انتخاب ارائه دهنده خدمات لجستیک : رویکرد فرایند تحلیل شبکه ای (ANP)

This article presents a comprehensive methodology for the selection of a logistic service provider. The proposed methodology consists of two parts: (i) preliminary screening of the available providers, and (ii) analytic network process (ANP)-based final selection. The criteria, which are relevant in the selection of a provider, have been identified and used to construct an ANP model. Thereafter, the application of ANP for the final selection of a provider has been demonstrated through an illustrative example. The results of this example indicate that compatibility between the user and the provider companies is the most important determinant, which influences the final selection process. This approach also enables the decision-makers to better understand the complex relationships of the relevant attributes in the decision-making, which may subsequently improve the reliability of the decision.

The outsourcing of logistics activities to third-party logistics service providers, 3PL (hereinafter called provider), has now become a common practice. The commonly known drivers for outsourcing are [1], [2], [3] and [4] needs of the organizations to concentrate on core competencies, cost reduction, development of supply chain partnerships, restructuring of the company, success of the firms using contract logistics, globalization, improvement of services and efficient operations, etc. One of the most important reasons for outsourcing is the capabilities of the providers to support their clients with the expertise and experience that otherwise would be difficult to acquire or costly to have in-house [1]. According to the Langley et al. (2003) 3PL survey [5], the most common outsourced activities are warehousing, outbound transportation, customs brokerage, and inbound transportation. Keeping in view the growing trend of logistics outsourcing, many providers are now offering a variety of services. These services mainly involve business-to-business relationships, where not only the user is a critical stakeholder but also his customers who are directly affected by the quality of service of the provider [6]. Therefore, the user must exactly identify what it needs from the provider. Regarding logistics outsourcing, many researchers [1], [6], [7] and [8] have discussed, besides other issues, the criteria for the selection of a provider. However, the selection of a proper provider, which suits the needs of the outsourcing company (hereinafter called user), is not an easy task. The complexity of this task increases with an increase in the number of selection criteria [9]. Analytic hierarchy process (AHP) [10] is one of the widely used approaches to handle such a multi-criteria decision-making problem. However, a significant limitation of AHP is the assumption of independency among various criteria of decision-making. Analytic network process (ANP), on the other hand, captures interdependencies among the decision attributes and allows a more systematic analysis. It also allows inclusion of all the relevant criteria (tangible or intangible, objective or subjective, etc.) that have some bearing in arriving at the best decision [11]. Sarkis [12] has observed that ANP has been effectively used in decisions related to energy policy planning, product design, and equipment replacement. Contrary to AHP, ANP provides a more generalized model in decision-making without making assumptions about the independency of the higher-level elements from lower-level elements and also of the elements within a level. Despite all these merits, the applications of ANP are not very common in a decision-making problem. However, in recent years, there has been an increase in the use of ANP in multi-criteria decision-making problems. In the selection of a provider, the criteria are of both the types, subjective and objective. These criteria also have some interdependencies, which cannot be captured by the popular AHP method. Therefore, instead of using the commonly used AHP approach for solving such types of problems, we recommend the use of an ANP-based model for the selection of a provider. The objective of this paper is to introduce a comprehensive decision methodology for the selection of a provider that logistics managers and decision-makers can apply to their organization. The proposed methodology allows for evaluation of alternative providers in two steps: (i) initial screening of the providers, and (ii) ANP-based final selection. In this methodology, our focus is to demonstrate the application of ANP for the final selection of a provider. Therefore, in this paper an ANP-based model has been developed and illustrated for a case company. After introduction, the remainder of this paper is organized as follows. First, we review the literature on logistics outsourcing. The literature review includes the developments in logistics outsourcing, selection criteria for the provider, methods currently being used for the selection of a provider, and finally specific problems related to the selection of a provider. In the next part of this paper, we present the methodology for the selection of a provider. An ANP-based approach for the final selection of a provider is a part of this comprehensive methodology. In the later part of this paper, our focus is on the development of an ANP model and its solution. For the purpose of model development, we have identified and named four major criteria as determinants. All these determinants are supported by four sub-criteria, which we name dimensions. Each dimension in this model is separately supported by some enablers (Fig. 1). We conclude the paper with a discussion and managerial implications.

The major contribution of this paper lies in the development of a comprehensive methodology, which incorporates diversified issues, for the selection of a provider. The paper also provides for a review of the issues, which influence the selection of a provider. The ANP approach, as a part of this methodology, not only leads to a logical result but also enables the decision-makers to visualize the impact of various criteria in the final result. Further, we have demonstrated that the interdependencies among various criteria can be effectively captured using the ANP technique, which has rarely been applied in the context of outsourcing decisions. At a time when outsourcing of logistics activities has become a global trend, this paper provides an insight into the various aspects of logistics outsourcing. The proposed methodology serves as a guideline to the logistics managers in outsourcing-related decisions. The ANP approach is capable of taking into consideration both qualitative and quantitative criteria. Similar ANP-based models may also be developed in other contexts as well. But, as the development and evaluation of these models demand significant time and efforts from the decision-makers in the formation of pairwise comparison matrices, these should be used for long-term strategic decisions only where the investments made in the lengthy and cumbersome process of decision-making are recovered in due course of time. Further, though the technique is computationally intensive, the benefits of risk reduction will outweigh the cost and time. The ANP approach illustrated in this paper has a few limitations as well. For example, the outcome of the model is dependent on the inputs provided by the logistics manager of the case company. The possibility of bias of the decision-maker towards any particular provider cannot be ruled out while applying this model. Therefore, group decisions should be preferred in the pairwise comparison. Moreover, the formation of pairwise comparison matrices is a time-consuming and complex task. Inconsistency may also occur in the pairwise comparison of matrices, which may give wrong results. This study raises several important issues that warrant further research. For example, the model may also be subjected to a sensitivity analysis. Further evaluation and refinement of the model using additional field studies may prove beneficial in developing an intelligent system, which would advise the decision-makers about the low significance of certain enablers, dimensions, and determinants. Accordingly the decision attributes with low significance value may be dropped from the model, resulting in its simplification. Finally, user-friendly and intelligent software may also be developed on the basis of this model.