics, globalization, improvement of services, and efficient operations, companies need to outsource their logistics activities to 3PL service providers [
The LSP selection is a complex multicriteria decision making (MCDM) problem that includes both quantitative and qualitative criteria some of which can conflict each other and is vital in enhancing the competitiveness of companies [
Because of some troubles in MCDM problems such as subjectivity, uncertainty, and ambiguity in assessment process [
This research evaluates the performance of 3rd party LSPs of a tire company in a developing country, Turkey, via the proposed FAHP and fuzzy TOPSIS techniques with MCDM. The fuzzy AHP is used to determine the preference weights of evaluation criteria. Then, this research illustrates that the fuzzy TOPSIS is integrated with fuzzy AHP to evaluate and determine performance levels of seven logistics service providers (LSPs) and find out the best alternatives among these seven LSP companies.
The remainder of this paper is organized as follows. Section
According to definition by the Council of Supply Chain Management Professionals [
Logistics is an integration of information, transportation, material handling, stock and storage, and packaging operations. Logistics activities contain purchasing, transportation, quality, control, customs and insurance, handling, ware housing, inventory management, order processing, sales-demand forecast, logistics information management, distribution, labeling, packaging, fleet management, management of separate parts, product returns, and shipment planning [
Council of Logistics Management defined logistics as the process of planning, implementing, and controlling the efficient, cost effective flow and storage of raw materials, in-process inventory, finished goods, and related information from origin to consumption for the purpose of conforming to customer wants [
Logistics management [
There is an emerging trend for logistics outsourcing in the global market. Lambert et al. [
Lieb [
Lieb [
Third-party logistics can be defined as specialized companies from outside of the firm fulfill the some or all of the logistics activities performed traditionally within the organization through outsourcing [
Third-party logistics is the function by which the owner of goods (the client company) outsources various elements of the supply chain to a third-party logistics company that can perform the management function of the clients inbound freight, customs, warehousing, order fulfillment, distribution, and outbound freight to the clients [
Bask [
According to a survey performed by Forrester Research, 78% of Fortune 500 companies have outsourced transportation services, 54% of them have outsourced their distribution services, and 46% of them have outsourced their manufacturing activities. As a result, third-party logistics sector reached a scale of 50 billion $ throughout the world. To prefer outsourcing in primarily transportation and shipping services cause to transform some specialized transportation and shipping companies into third-party-logistics companies which are able to serve in all logistics functions [
3PL service providers can be defined as external suppliers which fulfill a portion or all of a company’s logistics functions of a company. Logistic functions released to third-party companies are services such as especially transportation, storage, distribution. These functions are required a high level of business investment [
Logistics industry constitutes approximately 10–15% of the total global GDP and is an integral portion of Turkey’s economy. The Turkey logistics sector’s value in 2008 was 60 billion U.S. dollar. Current size of 3PL service providers is 22 billion U.S. dollar. According to LODER, Turkey’s current logistics industry size is estimated to be USD 80–100 billion and is forecasted to reach USD 108–140 billion by 2017. The average growth in the fields of transportation, storage and communication was 6.4% between 2003 and 2013 [
According to Logistics Performance Index (LPI) prepared by World Bank, Turkey is ranked 27th with 3.22 point. Turkey moved up from 39th place in 2010 to 27th in 2013, out of the 155 countries in the index. Moreover, it is ranked third in the top 10 upper middle income performing countries. [
There are a large number of logistics provider firms in Turkey. These are newly founded small and medium sized firms with a transportation background. The most important Turkish logistics service providers are Arkas Denizcilik, Omsan, Barsan, Reysas, Borusan, Balnak, Türksped, and Horoz Lojistik. Rapidly growing trade with Turkey has created a promising perspective for the logistics sector, and the trend is expected to continue. For this reason, international logistics companies are increasing their presence in the country [
Deciding to use a third party LSP is a decision that depends on a variety of factors that differ from company to company. The decision to outsource certain business functions will depend on the company’s plans, future objectives, product lines, expansion, acquisitions, and so forth [
Measures indicating the success of logistics management can be summarized as cost reduction, maximized on time delivery, minimized lead times, rapid respond to the market, higher flexibility, increased number of solution alternatives, improved information reliability, faster communication, minimized rate of consumption, damage and loss, minimized number of total inventory through the supply chain, transformation of fixed costs into variable costs, increased efficiency and productivity in logistics activities, reduction of logistics management expenses, focus on core competencies, improved customer relations, customer focus, and creating win-win relationships in the supply chain [
The needs of the firm can be satisfied by the third party logistics organization in optimum by defining the firm’s goals and selection criteria. To know what metrics are used to evaluate the selection criteria of logistics service provider is an important issue. Generally, the companies have a variety of different characteristics related suppliers; but, if they use same methodology to evaluate the different types of suppliers, and the result cannot represent the real situation. Therefore, when determining the logistics service provider criteria, it should be considered that the criteria of selection differ in the different types of LSP [
According to Menon et al. [
In 2003, the International Warehouse Logistics Association, which comprises more than 550 logistics companies of North America, identified third-party LSP selection criteria (in a descending order) as follows: price, reliability, service quality, on-time performance, cost reduction, flexibility and innovation, good communication, management quality, location, customize service, speed of service, order cycle time, easy to work with, customer support, vendor reputation, technical competence, special expertise, systems capabilities, variety of available services, decreased labor problems, personal relationships, decreased asset commitment, and early notification of disruptions [
Because of increasing importance of logistics outsourcing, selecting correct third-party LSP is a more critical issue for companies. There are lots of factors affecting selection of the service provider. Therefore it is a multicriteria decision making (MCDM) problem. In the literature, a variety number of techniques are used to evaluate third party performance and some MCDM methods are used to select 3PL service provider. For example, Yan et al. [
Yeung et al. [
Jharkharia and Shankar [
Liu and Wang [
Kannan et al. [
Hamdan and Rogers [
Kumar [
Liu et al. [
Some of these studies are summarized in Table
Summary of methods for 3PL providers selection.
Techniques | References |
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Analytic hierarchy process (AHP) and fuzzy AHP | Zhang et al., [ |
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Analytic network process (ANP) | Meade and Sarkis [ |
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Analysis of variance (ANOVA) |
Yeung et al., [ |
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Technique for order preference by similarity to ideal solution (TOPSIS) | Bottani and Rizzi [ |
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Case-based reasoning (CBR) | Yan et al., [ |
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Data envelopment analysis (DEA) | Haas et al., [ |
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İntegrating interpretive structural model (ISM) and ANP | Thakkar et al., [ |
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Integrated methods | |
(i) Case-based reasoning (CBR), rule-based reasoning (RBR), and compromise programming techniques in fuzzy environment | Işıklar et al., [ |
(ii) Fuzzy Delphi and fuzzy TOPSIS | Gupta and Bhardwaj [ |
(iii) ANP and TOPSIS | Murray [ |
(iv) Analytic hierarchy process (AHP) and data envelopment analysis (DEA) | Zhang [ |
(v) Fuzzy Delphi method, fuzzy inference method, and a fuzzy linear assignment approach | Liu and Wang [ |
(vi) Interpretive structural modeling (ISM) and fuzzy technique for order preference by similarity to ideal solution (TOPSIS) | Kannan et al., [ |
(vii) Delphi method and analytical network process (ANP), | Chen and Wu [ |
(viii) Borda function theory and gray rational analysis | Cao et al., [ |
(ix) AHP and goal programming | Sheng et al., [ |
(x) Vector machine (SVM) and fuzzy analytic hierarchy process (FAHP) | Liu et al., [ |
(xi) AHP, TOPSIS fuzzy | Kumar [ |
In this study we used an integrated method via analytic hierarchy process (AHP) and (TOPSIS) with fuzzy logic to select the best logistics service provider. The methodology applies the Fuzzy AHP and TOPSIS to help the decision makers for the evaluation of logistics service providers in a fuzzy environment where the vagueness and subjectivity are handled with linguistic values parameterized by triangular fuzzy numbers. Fuzzy AHP is used to determine the weight to criteria for 3PL rating. It has been further used in TOPSIS to determine weights of evaluation criteria. Fuzzy TOPSIS is a good tool to determine the order preferences of 3PLs, and this method has been used for ranking of service providers and to find the difference between alternatives to ideal [
Combining fuzzy AHP with fuzzy TOPSIS to evaluate the alternatives according to the decision makers’ preference orders is very useful when the performance ratings are vague and imprecise. The usage of fuzzy-AHP weights in TOPSIS makes the decisions more realistic and reliable [
Methodology used in this study is shown in Figure
Methodology of the study.
The selection and the evaluation stage of the service provider organization will be important after evaluation of those listed criteria. A systematic approach is necessary to make an effective selection among potential service providers. In this study, the most important eight criteria were used in the evaluation process of the logistics service providers. In order to support objectiveness in selection process, clear definitions of those criteria are listed and defined in Table
Explanation of LSP selection criteria.
Selection criteria | Definition of the criteria | References |
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On-time delivery | Deliver a product or service that meets customer requirements against a specification for delivery time. On-time delivery is measured as percent achievement within a window of time that brackets the customer-requested date and/or the business' committed date, and is not improved by quoting long lead times and turning down tough business. | Stock et al., [ |
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Price | A key determinate in the purchasing decision. It is the price that a good or service is offered at, or will fetch, in the marketplace | Çakır et al., [ |
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Product availability | Reaching or to be able to find a product at the time you need or want it. Retailers and manufacturers across the world are losing out to store and brand switching as consumers substitute products which are unavailable or difficult to find. | Özbek and Eren, [ |
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Reliability | This criterion ensures that products or services are reliable and contribute to overall customer satisfaction. | Lynch [ |
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Firm background | The achievements of the factory in the past concerning the service or the product that has been provided by now will be evaluated in this criterion. | Çakır et al., [ |
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Firm reputation | Considered as a component of the identity as defined by others. Reputation is a fundamental instrument of social order, based upon distributed, spontaneous social control. | Kabir [ |
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Knowledge sharing | Traditional information sharing referred to one-to-one exchanges of data between a sender and receiver. These information exchanges are implemented via dozens of open and proprietary protocols message and file formats. As criterion, information sharing is a platform that will provide controlled data and information exchange between the customer and the supplier through predefined policies, guidelines, and standards to keep privacy, security, and data quality. | Lynch [ |
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Flexibility | The ability of the system to adapt to market demands. In another words, the ability of a system to respond to potential internal or external changes affecting its value delivery, in a timely and cost-effective manner. Thus, flexibility is the ease with which the system can respond to uncertainty in a manner to sustain or increase its value delivery. | Çakır et al., [ |
To deal with vagueness of human thought, Zadeh [
A fuzzy set is a class of objects with a continuum of grades of membership. Such a set is characterized by a membership (characteristic) function, which assigns to each object a grade of membership ranging between zero and one. A fuzzy set
A fuzzy number is a fuzzy subset in the universe of discourse
Membership function of triangular fuzzy number.
A linguistic variable is a variable whose values are linguistic terms. Linguistic variables are very useful in relation to situations which are unclear to be described in quantitative expressions. For example, “weight" is a linguistic variable, its values are very low, low, medium, high, very high, and so forth. These linguistic values can also be represented by fuzzy numbers [
Suppose that
Two triangular fuzzy numbers.
Both triangular and trapezoidal fuzzy numbers are used for fuzzy set theory. Using TFNs is preferred because of their computational ease. In this study, it is suitable to work with TFNs because of their computational simplicity and their usefulness in providing representation and information processing in a fuzzy environment. In this study TFNs in the FAHP is adopted. Reason of using TFNs for pairwise comparisons in fuzzy AHP is that a TFN corresponding to the expressed verbal condition in the pairwise comparison process has only one value which has the highest membership degree [
The analytic hierarchy process (AHP) is one of the extensively used multicriteria decision-making methods. One of the main advantages of this method is the relative ease with which it handles multiple criteria. In addition to this, AHP is easier to understand and it can effectively handle both qualitative and quantitative data. The use of AHP does not involve cumbersome mathematics. AHP involves the principles of decomposition, pair wise comparisons, and priority vector generation and synthesis. Though the purpose of AHP is to capture the expert’s knowledge, the conventional AHP still cannot reflect the human thinking style. Therefore, fuzzy AHP, a fuzzy extension of AHP, was developed to solve the hierarchical fuzzy problems. In the fuzzy-AHP procedure, the pairwise comparisons in the judgment matrix are fuzzy numbers that are modified by the designer’s emphasis [
In the fuzzy AHP, triangular fuzzy numbers are utilized to develop the scaling scheme in the judgement matrices, and interval arithmetic is used to solve the fuzzy eigenvector [
The procedure of the fuzzy AHP approach involves four essential steps as follows [
Define the problem and state clearly the objectives and results.
Decompose the complex problem into a hierarchical structure with decision elements (criteria and alternatives).
Employ pairwise comparisons among decision elements and form comparison matrices with fuzzy numbers.
Use the extent analysis method to estimate the relative weights of the decision elements.
(See [
In this study, fuzzy TOPSIS method developed by Chen [
Assume that a decision group contains
As mentioned above, a fuzzy multicriteria group decision-making problem can be concisely presented in matrix format as
The normalization method stated above is to protect the property that the ranges of normalized triangular fuzzy numbers belong to
Considering the different importance of each criterion, the weighted normalized fuzzy decision matrix can be constructed as follows:
With respect to the weighted normalized fuzzy decision matrix, it is known that the elements
The distance of each alternative from
A closeness coefficient is defined in order to determine the ranking order of all alternatives once the
Obviously, an alternative
In this section, we presented an illustrative example by using the methodology shown in Figure
Five people working in the logistics department of the company were determined to select evaluation criteria, to make pairwise comparisons for AHP in order to determine weights of criteria, and to evaluate alternatives via TOPSIS method. One of them is the manager of the logistic department and one of them is chief in the logistic department. Three of them are the normal staffs working in the logistics department.
Seven evaluation criteria are determined. These are on time delivery (OTD), price (P), product availability (PA), reliability (R), firm’s background (FB), firm reputation (FR), knowledge sharing (KS), and flexibility (F).
Seven logistics service providers are determined as alternatives. These alternatives are LSP1, LSP2,…, and LSP7.
In this step hierarchical construction of the problem was prepared as shown in Figure
Hierarchical structure of the problem.
In this step, first, linguistic variables for Fuzzy importance level are determined as shown in Table
Fuzzy triangular numbers for pairwise comparison of criteria [
Linguistic variables | Triangular fuzzy numbers | Reverse Triangular fuzzy numbers |
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Equal importance (EI) | (1, 1, 1) | (1/1, 1/1, 1/1) |
EI-WI | (1, 2, 3) | (1/3, 1/2, 1) |
Weak importance (WI) | (2, 3, 4) | (1/4, 1/3, 1/2) |
WI-FSI | (3, 4, 5) | (1/5, 1/4, 1/2) |
Fairly strong importance (FSI) | (4, 5, 6) | (1/6, 1/5, 1/4) |
FSI-VSI | (5, 6, 7) | (1/7, 1/6, 1/5) |
Very strong importance (VSI) | (6, 7, 8) | (1/8, 1/7, 1/6) |
VSI-AI | (7, 8, 9) | (1/9, 1/8, 1/7) |
Absolute importance (AI) | (8, 9, 9) | (1/9, 1/9, 1/8) |
Pairwise comparison of criteria with linguistic variables.
Criteria | OTD |
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PA |
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FB | FR | KS |
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OTD | EI | EI-WI | WI | WI-FSI | FSI | FSI-VSI | VSI | VSI-AI |
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Eİ | EI-WI | WI | WI-FSI | FSI | FSI-VSI | VSI | |
PA | Eİ | EI-WI | WI | WI-FSI | FSI | FSI-VSI | ||
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Eİ | EI-WI | WI | WI-FSI | FSI | |||
FB | Eİ | EI-WI | WI | WI-FSI | ||||
FR | Eİ | EI-WI | WI | |||||
KS | Eİ | EI-WI | ||||||
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Eİ |
Fuzzy weights of criteria.
Criteria |
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OTD | 0.18 | 0.28 | 0.44 |
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0.14 | 0.22 | 0.36 |
PA | 0.10 | 0.17 | 0.28 |
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0.07 | 0.13 | 0.21 |
FB | 0.05 | 0.09 | 0.15 |
FR | 0.03 | 0.06 | 0.10 |
KS | 0.02 | 0.04 | 0.07 |
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0.01 | 0.02 | 0.04 |
In this step, first, linguistic variables for fuzzy evaluation of alternatives are determined as shown in Table
Linguistic variables used for TOPSIS [
Linguistic variables | Triangular fuzzy numbers | Reverse triangular fuzzy numbers |
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Mostly bad (MB) | 0, 1, 1 | 1/1, 1/1, 0 |
Bad (B) | 0, 1, 3 | 1/3, 1/1, 0 |
Moderately Bad (MB) | 1, 3, 5 | 1/5, 1/3, 1/1 |
Moderate | 3, 5, 7 | 1/7, 1/5, 1/3 |
Moderately Good (MG) | 5, 7, 9 | 1/9, 1/7, 1/5 |
Good (G) | 7, 9, 10 | 1/10, 1/9, 1/7 |
Mostly good (MG) | 9, 10, 10 | 1/10, 1/10, 1/9 |
Evaluation of alternatives with linguistic variables.
OTD |
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PA |
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FB | FR | KS |
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LSP1 | MG | G | M | MG | MG | G | G | MG |
LSP2 | B | MB | MG | G | G | G | M | G |
LSP3 | MG | MB | MG | G | MG | MG | G | MG |
LSP4 | MG | MG | MG | G | MG | G | G | G |
LSP5 | MG | MG | MG | MG | MG | MG | G | G |
LSP6 | G | M | MG | G | G | G | G | MG |
LSP7 | G | MG | MG | MG | MG | M | MG | MB |
Fuzzy decision matrix.
On time delivery (OTD) | Price ( |
Product availability (PA) | Reliability |
Firm’s background (FB) | Firm reputation (FR) | Knowledge sharing (KS) | Flexibility |
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LSP1 | 9 | 10 | 10 | 7 | 9 | 10 | 3 | 5 | 7 | 9 | 10 | 10 | 9 | 10 | 10 | 7 | 9 | 10 | 7 | 9 | 10 | 9 | 10 | 10 |
LSP2 | 0 | 1 | 3 | 1 | 3 | 5 | 5 | 7 | 9 | 7 | 9 | 10 | 7 | 9 | 10 | 7 | 9 | 10 | 3 | 5 | 7 | 7 | 9 | 10 |
LSP3 | 9 | 10 | 10 | 1 | 3 | 5 | 9 | 10 | 10 | 7 | 9 | 10 | 9 | 10 | 10 | 9 | 10 | 10 | 7 | 9 | 10 | 9 | 10 | 10 |
LSP4 | 9 | 10 | 10 | 5 | 7 | 9 | 5 | 7 | 9 | 7 | 9 | 10 | 9 | 10 | 10 | 7 | 9 | 10 | 7 | 9 | 10 | 7 | 9 | 10 |
LSP5 | 9 | 10 | 10 | 9 | 10 | 10 | 9 | 10 | 10 | 9 | 10 | 10 | 9 | 10 | 10 | 9 | 10 | 10 | 7 | 9 | 10 | 7 | 9 | 10 |
LSP6 | 7 | 9 | 10 | 3 | 5 | 7 | 5 | 7 | 9 | 7 | 9 | 10 | 7 | 9 | 10 | 7 | 9 | 10 | 7 | 9 | 10 | 9 | 10 | 10 |
LSP7 | 7 | 9 | 10 | 5 | 7 | 9 | 9 | 10 | 10 | 5 | 7 | 9 | 5 | 7 | 9 | 5 | 7 | 5 | 5 | 7 | 9 | 1 | 3 | 5 |
In this step, the normalized decision matrix
Standardized Decision Matrix.
On time delivery (OTD) | Price ( |
Product availability (PA) | Reliability ( |
Firm’s background (FB) | Firm reputation (FR) | Knowledge sharing (KS) | Flexibility ( |
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LSP1 | 0.9 | 1 | 1 | 0.7 | 0.9 | 1 | 0.3 | 0.5 | 0.7 | 0.9 | 1 | 1 | 0.9 | 1 | 1 | 0.7 | 0.9 | 1 | 0.7 | 0.9 | 1 | 0.9 | 1 | 1 |
LSP2 | 0 | 0.1 | 0.3 | 0.1 | 0.3 | 0.5 | 0.5 | 0.7 | 0.9 | 0.7 | 0.9 | 1 | 0.7 | 0.9 | 1 | 0.7 | 0.9 | 1 | 0.3 | 0.5 | 0.7 | 0.7 | 0.9 | 1 |
LSP3 | 0.9 | 1 | 1 | 0.1 | 0.3 | 0.5 | 0.9 | 1 | 1 | 0.7 | 0.9 | 1 | 0.9 | 1 | 1 | 0.9 | 1 | 1 | 0.7 | 0.9 | 1 | 0.9 | 1 | 1 |
LSP4 | 0.9 | 1 | 1 | 0.5 | 0.7 | 0.9 | 0.5 | 0.7 | 0.9 | 0.7 | 0.9 | 1 | 0.9 | 1 | 1 | 0.7 | 0.9 | 1 | 0.7 | 0.9 | 1 | 0.7 | 0.9 | 1 |
LSP5 | 0.9 | 1 | 1 | 0.9 | 1 | 1 | 0.9 | 1 | 1 | 0.9 | 1 | 1 | 0.9 | 1 | 1 | 0.9 | 1 | 1 | 0.7 | 0.9 | 1 | 0.7 | 0.9 | 1 |
LSP6 | 0.7 | 0.9 | 1 | 0.3 | 0.5 | 0.7 | 0.5 | 0.7 | 0.9 | 0.7 | 0.9 | 1 | 0.7 | 0.9 | 1 | 0.7 | 0.9 | 1 | 0.7 | 0.9 | 1 | 0.9 | 1 | 1 |
LSP7 | 0.7 | 0.9 | 1 | 0.5 | 0.7 | 0.9 | 0.9 | 1 | 1 | 0.5 | 0.7 | 0.9 | 0.5 | 0.7 | 0.9 | 0.5 | 0.7 | 0.5 | 0.5 | 0.7 | 0.9 | 0.1 | 0.3 | 0.5 |
The weighted normalized decision matrix is constituted by multiplying the normalized decision matrix by its associated weights by (
Weighted standardized decision matrix.
On time delivery (OTD) | Price ( |
Product availability (PA) | Reliability ( |
Firm’s background (FB) | Firm reputation (FR) | Knowledge sharing (KS) | Flexibility ( |
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LSP1 | 0.162 | 0.280 | 0.440 | 0.098 | 0.198 | 0.360 | 0.030 | 0.085 | 0.196 | 0.063 | 0.130 | 0.210 | 0.045 | 0.090 | 0.150 | 0.021 | 0.054 | 0.100 | 0.014 | 0.036 | 0.070 | 0.009 | 0.020 | 0.040 |
LSP2 | 0.000 | 0.028 | 0.132 | 0.014 | 0.066 | 0.180 | 0.050 | 0.119 | 0.252 | 0.049 | 0.117 | 0.000 | 0.035 | 0.081 | 0.150 | 0.021 | 0.054 | 0.100 | 0.006 | 0.020 | 0.049 | 0.007 | 0.018 | 0.040 |
LSP3 | 0.162 | 0.280 | 0.440 | 0.014 | 0.066 | 0.180 | 0.090 | 0.170 | 0.280 | 0.049 | 0.117 | 0.210 | 0.045 | 0.090 | 0.150 | 0.027 | 0.060 | 0.100 | 0.014 | 0.036 | 0.070 | 0.009 | 0.020 | 0.040 |
LSP4 | 0.162 | 0.280 | 0.440 | 0.070 | 0.154 | 0.324 | 0.050 | 0.119 | 0.252 | 0.049 | 0.117 | 0.000 | 0.045 | 0.090 | 0.150 | 0.021 | 0.054 | 0.100 | 0.014 | 0.036 | 0.070 | 0.007 | 0.018 | 0.040 |
LSP5 | 0.162 | 0.280 | 0.440 | 0.126 | 0.220 | 0.360 | 0.090 | 0.170 | 0.280 | 0.063 | 0.130 | 0.210 | 0.045 | 0.090 | 0.150 | 0.027 | 0.060 | 0.100 | 0.014 | 0.036 | 0.070 | 0.007 | 0.018 | 0.040 |
LSP6 | 0.126 | 0.252 | 0.440 | 0.042 | 0.110 | 0.252 | 0.050 | 0.119 | 0.252 | 0.049 | 0.117 | 0.000 | 0.035 | 0.081 | 0.150 | 0.021 | 0.054 | 0.100 | 0.014 | 0.036 | 0.070 | 0.009 | 0.020 | 0.040 |
LSP7 | 0.126 | 0.252 | 0.440 | 0.070 | 0.154 | 0.324 | 0.090 | 0.170 | 0.280 | 0.035 | 0.091 | 0.189 | 0.025 | 0.063 | 0.135 | 0.015 | 0.042 | 0.050 | 0.010 | 0.028 | 0.063 | 0.001 | 0.006 | 0.020 |
We determined FPIS and FNIS as
We calculated the distance of each alternative from FPIS and FNIS. The separation measures
In this step, the relative closeness to the ideal solution is calculated from (
Separation measures and relative closeness to ideal solution.
Alternatives |
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Closeness to ideal solution |
Ordering |
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LSP1 | 7,056233 | 4,800213 | 0,404861 | 4 |
LSP2 | 7,482334 | 2,840077 | 0,275137 | 7 |
LSP3 | 7,113461 | 5,90693 | 0,453668 | 3 |
LSP4 | 7,135847 | 4,490351 | 0,386227 | 5 |
LSP5 | 6,960129 | 13,03951 | 0,651987 | 2 |
LSP6 | 7,210204 | 4,154227 | 0,365546 | 6 |
LSP7 | 7,132629 | 35,31464 | 0,831965 | 1 |
Outsourcing has become a common practice in many industries, specifically in the logistics activities. Because more companies outsource their logistics operations, selecting appropriate and preferable third party LSPs has increasingly become a critical issue and a strategic decision for companies outsourcing their logistics operations.
This study provides a practical approach and methodology for companies to select the best third party LSP meeting their requirements. LSP selection process started the determination of quantitative and qualitative factors to select the best LSP. In this study LSP provider selection via integrating approach of fuzzy AHP and fuzzy TOPSIS method has been presented. Evaluation criteria were determined as on time delivery (OTD), price (P), product availability (PA), reliability (R), firm’s background (FB), firm reputation (FR), knowledge sharing (KS), flexibility (F).
This study proposes a methodology to provide a simple approach to evaluate alternative LSP firms and help decision maker to select the best one. By using improved AHP with fuzzy set theory, the qualitative judgment can be qualified to make comparison more intuition and reduce or eliminate assessment bias in pairwise comparison process. Finally this paper defines an approach that integrates fuzzy TOPSIS algorithm with fuzzy AHP to support LSP evaluation and selection decisions. By means of the extent fuzzy approach, the uncertainty in the data could be effectively represented and processed to make a more effective decision.
As a result of this study, alternative LSP7 is determined as the best LSP which has the highest
Researchers such as Kabir [
This study has some limitations. One of them is that only qualitative criteria were used to evaluate performance of LSPs. Quantitative criteria can be used together with qualitative data. Another limitation of this study is that any subcriteria are not used as evaluation criteria, only main criteria are used for evaluation. Another limitation of this study is that the focus of the paper is on LSPs of a tire manufacturing company, but the analysis and methodology of 3PL providers’ selection can be successfully adopted by other sectors. Because this study used a small sample size and was performed in the tire manufacturing industry, this situation limits the generalization of the results. To generalize the results, similar studies can be performed in different industries with a different data set.
The main contribution of this paper includes application of integrated AHP and TOPSIS framework with support of fuzzy approach to measure the relative strength of the third-party LSPs. We hope that results of this research can be used a reference by the tire companies to select the best logistics service provider partner.
The proposed methodology of this study is easy to implement and quite reliable for ranking the alternatives. Applicability of the proposed methodology has been proposed in a tire company for the selection of the third-party LSPs. The approach can also be applied effectively to help any managerial decision-makings. The findings provide valuable insights for logistics practitioners, academicians, and educators. For further research, other multicriteria evaluation methods can be used and the obtained results can be compared with the ones found in this paper. Also, the methodology of third-party LSPs selection can be successfully adapted to other sectors with different data sets.
The authors declare that there is no conflict of interests regarding the publication of this paper.