INTRODUCTION:

The pharmaceutical industry is socially important, since it ensures public access to medications. The sector of bioengineering and biological products has been gaining new positions in the global pharmaceutical production in the recent years, gradually replacing traditional medicines. They have just recently emerged in the world of medicine but have already conquered the market – not only due to their treatment capabilities, but also due to the demands they place on a pharmaceutical supply chain. Most biopharmaceuticals require distribution both by temperature and by time.

Aside from biopharmaceuticals, various types of precision medical breakthroughs have recently emerged, such as cell therapy, biomarker testing, and regenerative medicine of stem cells. Transportation of materials for these types of therapy, as well as blood products and some vaccines also need to maintain a certain temperature regime for their preservation. In this regard, the problem of arranging transportation and storage of medications and vaccines in the cold chain mode is a relevant problem throughout the world.

A cold chain is a set of general conditions that allow to maintain the required temperature range in the chain. Requirements of the cold chain must be faultlessly met by all participants in the logistics chain: manufacturers, carriers, warehouse operators, pharmacists, and medical and veterinary personnel. If at least one link of the cold chain is unreliable even by one criterion, everyone's efforts become useless.

According to the Biopharma Cold Chain Sourcebook¹, the growth rate for temperature-controlled products is twice that of uncontrolled products. This may indicate future importance of the cold supply chain in the pharmaceutical logistics system. Another growth driver in the cold chain logistics is strengthening the good distribution practices (GDPs), including the requirements for temperature control over all types of pharmaceutical products in order to ensure quality of medications.

Given the costs and time required to develop and manage an efficient, flexible, reliable, and sustainable cold chain, many manufacturers consider outsourcing of many logistic functions.

Cold chain logistics outsourcing also allows pharmaceutical manufacturers adapting to international market and regulatory requirements, which can vary greatly among countries, more easily.

The growing demand for logistics outsourcing emphasizes the growing importance of evaluating and selecting cold chain logistics providers. This is very important for improving the company's competitiveness and positively impacts extending the company's life. Experts agree there is no best way to evaluate and select suppliers, and therefore organizations use different approaches. Selecting a logistics service provider is a multicriteria problem that includes quantitative and qualitative criteria, some of which may be contradicting².

The general purpose of the supplier evaluation is to reduce risks and maximize the total value to the buyer³. Obviously, it is nearly impossible to find a supplier that would be perfect in all the terms. Besides, some criteria are quantitative, while others are qualitative, which is certainly a drawback of the existing approaches. As such, a method is required that could cover both subjective and objective evaluation measures.

The purpose of this study is to present the method and identify the most important criteria for selecting the third-party logistics (3PL) for the cold chain of pharmaceutical products. The rest of this article is structured in the following way. Part 2 provides a literature review on logistics outsourcing and methods for selecting logistics service providers. The application of the method of evaluating 3PL operators, which is based on the hierarchy analysis method (HAM), is presented in Part 3. The selection model is confirmed by the results of a case study in Part 4. The possibility and problems of implementing the presented method when selecting a logistics operator are discussed in Part 5, and finally, the concluding notes are provided in the final Part 6.

LITERATURE REVIEW:

The issue of outsourcing logistics services has attracted lots of attention in the past 15 years. Different views on the importance of logistics outsourcing became apparent at the beginning of the discussion. R. Manzini, A. Pareschi, and A. Persona suggested that outsourcing, third-party logistics, and contract logistics usually mean the same⁴.

J. Arif and F. Jawab define the relationship of logistics outsourcing more broadly – as "long- and short-term contracts or alliances between manufacturing and service firms and third-party logistics providers"⁵.

Strategic alliances are an important form of interorganizational collaboration, which is widely covered in the literature⁶.

Strategic alliances allow partners uniting their resources and strengths in order to achieve their goals, share risks, gain knowledge, and access new markets. Strategic alliances have spread in all sectors of logistics in the past decade. The following three most important types of strategic alliances associated with the supply chain attracted the attention of researchers: 3PL, partnerships between retailers and suppliers, and integration of distributors⁷.

This study focuses on the first form of strategic alliances. 3PL involves third parties to perform logistic functions that were traditionally performed within an organization⁸. A third party can undertake the entire logistics process or just certain parts of it. As such, the 3PL evaluation and the subsequent selection of a strategic alliance partner in the logistics value chain are of strategic importance for achieving the competitive advantage of an enterprise⁹.

However, many alliances fail, despite their popularity in all business fields¹⁰. Aside from the inherent risk, the incompatibility of alliance partners is another most frequently cited reason¹¹.

Selecting the right partner for a strategic alliance is an important factor that impacts the alliance performance in the logistics value chain¹².

There are several analytical models that secure support for strategic decisions when selecting 3PL providers. Analytical models for partner selection range from simple weighted evaluation models to complex mathematical programming approaches. The most common approaches to selecting a logistic operator include various multicriteria decision making methods (MCDM), such as the AHP¹³ and the analytical network process¹⁴, statistical methods, such as basic component analysis and factor analysis¹⁵, data analysis methods, such as cluster analysis, discriminant analysis, data envelopment analysis¹⁶, and intelligence methods¹⁷.

The literature on the partner selection is mainly devoted to methodological aspects and covers qualitative research methods. Few studies are based on mathematical or quantitative approaches to decision making¹⁸.

There are numerous fuzzy AHP methods proposed by various authors. These methods represent systematic approaches to the problem of alternative selection and justification using the concepts of the fuzzy set theory and the hierarchical structure analysis¹⁹.

C.S. Grewal, K.K. Sareen, and S. Gill suggest using the AHP to solve the supplier selection problem due to its ability to process qualitative and quantitative criteria, simple and understandable decision-making procedure²⁰.

S. Percin presented a combined valuation approach with fuzzy expert systems for supplier evaluation in 2009²¹.

A.N. Haq and G. Kannan developed a supplier selection system using fuzzy logic²².

T.U. Daim, A. Udbye, and A. Balasubramanian announced their case study demonstrating a model using the AHP and the quality management system principles in developing the supplier selection model²³.

The criteria for the 3PL operator selection, which underlay the fuzzy AHP model development, were reviewed in the papers of K. Mathiyazhagan²⁴, A. Aguezzoul²⁵, M.N. Qureshi, D. Kumar, and P. Kumar²⁶.

METHODS:

This study suggests a system to support a decision on selecting a logistics service provider based on the AHP. This is a well-known method of integrating qualitative and quantitative criteria in the decision-making process.

The source data were obtained through requests for offers from 3PL providers and from public sources. The opinions of the logistics managers of the manufacturing company were taken into account when pairwise comparison matrices were built.

The study consists of several stages:

Stage 1 – criteria development and building a hierarchy of solutions.

A fuzzy AHP model is based on the five key criteria for selecting a provider to create a cold chain presented in Table 1.

Table 1.The key criteria for selecting a provider for the cold chain of pharmaceutical products

Criteria of selection	Relevance in the logistics outsourcing
1. Cost of service	This is about the total cost of logistics outsourcing, which should be as low as possible.
2. Long-term relationships	Long-term relationships, including common risks and benefits, secure the cooperation between the User and the supplier. They also help control the opportunistic behavior of suppliers.
3. Operational performance	Decent supplier operational performance can be measured by such indicators as delivery performance, IT capabilities, availability of tools for monitoring the compliance with the established temperature and time regimes at all stages, availability of certificates necessary for technical means of the cold chain, detailed accounting information, system security, response speed, and data privacy.
4. Financial performance	Stable financial performance of the supplier secures continuity of service and regular modernization of equipment and services used in logistics operations.
5. Reputation of the logistics operator	Reputation of the provider reflects the opinion of others about how efficiently it meets customer demands. Reputation of the 3PL plays an important role in its choice. This is more relevant in the initial selection of suppliers.

Source: compiled by authors.

The purpose is to select the best logistics provider for a particular company. As such, this purpose is at the top of the hierarchy. The hierarchy descends from general criteria at the second level to subcriteria at the third level to alternatives at the lower, fourth level. A hierarchical representation of the decision-making model for choosing the best logistics service provider is provided in Figure 1.

Table 4. Pairwise comparison matrix of the key criteria for selecting a cold chain provider

	CST	FP	OP	RPT	LTR	Geometric mean	Normalized priority vector (criteria weight)
CST	1	2	1/3	1/4	4	0.92	0.14
FP	1/2	1	1/4	1/5	1/2	0.42	0.06
OP	3	4	1	2	5	2.61	0.40
RPT	4	5	1/2	1	4	2.09	0.32
LTR	1/4	2	1/5	1/4	1	0.48	0.07
Total	8.75	14.00	2.28	3.70	14.50	6.51	1.00
Eigenvalue of the matrix λmax							5.30
Expert consistency index (CI)							0.08
Consistency Ratio (CR)							6.74 %

Source: Compiled by authors.

Table 5. Priority weights of the key criteria, subattributes and alternatives

Key criteria	Weight of the key criterion	Subattributes	Weight of the subattributes	A	B	C	Weight of A	Weight of B	Weight of C
Cost of services CST	0.27	Т	0.66	0.63	0.22	0.15	0.112	0.039	0.027
		ТОР	0.26	0.1	0.67	0.23	0.007	0.047	0.016
		EXC	0.08	0.1	0.32	0.63	0.002	0.007	0.014
Financial performance FP	0.14	FS	0.67	0.1	0.26	0.64	0.009	0.024	0.060
		FBP	0.1	0.08	0.63	0.29	0.001	0.009	0.004
		RS	0.23	0.08	0.7	0.23	0.003	0.023	0.007
Operational performance OP	0.43	FA	0.54	0.1	0.36	0.54	0.023	0.084	0.125
		IT	0.29	0.07	0.29	0.64	0.009	0.036	0.080
		DP	0.1	0.64	0.1	0.26	0.028	0.004	0.011
		FOD	0.07	0.27	0.64	0.09	0.008	0.019	0.003
3PL reputation (RPT)	0.06	QS	0.38	0.1	0.7	0.2	0.002	0.016	0.005
		GS	0.06	0.27	0.61	0.12	0.001	0.002	0.000
		MK	0.11	0.24	0.69	0.07	0.002	0.005	0.000
		EPP	0.44	0.06	0.3	0.63	0.002	0.008	0.017
Long-term relationships (LTR)	0.09	SQ	0.21	0.41	0.1	0.49	0.008	0.004	0.004
		INF	0.09	0.53	0.15	0.32	0.004	0.001	0.003
		MQ	0.51	0.09	0.66	0.24	0.004	0.030	0.011
		RM	0.15	0.11	0.68	0.21	0.001	0.009	0.003
		AS	0.04	0.2	0.1	0.7	0.001	0.000	0.003
TOTAL							0.227	0.368	0.393

Source: Compiled by authors.

If the total weight of the alternatives is compared, it can be seen that alternative C, which has the highest priority weight, can be chosen as the best logistics service provider. This logistics provider will be able to meet the manufacturer's demands for the cold chain of pharmaceutical products supply.

The sequence of alternatives depending on their significance weights is as follows: alternative C, alternative B, and alternative A.

The results allow to draw a conclusion that the most important factor in the selection of a logistics service provider for building a cold supply chain is the criteria of operational performance (significance is 0.43). In terms of operational activities, the size and quality of fixed assets and IT capabilities are the most significant subcriteria. Companies should pay attention to improving the assets quality – in particular, to ensure that the warehouse infrastructure and transportation vehicles comply with the standards for medication storage and transportation. Besides, the 3PL providers need to improve IT capabilities for building cold supply chains.

DISCUSSION:

The proposed method allows to simplify a complex multicriteria problem of decision making. It can also be used to quantify many subjective judgments required for evaluating various alternative logistics service providers. Another advantage of this method is that aside from supporting group decision making, it also allows to record various considerations during this process.

This approach has certain limitations. The AHP assumes a linear independence of criteria and alternatives. The analytical network process (ANP) is more appropriate if there is dependency among the criteria, but the ANP requires much larger number of comparisons, which can be huge among practical solutions. This may become a new direction for further research.

The AHP model is appropriate when a goal is clearly defined and there is a set of relevant criteria and alternatives. If there are several criteria, the AHP is one of the very few multicriteria approaches that can process so many criteria, especially if some of them are qualitative. Since the human decision-making process usually includes fuzziness and uncertainty, a fuzzy AHP can be used to solve the problem. The well-organized fuzzy AHP information system promotes facilitation of the decision making.

This study can be useful if the results are brought to the attention of various stakeholders. The results of the study indicate that alternative C is the best choice for a pharmaceutical company that wants to outsource logistics.

It is appropriate to discuss the priority values of the determinants that influence this decision. The operational indicators (43%) are the most important determining factor in choosing a logistics service provider. It is followed by the cost of services (27%), financial performance (14%), long-term relationships (9 %), and 3PL reputation (6%). These results suggest that logistics service providers need to improve their operational and financial performance, primarily by providing advanced technologies for strategy development, planning, collaboration, data management, decision support, integration, and flexibility.

Selection of a logistics operator for the cold chain of pharmaceutical products supply can be primarily associated with the availability of an appropriate logistics infrastructure and advanced information technologies in supply chain management and change management. It must be noted that the requirements for the logistics infrastructure in the supply chain for pharmaceutical products become tougher. For example, new rules for the medications storage and transportation came into effect on March 1, 2017, according to which the number of areas increased, the list of equipment required for the pharmaceutical warehouse was extended, and a quality system and mandatory temperature mapping of pharmaceutical warehouses were introduced. The 3PL expertise in developing pharmaceuticals transportation and distribution policies also determines the choice of a logistics provider.

This study presented a model for three alternative suppliers, but it is able to compare more than three suppliers due to complexity. It must be emphasized that, despite the use of a reliable algorithm for systematic decision making, the fuzzy AHP approach must be used with care. For example, a user must compare potential suppliers by a number of pairwise comparison matrices in his application. In these comparisons, the user must verify the suppliers' capabilities and should not rely solely on the information provided by potential suppliers. Experts recommend user companies to rate suppliers by what they have done, not by what they are only going to do.

Although the input data for the pairwise comparison matrices are based on responses to requests for offers and search by the logistics manager of the suppliers' websites in this case, a shift in the choice of the decision maker towards a particular supplier cannot be excluded.

Methods of group decision making must be used in order to avoid such situations. For example, brainstorming and sharing ideas often lead to a better understanding of the problems than would be possible for a single decision maker.

Building scenarios or a Delphi method can also be used for pairwise comparisons. Consensus can be achieved by agreeing on the geometric means of individual judgments in the case of a group decision-making process. In the absence of consensus, a voting may also be hold to achieve a more acceptable value. Compared to low-level factors, the consensus is more desirable for determinants and dimensions than a higher level of the fuzzy AHP model.

Using software and decision support systems can also reduce the complexity of implementing a group decision-making process.

In the light of the results of this study, it can be noted that they are valid only for a certain company in its individual decision-making environment and should not be generalized to establish the superiority of one provider over others. Besides, the proposed method may require considerable time and resources from managers and decision makers. However, this method can help reduce the risk of wrong investment decisions when looking for investments in a long-term contract for outsourcing logistics.

CONCLUSIONS:

The process of selecting a logistics service provider becomes increasingly important in the current complex environment. The selection process involves determining both quantitative and qualitative factors to select the best supplier.

Decision makers face the uncertainty of subjective perception and expertise in the decision-making process. The fuzzy theory can be used in many decision-making areas like this. The fuzzy AHP approach is particularly efficient in reducing uncertainty when determining weights of various criteria and when determining the influence of each alternative supplier on the attributes under consideration.

A choice of a 3PL supplier for a pharmaceutical company seeking to optimize and efficiently manage a cold supply chain through outsourcing logistics has been proposed in this study. The study is valuable in terms of understanding the 3PL concept and practice, as well as understanding the importance of the logistics provider selection.

The cost of services, financial performance, operating performance, 3PL reputation, and long-term relationships are the key criteria for making a decision. These criteria were evaluated to obtain the degree of preference associated with each alternative logistics provider for choosing the most appropriate cold chain of pharmaceutical products supply. The data can be efficiently presented and processed to make a better decision using a fuzzy approach to determining their ambiguity.

As a result of this study, alternative C was selected as the best logistics service provider for the cold chain of pharmaceutical products supply, having the highest priority weight.

The major value of the study is the development of an integrated method that not only leads to a logical result but also allows decision-makers visualizing the influence of various criteria on the final result. The proposed method can be used as a guide for logistics managers in making outsourcing-related decisions. Similar AHP-based models may also be developed in other contexts. However, since the development and evaluation of these models require considerable time and efforts from decision makers to form pairwise comparison matrices, they should be used for long-term strategic decisions when investments in a long and cumbersome decision-making process are repaid within a specified timeframe. Besides, even though this method requires significant computational resources, the benefits of reducing risk outweigh the costs and time spent.

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Received on 03.10.2019 Modified on 14.12.2019

Research J. Pharm. and Tech. 2020; 13(10):4641-4647.

DOI: 10.5958/0974-360X.2020.00817.3

	K₁	K₂	K₃	Geometric mean	Normalized priority vector
K₁	R₁₁	R₁₂	R₁₃		W₁₁=R₁/R
K₂	R₂₁	R₂₂	R₂₃		W₁₂=R₂/R
K₃	R₃₁	R₃₂	R₃₃		W₁₃=R₃/R
Sum	S₁=R₁₁+R₂₁+R₃₁	S₂=R₁₂+R₂₂+R₃₂	S₃=R₁₃+R₂₃+R₃₃	R=R₁+R₂+R₃
λ_max	S₁W₁₁+S₂W₁₂+S₃*W₁₃

Matrix size	1	2	3	4	5	6	7	8	9	10
Random consistency (RC) value	0	0	0.58	0.9	1.12	1.24	1.32	1.41	1.42	1.49