Supply Chain Sustainability Drivers: Identification and Multi-criteria Assessment

1. Introduction

The variability and uncertainty of many supply chain performance factors complicate decision-making. Globalization, shorter product life cycles, government regulation, and the development of innovations lead to the emergence of new products and services, changes in the goals and behavior of stakeholders in supply chains [1]. However, each supply chain participant assesses the impact of environmental factors differently. Such assessments do not always coincide with the objective intensity and strength of the impact of factors that have a different impact on the elements of supply chains. This complicates the coordinated functioning of the supply chain links and reduces their efficiency. Therefore, solving the problem of prioritizing factors, determining their importance and the degree of their influence on supply chain elements is necessary for effective decision-making.

At present, the efficiency of supply chain performance is increasingly being assessed by the sustainability criterion — a complex criterion for achieving economic, social and environmental goals. This is the peculiarity of the supply chain structure, when the elements of the same chain can be in regions with different natural, climatic and political conditions, different socio-economic levels. The only factor that has a similar impact on different elements of supply chains is the need to address a set of global environmental problems related to greenhouse gas emissions, environmental pollution, exhaustion of natural resources and climate change.

According to UN Secretary General Antonio Guterres “Every country, city, financial institution and company must adopt zero emission plans and act now to get on the right track towards this goal, which means reducing global emissions by 45% by 2030 compared to 2010 levels” [2]. Following this trend, many of the world's leading organizations are planning to achieve carbon neutrality in their operations and supply chains [3]. Most countries have adopted Sustainable Development Goals (SDGs) and are implementing the concept of Sustainable Development. Growing pressure from stakeholders, governmental and non-governmental organizations forces various industries to implement sustainable supply chain management initiatives — Sustainable Supply Chain Management (SSCM) [4]. Integrating the concept of sustainable development into supply chain operations allows companies to create a competitive advantage [5]. Therefore, sustainability and SDGs are becoming increasingly relevant for inclusion in business logistics and supply chain management [6].

The implementation of sustainability initiatives depends on various internal and external drivers and barriers [7]. Recently, the composition of these drivers and barriers of sustainable and green supply chains has been actively debated in the scientific community [5,8,9]. Answers are sought as to what drivers influence sustainability [10,11,12], how to measure the performance of sustainable supply chains [8,13], and what models and methods to use for evaluation [14]. Many researchers have investigated individual drivers of SSCM. Despite the acknowledged importance of addressing this issue, the task of systematizing the known drivers of SSCM, determining their significance and the degree of their influence on supply chain elements to make effective decisions remains unsolved.

The main goal of our study is to systematize the factors, drivers, and barriers of supply chain sustainability to design a universal sustainability assessment framework using multi-criteria analysis. This study makes both theoretical and practical contributions to the field of sustainable supply chain management. First, based on the literature review and the structural-functional approach, we performed the identification and developed a universal system of supply chain sustainability drivers. The main idea of the systematization is to identify and group various factors that affect the performance of supply chain elements in their logistics functions. Second, we developed a methodology for ranking supply chain sustainability drivers using multi-criteria decision-making (MCDM) techniques. The methodology will help stakeholders to make effective decisions on logistics flow management using a justified sustainability assessment of supply chain elements as well as the factors impacting the performance of their logistics functions.

The paper is organized as follows. The second part reviews the existing research in the field of assessing the drivers and barriers of supply chain sustainability, identifies the main shortcomings of the existing approaches to sustainability assessment. The third part contains the description of the original methodology for assessing the drivers of supply chain sustainability. The methodology is based on the proposed universal system of sustainable supply chain drivers, which considers the structure and functions of supply chain elements, as well as a combined multi-criteria model of driver ranking. The fourth part presents a case study of sustainable supply chain driver ranking using the developed MCDM model. The conclusion presents the main results and the prospects of the future research.

2. Literature Review

The paper analyzes 89 articles on the problems of supply chain sustainability assessment (Table 1). The review of the publications allowed us to identify the following main research areas:

• factors, drivers, and barriers of supply chain sustainability, as well as indicators of their assessment.
• models and methods for assessing the performance and sustainability of different types of supply chains.
• case studies of supply chains of different types and structures.

The generalized results of the analysis are presented in Fig. 1.

Figure 1. Results of the grouping of studies SSCM factors, drivers, and barriers.

Figure 1. Results of the grouping of studies SSCM factors, drivers, and barriers.

The distribution of articles by type of research indicated that case studies predominate, with a share of 79%. Conceptual studies and review articles account for 12% and 9%, respectively. The results for each group of articles are analyzed below.

2.1. Review Papers

Paper [8] emphasizes supply chain structure, inventory control policy, information sharing, customer demand, forecasting method, lead time and review period length as the main factors of supply chain efficiency. According to the authors [8], an optimal set of parameters of these factors increases the efficiency of the supply chain. By analyzing 1559 sustainable supply chain drivers (from 217 papers) [9], the authors compiled a list of 40 unique SSCM drivers. All SSCM drivers are grouped into two groups — internal and external drivers. The group of internal drivers includes corporate strategy, organizational culture, organizational resources, organizational characteristics. The group of external drivers includes regulatory pressures, societal pressures, market pressures. In [12] the results of the study of critical factors for the realization of sustainable supply chain innovations are presented. Fourteen main categories of critical factors are identified, the most significant of which are cooperation, strategic orientation, culture, practice and political context. The paper [15] investigated the realization of sustainable supply chains. The authors researched 115 papers and identified 69 attributes of supply chain visibility, which were grouped into 15 groups of factors. In [11], based on the review of 47 articles, an ABCDE framework is proposed which includes five groups of supply chain visibility factors: antecedents (A), barriers and challenges (BC), drivers (D), and effects (E), and provides a holistic view of supply chain visibility. In [16], based on the analysis of 59 research papers, 37 drivers and 36 barriers are systematized as the main factors affecting the performance of reverse logistics. In [17], based on a literature review, a grouping of 10 drivers and 15 barriers of green supply chains is proposed considering internal and external factors. A review of 188 papers [18] identified 15 success factors to implement sustainable supply chain management. Based on a systematic review of 362 papers [5], authors found 22 drivers and 19 barriers of sustainable supply chains. The authors note the prevalence of case studies, which help to analyze the latest advances in SSCM for a specific supply chain.

2.2. Conceptual Studies

The authors in [19] concluded that empirical studies on green supply chain management factors show different results. They investigated six factors and showed that green manufacturing is the most influential and the green logistics factor is influenced by all other factors. The results of ranking 13 green supply chain factors in [20] found a strong influence of lack of leadership activity. This is due to the view of the management that implementing a green supply chain will bring negative effects on finance. Authors [21] investigated synchromodal logistics critical success factors. Six groups of factors were identified: network, collaboration, and trust; sophisticated planning; ICT (Information and Communication Technologies) and ITS (Intelligent Transportation Systems); physical infrastructure; legal and political framework; awareness and mental shift; pricing/cost/service. Furthermore, the technologies that influence these critical factors are substantiated.

Paper [10] investigated the internal environment factors of supply chains and identified eight groups of factors, the most significant of which are top management commitment, reverse logistic management, materials store and management. Out of the 8 green supply chain factors investigated in [22], stakeholder cooperation is the most important factor of eco design. Analyzing the factors underlying successful sustainable supply chain management [23] found that signaling, information provision and the adoption of standards are crucial preconditions for strategy commitment, mutual learning, the establishment of ecological cycles and hence for the overall success of SSCM. In [24] the factors influencing the development of supply chains are considered and a model for the development of the regional logistics industry is proposed. The authors in [25] suggested a model of lean and sustainable supply chain management considering 8 factors with 29 influencing determinants. The model [26] of green-lean supply chain management integration considers 15 factors. The paper [7] identifies exogenous drivers and endogenous drivers of supply chains and recommends initiatives to move towards a sustainable state.

2.3. Case Studies

The Green Supply Chain and Sustainable Supply Chain are the most frequent objects of study in case studies. They account for 57% and 18% of all case studies, respectively. Other case studies investigate the drivers [27] and barriers [28] of Close-Loop Supply Chain, Resilience Supply Chain factors [29], critical success factors of reverse logistics [30]. The distribution of case study by country shows the predominance of India (31% of articles) and China (12% of articles). Bangladesh, Germany, UK, Turkey each account for 5% of the analyzed articles, while Taiwan, EU, Thailand, Vietnam each account for 2%. Examples of supply chain research in Thailand, the Middle East, Brasília, Egypt, Iran, Jordan, Korea, Lithuania, Malaysia, New Zealand, Pakistan, Spain, USA are represented by one article in the analyzed sample.

Factors (37% of the total number of case study articles), drivers (22% of the articles) and barriers (19% of the articles) are the most frequent subjects of study. Simultaneous study of drivers and barriers is found in 7% of case study articles. Several studies focus on the study of drivers, drivers, and barriers of individual supply chain elements: input element [31,32,33,34], processing element [35,36,37,38,39], cumulative element [40], transport element [41,42], output element [43,44,45,46]. However, most studies (about 50% of all studies) focus on the comprehensive assessment of the impact of factors on the entire supply chain.

The most used methods for assessing supply chain sustainability factors are: Interpretive Structural Modelling (ISM); Matriced' Impacts Cruoses Multiplication Applique a un Classement (MICMAC) and factor analysis. These methods account for 20% and 10% respectively. The most frequent MCDM methods are DEcision-Making Trial and Evaluation Laboratory, DEMATEL (9% of articles), Fuzzy DEMATEL (7% of articles), Analytic Hierarchy Process, AHP (6% of articles).

Analyzing and evaluating factors, drivers, and barriers use two types of grouping: one-level (53% of articles) or two-level (40%). The number of factors (drivers) can vary and depends on the type of supply chain. In a one-level grouping, the number of factors (drivers) ranges from 3 to 41. In two-level grouping: at the first level — from 2 to 14; at the second level — from 6 to 73.

Table 1. Supply chain factor assessment studies.

№	Supply Chain Type	Object of Assessment	Number of Factors, Drivers, and Barriers	Methods and Models**	Reference
1	Supply Chain	Other	7 factors	—	[8]
2	Green Supply Chain	Supply Chain	7 factors	DEMATEL	[19]
3	Green Supply Chain	Other	13 factors	ISM	[20]
4	Sustainable Supply Chain	Supply Chain	7 drivers / 40 sub-drivers	SLR	[9]
5	Green Supply Chain	Supply Chain	8 factors	ISM-MICMAC	[10]
6	Green Supply Chain	Other	8 factors	FQFD	[22]
7	Sustainable Supply Chain	Supply Chain	8 factors	SLR/Survey	[23]
8	Green Supply Chain	Supply Chain	16 factors	DEMATEL	[4]
9	Green Supply Chain	Supply Chain	5 factors / 24 sub-factors	AHP-DEMATEL	[47]
10	Other Supply Chain	Other	6 factors	ANOVA	[48]
11	Green Supply Chain	Supply Chain	11 drivers	ISM-MICMAC	[49]
12	Green Supply Chain	Supply Chain	4 critical factors / 20 sub-factors	Factor Analysis	[50]
13	Other Supply Chain	Other	3 factors / 20 sub-factors	ANOVA	[51]
14	Reverse Supply Chain	Supply Chain	5 factors / 25 sub-factors	AHP-DEMATEL	[30]
15	Close-Loop Supply Chain	Other	3 drivers	—	[52]
16	Supply Chain	Supply Chain	3 drivers	AHP	[53]
17	Green Supply Chain	Processing element*	12 drivers	Fuzzy AHP	[35]
18	Sustainable Supply Chain	Supply Chain	13 factors	Hesitant Fuzzy DEMATEL	[54]
19	Sustainable Supply Chain	Supply Chain	14 critical factors / 62 sub-factors	SLR	[12]
20	Sustainable Supply Chain	Supply Chain	4 critical factors / 20 sub-factors	Factor Analysis	[55]
21	Resilience Supply Chain	Supply Chain	15 factors	Data Analysis	[29]
22	Sustainable Supply Chain	Supply Chain	15 factors	SLR	[15]
23	Supply Chain	Supply Chain	4 factors / 43 sub-factors	SLR	[11]
24	Supply Chain	Supply Chain	4 barriers	Cross-case Analysis	[3]
25	Green Supply Chain	Supply Chain	5 barriers / 22 sub-barriers	AHP	[56]
26	Reverse Supply Chain	Supply Chain	7 drivers / 37 sub-drivers 7 barriers / 36 sub-barriers	SLR	[16]
27	Green Supply Chain	Supply Chain	2 drivers / 10 sub-drivers 2 barriers / 15 sub-barriers	SLR	[17]
28	Green Supply Chain	Supply Chain	19 factors	ISM-MICMAC	[57]
29	Green Supply Chain	Supply Chain	26 critical factors	ISM-MICMAC	[58]
30	Sustainable Supply Chain	Supply Chain	7 critical factors / 32 sub-factors	ISM-MICMAC	[59]
31	Green Supply Chain	Input element*	7 drivers / 26 sub-drivers	Structural Equation Modelling	[31]
32	Sustainable Supply Chain	Input element*	7 drivers / 17 sub-drivers	Hierarchical Linear Modelling	[32]
33	Green Supply Chain	Supply Chain	8 drivers	ISM-MICMAC	[60]
34	Green Supply Chain	Input element*	5 factors / 15 sub-factors	Fuzzy DEMATEL	[61]
35	Green Supply Chain	Processing element*	13 drivers	ISM-MICMAC	[36]
36	Other Supply Chain	Output element*	8 barriers	Grey DEMATEL	[43]
37	Sustainable Supply Chain	Supply Chain	7 factors	GRA	[62]
38	Green Supply Chain	Supply Chain	18 barriers	ISM	[63]
39	Green Supply Chain	Output element*	13 factors	DEMATEL	[44]
40	Green Supply Chain	Supply Chain	5 factors / 28 sub-factors	Factor Analysis	[64]
41	Green Supply Chain	Supply Chain	15 barriers	Hierarchical Clustering Analysis	[65]
42	Green Supply Chain	Supply Chain	4 critical factors / 25 sub-factors	DEMATEL	[66]
43	Green Supply Chain	Supply Chain	10 factors	Fuzzy DEMATEL	[67]
44	Green Supply Chain	Transport element*	10 factors	Statistical Analysis	[41]
45	Green Supply Chain	Supply Chain	5 drivers / 18 sub-drivers	ANOVA	[68]
46	Green Supply Chain	Transport element*	8 drivers	Structural Equation Modelling	[42]
47	Green Supply Chain	Supply Chain	3 drivers	Survey	[69]
48	Sustainable Supply Chain	Input element*	17 drivers and 16 barriers	Delphi method	[33]
49	Supply Chain	Supply Chain	32 drivers	Structural Equation Modelling	[70]
50	Green Supply Chain	Supply Chain	26 barriers	ISM-MICMAC	[71]
51	Green Supply Chain	Supply Chain	14 barriers	ISM-MICMAC	[72]
52	Green Supply Chain	Input element*	10 barriers	ISM-MICMAC	[45]
53	Sustainable Supply Chain	Supply Chain	13 barriers	ISM-MICMAC	[73]
54	Supply Chain	Processing element*	10 drivers and 4 barriers	—	[37]
55	Green Supply Chain	Supply Chain	7 drivers and 10 barriers	Semi-structured interviews	[74]
56	Sustainable Supply Chain	Supply Chain	11 drivers	Fuzzy TISM-MICMAC	[75]
57	Sustainable Supply Chain	Supply Chain	3 drivers / 11 sub-drivers 2 barriers / 11 sub-barriers	—	[76]
58	Green Supply Chain	Supply Chain	5 barriers / 22 sub-barriers	DEMATEL-Fuzzy EDAS-Fuzzy COPRAS	[77]
59	Supply Chain	Supply Chain	14 barriers	Fuzzy TISM-MICMAC	[78]
60	Close-Loop Supply Chain	Supply Chain	4 drivers / 21 sub-drivers	Grey DEMATEL	[27]
61	Close-Loop Supply Chain	Supply Chain	6 barriers / 35 sub-barriers	Pythagorean Fuzzy AHP-DEMATEL	[28]
62	Green Supply Chain	Supply Chain	9 critical factors	Fuzzy DEMATEL	[79]
63	Green Supply Chain	Output element*	25 critical factors	Factor Analysis	[46]
64	Other Supply Chain	Supply Chain	6 factors	GRA	[80]
65	Supply Chain	Supply Chain	6 factors / 13 sub-factors	AHP	[81]
66	Supply Chain	Supply Chain	2 factors / 10 sub-factors	—	[24]
67	Other Supply Chain	Transport element*	7 critical factors	—	[21]
68	Sustainable Supply Chain	Supply Chain	15 factors	ISM-MICMAC	[18]
69	Sustainable Supply Chain	Supply Chain	7 drivers and 6 barriers	AHP-TOPSIS, AHP-COPRAS	[82]
70	Green Supply Chain	Input element*	11 factors	SWARA-TOPSIS	[83]
71	Green Supply Chain	Supply Chain	3 critical factors / 12 sub-factors	DEMATEL	[84]
72	Green Supply Chain	Supply Chain	12 factors	ISM-MICMAC	[85]
73	Green Supply Chain	Input element*	5 factors / 17 sub-factors	ANP-TOPSIS	[86]
74	Green Supply Chain	Supply Chain	5 drivers	SEM	[87]
75	Other Supply Chain	Supply Chain	3 factors / 8 sub-factors	ANP-AHP-BOCR	[25]
76	Green Supply Chain	Processing element*	5 factors / 21 sub-factors	Confirmatory factor analysis	[88]
77	Sustainable Supply Chain	Processing element*	4 factors / 14 sub-factors	Factor Analysis	[38]
78	Supply Chain	Processing element*	3 barriers / 15 sub-barriers	Grey DEMATEL	[39]
79	Supply Chain	Cumulative element*	15 factors	ISM-MICMAC	[40]
80	Other Supply Chain	Transport element*	8 factors	—	[89]
81	Green Supply Chain	Supply Chain	5 drivers / 17 sub-drivers	Fuzzy DEMATEL-Fuzzy ANP-Fuzzy TOPSIS	[90]
82	Green Supply Chain	Supply Chain	3 barriers / 13 sub-barriers	AHP-ELECTRE I	[91]
83	Sustainable Supply Chain	Input element*	3 factors / 10 sub-factors	DEMATEL	[92]
84	Green Supply Chain	Input element*	5 factors / 21 sub-factors	AHP	[34]
85	Green Supply Chain	Input element*	15 factors	Fuzzy DEMATEL	[26]
86	Supply Chain	Supply Chain	10 factors	Grey system theory	[1]
87	Sustainable Supply Chain	Supply Chain	22 drivers and 19 barriers	SLR	[5]
88	Green Supply Chain	Supply Chain	2 drivers / 6 sub-drivers	—	[7]
89	Green Supply Chain	Supply Chain	7 factors / 47 sub-factors	Factor Analysis	[93]

* The authors identified the elements of the supply chain based on a structural-functional approach. A detailed description of the structural elements of the supply chain and their functions is presented in [94] and section 3.1 of this paper. ** AHP – Analytic Hierarchy Process; ANOVA – Analysis of Variance; ANP – Analytic Network Process; BOCR – Benefit, Opportunity, Cost, and Risk; COPRAS – Complex Proportional Assessment; DEMATEL – DEcision MAking Trial and Evaluation Laboratory; EDAS – Evaluation based on Distance from Average Solution; ELECTRE – ELimination and Choice Expressing the Reality; FQFD – Fuzzy Quality Function Deployment; GRA – Grey Relation Analysis; ISM – Interpretive Structural Modeling; MICMAC – Matriced Impacts Cruoses Multiplication Applique a un Classement techniques; SWARA – Stepwise Weight Assessment Ratio Analysis; SLR – Sytematic literature review; TISM – Total Interpretive Structural Modelling; TOPSIS – Technique for the Order of Preference by Similarity to Ideal Solution; SEM – Structural Equation Modelling.

Table 1. Supply chain factor assessment studies.

№	Supply Chain Type	Object of Assessment	Number of Factors, Drivers, and Barriers	Methods and Models**	Reference
1	Supply Chain	Other	7 factors	—	[8]
2	Green Supply Chain	Supply Chain	7 factors	DEMATEL	[19]
3	Green Supply Chain	Other	13 factors	ISM	[20]
4	Sustainable Supply Chain	Supply Chain	7 drivers / 40 sub-drivers	SLR	[9]
5	Green Supply Chain	Supply Chain	8 factors	ISM-MICMAC	[10]
6	Green Supply Chain	Other	8 factors	FQFD	[22]
7	Sustainable Supply Chain	Supply Chain	8 factors	SLR/Survey	[23]
8	Green Supply Chain	Supply Chain	16 factors	DEMATEL	[4]
9	Green Supply Chain	Supply Chain	5 factors / 24 sub-factors	AHP-DEMATEL	[47]
10	Other Supply Chain	Other	6 factors	ANOVA	[48]
11	Green Supply Chain	Supply Chain	11 drivers	ISM-MICMAC	[49]
12	Green Supply Chain	Supply Chain	4 critical factors / 20 sub-factors	Factor Analysis	[50]
13	Other Supply Chain	Other	3 factors / 20 sub-factors	ANOVA	[51]
14	Reverse Supply Chain	Supply Chain	5 factors / 25 sub-factors	AHP-DEMATEL	[30]
15	Close-Loop Supply Chain	Other	3 drivers	—	[52]
16	Supply Chain	Supply Chain	3 drivers	AHP	[53]
17	Green Supply Chain	Processing element*	12 drivers	Fuzzy AHP	[35]
18	Sustainable Supply Chain	Supply Chain	13 factors	Hesitant Fuzzy DEMATEL	[54]
19	Sustainable Supply Chain	Supply Chain	14 critical factors / 62 sub-factors	SLR	[12]
20	Sustainable Supply Chain	Supply Chain	4 critical factors / 20 sub-factors	Factor Analysis	[55]
21	Resilience Supply Chain	Supply Chain	15 factors	Data Analysis	[29]
22	Sustainable Supply Chain	Supply Chain	15 factors	SLR	[15]
23	Supply Chain	Supply Chain	4 factors / 43 sub-factors	SLR	[11]
24	Supply Chain	Supply Chain	4 barriers	Cross-case Analysis	[3]
25	Green Supply Chain	Supply Chain	5 barriers / 22 sub-barriers	AHP	[56]
26	Reverse Supply Chain	Supply Chain	7 drivers / 37 sub-drivers 7 barriers / 36 sub-barriers	SLR	[16]
27	Green Supply Chain	Supply Chain	2 drivers / 10 sub-drivers 2 barriers / 15 sub-barriers	SLR	[17]
28	Green Supply Chain	Supply Chain	19 factors	ISM-MICMAC	[57]
29	Green Supply Chain	Supply Chain	26 critical factors	ISM-MICMAC	[58]
30	Sustainable Supply Chain	Supply Chain	7 critical factors / 32 sub-factors	ISM-MICMAC	[59]
31	Green Supply Chain	Input element*	7 drivers / 26 sub-drivers	Structural Equation Modelling	[31]
32	Sustainable Supply Chain	Input element*	7 drivers / 17 sub-drivers	Hierarchical Linear Modelling	[32]
33	Green Supply Chain	Supply Chain	8 drivers	ISM-MICMAC	[60]
34	Green Supply Chain	Input element*	5 factors / 15 sub-factors	Fuzzy DEMATEL	[61]
35	Green Supply Chain	Processing element*	13 drivers	ISM-MICMAC	[36]
36	Other Supply Chain	Output element*	8 barriers	Grey DEMATEL	[43]
37	Sustainable Supply Chain	Supply Chain	7 factors	GRA	[62]
38	Green Supply Chain	Supply Chain	18 barriers	ISM	[63]
39	Green Supply Chain	Output element*	13 factors	DEMATEL	[44]
40	Green Supply Chain	Supply Chain	5 factors / 28 sub-factors	Factor Analysis	[64]
41	Green Supply Chain	Supply Chain	15 barriers	Hierarchical Clustering Analysis	[65]
42	Green Supply Chain	Supply Chain	4 critical factors / 25 sub-factors	DEMATEL	[66]
43	Green Supply Chain	Supply Chain	10 factors	Fuzzy DEMATEL	[67]
44	Green Supply Chain	Transport element*	10 factors	Statistical Analysis	[41]
45	Green Supply Chain	Supply Chain	5 drivers / 18 sub-drivers	ANOVA	[68]
46	Green Supply Chain	Transport element*	8 drivers	Structural Equation Modelling	[42]
47	Green Supply Chain	Supply Chain	3 drivers	Survey	[69]
48	Sustainable Supply Chain	Input element*	17 drivers and 16 barriers	Delphi method	[33]
49	Supply Chain	Supply Chain	32 drivers	Structural Equation Modelling	[70]
50	Green Supply Chain	Supply Chain	26 barriers	ISM-MICMAC	[71]
51	Green Supply Chain	Supply Chain	14 barriers	ISM-MICMAC	[72]
52	Green Supply Chain	Input element*	10 barriers	ISM-MICMAC	[45]
53	Sustainable Supply Chain	Supply Chain	13 barriers	ISM-MICMAC	[73]
54	Supply Chain	Processing element*	10 drivers and 4 barriers	—	[37]
55	Green Supply Chain	Supply Chain	7 drivers and 10 barriers	Semi-structured interviews	[74]
56	Sustainable Supply Chain	Supply Chain	11 drivers	Fuzzy TISM-MICMAC	[75]
57	Sustainable Supply Chain	Supply Chain	3 drivers / 11 sub-drivers 2 barriers / 11 sub-barriers	—	[76]
58	Green Supply Chain	Supply Chain	5 barriers / 22 sub-barriers	DEMATEL-Fuzzy EDAS-Fuzzy COPRAS	[77]
59	Supply Chain	Supply Chain	14 barriers	Fuzzy TISM-MICMAC	[78]
60	Close-Loop Supply Chain	Supply Chain	4 drivers / 21 sub-drivers	Grey DEMATEL	[27]
61	Close-Loop Supply Chain	Supply Chain	6 barriers / 35 sub-barriers	Pythagorean Fuzzy AHP-DEMATEL	[28]
62	Green Supply Chain	Supply Chain	9 critical factors	Fuzzy DEMATEL	[79]
63	Green Supply Chain	Output element*	25 critical factors	Factor Analysis	[46]
64	Other Supply Chain	Supply Chain	6 factors	GRA	[80]
65	Supply Chain	Supply Chain	6 factors / 13 sub-factors	AHP	[81]
66	Supply Chain	Supply Chain	2 factors / 10 sub-factors	—	[24]
67	Other Supply Chain	Transport element*	7 critical factors	—	[21]
68	Sustainable Supply Chain	Supply Chain	15 factors	ISM-MICMAC	[18]
69	Sustainable Supply Chain	Supply Chain	7 drivers and 6 barriers	AHP-TOPSIS, AHP-COPRAS	[82]
70	Green Supply Chain	Input element*	11 factors	SWARA-TOPSIS	[83]
71	Green Supply Chain	Supply Chain	3 critical factors / 12 sub-factors	DEMATEL	[84]
72	Green Supply Chain	Supply Chain	12 factors	ISM-MICMAC	[85]
73	Green Supply Chain	Input element*	5 factors / 17 sub-factors	ANP-TOPSIS	[86]
74	Green Supply Chain	Supply Chain	5 drivers	SEM	[87]
75	Other Supply Chain	Supply Chain	3 factors / 8 sub-factors	ANP-AHP-BOCR	[25]
76	Green Supply Chain	Processing element*	5 factors / 21 sub-factors	Confirmatory factor analysis	[88]
77	Sustainable Supply Chain	Processing element*	4 factors / 14 sub-factors	Factor Analysis	[38]
78	Supply Chain	Processing element*	3 barriers / 15 sub-barriers	Grey DEMATEL	[39]
79	Supply Chain	Cumulative element*	15 factors	ISM-MICMAC	[40]
80	Other Supply Chain	Transport element*	8 factors	—	[89]
81	Green Supply Chain	Supply Chain	5 drivers / 17 sub-drivers	Fuzzy DEMATEL-Fuzzy ANP-Fuzzy TOPSIS	[90]
82	Green Supply Chain	Supply Chain	3 barriers / 13 sub-barriers	AHP-ELECTRE I	[91]
83	Sustainable Supply Chain	Input element*	3 factors / 10 sub-factors	DEMATEL	[92]
84	Green Supply Chain	Input element*	5 factors / 21 sub-factors	AHP	[34]
85	Green Supply Chain	Input element*	15 factors	Fuzzy DEMATEL	[26]
86	Supply Chain	Supply Chain	10 factors	Grey system theory	[1]
87	Sustainable Supply Chain	Supply Chain	22 drivers and 19 barriers	SLR	[5]
88	Green Supply Chain	Supply Chain	2 drivers / 6 sub-drivers	—	[7]
89	Green Supply Chain	Supply Chain	7 factors / 47 sub-factors	Factor Analysis	[93]

* The authors identified the elements of the supply chain based on a structural-functional approach. A detailed description of the structural elements of the supply chain and their functions is presented in [94] and section 3.1 of this paper. ** AHP – Analytic Hierarchy Process; ANOVA – Analysis of Variance; ANP – Analytic Network Process; BOCR – Benefit, Opportunity, Cost, and Risk; COPRAS – Complex Proportional Assessment; DEMATEL – DEcision MAking Trial and Evaluation Laboratory; EDAS – Evaluation based on Distance from Average Solution; ELECTRE – ELimination and Choice Expressing the Reality; FQFD – Fuzzy Quality Function Deployment; GRA – Grey Relation Analysis; ISM – Interpretive Structural Modeling; MICMAC – Matriced Impacts Cruoses Multiplication Applique a un Classement techniques; SWARA – Stepwise Weight Assessment Ratio Analysis; SLR – Sytematic literature review; TISM – Total Interpretive Structural Modelling; TOPSIS – Technique for the Order of Preference by Similarity to Ideal Solution; SEM – Structural Equation Modelling.

Thus, the analysis of scientific works in the field of supply chain sustainability assessment allows us to draw the following general conclusions.

1. There is an increase in the number of scientific publications devoted to sustainable and green supply chain management, including works related to the identification of factors, drivers, and barriers of SSCM. At the same time, there is a discrepancy in the terminology used. The authors use different concepts — “factors”, “critical factors”, “critical success factors”, “drivers and barriers” to identify the causes that affect the sustainability of supply chains. This variety of concepts makes it difficult to understand and systematize the causes of supply chain sustainability. Moreover, there is a frequent duplication of the same reasons with different names, which, as a consequence, complicates the assessment of supply chain sustainability.

2. We were unable to find generally accepted systems of factors and indicators for assessing the impact of these factors on SSCM. Researchers use different factors and indicators for assessing these factors, depending on the type of supply chain, the elements under study or the tasks to be solved. In most cases, two grouping types of sustainability factors are used. The first one-level type contains only factors. In the second type, a second additional level of sub-factors is introduced. In one-level grouping, the number of factors ranges from 3 to 41 (average 13). The number of factor groups ranges from 2 to 14 (average 5) in the two-level grouping, and the number of sub-factors in the groups ranges from 6 to 73 (average 24).

3. We have identified a wide variety of supply chain sustainability factors. Many authors note the complexity of collecting input data for their assessment. This explains the researchers' wide use of multi-criteria analysis methods, expert methods, including those based on the provisions of fuzzy set theory, gray systems theory and similar. The most frequently used methods are Interpretive Structural Modeling and MICMAC approach (20% of the total number of methods used), Factor Analysis (about 10%), DEMATEL and Fuzzy DEMATEL (respectively 9% and 7%), AHP (about 6%) and others (see Table 1). The use of Crisp prevails in the studies (74% of the total number of papers analyzed). The statements of Fuzzy Set Theory and Gray System Theory are found in 18% and 8% of the analyzed papers, respectively.

4. The disadvantage of most of the approaches used by researchers is the lack of a systematic approach to assessing the sustainability of all elements and processes in supply chains in interconnection. The most frequent objects of assessment are individual elements of the supply chain and their functions — “sustainable supply (input element)” and “sustainable production (processing element)” (11% and 8%, respectively). The least number of studies falls on “sustainable warehousing (cumulative element)”.

3. Methodology for Assessing Sustainable Supply Chain Drivers

The proposed methodology for assessment of sustainable supply chain (SSC) drivers includes methods for describing the structure and functions of SSC, a system of SSC drivers, and a substantiated system of SSC assessment criteria. This methodology formed the basis of the driver assessment and ranking framework.

3.1. Sustainable Supply Chain Structure and Functions

The structural elements of the supply chain and their functions are identified in accordance with the concept proposed in [94]. The supply chain is considered as a fragment of the global logistics system and is linearly ordered along the logistics flows, a set of logistics elements. The specification of logistics elements is based on the idea of functional isolation of an element and the limitation of its decomposition within a certain management task. In addition, the specificity of the element is determined by a set of certain functions, the fulfillment of which ensures the achievement of both its local goal and the goal for the entire logistics system. In [94] the following supply chain elements are identified: input, processing, cumulative, transport, output, and control elements.

Each element of the supply chain (SC) performs the following basic functions to influence logistics flows (Fig. 2):

• Input element (E1) the entry of material flows into the system, i.e., the purchase of necessary raw materials, supplies, or services.
• Processing element (E2) changes the qualitative properties of material flows, their transformation from raw materials into finished products.
• Cumulative element (E3) regulation of the speed of material flows as a result of their inhibition, accumulation, and storage.
• Transport element (E4) acceleration and movement of material flows.
• Output element (E5) withdrawal of material flow from the system, marketing, and distribution of finished products and services.
• Control element (E6) providing information and financial connection between other LS elements, controlling their functions and operations, regulating the promotion of information and financial flows in the SC.

The basis functions are logistic elements' generalized functions. The realization of basis functions is achieved by performing specific supporting functions accordingly [95,96,97].

Figure 2. Supply chain structure and functions.

Figure 2. Supply chain structure and functions.

The presented structural-functional approach to describing the logistics system is fundamentally different from the common way of identifying functional areas of logistics, such as transportation, sales, production, supply, and warehousing logistics. The disadvantage of this traditional functional approach is the “binding” of logistics functions and operations to infrastructural elements of logistics chains — warehouses, industrial enterprises, supply and sales departments, transportation. As a result, one and the same method of logistics flow management can be realized in different functional areas of logistics [98]. For example, the same “green” solutions are implemented using different methodological frameworks, regulated by different, often contradictory, regulatory and legal rules [94].

SC sustainability is achieved as a result of orientation of each of its elements to the overall objectives of the logistics system — economic, technological, social and environmental. Effective coordination of SC elements requires systematization and research of SC sustainability factors (drivers). Assessment of the impact of these factors (drivers) on SC elements is necessary for making decisions to achieve the goals of the concept of sustainable development in relation to supply chains.

3.2. Sustainable Supply Chain Driver System

Supply Chain Sustainability Drivers are a causes, resources, and actions that have a meaningful impact on accelerating the process of supply chain sustainability. Table 2 presents the results of the systematization of sustainability SC drivers. As the main feature of systematization, we used the supporting functions of the SC elements.

Table 2. Sustainable supply chain driver system.

Supply chain elements	Sustainable supply chain drivers	References
Input element	D1.1 – Environmentally friendly raw materials (at supplier)	[19,31,43,50,55,56,59,60,61,64,66,67,68,69,72,77]
	D1.2 – Raw materials able to reuse or recycle	[12,16,19,27,31,33,44,46,49,56,64]
	D1.3 – Raw materials procurement system	[30,31,36,50,52,53,57,59,60,64,66,67,81]
	D1.4 – Eco-friendly suppliers	[4,10,11,17,22,28,32,33,42,47,48,49,50,55,57,58,60,61,63,64,66,67,68,72,75,76,77]
	D1.5 – Delivery distance of raw materials	[31,32,64]
	D1.6 – Type of packaging for raw materials	[10,16,36,46,52,53,60,61,64,68,73]
	D1.7 – Raw material eco-labeling	[16,46,59]
	D1.8 – E-commerce with supplier	[62,75]
Processing element	D2.1 – Eco-friendly raw materials from the manufacturer	[10,12,19,43,51,56,59,60,64,66,68,69,72,77]
	D2.2 – Reusable or recyclable raw materials	[16,27,44,46,56,64,77]
	D2.3 – Eco-friendly equipment	[9,19,33,44,51,59,64,69]
	D2.4 – Energy and resource saving technologies	[4,22,33,35,36,44,47,49,69,72]
	D2.5 – Eco-friendly production technologies	[4,9,19,22,33,35,36,44,47,51,56,57,58,61,63,64,65,69,70,71,72,73,77,78,81]
	D2.6 – Environmental protection systems	[20,32,35,51,61,64]
	D2.7 – Industrial waste	[10,12,16,20,28,30,31,36,41,44,50,52,58,64,66,67,72,73,77]
	D2.8 – Labor conditions	[51,55,58,59]
	D2.9 – Eco-learning	[4,9,12,16,19,23,28,30,31,32,36,47,48,54,56,57,58,59,62,64,65,72,73,74,77,78,79,81]
Transport element	D3.1 – Transport type	[19,51,61,69,81]
	D3.2 – Transport links type	[51]
	D3.3 – Route of transportation	[64]
	D3.4 – Cargo flow structure	[81]
	D3.5 – Frequency and size of shipments	[31,36,50,52,53,57,59,60,81]
	D3.6 – Compliance of transportation vehicles with legal regulations	[19,35,44,51,61,64,69]
	D3.7 – Fuel type	[51,64]
	D3.8 – Technical condition of vehicle fleet	[64]
	D3.9 – Vehicle type and model	[61,64]
	D3.10 – Vehicles loading degree	[64]
	D3.11 – Equipment of rolling stock with navigation and telecommunication systems	[11]
	D3.12 – Eco-driving	[41,89]
Cumulative element	D4.1 – Eco raw materials and materials for warehouse construction	[72]
	D4.2 – Warehouse type	[40,83,86]
	D4.3 – Spatial organization of warehouse facilities	[44]
	D4.4 – Energy-saving technologies	[4,22,33,36,44,47,49,69,72,73]
	D4.5 – Environment protection systems	[20,35,61,64]
	D4.6 – Eco-friendly loading and unloading equipment	[19,35,44,64]
	D4.7 – Mechanization and automation of loading and unloading operations	[15,29,44]
	D4.8 – Inventory management system	[8,10,32]
	D4.9 – Placement and storage of products and waste	[10,12,16,20,36,44,47,50,52,53,58,66,72]
	D4.10 – Type of packaging for products	[36,46,52,53,61,64,68,73]
	D4.11 – Labor conditions	[19,59]
Output element	D5.1 – Eco-marketing	[4,16,20,22,27,28,30,33,35,36,41,43,46,47,48,49,58,59,63,64,65,67,68,69,72,75,76,77,81]
	D5.2 – Eco-friendly sales channels	[11,12,16,17,22,27,33,46,48,50,51,55,57,58,59,64,72,75,76]
	D5.3 – Tare and packaging return system	[10,16,27,36,50,60,61,64]
	D5.4 – Type of packaging for products	[10,16,36,46,52,53,61,64,68,73]
	D5.5 – Eco-labeling of products	[15,29,46,58,59,60]
	D5.6 – E-commerce with consumer	[62,75]
Control element	D6.1 – Environment strategy	[4,9,11,12,16,19,20,22,23,27,28,30,31,32,33,35,36,41,42,46,47,48,49,50,52,53,55,56,57,58,61,63,65,66,68,70,71,73,74,75,76,77,78,79,80,81]
	D6.2 – Environmental audit	[16,20,22,36,48,50,59,66]
	D6.3 – Corporate Information Systems	[9,11,12,15,16,17,23,29,31,33,43,47,50,54,56,59,62,63,65,66,70,71,74,75,76,79,80,81]
	D6.4 – Information and communication technologies	[3,8,10,11,15,23,27,29,30,33,35,42,46,50,57,58,59,62,63,66,70,71,72,73,75,76,78,79,80,81]
	D6.5 – Intelligent Transportation Systems	[21,83,89]
	D6.6 – Corporate social responsibility	[3,4,9,12,15,17,19,20,22,23,28,29,32,35,36,41,46,47,50,51,52,53,54,55,56,58,59,63,64,65,66,72,74,76,78,79]
	D6.7 – Return and reverse flow management systems	[4,10,16,27,28,36,46,47,49,52,53,56,60,61,65,73,77]
	D6.8 – Recycling processes for waste, packaging, finished products	[10,12,16,20,22,27,28,30,31,41,44,46,47,50,52,53,58,61,64,66,67,68,69,72,77]

Table 2. Sustainable supply chain driver system.

Supply chain elements	Sustainable supply chain drivers	References
Input element	D1.1 – Environmentally friendly raw materials (at supplier)	[19,31,43,50,55,56,59,60,61,64,66,67,68,69,72,77]
	D1.2 – Raw materials able to reuse or recycle	[12,16,19,27,31,33,44,46,49,56,64]
	D1.3 – Raw materials procurement system	[30,31,36,50,52,53,57,59,60,64,66,67,81]
	D1.4 – Eco-friendly suppliers	[4,10,11,17,22,28,32,33,42,47,48,49,50,55,57,58,60,61,63,64,66,67,68,72,75,76,77]
	D1.5 – Delivery distance of raw materials	[31,32,64]
	D1.6 – Type of packaging for raw materials	[10,16,36,46,52,53,60,61,64,68,73]
	D1.7 – Raw material eco-labeling	[16,46,59]
	D1.8 – E-commerce with supplier	[62,75]
Processing element	D2.1 – Eco-friendly raw materials from the manufacturer	[10,12,19,43,51,56,59,60,64,66,68,69,72,77]
	D2.2 – Reusable or recyclable raw materials	[16,27,44,46,56,64,77]
	D2.3 – Eco-friendly equipment	[9,19,33,44,51,59,64,69]
	D2.4 – Energy and resource saving technologies	[4,22,33,35,36,44,47,49,69,72]
	D2.5 – Eco-friendly production technologies	[4,9,19,22,33,35,36,44,47,51,56,57,58,61,63,64,65,69,70,71,72,73,77,78,81]
	D2.6 – Environmental protection systems	[20,32,35,51,61,64]
	D2.7 – Industrial waste	[10,12,16,20,28,30,31,36,41,44,50,52,58,64,66,67,72,73,77]
	D2.8 – Labor conditions	[51,55,58,59]
	D2.9 – Eco-learning	[4,9,12,16,19,23,28,30,31,32,36,47,48,54,56,57,58,59,62,64,65,72,73,74,77,78,79,81]
Transport element	D3.1 – Transport type	[19,51,61,69,81]
	D3.2 – Transport links type	[51]
	D3.3 – Route of transportation	[64]
	D3.4 – Cargo flow structure	[81]
	D3.5 – Frequency and size of shipments	[31,36,50,52,53,57,59,60,81]
	D3.6 – Compliance of transportation vehicles with legal regulations	[19,35,44,51,61,64,69]
	D3.7 – Fuel type	[51,64]
	D3.8 – Technical condition of vehicle fleet	[64]
	D3.9 – Vehicle type and model	[61,64]
	D3.10 – Vehicles loading degree	[64]
	D3.11 – Equipment of rolling stock with navigation and telecommunication systems	[11]
	D3.12 – Eco-driving	[41,89]
Cumulative element	D4.1 – Eco raw materials and materials for warehouse construction	[72]
	D4.2 – Warehouse type	[40,83,86]
	D4.3 – Spatial organization of warehouse facilities	[44]
	D4.4 – Energy-saving technologies	[4,22,33,36,44,47,49,69,72,73]
	D4.5 – Environment protection systems	[20,35,61,64]
	D4.6 – Eco-friendly loading and unloading equipment	[19,35,44,64]
	D4.7 – Mechanization and automation of loading and unloading operations	[15,29,44]
	D4.8 – Inventory management system	[8,10,32]
	D4.9 – Placement and storage of products and waste	[10,12,16,20,36,44,47,50,52,53,58,66,72]
	D4.10 – Type of packaging for products	[36,46,52,53,61,64,68,73]
	D4.11 – Labor conditions	[19,59]
Output element	D5.1 – Eco-marketing	[4,16,20,22,27,28,30,33,35,36,41,43,46,47,48,49,58,59,63,64,65,67,68,69,72,75,76,77,81]
	D5.2 – Eco-friendly sales channels	[11,12,16,17,22,27,33,46,48,50,51,55,57,58,59,64,72,75,76]
	D5.3 – Tare and packaging return system	[10,16,27,36,50,60,61,64]
	D5.4 – Type of packaging for products	[10,16,36,46,52,53,61,64,68,73]
	D5.5 – Eco-labeling of products	[15,29,46,58,59,60]
	D5.6 – E-commerce with consumer	[62,75]
Control element	D6.1 – Environment strategy	[4,9,11,12,16,19,20,22,23,27,28,30,31,32,33,35,36,41,42,46,47,48,49,50,52,53,55,56,57,58,61,63,65,66,68,70,71,73,74,75,76,77,78,79,80,81]
	D6.2 – Environmental audit	[16,20,22,36,48,50,59,66]
	D6.3 – Corporate Information Systems	[9,11,12,15,16,17,23,29,31,33,43,47,50,54,56,59,62,63,65,66,70,71,74,75,76,79,80,81]
	D6.4 – Information and communication technologies	[3,8,10,11,15,23,27,29,30,33,35,42,46,50,57,58,59,62,63,66,70,71,72,73,75,76,78,79,80,81]
	D6.5 – Intelligent Transportation Systems	[21,83,89]
	D6.6 – Corporate social responsibility	[3,4,9,12,15,17,19,20,22,23,28,29,32,35,36,41,46,47,50,51,52,53,54,55,56,58,59,63,64,65,66,72,74,76,78,79]
	D6.7 – Return and reverse flow management systems	[4,10,16,27,28,36,46,47,49,52,53,56,60,61,65,73,77]
	D6.8 – Recycling processes for waste, packaging, finished products	[10,12,16,20,22,27,28,30,31,41,44,46,47,50,52,53,58,61,64,66,67,68,69,72,77]

3.3. Sustainable supply Chain Drivers’ Assessment Criteria System

It is proposed to evaluate SSC drivers using a two-level system of criteria developed by the authors [99]. The system of criteria is based on the systematization of research and practice of using different indicators for assessing logistics flows and individual elements of supply chains for compliance with the goals and principles of sustainable development. The main distinguishing feature of the proposed system is the comprehensiveness of the assessment of all logistics flows and SSC elements for compliance with the concept of sustainable development. The system includes five groups of criteria of the first level and fifteen sub-criteria of the second level (Table 2).

Table 3. Sustainable supply chain drivers assessment criteria system [99].

Criteria	Characteristic	Sub-criteria	Characteristic
Economic criteria (С1)	The efficiency of using all types of resources in the SSC	Profit (C1.1)	Difference between total revenue and operating costs
		Operating expenses (C1.2)	The sum of all costs associated with converting investments into profits
		Fixed investment (C1.3)	Cash flow for the formation of fixed assets
Energy-ecological criteria (С2)	The efficiency of energy use during the movement of logistics flows and their impact on the environment	The energy intensity (C2.1)	The amount of energy spent on the movement of the logistics flow
		Greenhouse gas emissions of CO2 (C2.2)	The total volume of greenhouse gas emissions from all sources involved in the movement of the logistics flow
Quality criteria (С3)	The safety and timeliness of movement and processing of logistics flows, as well as the quality of their management	Safety of cargo transportation (C3.1)	Comprehensive indicator of the material flow movement without damage, pollution, and loss
		Timeliness of cargo transportation (C3.2)	Comprehensive indicator of the material flow movement by the appointed date, regularly, or at the required speed
		The coefficient of flow controllability (C3.3)	The ratio of the number of information messages on compliance with the indicators of safety and timeliness of transportation to the total number of management decisions
Statistical criteria (С4)	The patterns of change in the controlled sub-criteria of logistics flows	The coefficient of flows irregularity (C4.1)	Deviation of the logistics flows physical parameters of from their average values
		The coefficient of complexity structure of flow (C4.2)	The number of streams within the logistic flow
		The coefficient of flows discreteness (C.4.3)	The number of elements of the logistic flow in the stream
		The coefficient of differentiability of flow (C4.4)	Changing the structure of the logistics flow (number of streams) in the process of movement
Flow’s physical criteria (С5)	The intensity of logistics flows and their spatio-temporal changes	The mass (quantity) of flow (C5.1)	The total number of elements in the logistics flow
		The speed of flow (C5.2)	The speed of movement of the logistics flow elements
		The length of the route (C5.3)	Distance traveled by a logistic flow element while moving along a route

Table 3. Sustainable supply chain drivers assessment criteria system [99].

Criteria	Characteristic	Sub-criteria	Characteristic
Economic criteria (С1)	The efficiency of using all types of resources in the SSC	Profit (C1.1)	Difference between total revenue and operating costs
		Operating expenses (C1.2)	The sum of all costs associated with converting investments into profits
		Fixed investment (C1.3)	Cash flow for the formation of fixed assets
Energy-ecological criteria (С2)	The efficiency of energy use during the movement of logistics flows and their impact on the environment	The energy intensity (C2.1)	The amount of energy spent on the movement of the logistics flow
		Greenhouse gas emissions of CO2 (C2.2)	The total volume of greenhouse gas emissions from all sources involved in the movement of the logistics flow
Quality criteria (С3)	The safety and timeliness of movement and processing of logistics flows, as well as the quality of their management	Safety of cargo transportation (C3.1)	Comprehensive indicator of the material flow movement without damage, pollution, and loss
		Timeliness of cargo transportation (C3.2)	Comprehensive indicator of the material flow movement by the appointed date, regularly, or at the required speed
		The coefficient of flow controllability (C3.3)	The ratio of the number of information messages on compliance with the indicators of safety and timeliness of transportation to the total number of management decisions
Statistical criteria (С4)	The patterns of change in the controlled sub-criteria of logistics flows	The coefficient of flows irregularity (C4.1)	Deviation of the logistics flows physical parameters of from their average values
		The coefficient of complexity structure of flow (C4.2)	The number of streams within the logistic flow
		The coefficient of flows discreteness (C.4.3)	The number of elements of the logistic flow in the stream
		The coefficient of differentiability of flow (C4.4)	Changing the structure of the logistics flow (number of streams) in the process of movement
Flow’s physical criteria (С5)	The intensity of logistics flows and their spatio-temporal changes	The mass (quantity) of flow (C5.1)	The total number of elements in the logistics flow
		The speed of flow (C5.2)	The speed of movement of the logistics flow elements
		The length of the route (C5.3)	Distance traveled by a logistic flow element while moving along a route

3.4. A Framework for Sustainable Supply Chain Drivers’ Multi-Criteria Assessment

The two-level criteria system presented in the previous section is proposed to be used to evaluate SSC drivers by multi-criteria methods. The flow chart of the developed assessment framework is presented in Fig. 3. This framework is universal and can be applied both for evaluating the set of drivers identified by the authors and for any other combination of drivers. The combination of drivers depends on the structure of a particular SSC as well as the specific elements that comprise it.

Figure 3. A framework for sustainable supply chain drivers’ multi-criteria assessment.

Figure 3. A framework for sustainable supply chain drivers’ multi-criteria assessment.

Stage I. A specific supply chain is formalized as a model consisting of certain logistic elements. The boundaries of the supply chain are defined. Management objectives for achieving sustainability goals are selected. The supply chain structure [94] and management objectives determine the composition of indicators (criteria) [99] for assessing and selecting SSC drivers (alternatives). Scientific literature and best practices of implementing green principles and technologies are sources of information for justification and systematization of SSC drivers and criteria for their evaluation. Supporting functions of SC elements [98] are recommended to be used as the main feature of systematization. The system of driver evaluation criteria should meet the requirements of comprehensiveness of logistics flows assessment and consistency of logistics flows [99].

We recommend using the criteria and drivers’ systems presented in Sections 3.2 and 3.3 to be considered as the most comprehensive and universal systems. Systems with a smaller number of criteria and drivers can be used for supply chains with fewer logistics elements or sustainability objectives.

Stage II. Expert scoring of the identified criteria, sub-criteria and drivers. Expert assessment is performed using a standardized methodology. The methodology of expert scoring includes the formation of an expert group of at least five people, development of questionnaires, filling out questionnaires and verification of the data obtained. It is recommended to use simple linguistic variables (Table 4) for scoring. The obtained scores are the initial data for ranking the criteria and sub-criteria at the next stage of the framework.

Stage III. Ranking of criteria, sub-criteria and drivers. The weight of criteria and sub-criteria is calculated using the DEMATEL method. Our research has shown that the accuracy of the ranking results depends on the composition and size of the expert group. Therefore, we recommend using different scales to assess the importance of criteria and sub-criteria and then comparing the results obtained. Crisp, Fuzzy and Grey number scales are used in the present study. The correspondence of these scales to the experts' linguistic scores is presented in Table 4.

The scores obtained using the three scales are used to rank the criteria and sub-criteria using the C-DEMATEL, F-DEMATEL and G-DEMATEL methods, respectively. DEcision MAking Trial and Evaluation Laboratory (DEMATEL) [100] allows establishing direct and inverse relationships between criteria, and to visualize these causal relationships in the form of a map of network relationships. DEMATEL is used to assess the strength of influence of criteria on each other. The combination of DEMATEL with fuzzy set theory and gray systems' theory provides decision-making considering uncertain, ambiguous or incomplete information and thus provides more reliable analysis and evaluation results. The main steps of DEMATEL using Crisp, Fuzzy and Grey are presented in Table 5.

The three versions of the criteria and sub-criteria scores are further used to rank the drivers in combination with the CRADIS method – Compromise Ranking of Alternatives from Distance to Ideal Solution [101]. CRADIS allows you to calculate the deviations of alternatives from ideal and anti-ideal solutions. The CRADIS method is not a new method, but it is a new approach to combining known MCDMs: ARAS (Additive Ratio ASsessment), MARCOS (Measurement of Alternatives and Ranking according to Compromise Solution), and TOPSIS (Technique for the Order of Preference by Similarity to Ideal Solution) methods.

The calculations using CRADIS are performed in the following steps.

Step III.6. Formation of an initial decision-making matrix consisting of a set of n criteria and m alternatives

$$X=\left[\begin{array}{cccc}{x}_{11}& x_{12}& \cdots & x_{1n}\\ x_{21}& x_{22}& \dots & x_{2n}\\ ⋮& ⋮& \ddots & ⋮\\ x_{m1}& x_{m2}& \dots & x_{mn}\end{array}\right],$$

(1)

where x_mn – assessment of i-th alternative, i=1, 2,…,m according to the j-th criterion, j=1,2,…,n.

Step III.7. Normalization of the initial decision matrix X using the following formulas

$$N={\left[n_{ij}\right]}_{m\times n},i=1,2,\dots ,m;j=1,2,\dots ,n,$$

(2)

$$n_{ij}={\displaystyle \frac{x_{ij}}{x_{j\max }}},ifj\in C$$

(3)

$$n_{ij}={\displaystyle \frac{x_{j\min }}{x_{ij}}},ifj\in B$$

(4)

where C – cost criteria, B – benefit criteria.

Step III.8. Calculation of the weighted matrix V by multiplying the values of the normalized matrix elements n_ij by the corresponding weight coefficients w_j of the criteria

$$V={\left[v_{ij}\right]}_{m\times n},i=1,2,\mathrm{3...},m;j=1,2,\mathrm{3...},n,$$

(5)

$$V=n_{ij}\times w_j,$$

(6)

$${\displaystyle \sum _{i=1}^m{w}_j}=1.$$

(7)

Step III.9. Calculation of ideal and anti-ideal solutions. Ideal solutions t_i are the largest values of v_ij in the weighted matrix of solutions, and anti-ideal solutions tai, respectively, are the smallest values

$$t_i=\max v_{ij},$$

(8)

$$t_{ai}=\min v_{ij},$$

(9)

Step III.10. Calculation of deviations from ideal and anti-ideal solutions

$$d^{+}=\max t_i-v_{ij},$$

(10)

$$d^{-}=v_{ij}-\min t_{ai}.$$

(11)

Step III.11. Calculation of the degree of deviation of alternatives from ideal and anti-ideal solutions

$$s_i^{+}={\displaystyle \sum _{j=1}^n{d}^{+}},$$

(12)

$$s_i^{-}={\displaystyle \sum _{j=1}^n{d}^{-}}.$$

(13)

Step III.11. Calculation of the utility functions for each alternative regarding deviations from the optimal alternatives

$$K_i^{+}={\displaystyle \frac{s_0^{+}}{s_i^{+}}},$$

(14)

$$K_i^{-}={\displaystyle \frac{s_i^{-}}{s_0^{-}}},$$

(15)

where $s_0^{+}\text{}$– optimal alternative with the smallest distance from the ideal solution, i.e. $s_0^{+}\text{}=\min (s_i^{+}),\forall m$, $s_0^{-}$ – optimal alternative, as far as possible from the anti-ideal solution, i.e. $s_0^{-}\text{}=\max (s_i^{-}),\forall m$.

Step III.12. Calculation of average values of utility functions for alternatives Q_i and ranking of alternatives according to their values

$$Q_i={\displaystyle \frac{K_i^{+}+K_i^{-}}{2}}.$$

(16)

The best alternative is the alternative with the maximum value of the utility function Q_i.

Stage IV. Quality assessment of ranking results and development of management decisions. The value of the Mean Relative Error of the Ranking results (MRER) of drivers obtained by different methods is proposed to be generally calculated by the following formula

$$MRER={\displaystyle \frac{{\displaystyle \sum _{i=1}^M{\displaystyle \sum _{l=1}^{L-1}\left(\left|R_{il}-R_{il+1}\right|+\left|R_{iL}-R_{i1}\right|\right)}}}{M\times L}},$$

(17)

where R_il – rank of the i-th alternative, calculated by the l-th method, M – number of alternatives, L – number of ranking methods, L ≥ 3.

Finally, the sensitivity of the obtained results is assessed by ranking the alternatives by different MCDMs and then calculating the Spearman coefficient. If the MRER or Spearman coefficient values exceed the acceptable values, it is necessary to increase the size of the expert group or change its composition.

The resulting SSC driver ranks are used to develop and justify decisions on the implementation of green logistics methods and instruments [96].

4. Numerical Example

An example of driver ranking for all elements of the supply chain is presented in this section. The supply chain model under study is presented in Section 3.1. The SSC driver system for this model is described in Section 3.2 (Table 2) and contains 54 alternatives. The drivers were ranked using a criteria system containing 15 attributes (Section 3.3, Table 3).

In the second stage of the framework for ranking the identified drivers (Figure 3), we formed a panel of five experts (Table 6).

Initially, the experts performed an assessment of the importance of criteria and sub-criteria of logistic flows in SSC. The linguistic scale presented in Table 4 was used for the assessment. Then the experts assessed the impact of drivers on criteria and sub-criteria on a five-point scale: 1 — very low, 2 — low, 3 — medium, 4 — high, 5 — very high. The results of expert assessments are presented in Table 7 and Table 8.

Total matrices of links between criteria and network relationship maps obtained as a result of DEMATEL method calculation are presented in Figure 4. The shaded cells in the tables show the presence of significant relationships between the criteria. Significant are the relationships whose value is higher than the threshold values set by the experts: α_Crisp=0,755, α_Fuzzy=0,373 and α_Grey=0,345. The threshold value is calculated as the average of all estimates of the total direct linkage matrix T. Criteria C1, C3 and C5 are assigned to the “cause” group and C2 and C4 to the “effect” group in all models according to the values of (D_i – R_i).

Similar calculations are performed for the evaluation of sub-criteria. Maps of network relations of sub-criteria are presented in Figure 5.

Criteria and sub-criteria are ranked according to the value of weighting coefficients, which are calculated based on the results of assessing the number of relationships (Di + Ri) and the strength of influence (D_i – R_i) between criteria or sub-criteria (Table 9).

The obtained criterion and sub-criteria ranks are used to evaluate the SSC drivers using the CRADIS method. The results of SCC driver ranking using the three models C-DEMATEL-CRADIS, F-DEMATEL-CRADIS and G-DEMATEL-CRADIS are presented in Table 10 and Figure 6.

The three most significant SSC drivers are D6.3 “Corporate Information Systems” (rank #1), D6.5 “Intelligent Transportation Systems” (rank #2), and D3.8 “Technical condition of vehicle fleet” (rank #3). The three least significant drivers are D1.1 “Environmentally friendly raw materials (at supplier)” (rank #54), D2.2 “Reusable or recyclable raw materials” (rank #53), and D6.8 “Recycling processes for waste, packaging, finished products” (rank #52).

The ranking of SCC drivers separately for each element of the supply chain allowed to establish the most important (rank #1) drivers of each element: D1.8 “E-commerce with supplier” (Input element), D2.3 “Eco-friendly equipment” (Processing element), D3.8 “Technical condition of vehicle fleet” (Transport element), D4.3 “Spatial organization of warehouse facilities” (Cumulative element), D5.6 “E-commerce with consumer” (Output element), D6.3 “Corporate Information Systems” (Control element).

The following drivers have the least impact: D1.1 “Environmentally friendly raw materials (at supplier)” (Input element, rank #8), D2.2 “Reusable or recyclable raw materials” (Processing element, rank #9), D3.6 “Compliance of transportation vehicles with legal regulations” (Transport element, rank #12), D4.9 “Placement and storage of products and waste” (Cumulative element, rank #11), D5.3 “Tare and packaging return system” (Output element, rank #6), D6.8 “Recycling processes for waste, packaging, finished products” (Control element, rank #8).

The results of the SSC drivers’ ranking for each supply chain element are presented in Fig. 7.

The evaluation of the quality of ranking by the three methods using MRER (17) showed a satisfactory result, MRER = 188/54×3 = 1.16. That is, the average difference between the ranks calculated by different methods is close to one.

Sensitivity analysis of the ranking results was performed by comparing the results of CRADIS, with the results of three other multi-criteria methods. We chose for comparison the methods TOPSIS (Technique for the Order of Preference by Similarity to Ideal Solution) [102], ARAS (Additive Ratio Assessment) [103], MARCOS (Measurement of Alternatives and Ranking according to COmpromise Solution) [104]. Table 11 and Figure 8 show the results of the comparison.

Spearman rank correlation coefficient calculation showed a high correlation between the ranking results of different MCDM methods. The average correlation coefficient was for all models 0.966, for Crisp models – 0.9679, for Fuzzy models – 0.9647, and for Grey models – 0.9658 (Table 12).

The obtained results of the assessment of supply chain sustainability drivers and their impact on the elements of supply chains are the basis for the formation of programs and solutions (Table 13) for the implementation of green logistics methods and instruments [96].

5. Conclusions

The paper presents a framework for assessing and ranking the drivers of sustainable supply chain development. The peculiarities of the framework are the use of a universal supply chain model, which includes structural elements that realize all known functional areas of logistics. An original two-level system of criteria and sub-criteria for evaluating logistics flows is used as assessing criteria. It is shown how the use of these criteria allows the evaluation of supply chain sustainability drivers. Fifty-four sustainability drivers are identified based on a literature review of logistics research and supply chain management practices. The assessment and ranking of the drivers is proposed to be performed using a combination of DEMATEL and CRADIS multi-criteria methods.

The three most significant SSC drivers are identified. These drivers are “Corporate Information Systems”, “Intelligent Transportation Systems”, and “Technical condition of vehicle fleet”. The three least significant drivers are “Environmentally friendly raw materials (at supplier)”, “Reusable or recyclable raw materials”, and “Recycling processes for waste, packaging, finished products”. Similar ranking is performed for each structural element of the studied supply chain model.

An example of using the ranked drivers to select sustainable development methods and instruments is shown. Thus, it is proposed to consider the identified drivers and the framework for their selection as a universal basis for justification and implementation of management decisions on coordinated supply chain sustainability. The limitation of the study is the assumption of coordination by the managing element of the supply chain of the functioning of its participants for the realization of sustainability goals. The proposed framework and the approach used generally do not sufficiently account for supply chain actors' competition and conflicting economic objectives. Future research will be aimed at improving the framework, supply chain model, systems of sustainability criteria and drivers for the conditions of competitive environment and justification of dynamic equilibrium of supply chain participants' interests.