Multistakeholder Strategic Third-Party Logistics ...

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Transportation Journal, Volume 54, Number 3, Summer 2015, pp. 312-338 (Article) 3XEOLVKHGE\3HQQ6WDWH8QLYHUVLW\3UHVV

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Multistakeholder Strategic Third-Party Logistics Provider Selection: A Real Case in China Jian-Jun Wang, Meng-Meng Wang, Feng Liu, and Haozhe Chen

Abstract

Outsourcing logistics to third-party logistics (3PL) providers is becoming an increasing trend for companies seeking improved customer service and competitiveness. Choosing a suitable and competitive logistics provider has become more important than ever. In this study, we developed an evaluation and selection framework on strategic 3PL provider selection on the basis of a real case application in China. Multiple criteria and multiple stakeholders were considered to balance the various measurements in this framework. In the actual application, we chose the PROMETHEE technique as our specific evaluation method and we also assessed its effectiveness with further analysis. The results show that our proposed holistic framework is well suited for the strategic 3PL provider selection problem. Keywords

3PL provider selection, multiple stakeholders, evaluation framework, multiple criteria, PROMETHEE

Jian-Jun Wang Dalian University of Technology [email protected]

Haozhe Chen Corresponding Author Iowa State University [email protected]

Meng-Meng Wang Dalian University of Technology [email protected]

Transportation Journal, Vol. 54, No. 3, 2015 Copyright © 2015 The Pennsylvania State University, University Park, PA

Feng Liu Dongbei University of Finance and Economics [email protected]

TJ 54.3_02_Wang-Chen-Wang-Liu.indd 312

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Wang, et al.: Multistakeholder Strategic 3PL Provider Selection \ 313

Introduction

In response to the more global and highly competitive business environment, companies have to improve the efficiency of their supply chain continuously (Shen and Chou 2010). Outsourcing logistics functions to outside professional third-party logistics (3PL) providers has burgeoned and evolved as a viable means for companies to enhance the efficiency and thus enable them to focus on their core competencies. The growth of 3PL practice is mainly attributed to companies’ inner resource constraints and its appealing benefits, such as efficiency improvement, service quality enhancement, transportation cost reduction, supply chain restructuring, and marketplace legitimacy development (Bhatnagar, Sohal, and Millen 1999; Hertz and Alfredsson 2003; Skjoett-Larsen 2000). Research on 3PL topics also grew substantially in recent years (Ashenbaum, Maltz, and Rabinovich 2005; Maloni and Carter 2006), and 3PL provider selection still remains an important research topic (Maloni and Carter 2006). The reason is that from a supply chain perspective, 3PL providers hold a unique position interfacing with outsourcing companies, end customers, and the marketplace by offering a variety of services, such as transportation, warehousing, inventory management, product returns service, and valueadded activities (such as secondary assembly and installation of products) (Göl and Çatay 2007). The 3PL providers’ performance can directly affect the supply chain performance of other companies and end customers. Because good provider selection can make a significant difference to an outsourcing company’s future development and competitiveness, it is necessary for the outsourcing company to consider it a strategic decision. Therefore, the importance of an effective 3PL provider selection mechanism cannot be overemphasized. Selecting the most suitable 3PL provider involves much more than scanning a series of price lists; instead, the choice relies on a systematic consideration that involves a wide range of factors (Ho et al. 2012). In extant literature, different evaluation criteria and evaluation methods have been developed and applied to address the 3PL provider selection problem. Traditionally, the selection process often depended on subjective judgment on the basis of the decision-makers’ individual understanding of 3PL providers, lacking both theoretical support and systems considerations (Xu 2000). Such an approach might not result in optimal decisions. Furthermore, the perceptual differences regarding important selection criteria between 3PL providers and customers can negatively impact service outcomes (Premeaux 2002). Conventional approaches often emphasized


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the cost aspect in the evaluation of potential service providers (Robinson, Thomas, and Manrodt 2013; Weber, Current, and Benton 1991). In recent years, as more companies start to treat 3PL providers as strategic partners and strive to develop long-term relationships with them, more comprehensive quantitative and qualitative criteria, including social, political, and customer satisfaction concerns have been added to the traditional factors of cost, quality, delivery, and service (Liu and Wang 2009; Robinson, Thomas, and Manrodt 2013). For example, Liu and Wang (2009) developed an evaluation framework with 17 criteria for the selection of 3PL providers in Taiwan. Their results indicated that logistics information system, customer service, on-time delivery, capability to handle specific business requirements, responsiveness, and accessibility to contact persons in urgency are the most influential factors. Also, Li et al. (2012) designed an evaluation system based on fuzzy sets focusing on four first-level criteria such as management success, business strength, service quality, and business growth, and 20 s-level criteria. However, some of the important emerging factors, such as sustainability issues and environmental problems, have not been given enough attention in the 3PL provider selection practices and should be incorporated into the evaluation criteria (Christina and Stefan 2010). Various individual and integrated multicriteria decision-making (MCDM) approaches have been developed in the research stream regarding 3PL provider selection. Ho, Xu, and Dey (2010) analyzed and summarized existing MCDM approaches that were prevalently applied in international journals, such as AHP (analytic hierarchy process), ANP (analytic network process), CBR (case-based reasoning), DEA (data envelopment analysis), TOPSIS (technique for order preference by similarity to ideal solution). Ho, Xu, and Dey (2010) pointed out that these approaches did not take into consideration the impact of requirements of various stakeholders in the 3PL provider selection process. In reality, the involvement and input of multiple stakeholders will help companies better understand the essential requirements of various departments and make the outsourcing selection more effective (Ho, Dey, and Lockstrom 2011). Maloni and Carter (2006) also pointed out that research in 3PLs should ultimately support current and future professional practice and should seek practitioner involvement. As a response to this call, the current study was undertaken to advance existing research selection by developing a strategic 3PL provider selection framework based on a real case in China, in which multiple criteria and multiple stakeholders were engaged to evaluate the providers. Based on the above discussion, we employed the PROMETHEE


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method as our specific evaluation tool. Compared with other MCDM methods, the PROMETHEE method has remarkable advantages in conceptualization simplicity (Brans, Vincke, and Mareschal 1986) and transparent computational procedure (Behzadian et al. 2010; Macharis et al. 2004; Wang and Yang 2007), which have contributed to its rapid development and application in practice (Brans and Mareschal 2005). In addition, it is also an excellent method for resolving multiple criteria along with various stakeholders. Thus, there are sufficient justifications for using this method in our current research context of strategic 3PL provider selection. The remainder of this article is organized as follows. A brief description of the extant literature about 3PL provider selection is summarized first. We then develop a strategic 3PL provider selection framework for an auto parts manufacturer, which is followed by an analysis of the selection process to validate the selection results. Finally, we present our discussion and conclusion. Literature Review

As discussed previously, the globalization of today’s supply chains has made selecting a suitable 3PL provider a critical decision for many companies. As many managers have realized, this is a complex, multicriteria decision-making problem and thus multiple criteria (both quantitative and qualitative) need to be considered in the decision process. Researchers have made great efforts to tackle the 3PL provider selection problem with a wide range of criteria and methods. In order to identify the most used criteria and methods in the 3PL provider selection process, Aguezzoul (2014) reviewed and analyzed 67 journal articles specific to the 3PL provider selection problem for the period 1994– 2013. The large number of factors that were considered in the selection process by these articles indicates the importance of the decision. Criteria such as cost, relationship, service, quality, information/equipment system, flexibility, delivery, professionalism, financial position, location, and reputation are in the focus of the most commonly used criteria. For example, Meade and Sarkis (2002) developed a model, in which time, quality, cost, and flexibility were evaluated, to select the best third-party reverse logistics service provider. Bottani and Rizzi (2006) identified twelve factors that influenced the 3PL provider selection decision. Despite the large number of factors that have appeared in the extant literature, some important and relevant variables such as sustainability and environmental performance are largely left out in the 3PL provider selection process to date (Christina and Stefan 2010).


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To be specific, the extensive body of literature regarding methods modeling the process of evaluating potential 3PL provider can be categorized into five groups, namely, MCDM techniques, statistical approaches, artificial intelligence, mathematical programming, and hybrid methods. Thakkar et al. (2005) developed an ANP and interpretive structural model to select the best 3PL provider among various alternatives. Later, Göl and Çatay (2007) introduced the AHP approach to determine the most desirable 3PL provider. Extending these studies, Perçin and Min (2013) proposed a hybrid approach combing the QFD and fuzzy linear regression method to select the appropriate 3PL in a real-world setting. Because of its advantages in coping with multiple qualitative and quantitative criteria, MCDM techniques such as FST, AHP, ANP, and TOPSIS have been very popular in extant literature. However, each individual technique has its strengths and weaknesses. Therefore, integrating individual MCDM techniques with methods in the same category or with other techniques becomes a desirable approach. These types of integrated approaches can more effectively model different stages of the 3PL selection process such as the identification of criteria weighting, the elimination of unsuitable providers, and the final decision of the selection. Table 1 presents a review of the related articles. Table 1/Summary of the Related Articles on 3PL Selection Article

Key Features

Analytical Method(s)

Menon et al. 1998

Focused on how competitiveness and external environment affect the criteria

Correlation method

Lehmusvaara, Tuominen, and Korpela 1999

Centered on the truck carrier 3PL provider selection

AHP and mixedinteger LP

Meade and Sarkis 2002

Conducted the selection in reverse logistics

ANP

Yan, Chaudhry, and Chaudhry 2003

Took the input from human experts into account in the selection

CBR

Thakkar et al. 2005

Captured the interdependency among the selection criteria

Interpretive structural method and ANP

Bottani and Rizzi 2006

The uncertainty and inaccuracy associated with the weights of selection criteria is modeled

Fuzzy TOPSIS

Min and Joo 2006

One of the first to evaluate the comparative performance of the leading 3PL providers in the USA

DEA

Göl and Çatay 2007

Based on the Turkish automotive industry background for selection

AHP

Zhou et al. 2008

Evaluated the comparative operating efficiency of 10 Chinese providers

DEA

(Continues)


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Article

Key Features

Analytical Method(s)

Liu and Wang 2009

Developed the three-phase 3PL selection procedure

Delphi method, fuzzy LP, and fuzzy inference

Liou and Chuang 2010

Addressed the dependent relationships among criteria

ANP, VIKOR, and DEMATEL

Li et al. 2012

Represented a linear programming model to maximize the comprehensive evaluation of the selection

Fuzzy sets

Ho et al. 2012

Considered the stakeholders requirements in the process

QFD and fuzzy AHP

Perçin and Min 2013

Reflected the relation between the customer needs and 3PL characteristics

QFD, and fuzzy line regression

Considering the growing importance of logistics outsourcing in supply chain management, a holistic perspective taking account of the “voices” of different stakeholders becomes a meaningful and necessary initiative both in practice and academic research. Along this line, Ho et al. (2012) developed an integrated QFD and fuzzy AHP approach to select the optimal 3PL provider for a Hong Kong–based enterprise, taking four company stakeholders’ opinions into consideration. Unfortunately, the Ho et al. research (2012) is a rare exception in the sense of emphasizing practical application because most extant research attempts have mainly focused on the methodological investigation. In order to provide sound theory support for supply chain professionals’ decision-making, much more effort on tying academic research with actual practical application on the subject of 3PL provider selection process is imperative and warranted. Therefore, with the aim of enriching the existing body of knowledge, we developed a strategic 3PL provider evaluation framework for a Chinabased company. Moreover, we extended the system of criteria by considering the business environment specific to the company of interest, and we took into consideration the opinions of multiple stakeholders in the 3PL provider selection problem. It is expected that our evaluation framework can be easily modified and applied to assist other companies’ decisionmaking processes. The Evaluation Framework: A Real Case Study

The company of focus in the current study is an auto parts manufacturer located in Dalian, China, who produces and sells various automobile tires for its dealers on a national scale. This company is currently in its rapid development stage and experiencing a massive expansion to


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meet the growing demand. It is imperative for the company to focus on its core competency and service quality in this process. One of the biggest challenges is distributing its products to its growing dealer network, which can be a substantial investment for the company. Thus, choosing an appropriate 3PL provider appears to be a viable strategic option. In order to establish a long-term cooperative relationship that would fit the company’s strategies, the auto parts manufacturer would consider the selection based on satisfaction of the company’s basic needs (e.g., logistics capability, customer service, and so on) and value-adding benefits (e.g., a continual improvement system and compatibility between cooperation). In order to assist the company in evaluating their potential providers, we developed a strategic 3PL provider selection framework (as shown in fig. 1). In the following section, we describe the detailed selection process for this auto parts manufacturer. Step 1.1: Acquire the Potential 3PL Providers

In the initial screening phase, the auto parts manufacturer would identify all potential providers based on the company’s needs. In order to obtain the initial provider list, we used the following approaches: (1) search various major Chinese logistics websites, (2) directly contact the well-known and established providers, and (3) gather information from newspapers and business magazines. After collecting and compiling the obtained information, 12 potential 3PL providers were identified and were sent the bidding invitations. Nine of them were available for the further consideration. Step 1.2: Eliminate the Unsuitable 3PL Providers Based on Basic Requirements

Given that the main task of the 3PL provider is providing satisfactory logistics service for the company, especially to support the sales department’s activities, it is critical to solicit the sales department’s input on the basic requirements for selection. These requirements must be met to ensure effective logistics operations, such as improving timeliness of deliveries and providing accurate information about the status of all shipments and the condition of the trucks. Elimination of the unqualified providers helps save time and effort in the decision process. Therefore, a panel of experts from the sales department proposed the baseline criteria after evaluating the company’s actual business operations. Then, they offered their opinions about the necessary requirements for logistics service using the Delphi method. The resulting criteria mainly focused on transportation capability, storage, and information systems. In accordance with their opinions, we


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established six preliminary screening criteria for elimination (see table 2). Based on these elimination criteria, a questionnaire was carefully designed and distributed to obtain the necessary information from the nine providers. Comparing the providers’ responses with the company’s actual needs, we narrowed the pool to five providers who had the necessary capabilities. For confidentiality purposes, these companies will be named as company A1, A2, A3, A4, and A5 hereafter.

Screening phase

Acquisition of initial providers

Identify the screening criteria

Evaluation phase

Stakeholder, criteria and method selection phase

Screen qualified providers

Identify the stakeholders

Identify the evaluation criteria

Select the evaluation method

DM1 weights

DMi weights

DMR weights

Data matrix

Data matrix

Data matrix

DM1 ranking

DMi ranking

DMR ranking

Providers ranking GAIA and sensitivity analysis Figure 1 The Framework for Strategic Third-party Logistics Provider Selection


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Table 2/Elimination Criteria and Explanation for Provider Selection Criterion

Purpose and Scope of the Criterion

Storage

To ensure the space and number are sufficient for storing and ensure uniformity in storage operations for the auto parts manufacturer across all locations. The storage conditions are crucial for goods keeping.

Transportation

To ensure the availability of trucks/shipping etc. for delivering.

Logistics network

The distribution of networks should be sufficient for the deals across the nation.

Information systems

For logistics service, a set of complete information systems is requisite. All receipts in and out must be entered into the company’s SAP system.

Order management and dispatch

Adequate client order management and dispatch are important for improving client satisfaction. The dispatch process should be operated following the auto parts manufacturer’s policy, first in first out (FIFO).

Industry certification

To ensure the legitimacy of services provided.

Step 2.1: Identify Company Stakeholders

In order to achieve a balanced perspective of the selection decision, a group of experts who possess rich knowledge and experience in logistics was formed and involved in the process. The group members included different internal and external stakeholders based on their positions and relevant experience. In addition to sales department (DM1) and purchasing department (DM2), the group also included a panel of senior managers (CEO, COO, CFO, CTO, CHO) (DM3), a panel of three main customers (DM4), and a panel of consultants (one operations management professor, one logistics professor, one local government officer, and two Dalian Logistics Association members) (DM5). Step 2.2: Determine the 3PL Evaluation Criteria

At first, each DM applied the Delphi method to identify a list of proposed criteria based on their knowledge and experience. They were aware that the goal was to provide a consistent criterion family. The five collected lists were consolidated into an aggregated list and this list was presented to the DMs for further comments and refinements. This process was repeated until an agreement was reached. After carefully classifying and analyzing the group’s opinion, we normalized their opinion by referring to existing research on the provider evaluation/selection criteria (Bottani and Rizzi 2006; Genovese et al. 2013; Göl and Çatay 2007; Handfield et al. 2002; Hertz and Alfredsson 2003; Ho, Dey, and Lockstrom 2011; Jharkharia and Shankar 2007; Lehmusvaara, Tuominen, and Korpela 1999; Li et al. 2012; Liou and Chuang 2010; Liu and Wang 2009; Meade and Sarkis 2002; Min and Joo 2006; Murphy and Daley 1997; Perçin and Min 2013; Thakkar et al.


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Table 3/Evaluation Criteria and Explanation for Provider Selection Criterion

Purpose and Scope of the Criterion

General Company Consideration (C1) Cost (C11)

Pricing information derived from the quotations.

Financial position (C12)

The provider’s financial situation obtained from balance sheets and income statements.

Logistics equipment (C13)

The provider’s facility and equipment utilization.

Staff’s quality (C14)

The provider employees’ education background and training experience.

Market share (C15)

The provider’s logistics service scale.

Geographic location (C16)

The geographic area served by the provider.

Experience in the industry (C17)

The provider’s experience in similar industries.

Capability (C2) Optimization capability (C21)

The provider’s ability to optimize route planning, load planning, and truck/container planning.

IT capability (C22)

The provider’s ability to use computer systems to capture information on receiving, tracking, tracing, and confirmation etc.

Management capability (C23)

The provider’s capability of general management.

Responsiveness (C24)

The provider’s ability to handle unexpected events.

Compatibility (C25)

The provider’s culture compatibility and structural compatibility with our firm.

Service quality (C3) Delivery quality (C31)

On time delivery.

Client satisfaction (C32)

Service quality derived from the evaluation of prior clients.

Conflict resolution (C33)

The provider’s ability to resolve conflicts in a timely and effective manner.

Development Prospect (C4) Investment intention (C41)

The provider’s interest in investing in the areas that fit our firm’s development needs.

Continual improvement(C42)

The provider’s continuous improvement efforts (such as reduce time to deliver, reduce cost, improve service level etc.).

Growth forecasts (C43)

The provider’s growth capability based on the number of days it takes the provider to respond a capacity increase of 20 percent.

Guanxi (C5)

The provider’s guanxi network is evaluated with its guanxi connections with governments, competitors, industrial authorities, and other authorities (taxation bureaus, banks, industrial and commercial administrative bureaus, etc.).

Environmental Performance (C6)

The provider’s commitment to environmental issues and sustainable development.


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2005; Weber, Current, and Benton 1991; Williams, Garver, and Taylor 2013; Yan, Chaudhry, and Chaudhry 2003; Zhou et al. 2008). Then, each DM voted on each criterion. Finally, a general consensus was resulted on the finial evaluation criteria, including 6 first-level criteria and 18 s-level criteria (shown in table 3). In addition to the general company consideration (C1), capability (C2), service quality (C3), and improvement prospect (C4), we also identified guanxi (C5) and environmental performance (C6) as important factors in the process. It is noted that guanxi, a China-specific concept of drawing on a web of connections to secure favors in personal and organizational relations (Park and Luo 2001), is a unique and relevant sociocultural construct in Chinese business practices. Having good guanxi connections would benefit a company in accessing limited resources, preferential treatment in business, and increasing accessibility to controlled information (Lee, Pae, and Wong 2001). A 3PL provider who enjoys good guanxi connections with governments, customers, or competitors can manage the logistics activities more smoothly due to its privileges. In addition, environmental performance of the 3PL providers also appeared in our final criteria list because protecting the environment has become a major concern for many parties such as customers, companies, and government. Step 2.3: Select an Appropriate Evaluation Method

Because both quantitative and qualitative criteria were used by the company, we determined that the PROMETHEE method was suitable for the evaluation process for the following reasons. First, the PROMETHEE method is widely used in strategic decisions with multicriteria and multistakeholders analysis. Especially when dealing with conflicting voices, the compensation between good scores on some criteria and bad scores on other criteria is avoided in the PROMETHEE method (Macharis et al. 2004; Wang and Yang 2007). Second, the simplicity of this method in conceptualization and software packages operation (Brans, Vincke, and Mareschal 1986; Macharis et al. 2004) enhances its user friendliness in practice. Third, this method has a distinctive advantage in visualizing the decision problem, which can be beneficial for the decision-maker to obtain a good view on the analysis. In sum, we believed this method was well suited to solve our 3PL selection problem and we had the support from all of the involved stakeholders on using this method. A description of the PROMETHEE method is provided in the appendix. Applying this method, the detailed steps of the evaluation process are as follows:


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Step 3.1: Determine The Weights Of Evaluation Criteria

Since the PROMETHEE method does not have specific guidelines for determining weights, we thus applied the AHP to obtain the information. According to the concepts of AHP (see Saaty and Vargas 2012), the weight of each criterion was assigned by each DM (as shown in table 4). The consistency ratio (CR) indices were calculated to confirm the acceptability of judgments. All CRs were less than 0.1, confirming that the evaluations were sufficiently consistent. It was evident that the company had an intention of pursuing long-term relationships with its 3PL provider for strategic reasons. DM1 considered capability and guanxi more Table 4/Evaluation Criteria for Provider Selection Id

DM1

DM2

DM3

DM4

DM5

C1

11.27%

15.12%

14.58%

12.81%

14.50%

C11

1.19

3.16

1.02

1.33

2.27

C12

1.02

1.94

1.78

1.47

2.36

C13

1.87

2.59

1.96

1.98

2.76

C14

2.26

1.86

2.24

2.41

1.75

C15

1.62

1.40

3.0

1.61

1.38

C16

2.04

2.35

2.56

1.62

2.24

C17

1.27

1.82

2.01

2.39

1.73

C2

20.13

18.50

18.71

14.49

14.80

C21

5.07

4.66

4.29

3.75

3.25

C22

5.07

4.66

3.44

2.47

3.25

C23

2.98

4.04

3.96

2.84

3.81

C24

4.48

3.12

3.35

3.27

2.87

C25

2.53

2.02

3.68

2.15

1.62

C3

23.19

23.08

21.05

22.94

27.37

C31

12.51

10.23

10.58

10.17

8.13

C32

6.89

8.94

6.06

8.89

14.77

C33

3.79

3.91

4.42

3.88

4.47

C4

11.88

14.22

16.30

16.37

16.02

C41

4.90

3.70

4.24

3.21

3.14

C42

3.09

5.87

6.73

8.07

4.98

C43

3.89

4.66

5.34

5.09

7.90

C5

18.71

14.22

14.58

12.81

14.76

C6

14.81

14.86

14.77

20.57

12.54

Note: CRDM1 = 0.02; CRDM1 = 0.03; CRDM3 = 0.02; CRDM4 = 0.02; CRDM5 = 0.03


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Table 5/Evaluation Matrix of DM1 Alternatives

Criteria

A1

A2

A3

A4

A5

C11 (10M¥)

4.3

5.2

5.8

7.4

6.1

C12

Average

Good

Good

Good

Good

C13

Good

Good

Good

Very good

Average

C14

Average

Good

Good

Good

Good

C15 (%)

1.5

1.5

1.8

2.0

1.8

C16

Good

Good

Average

Average

Good

C17

High

Moderate

Moderate

Moderate

High

C21

Average

Good

Average

Good

Good

C22

Good

Good

Good

Very good

Good

C23

Average

Average

Good

Good

Good

C24

Very good

Very good

Average

Good

Average

C25

Average

Average

Good

Bad

Good

C31

Good

Good

Good

Very good

Average

C32

High

Moderate

High

Moderate

High

C33

Average

Good

Good

Good

Good

C41

High

Moderate

High

Moderate

High

C42

Average

Good

Good

Good

Average

C43

Average

Good

Average

Good

Good

C5

Good

Good

Average

Average

Good

C6

Low

Very low

Moderate

High

Very low

Note: ¥: RMB (China Yuan)

important than other DMs; DM2 viewed general company considerations, such as cost, logistics equipment, to be more important; and DM4 had a preference for environmental issues. Step 3.2: Construct The Evaluation Matrix Of Each Stakeholder DM

Every decision-maker was asked to give his/her evaluation on the alternatives applying the PROMETHEE method. The evaluation matrix is usually obtained with the criteria’s own units. In our study, the quantitative criteria, the cost (C11), and market share (C15) were expressed in the actual numbers. The qualitative criteria were structured with 5-point Likert scales and each semantic value included in the set {very low/bad, low/bad, moderate/average, high/good, very high/good} was associated with a numerical value ranging from 1 to 5. Based on the materials provided, each DM provided his or


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Table 6/Preference Functions of Each DM Criteria

DMs DM1w

DM2 Type II

C12

Type V

Type IV q = 0.5, Type V p = 1.5

C13

Type IV q = 0.5, p = 1.5

Type V

q = 0.5, Type VI s = 2.5 p = 2.0

C14

Type V

q = 0.5, p = 2.5

Type V

q = 1.0, Type IV q = 1.0, Type VI s = 3.0 p = 2.0 p = 2.0

C15

Type II

q = 0.2


C16

Type II

q = 1.0

Type IV q = 0.5, Type IV q = 0.5, Type IV q = 0.5, Type IV q = 0.5, p = 2.0 p = 1.5 p = 2.5 p = 1.5

C17

Type III p = 2.5

Type III

Type III p = 3.0

Type V

q = 0.5, Type V p = 2.5

q = 0.5, p = 2.5

C21

Type II

Type IV q = 0.5, Type III p = 3.0 p = 1.5

Type II

q = 1.5

Type V

q = 0.5, p = 2.0

C22

Type III p = 2.0

Type III

q = 0.5, Type VI s = 3.0 p = 1.0

Type III

p = 2.0

C23

Type IV q = 0.5, p = 1.5

Type VI s = 3.0

C24

Type III p = 2.0

Type IV q = 0.5, Type IV q = 0.5, Type V p = 1.5 p = 1.0

q = 0.5, Type IV q = 0.5, p = 2.0 p = 2.0

C25

Type IV q = 0.5, p = 1.5


q = 1.0

C31

Type V

q = 0.5, p = 1.5

Type III

p = 1.5

Type V

C32

Type III p = 1.5

Type III

p = 1.5

Type VI s = 2.5

Type III p = 2.0

Type III

C33

Type IV q = 1.0, p = 3.0

Type III

p = 2.0

Type III p = 2.0

Type VI s = 3.0

Type IV q = 1.0, p = 2.0

C41

Type III p = 2.5

Type V

q = 1.0, Type V p = 2.5

C42

Type IV q = 0.5, p = 1.5


Type III p = 2.0

C43

Type III p = 3.0

Type V

q = 0.5, Type III p = 2.0 p = 2.0

Type V

C5

Type V

q = 0.5, p = 2.0

Type III

p = 2.0

Type II

C6

Type II

q = 1.0

Type II

q = 1.5


q = 0.5

p = 2.0

Type V

q = 1.5

Type V

DM5

Type VI s = 1.3

p = 2.5

Type II

DM4

C11

q = 0.5, p = 1.5

q = 1.2

DM3

q = 0.5, Type VI s = 2.0 p = 2.0

q = 0.5, Type III p = 0.5 p = 2.0 Type II

q = 2.0

q = 0.1, Type III p = 1.0 p = 0.8

Type VI s = 2.0

Type VI s = 3.0 Type V

q = 0.5, p = 1.5

Type IV q = 1.0, p = 2.0 Type II

q = 0.2

Type IV q = 0.5, Type IV q = 0.5, p = 1.5 p = 1.0

Type II

Type III

p = 1.5

q = 0.5, Type IV q = 0.5, Type IV q = 0.5, p = 2.5 p = 1.0 p = 1.5

q = 0.5, Type III p = 2.0 p = 2.0

q = 1.0

P = 1.5

Type V

q = 0.5, p = 2.0

Type V

q = 0.5, p = 2.0

q = 1.0, Type V p = 2.0

q = 0.5, p = 2.0


q = 0.5, p = 1.5

q = 0.5, Type II p = 1.5

q = 1.0

her evaluation on each criterion. Table 5 presents the evaluation of DM1 for illustration.


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Step 3.3: Determine the Preference Functions of Each Stakeholder DM with Respect to the Evaluation Criteria

First, each DM was provided with the definitions of six basic functions along with the thresholds (see the appendix) for each criterion. Then, an informational document was provided to help them better understand various parameters involved in the six preference functions. Thus, stakeholders could determine their own preference functions on the evaluation criteria. The preference functions used by DMs are shown in table 6. Step 3.4: Rank the Individual Preference Order of Stakeholders

Based on the preparation above, we obtained the final values of positive (φ+), negative (φ-) and net (φ) outranking flows in table 7. The results indicated that each DM had his or her own preference during the 3PL provider selection. In our application, each alternative had its strengths and weaknesses and each DM had his or her preferences, which would lead to different ranking results. It can be seen that not every DM was able to select the best alternative based on PROMETHEE I. DM1 considered alternative A2 was best, and alternatives A5 and A1 outranked alternative A3, but it was difficult for DM1 to rank the alternatives A5 and A1 for the reason that alternative A5 obtained high scores on optimization capability and management capability for which alternative A1 obtained low scores and the opposite occurred for responsiveness and delivery quality. In DM2’s consideration, alternatives A5, A2, and A1 were incomparable, which was caused by evaluations that A1 had strength in cost, experience in the industry, and client satisfaction; A2 had strength in IT capability, responsiveness, and continual improvement; while A5 excelled in geographic location, optimization capability, and management capability. DM3 outranked alternatives A2 and A5 over other alternatives, but DM3’s preference between them could not be determined. The reason is that alternative A2 obtained high scores on continual improvement, investment intention, and guanxi, for which alternative A5 obtained low scores; the opposite occurred for client satisfaction and compatibility. Similarly, DM4 also considered that A5 and A2 were better than other alternatives with incomparable scores. In DM4’s opinion, A5 had advantages in financial position and client satisfaction over A2, while A2 had better responsiveness and continual improvement. As for DM5, alternative A5 was chosen as the best, followed by alternatives A2 and A1, which had equal levels of ranking results. Higher scores on growth forecasts and environmental performance for A2 and experience in the industry and client satisfaction for A1 were allocated by DM5.


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φ

0.18

0.18

0.08

0.14

0.15

DM1

DM2

DM3

DM4

DM5

+


0.10

0.08

0.10

0.14

0.13

0.06

0.05

-0.02

0.05

0.05

0.17

0.14

0.12

0.19

0.22

φ

φ

φ

0.10

0.07

0.06

0.14

0.09

-

φ

+

A2

-

A1

0.07

0.07

0.05

0.06

0.13

φ

0.10

0.08

0.04

0.09

0.09

φ +

Table 7/PROMETHEE Flows and Ranking Orders

0.15

0.16

0.13

0.15

0.25

φ-

A3

-0.06

-0.08

-0.09

-0.06

-0.17

φ

0.12

0.11

0.08

0.16

0.19

φ +

0.28

0.24

0.07

0.26

0.27

φ -

A4

-0.16

-0.13

0.01

-0.11

-0.08

φ

0.18

0.16

0.11

0.17

0.22

φ +

0.09

0.07

0.06

0.11

0.14

φ -

A5

0.09

0.08

0.05

0.06

0.07

φ

A5>{A2,A1}>{A3,A4}

{A5,A2}>A1>{A3,A4}

{A2,A5}>{A4,A1}>A3

{A5,A2,A1}>{A3,A4}

A2>{A5,A1}>{A4,A3}

PROMETHEE I

A5>A2>A1>A3>A4

A5>A2>A1>A3>A4

A2>A5>A4>A1>A3

A5>A2>A1>A3>A4

A2>A5>A1>A4>A3

PROMETHEE II

Ranking Order of DMs


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To further analyze the stakeholder’s preferences of these alternatives, the net outranking flows (PROMETHEE II) were computed in table 7. Clearly, alternative A2 was given priority by the DM1, and alternative A5 was considered the best by the DM4 and DM5. For DM2 and DM3, there was not enough evidence showing which alternative was better since alternative A2 and A5 received the same scores. A careful examination of the partial and complete ranking results yielded an interesting and important observation. As for DM2 and DM3, it was impossible to give priority among the alternatives from the individual partial and complete ranking. If there were only one decision-maker, for example, a purchasing department (DM2), the 3PL selection would be very difficult because of the lack of convincing support. Therefore, the input from other concerned stakeholders is necessary not only in theory but also in practice. Step 3.5: Generate the Group Ranking Order by Synthesizing Stakeholders’ Opinions

Prior analysis of results of individual stakeholders indicated that conflicting superiority was given to alternatives A2 and A5. In order to get a holistic consensus at the organizational level, we applied the PROMETHEE GDSS method to take the multiple stakeholders into account. Because there were no formal guidelines regarding how to decide the relative importance of each stakeholder in PROMETHEE, we used equal importance for all stakeholders with permission from the company. The final group partial and complete rankings of the alternatives are A2≻A5≻A1≻{A3, A4} and A2≻A5≻A1≻A3≻A4. Clearly, alternative A2 outranked all other alternatives and was assigned superiority after the decision-maker group compared all the alternatives. Using the group decision technology, a consistent and satisfactory result was achieved. Alternative 2 gained the support from all the decision-makers due to its advantages in capability, improvement prospect, guanxi, and environmental performance. This alternative was also in line with the company’s long-term strategy. Step 3.6: GAIA Analysis

In order to develop a visual understanding of the discrimination of the criteria and DMs’ opinions, the GAIA plane was used with a holistic view of the decision problem. Respectively, the GAIA-Criteria analysis and GAIA-Scenarios analysis are shown in figures 2 and 3 to illustrate the origin of conflict. In figure 2, different alternatives are presented by points and criteria are presented by vectors. Here, each criterion vector represents a weighted


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C3

C6 A3

A1 A5

C5 pi

C1

A4

A2

C4

C2 Figure 2 GAIA-Criteria Analysis from the Group

DM5

DM2 DM4

pi

DM1 DM3 Figure 3 GAIA-Scenarios Analysis from the Group


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aggregate of the different weights given by DMs. Criteria vectors expressing similar preferences are oriented in the same direction, while conflicting criteria are pointing in opposite directions. The criterion service quality (C3) had a high differentiation power and expressed independent preferences, different from other criteria. It was clear that general company consideration (C1), capability (C2), and improvement prospect (C4) had similar preferences. In contrast, capability (C2) and environmental performance (C6) were given different preferences. It was also possible to clearly differentiate the quality of the alternatives with respect to the different criteria. Both A2 and A5 were relatively strong on guanxi (C5) and environmental performance (C6); A3 was particularly strong on service quality (C3). In the GAIA plane, the vector pi represents the direction of the compromise corresponding to the weights of the criteria. The alternatives located in that direction are often appreciated by decision-makers. According to the weights associated with the criteria, pi was oriented in the direction of the alternative A2 (see fig. 2), which is consistent with the PROMETHEE GDSS ranking. The GAIA-Scenarios can be used to identify coalitions of each DM as well as the origin of conflicts among the DMs. In figure 3, each vector represents the ranking results of the different DMs for the decision problem. It can be seen that DM1 (sales department) and DM3 (senior managers) had similar views on the ranking, while DM2 (purchasing department), DM4 (customers), and DM5 (consultants) shared common views. There are plausible explanations for this distribution. DM1 and DM3 are mainly concerned with the strategic and crucial elements. The social and economic advantages, such as environment and cost, were at the center of considerations of DM2, DM4, and DM5. Similarly, the pi in figure 3 represents the direction of the compromise corresponded to ranking results aggregated the rankings of DMs. Clearly, pi was in oriented in the direction, where there was not much variation with any DMs. This means that the group decisionmakers did not have strong conflicts with the final group ranking, and the satisfactory ranking results were achieved using the proposed method. Step 3.7: Sensitivity Analysis

With the application of a stability intervals analysis, we gave each criterion and each DM a limit within which its weight could be modified without changing the PROMETHEE II complete ranking as shown in tables 8 and 9. From the results of the sensitivity analysis (see table 8), we observed that service quality (C3) and environmental performance (C6) had greater impact


18/07/15 2:05 AM

Weight

0.1127

0.2013

0.2319

0.1188

0.1871

0.1481

Criteria

C1

C2

C3


C4

C5

C6

0.0412

0.0000

0.0000

0.0000

0.0727

0.0000

Min

DM1

Max

0.4801

Infinity

1.2747

0.3111

0.5781

0.5649

0.1486

0.1422

0.1423

0.2308

0.1850

0.1512

Weight

0.0854

0.0000

0.1197

0.2254

0.1566

0.0000

Min

DM2

Table 8/Stability Internal Analysis of Criteria Max

Infinity

0.1498

0.1606

0.2489

0.2016

0.1882

0.1477

0.1458

0.1631

0.2106

0.1872

0.1457

Weight

0.0000

0.0000

0.0160

0.0000

0.1657

0.0011

Min

DM3 Max

Infinity

Infinity

0.2106

0.4676

0.6569

0.1879

0.2057

0.1281

0.1637

0.2294

0.1448

0.1281

Weight

0.1334

0.0000

0.1050

0.1706

0.0000

0.0244

Min

DM4 Max

Infinity

Infinity

0.1967

0.3078

0.2269

Infinity

0.1254

0.1476

0.1602

0.2737

0.1480

0.1449

Weight

0.0721

0.0000

0.1204

0.1912

0.1037

0.0000

Min

DM5 Max

0.2125

Infinity

0.2633

0.3034

0.6676

0.9777


18/07/15 2:05 AM


on the complete ranking for DM1 and DM2. As for DM3, the more influential criterion was capability (C2). Compared with other criteria, the service quality (C3) and development prospect (C4) had a greater effect on the complete ranking for DM4. For DM5, there were two criteria—capability (C2) and development prospect (C4)—with greater influence on the complete ranking. A recommendation based on table 9 is that it is advisable to pay more attention to the opinions of DM1, DM3, and DM4 as they have larger influences on the PROMETHEE II complete ranking relative to other DMs. Table 9/Stability Internal Analysis of DMs DM1

DM2

DM3

DM4

DM5

Weight

0.2000

0.2000

0.2000

0.2000

0.2000

Min

0.1333

0.1502

0.1777

0.0000

0.1478

Max

0.2284

0.9121

0.3546

0.2227

0.4366

Discussion

In this study, we developed an evaluation and selection framework for the strategic 3PL provider selection problem based on a real case company in China. In order to facilitate the adaptation of this selection framework to similar strategic decision making scenarios, we provide in this section some helpful tips for effective implementation. First, the goal of our application was to provide assistance in selecting the most appropriate 3PL provider through effectively combining conflicting voices of multistakeholders with a holistic perspective. In reality, each company has its different internal and external stakeholders, and each stakeholder has different considerations because of the variance in knowledge, experiences, business positions, and preferences in the process of identifying the evaluation matrix, criterion weights and preference functions. Therefore, which stakeholders should be included in the selection process should be carefully evaluated and it varies from company to company. For example, in our case the internal stakeholders included the sales department (DM1) whose primary task is distributing the products; the purchasing department (DM2) who have responsibility for the procurement; and a panel of senior managers (DM3) with a better understanding of the company’s overall strategic development goals. In addition, the “voice” of the customers (DM4) was also included for the auto parts manufacturer to ensure and improve its customer satisfaction. In order to have a clear recognition of the local business environment and the industry prospects,


18/07/15 2:05 AM


a panel of consultants (DM5) also included other concerned stakeholders such as local government, environmental groups, and industry experts in the decision making group. Second, in the preliminary screening phase, six elimination criteria focusing on logistics service equipment and capability were used for screening the qualified providers. It is worthwhile mentioning that the elimination criteria were identified to disqualify the providers who could not meet the basic requirements. Such a procedure can save the company a significant amount of time and effort. The actual criteria used can vary based on specific situations. For example, the cost quoted by providers was not considered in our case because the focal company’s aim was to achieve better efficiency and service and the cost was a complementary factor but not a determining factor. Third, developing evaluation criteria definition was the first action that the DMs should take as a group. Defining appropriate criteria was a difficult but critical process. A detailed identification of the overall goals of the selection and an analysis from these goals to specific objectives were fundamental to the criteria definition development process. Such a process was extremely helpful in terms of identifying important criteria such as guanxi, which is largely overlooked in previous outsourcing literature as well as practice. As a unique and important feature in the Chinese society, a good guanxi network would smooth the cooperation between organizations and is particularly important in coping with challenging situations. It is certainly possible that the application of our methods in other contexts could yield other important criteria. Fourth, the determination of a decision matrix and weights required a significant amount of subjective judgment of the stakeholders based on the information provided by the 3PL providers. Although the stakeholders possess adequate experience, knowledge, and expertise in the logistics area, some evaluations may still be suboptimal due to insufficient information or ambiguous description. Therefore, it is important to obtain as comprehensive and accurate information about the 3PL providers as possible before the selection process. Finally, determining the preference functions and thresholds was difficult for the stakeholders when they were unfamiliar with those functions and thresholds. The main challenge was to define the point where the difference is too small to accept any preference (q threshold) and where strict preference begins to happen (p threshold). Guidance about the preference functions and thresholds are necessary prior to the selection. Particularly, the meaning of the thresholds q,p,s should be explained clearly in advance.


18/07/15 2:05 AM


Conclusion and Future Research

With a focus on applicability in practice, our study developed a strategic 3PL provider evaluation and selection framework for an auto parts manufacturer in China. Besides the multiple criteria (both qualitative and quantitative), five concerned stakeholders participated in the selection process for a balanced consideration. To be more specific, six first-level criteria, including general company consideration, capability, service quality, development prospect, guanxi, and environmental performance, and 18 s-level criteria were proposed according to the company’s requirements for development. Differing from existing studies, we identified two additional important criteria (guanxi and environment performance) in the China context. Interestingly, the sales department (DM1) values guanxi more than other DMs, and customers (DM4) put more emphasis on environmental performance. The inclusion of these additional variables ensured a more balanced result of the selection process. Our PROMETHEE method combined multiple criteria and input from various stakeholders, and our verification results suggested that it is an effective and robust tool to address the multicriteria decision problem. Multistakeholder and multiperspective approaches can help the outsourcing company better understand the essential requirements from different parties and make the sourcing decisions more effective (Ho, Dey, and Lockstrom 2011). The method presented in this article can be easily applied by supply chain professionals to achieve much improved 3PL provider selection outcome. More important, this method is robust and can be easily adopted or adapted to assist other supplier selection processes. However, we do believe that future research could refine certain details of the methods. One example is the approach to determine weights of different criteria. Because there is no specific means for the weight definition in the PROMETHEE approach, we applied the AHP method to determine the weights in this study. Although the AHP method has the advantage in determining weights with decomposition and hierarchies of criteria, other methods may be able to better combine objective and subjective perspectives and provide a more refined outcome. Appendix The PROMETHEE method is widely adapted to address multicriteria strategic problems of the following type:max{ f1(a),…,fn(a)|a∈A}. Where A is a finite set of possible alternatives, and fi(a) is an evaluation of alternative a on criterion i. When we compare two alternatives a,b ∈ A, a preference function p(x), which is a monotonically increasing function of the observed deviation (d) between f(a) and f( b), must be considered to express the


18/07/15 2:05 AM


result of these comparisons. To facilitate the selection of a specific preference function, six basis shapes: (1) usual criterion, (2) U-shape criterion, (3) V-shape criterion, (4) level criterion, (5) V-shape with indifference criterion, and (6) Gaussian criterion were proposed by Brans and Vincke (1985), in each case no more than two parameters (thresholds q, p or s) have to be fixed. Indifference threshold q represents the largest deviation to consider as negligible on that criterion. Preference threshold p represents the smallest deviation to consider decisive in the preference of one alternative over another. Gaussian threshold s is only used with the Gaussian preference function, and is an intermediate value between an indifference and a preference threshold. Based on the preference function, a set of the following quantities for alternatives a and b are computed to yield the ranking order: n

π (a, b ) = ∑ i =1 wi pi (a, b )

∑ b∈A π (a, b) n ∑ i =1 wi ∑ π (b, a) φ − (a ) = b∈An ∑ i =1 wi φ + (a ) =

φ (a ) = φ + (a ) − φ − (b )

Where wi are the weights of criterion i, and π(a,b) is an overall preference index of a over b. The positive outranking flow φ+(a) indicates how alternative a dominates all the other alternatives of A. Symmetrically, the negative outranking flow φ−(a) indicates how alternative a is dominated by all the other alternatives of A. The net outranking flow φ (a) is a balance between the positive and negative outranking flows, reflecting the attractiveness of alternative a. The PROMETHEE method provides the group decision support system (GDSS) for multiple stakeholder analysis. The same concepts are used in the GDSS, with the following quantities as a weighted average of the individual outranking flows:

φG+ (a ) = ∑ βiφi+ (a ) i∈G

φG− (a ) φG (a ) =

= ∑ βiφi− (a ) i∈G

φG+ (a ) − φG− (a )

Where βi represents the relative importance of stakeholder i. A corresponding visualization tool, the geometrical analysis for interactive aid (GAIA), is available in this method. It displays graphically the relative position of the


10/07/15 3:10 AM


alternatives in terms of contributions to the various criteria. Also, in the GAIA plane, any conflicts among the various stakeholders’ points of view can be visualized. Additional tools such as the intervals analysis can be used to further analyze the sensitivity of the results in terms of weight changes. To get the further information about the PROMETHEE method, please note Brans and Mareschal (2005).

Note This research was supported by the National Natural Science Foundation of China (71271039, 70902033), New Century Excellent Talents in University (NCET-13-0082), the Foundation for Innovative Research Groups of the National Natural Science Foundation of China (71421001), and the Fundamental Research Funds for the Central Universities (DUT14YQ211).

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