Collaborative decision-making in supply chain management: A review and bibliometric analysis

Abstract

Although collaborative decision-making with multiple stakeholders in supply chain has become an important research issue in recent years, current research on the mechanism/process, methods, and performance assessment of collaborative decision-making still lags behind the practical needs in supply chain management. This paper reviews the existing literature in the past decade on collaborative supply chain from the perspective of decision-making in four stages with a causal framework. This paper reveals a general retrospect of the main subjects developed and investigated with related applications of cases, which includes both theoretical assumptions and practical data. The findings indicate that most articles in this field are based on a direct relationship between collaboration and performance improvement but lack of a mechanism between them. Additionally, dynamic collaboration with members in the whole supply chain both horizontally and vertically has not been conducted by previous studies. This paper provides a new conceptual framework and future research directions for further study in supply chain collaboration.

Keywords:

• bibliometric analysis
• collaborative decision-making
• review
• supply chain collaboration
• supply chain performance

PUBLIC INTEREST STATEMENT

Collaborative decision-making involving multiple stakeholders in the supply chain has gained importance in recent years, however, current research on the process, mechanisms, methods, and assessment of collaborative decision-making still lags behind practical needs in supply chain management. This paper presents a review of recent studies on collaborative decision-making in supply chain management and examines two key issues: the transmission mechanism between collaboration and performance and the dynamic collaboration with all members in the supply chain. Furthermore, the authors explore performance assessment indicators and research methods in supply chain management, offering insights into development trends and future research directions for both researchers and practitioners in supply chain management.

1. Introduction

Supply chain describes a relationship of members involved in the life cycle of a product or service. As the era has witnessed the process of globalization, more elements have been mentioned in supply chain studies. In the past decades, supply chain collaboration (SCC) has gained great importance to performance improvement from different aspects with consideration of more stakeholders. Fu and Piplani (2004) evaluate the value of collaboration between supplier and distributor in a two-echelon supply chain by numerical experiments. Arshinder et al. (2008) find that previous models did not provide quantified coordination with supply chain and thus the evaluation of the value of SCC is complicated and difficult. They then propose a framework to quantify coordination and suggest that the quantification of risk and uncertainty should be involved in research. Cannella and Ciancimino (2010) find that the impact of SCC on overall performance is greater than that of order smoothing, and the negative effect on customer service level of order smoothing would be largely eliminated in synchronized supply chains according to bullwhip effect. Narayanan and Moritz (2015) suggest that individuals’ level of cognitive reflection can influence the SCC performance.

As the competition is getting intense these days, supply chain managers and scholars realized that becoming a contributing member of a strong SCC enables their companies to create greater customer value at lower costs and thus mitigate intensifying competitive forces (Fawcett et al., 2012). Collaborative decision-making becomes one of the important criteria for the success of supply chain collaboration (Ramanathan & Gunasekaran, 2014). Successful SCC could lead member in the chain to gain success and continue their future partnerships (Ramanathan & Gunasekaran, 2014). Various capabilities and value creation could be a useful tool to deal with the keen competition (Soosay & Hyland, 2015). Goal alignment of supply chain, commitment to networking and decision-making are significantly and positively correlated with effectiveness in supply chain as well (Albishri et al., 2020). Environmental effect has become a global issue consequently as the sustainability development for supply chain, which cannot be ignored (Tseng et al., 2019). The collaboration with members in supply chain to reduce the carbon emissions was proposed as green supply chain collaboration (Benjaafar et al., 2013). However, the relationships of behavioural antecedents of collaboration between supply chain partners and supply chain integration receive few attentions from scholars (Tsanos et al., 2014).

The definition of SCC serves for different purposes and concerns. The subjects and behaviours of SCC are similar in the existing literature while the mechanism varies from one to another. Collaboration refers to negotiated cooperation between independent parties by exchanging capabilities and in sharing burdens to improve collective responsiveness and profitability (Chan & Prakash, 2012). While Sanchez et al. (2015) propose a new definition of horizontal logistics collaboration where logistics providers can work with retailers, retailers can work with manufacturers. In the parallel supply chains, the collaborative relationship(s) can identify and realize synergistic logistics activities among suppliers, customers and/or logistics service providers. To provide a comprehensive review of SCC, this paper defines SCC as horizontal and vertical subjects in the supply chain cooperate in decision-making to create more value and improve performance.

Some scholars have made contributions to reviewing the existing findings of the relationship between supply chain collaboration and performance. For instance, Trang et al. (2022) explore the effect of supply chain collaboration on the performance of cold supply chain of agriculture and foods and identify the driving factors from the related studies. Gimenez et al. (2012) conduct a structured literature review and explore the impact of assessment and collaboration on performance. The conclusion suggests that the collaboration is necessary to improve social and environment performance. Additionally, some scholars establish quantitative models to further explore the collaboration effect in their systematic reviews. Allred et al. (2011) conduct literature review, survey and interviews in their studies and suggest that the hypothesis that collaboration ability has a positive effect on performance. However, the mechanism between collaborative decision-making and performance of supply chain is still vague and the causal relationship between inputs and outputs in SCC is still anomalous. The unclear mechanism of SCC makes it difficult for supply chain manager to adjust their management strategies. Therefore, this paper aims at giving a comprehensive review of current studies on collaborative decision-making in supply chain management and investigating the research status quo and gaps. It is significant to provide development trends and future research directions for both researchers and supply chain managers to deal with the intensifying competition of supply chain.

2. Methodology

2.1. Search rules

To ensure the coverage and quality of existing literature on collaborative decision-making in supply chain to be collected, this paper retrieves journal articles from 2012 to 2021 in the Web of Science Core Collection database. Under the topic search (TS) criterion of TS=(supply chain) AND (TS=(collaboration) OR TS=(collaborative) OR TS=(cooperative) OR TS=(cooperation) OR TS=(coordination)) AND (TS=(decision-making)), a total of 1511 publications are retrieved for the following selection. It is noteworthy that as collaboration is regarded as a dynamic capability (Fawcett et al., 2012), “dynamic” is implicitly in the search rules.

2.2. Selection process

Four steps are applied to select the publications for bibliometric and systematic analysis as shown in Figure 1. First, the proceeding papers, editorial material and book chapters are excluded where only journal articles fall into the next box. Second, the research area criteria are applied to narrow down the focus on management research field. As it is of significance to screen the publications of high quality and rigor before synthesizing them, a quality filter based on the UK ABS list are applied in the third step (Durach et al., 2017; Franke & Foerstl, 2020). Only the articles published on the journals that rank the first and second tiers in UK ABS list of the research field of Operations and Technology Management and Operations Research and Management are included. Finally, with the content analysis, the unrelated publications are excluded.

Figure 1. Publication selection process.

2.3. Bibliometric approach

Bibliometric analysis is a quantitative and visualization approach to document analysis with the application of mathematical and statistical methods, from which the bibliographic features and regular patterns of existing literature can be revealed (Wang et al., 2021). Thus, the bibliometric approach is conducted in this paper to quantitatively analyse and visually present the selected literature with the application of VOSviewer software tool. Applying the visualization function of the software, the co-occurrence map is scripted based on the texts of abstracts and reflects the popular keywords in clusters. More information such as distribution of research fields and countries is also captured and analysed.

3. Bibliometric analysis

3.1. Time series analysis

The collaborative supply chain topic has aroused wide attention from scholars in the past ten years. The number of the literature has increased greatly from 2013. Figure 2 shows the growth trend of numbers of published papers and their citations from 2010 to 2021.

Figure 2. Growth trend of publications and citations.

3.2. Journal statistics analysis

Of the retrieved 187 journal papers, most are published in the International Journal of Production Economics and International Journal of Production Research. The journal distribution is mainly illustrated in Table 1.

Table 1. Journal Distribution

Journal	Number	% of 187
International Journal of Production Economics	47	25.13
International Journal of Production Research	37	19.79
European Journal of Operational Research	19	10.16
Annals of Operations Research	14	7.49
Supply Chain Management: An International Journal	12	6.42
Production and Operations Management	10	5.35
Omega International Journal of Management Science	9	4.81
Production Planning Control	9	4.81
Journal of Supply Chain Management	6	3.21
International Journal of Operations Production Management	5	2.67

3.3. Field analysis

The retrieved literature could be categorized in different research fields illustrated in Table 2. It can be found that operations research management science (83.42%), engineering manufacturing (55.08%), engineering industrial (54.01%) and management (33.69%) are the most popular fields in recent years.

Table 2. Fields Distribution

Research Field	Number	% of 187
Operations Research Management Science	156	83.42
Engineering Manufacturing	103	55.08
Engineering Industrial	101	54.01
Management	63	33.69
Business	15	8.02
Computer Science Interdisciplinary Applications	3	1.60

3.4. Country and region distribution analysis

Table 3 indicates that the authors from China, the USA, England, France, and Canada have accounted for more than 50% of all selected publications, which reflects SCC has been investigated mostly in these countries.

Table 3. Country/Region Distribution

Countries/Regions	Number	% of 187
Peoples R China	58	31.016
USA	50	26.738
England	27	14.439
France	16	8.556
Canada	13	6.952
Iran	13	6.952
Germany	12	6.417
India	12	6.417
Australia	8	4.278
Spain	8	4.278

3.5. Keyword analysis

The co-occurrence map is scripted to conduct the keyword analysis as shown in Figure 3. The co-occurrence analysis can reflect the frequency of two keywords showing up in the same literature. In the network map, the nodes refer to the selected text items and the size of the nodes shows the frequency of their occurrences. The linkage between different nodes shows the strength of their co-occurrence relationship. If two keywords show up in the same literature, there would be a linkage between them. More linkages that the two nodes have, stronger co-occurrence relationship they maintain. The location of the nodes refers to the level of degree centrality of the nodes. If the node locates in the centre of the map, the keyword may have the highest scores of degree centrality and have the most linkages with other keywords. The calculation of degree centrality is as follows.

(1) $\begin{array}{c}\mathit{Degree}\left(n_i\right)=\frac{d(n_i)}{\mathit{N}-1}\end{array}$(1)

Figure 3. Keyword co-occurrence map.

where $\mathit{d}(n_i)$ is the number of edges directly connected to node $\mathit{i}$, $\mathit{N}$ is the total number of nodes. In addition, the keyword occurred with high frequency are shown in the network map and divided into different clusters. The cluster reflects the relevance between keywords in different articles of the same research field. From Figure 3, there are four clusters categorizing the high-frequency keywords: green (13 items), blue (11 items), yellow (10 items) and red (14 items).

Keywords in the green cluster are “networks, channel coordination, selection and supply chain collaboration”. In this field, relevant studies investigated the partner selection especially supplier selection in supply chain management. Keywords in the blue cluster are “supply chain coordination, inventory management, policy, cost and demand”. The main decision-making problems in supply chain collaboration such as inventory management, information sharing, pricing, quality control, etc., are discussed in this cluster. Keywords in the yellow cluster are “integration, information, supply chain performance and trust”. In this field, the integration and efficiency are considered as the main aspects of performance assessment as well as sustainability is triggering attentions from scholars. Keywords in the red cluster are “game theory, decision-making, models and strategy”. Relevant studies have applied various theories and models to make collaborative plans and decisions.

4. Critical review

The bibliometric analysis helps develop the framework of the critical review as shown in Figure 4, in which the following five modules are identified from the four clusters in Figure 3. Specifically, the five modules include: 1) Stakeholders (Member selection and inner relationship—Yellow cluster); 2) Collaborative decision-making (How do the stakeholders interact with each other when making decisions—Red cluster); 3) Transmission mechanisms between collaboration and performance; 4) Performance assessment (What factors are concerned in performance—Blue cluster); 5) Technologies and methodologies applied—Green cluster. Based on the framework, the critical review is conducted as follows.

Figure 4. Framework of critical review.

4.1. Stakeholders in supply chain

Member selection in supply chain mainly depends on the attributes of the involved member. Reducing uncertainty in supplier selection is verified to have positive impacts on the improvement of performance in supply chain while the impacts could be affected by procedural rationality (Riedl et al., 2013). Feng et al. (2011) establish a multi-objective algorithm based on Tabu search including collaborative utility for selecting suppliers. Tirkolaee et al. (2020) propose a supply chain structure with three levels including supplier, central warehouse and wholesaler. Fuzzy Analytic Network Process is adopted when selecting suppliers to maximize the sustainable goal of supply chain. However, the number of members in supply chain could affect the performance as the growing number of involved members complicate and slow down the decision-making process in supply chain. Interactions among stakeholders make contributions in assisting decision-making in IT and collaborations to improve the performance. Vlahakis et al. (2020) propose a new proactive model based on Bayes network to support member selection and decision-making in supply chain purchasing and the effectiveness of the models is validated by the simulation. Multi-perspective of supplier selection enables the decision makers to actively participate and fully understand the decision-making process through knowledge sharing, which ensures the high quality of final decisions (Hlm et al., 2020).

The inner relationship among members in supply chain has been considered in the alliance these years. Many scholars conduct research on trust or commitment in member selection, especially for supplier selection. As trust and commitment can both ensure long-term collaboration from similar aspects, the difference between them can be ignored. Trust is assumed to directly affect organizational performance in logistic efficiency due to its long-term effects on the relationship between members (Ha et al., 2011). Thus, building a collaboration capability is regarded as a process to build trust (Fawcett et al., 2012). Trust enables collaborating organizations to focus on the long-term benefits of entering a relationship by enhancing competitiveness and reducing transaction costs (Tsanos et al., 2014). Furthermore, these concepts could be extended to reality as contractual governance, which was investigated by game-theoretic approach (Xu et al., 2018). Fawcett et al. (2012) conduct 50 structured interviews at each of two points at a time. The study shows that few of them achieved deeply cultural and structural change that could support high-level collaboration strategies, though some interviewees said that they are willing to invest for collaboration. Pibernik et al. (2011) propose a stochastic benefit-sharing rule and mathematic model in Joint Economic Lot Size model to make secure joint decision-making and profits sharing possibilities in SCC. Ghadge et al. (2017) develop a risk-sharing contract model under different power relationships and propose a perspective of dynamics of supply chain design and SCC from an automotive case study, which is inherently complicated and helpful to reduce risks.

Albeit some studies have mentioned the stakeholders of supply chain, deep understanding of them is still scarce. High-level collaboration is rare in the real world owing to the dynamic capability as listed: 1) Changes require net skills; 2) Collaboration requires structural enabler; 3) Developing a collaboration capability is challenging and resource-dependent (Ma et al., 2021). Collaborative planning and execution related to decision-making have a positive impact on the success of the collaboration, especially in the long-term future (Ramanathan & Gunasekaran, 2014). However, the collaboration would not succeed unless all the plans are executed effectively. Chen et al. (2019) suggest that the government should decide on subsidies for the company, and the manufacturer should decide on the innovation and cooperation with retailers, and the retailers should decide the wholesale and retail price of the product.

4.2. Collaborative operation of supply chain

In terms of operation, it stems from the definition mentioned before and contains information sharing in operation, the actual degree of collaboration, inventory management, investment, cooperation, coordination, pricing, quality control, etc. Collaboration can be seemed as a process of information sharing. Sahin and Robinsonjr (2005) mathematically formulated five supply chain strategies based on different levels of information sharing and coordination in a make-to-order vendor—manufacturer relationship. They find that the cost savings could be achieved in a fully integrated system, whereas they are not equally allocated across channel members. In addition, the asymmetries of negotiation focus, discussion bias, and evaluation bias are proposed to have a negative effect on group decision-making (Brodbeck et al., 2007).

The quality of information sharing makes different effects on the operation performance. The exchange between low- and high-quality information contributes to improving the operation performance. Whereas the incentive alignment and joint decision-making only improve operation performance when the information is of high quality (Wiengarten et al., 2010). The attributes of information such as availability, reliability, action-ability, and importance are combined with the reference demand model to predict sales (Ramanathan, 2012). Cannella (2014) investigates the capability of information exchange in supply chain by establishing difference equations in terms of internal process efficiency and customer service level.

Scholars find that the degrees of collaboration impacted on its effect, which should be taken into consideration for the sake of performance assessment (Cisneros-Cabrera et al., 2021). Culture and social structure could be a strong resisting force that needs to be properly recognized by managers, or else the collaboration will be affected (Fawcett et al., 2012). Inventory management pays attention to the collaboration between customers and suppliers and arouses attention from scholars these years. The existence of optimal (possibly non-unique) solution to Vendor Managed Inventory with consignment under adverse market conditions has been proved in Bieniek’s (2021), where the closed-form formulas for optimal quantities which may increase the expected profit of vendor is calculated either.

Wu (2011) discusses strategies for balancing economic and environmental priorities and decision-making under uncertainty based on a case study related to eight companies in different industries. The key sustainable supply chain management issues for each case are identified in the research. Breaching the perceived reciprocal obligations that characterize a relationship between an individual and organizational entity, a psychological contract, have a negative effect on SCC (Eckerd et al., 2013). Investment in collaboration can also contribute to the performance improvement of supply chain. From the perspective of investment in a long-term period, manufacturers could get a better pay-off from cooperative settings (Dastyar & Pannek, 2019).

Cooperation and coordination strategies are of significance for the profit improvement of each chain member in the supply chain and different models have been developed in recent research. Bai et al. (2017) develop a low-carbon cooperation practices framework based on DEMATEL and NK model with the validation of empirical data from three manufacturing organizations in China. Mukherjee and Carvalho (2021) propose a manufacturer-retailer vertical supply chain model and investigated the equilibrium decisions for pricing, greening investment and cost-sharing proportion agreement between firms under three dynamic market scenarios to achieve sustainability under cooperation. Roma and Perrone (2016) investigate the internal cooperation relationships between competitors in terms of cost-sharing mechanisms and found that even a fully collusive between two competitors can improve total welfare. The role of supply chain coordination has been highlighted in behavioural studies as well (Goudarzi et al., 2021). Cao et al. (2013) develop a coordination mechanism for a supply chain of one manufacturer and n Cournot competing retailers concerning the design of revenue sharing contract and investigated the optimal strategies to improve the efficiency of decentralized decision-making.

The pricing strategy and quality control in SCC also gain attention from scholars. The policies of non-cooperation and cooperation have been applied to examine the combined effects of pricing scheme and other factors for the deteriorating items (Chen, 2017). Hong et al. (2015) build a Stackelberg game model to investigate the optimal decisions of local advertising and pricing in the closed-loop supply chains and find that local advertising strongly influenced members’ pricing strategies. El Ouardighi proposes that the revenue sharing contract as one of coordinating schemes can help mitigate the double marginalization and improve the design quality and supply chain efficiency (El Ouardighi, 2014). As the development of sharing economy could be affected by the quality uncertainties, Wen and Siqin (2020) apply mean-variance theory to investigate the optimal average quality level that should be provided to the market.

4.3. Transmission mechanism between collaboration and performance

Though the bibliometric analysis provides the main direction for the literature review, the important topic, the causal relationship between SCC and the performance has not been well recognized. The impact and performance improvement of SCC are important to supply chain management. Hall and Potts (2003) demonstrate that cooperation between a supplier and a manufacturer may reduce the total system cost by at least 20%, or 25%, or by up to 100%, depending on the scheduling objective. The incentives and mechanisms for cooperation are also identified in the study. Bordley and Kirkwood (2004) apply numeric method in multi-objective decision and explain the results mathematically but the transmission is still unclear. Lin et al. (2014) enhance a mathematical model to measure capabilities and analyse the benefits of horizontal collaboration. The model comprehensively shows the mechanism from resources allocation, improvement of abilities of management, transmit strategies to tactics, even detailed plans for actions.

Collaboration in systems and strategies needs to work through supply chain responsiveness to enhance performance. Based on information sharing in collaboration among suppliers, forecasting demand could be more accurate and helpful in cost saving and profit earning (Ramanathan, 2013). Ojha et al. (2019) indicate that information sharing to coordinate orders in the supply chain can often reduce the negative impact of bullwhip effect. Similarly, Taleizadeh et al. (2020) establish game models based on Stackelberg game theory and fuzzy-theory to detect such a mechanism. The result shows that SCC positively enhances the efficiency of the whole chain. However, merely collaborative intent could seldom translate into collaboration capability and have positive impact (Fawcett et al., 2012).

Information technology (IT) is essential in path from collaboration to successful performance, since it can improve process of information sharing and link the inter-organizational information integration with IT-enabled collaboration decision-making (Jain et al., 2017). Appropriate communication technology can reduce the ambiguity and increase the ability to handle more information in forecasting (Ramanathan, 2013). Zhan and Tan (2018) propose an integrated infrastructure for breaking down information silos to improve performance. Huang et al. (2021) apply Stackelberg game in a dual-channel closed-loop supply chain and highlight the significance of information sharing.

Lateral collaboration can also reduce the overall cost of supply chain and the enterprises involved can improve the real-time decision-making process by adopting a suitable inventory policy (Felix & P, 2012). Specifically, the cooperation strategies are also proved to have positive effects on market creation, promotion, quality, training, joint supply purchases and research ventures towards the comprehensive performance of supply chain (Mesa & Gomez, 2015). Economic Order Quantity (EOQ) is put forward to calculate the optimal shipment considering cost of storage, transport, and order (Parsa et al., 2020). Marchi et al. (2016) illustrate that when investment opportunities exist, coordinating the investment and inventory replenishment with considering the option to share the cost of investment helps improve the performance.

4.4. Performance assessment of SCC

Cost-saving, quality improvement, efficiency, sustainability, environment protection are the most common key indicators discussed in performance assessment of SCC (Barbosa-Póvoa et al., 2018). Lotfi et al. (2021) further consider taking into account the emission of carbon dioxide, energy consumption, employment opportunities, etc., to evaluate the sustainability of supply chain. Liu et al. (2013) discuss the impact of punishment intensity, information sharing, and other factors by developing a multi-period quality coordination model. As Quality Function Distribution (QFD) can provide a quality design framework for SCC, Wang et al. (2019) develop a fuzzy QFD that complied with grey decision-making. Bhattacharya et al. (2014) propose a Green Supply Chain performance measurement framework using the Collaborative Decision Making (CDM) approach with the application of fuzzy-Analytic Network Process (ANP) based Green Balanced Score card to fit in the requirements of managers to evaluate the suppliers’ performances on environment standards.

Specific criteria are proposed for different industries owing to the different characteristics. For example, Parsa et al. (2020) investigate a closed-loop supply chains system of recycled content products with the participants of manufacturers, retailers, suppliers, material recovery facilities and recycling facilities and suggest that the profit can be maximized by cooperating through optimizing the shipments among them. The optimisation problem can be formulated as the following inter non-linear programming mode with constraints:

(2) $\begin{array}{c}\mathit{Max}\mathit{TP}=\mathit{T}{P}_m+\mathit{T}{P}_r+\mathit{T}{P}_s+\mathit{T}{P}_M+\mathit{T}{P}_R,\end{array}$(2)

(3) $\begin{array}{c}\mathit{subject}\mathit{to}\mathit{K}\le k_0,\mathit{l}\le l_0,\mathit{m}\le m_0,\mathit{and}\mathit{n}\le n_0,\end{array}$(3)

(4) $\begin{array}{c}\mathit{k},\mathit{l},\mathit{m},\mathit{n}\in Z^{+},\end{array}$(4)

(5) $\begin{array}{c}{T}_r\in R^{+}.\end{array}$(5)

where $\mathit{T}{P}_m$ is manufacturer’s profit, $\mathit{T}{P}_r$is retailer’s profit, $\mathit{T}{P}_s$ is supplier’s profit, $\mathit{T}{P}_m$ is material recovery facilities’ profit and $\mathit{T}{P}_R$ is recycling facilities’ profit. The parameter $\mathit{k},\mathit{l},\mathit{m},\mathit{n}$ represent the shipment among different components of supply chain. $T_r$ is retailer’s cycle time and can be any positive real number in the field of real numbers.

Psychological analysis could explain the relationship between information sharing and performance. Cantor and Macdonald (2009) apply construal level theory and find that the abstract problem-solving approach is better than the concrete problem-solving approach under the circumstance of limited information availability, and the types of problem-solving approaches make no difference when the information is mostly available.

Green supply chain collaboration has been put forward as a proactive approach to achieve sustainable development and enhance environmental performance in SCC (Lin, 2013). Bouchery et al. (2017) imply multi-object programming to reduce both cost and carbon dioxide emission. Zheng et al. (2018) propose the duopoly manufacturers’ decision-making under a cap-and-trade (CAT) system considering green technology investment and provide suggestions for the carbon quota of companies and emission settings of government. Similarly, Chen et al. (2019) develop water-saving supply chain equilibrium and coordination models under the scenario with/without government’s water CAT regulation and identify the positive effect on water-saving of the CAT.

Additionally, the criteria for assessment can be referred by some industries, and they need to be more specialized and the characteristics should be reflected. For agri-food supply chain, agent-based simulation is widely applied but the research on collaboration and competition, buyer-seller relationship and service is scarce (Utomo et al., 2018). Moreover, depending on the project-delivery method is not sufficient for SCC (Koolwijk et al., 2018) and there exist some deficiencies on procurement supply chain in the era of Pharma Industry 4.0 (Ding, 2018).

Social responsibility is considered as an important factor as well. Cruz (2008) develops a dynamic framework to analyse SCC with the goal of maximizing profit and minimizing emission and risk. Besiou and Van Wassenhove (2015) identify common characteristics of socially responsible operations, including complexity, unfamiliar context, counter-intuitive behaviour. From a wider perspective, Gray et al. (2020) emphasize the total value contribution to more benefits. However, as comprehensive data are difficult to assess (Wiengarten et al., 2010), previous studies mainly apply case studies in quantity or quality (Shahriari et al., 2015).

The resilience has been introduced as a significant part of performance to reflect the ability of the supply chain to start functioning after suffering from disruption (Bhattacharya et al., 2013). The adaptive capabilities, such as adaptive capability, collaboration among players, trust among players, sustainability, risk and revenue sharing, are identified as the key enablers to achieve resilience in supply chain (Jain et al., 2017; Kristianto et al., 2014). Jain et al. (2017) develop a hierarchy-based model for supply chain resilience with 13 key enablers, which may help organizations to improve the resilience potential. Lotfi et al. (2021) suggest that the application of renewable resources in supply chain management can improve the resilience of supply chain. Sawik (2020) suggests that the resilience of supply chain could be achieved by selection of multi-tier supply portfolio and pre-positioning of risk mitigation inventory beforehand based on a two-period decision-making model.

4.5. Technologies and methodologies applied in SCC

SSCM is defined as the management of material, information flows and cooperation, including all subjects in the supply chain while integrating economic, environmental and social dimension as “triple-bottom-line” selection factors (Darbari et al., 2019). Darbari et al. (2019) employ triple-bottom-line accounting in the context of increasing stakeholders’ involvement and global supply chain sustainability. Given ranking related to three dimensions, this approach could be used to assess how to choose, improve, discontinue and continue a supplier from a quantitative perspective. The quantitative analysis could reduce the negative environmental and social impacts and effectively avoid vague and imprecise judgments. The rapid depletion of natural resources and wealth disparity draws wide attention and more scholars focus on SSCM studies over the past decade (Govindan et al., 2013). Moreover, game theory is also a widely applied tool in SCC studies (Dastyar & Pannek, 2019; Huang et al., 2021; Xu et al., 2018). Martinez de Albeniz and Simchi Levi (2012) propose a model for a repeated supplier-buyer interaction to achieve subgame-perfect equilibrium and apply fuzzy set theory to design solution for quality performance. Lei et al. (2018) illustrate the benefit from joint pricing and fulfillment optimization of e-commerce retailers mathematically.

Various methods to assess the sustainability of supply chain have been developed. Qorri et al. (2018) provide a review for performance assessment in sustainability project management across the entire supply chain with sustainable indicators and stakeholders and suggest that the project management approaches based on Multi-Criteria Decision-Making (MCDM) methods and fuzzy set theory is thriving. Jiang et al. (2019) develop a hesitant triangular fuzzy geometric Bonferroni Mean operator to conduct MCDM study for performance assessment of SCC. Saaty (2013) compares the application of AHP and ANP in the multicriteria decision making in supply chain. Whereas these comparative approaches require the number of alternatives should be finite so that the grader are able to make the judgement. To deal with the limitation, Lima-Junior and Carpinetti (2016) indicate that Fuzzy QFD is available when the number of the alternatives of simultaneous evaluation is infinite as it does not require the parameterization of decision rules. However, the method ignores the interdependence of criteria and it lacks practical test and operational feasibility, which needs accurate and validated data.

Traditional goal programming and multi-objective optimization can reduce costs and develop single or multistage lot-sizing models for supply chain planning (Benjaafar et al., 2013). The approach is helpful to optimize investment decision on green technologies (Barman et al., 2022). However, some research merely measures sustainability performance between the supplier and manufacturer or focus on the single relationship merely (Qorri et al., 2018). More detailed review focusing on mathematical programming with coordination mechanism for decentralized decision-making can be seen in Rius-Sorolla et al. (2018), which suggests that the majority of models only consider tactical and operational levels and assume trust between companies.

Feng et al. (2010) develop a decision-making model for locating manufacturing centers in China and integrated factor analysis, interpretive structural modelling, Markov chain, fuzzy integral and the simple additive weighted method. Related studies are categorized by the applied methods, key purposes and interaction nodes in SCC as shown in Table 4.

Table 4. Technologies and methodologies applied in SCC studies

Method	Relevant nodes	Performance Dimension	Key concept	Representative
Analytic Network Process (ANP)	Stakeholders (supplier, customer, staff, and community)	Competitiveness and sustainability	Define importance and priority of key factors in performance management	Yadav and Singh (2020)
Step-Wise Weight Assessment Ratio Analysis (SWARA)	Supplier and customer	Customer’s engagement	Capture experts’ knowledge and judgement rating to evaluate the relative weight of criteria	Kaur et al. (2021)
Fuzzy Set Theory & Fuzzy Quality Function Deployment & Fuzzy Set Theory + Game Theory	Supplier and customer	Competitiveness (quality, on-time delivery, price, etc.)	Support the decision-making processes based on imprecise and uncertain information to make performance assessment and quality-oriented performance assessment	Ojha et al. (2019); Wang et al. (2019); Z. Jiang et al. (2019); Huang et al. (2021); Taleizadeh et al. (2020); Kuiti et al. (2019)
Dynamic Programming	Manufacturer and customer	Profit and competitiveness	Under the condition of uncertain demands, the model is constructed to satisfy the constraints and minimize the costs	Mandal et al. (2020); Cheng et al. (2022)
Vendor Managed Inventory (VMI)	Vendor, retailer and customer	Inventory, transportation, manufacturing, benefit and coordination/collaboration	Vendor managed inventory performance	Bieniek’s (2021); Y. Jiang et al. (2015)
Multiple Criteria Decision-making (MCDM)	Manufacturer and supplier	Efficiency and sustainability	Apply different criteria (regarded as performance) to choose a better solution	Tsai et al. (2021); Yang and Wang (2020); Çolak et al. (2020)
Simulation	Customer, supplier, manufacturer and retailer	Resilience, profit and costs	Measure resilience level of system by numerical simulation	Moosavi and Hosseini (2021); Kek et al. (2022)
	Customer, supplier, manufacturer and retailer	supply chain profit, efficiency and wasting of resources	Numerical simulation was carried out by the Back Propagation Neural Network (BPNN)	Wang (2022); Guan et al. (2022)
Compound Method Based on Financial Accounts (MC3)	Manufacturer, corporation and customer	Sustainability (in carbon footprint)	Quantify the effect of collaboration in organization-level actions on carbon emissions in green supply chain	Novaes das Virgens et al. (2020)
Economic Order Quantity (EOQ)	Manufacturer, government and customer	Profit and carbon emissions	Take inventory and costs into consideration to achieve the optimal level	Barman et al. (2022); Bouchery et al. (2017)
Joint Economic Lot Size	Farmer, processor, manufacturer, retailer, supplier, material recovery facility and recycling facility	Costs and profit	Apply collaborative decision in traditional EOQ with enhancement and EOQ is applied in certain industries by case	Sebatjane and Adetunji (2020); Parsa et al. (2020)
Triple Bottom Line	Manufacturer, supplier, third party logistics provider, retailer, customer, NGO and environment	Profit, carbon emissions and social impact	Laptop manufacturer designs a Closed-loop Supply Chain (CLSC) network to minimize environmental impact and maximize net profit and social impact	Darbari et al. (2019)

5. Discussions

5.1. Current findings and deficiencies

The manufacturer, supplier and customer are the main investigated subjects in previous supply chain studies while the government and environment are considered in green supply chain studies. Previous studies lack consideration of other different stakeholders and subjects in supply chain, which draws limitation of the strategies provided for supply chain management.

When it comes to methodologies and technologies, structural equation model is one of the most common models adopted to test identified factors in their effect or performance in SCC. It strongly supports the mechanism but it seldom considers the measured elements, such as emotion of members, environmental results in the future and other disturbance factors. The framework also requires large-scale survey and the specific effect is hardly to be detected. Another method for mechanism study is game theory. Some compile fuzzy set theory for quality decision-making as performance and most of them adopt mathematical deduction to investigate the transmission process. Though it can provide a long-term equilibrium for the real world with profound meanings, the assumptions of the game model are too strict to achieve the equilibrium in practice.

For performance assessment method, generally, MCDM is classified into Multi-Attribute Decision-Making and Multi-Objective Decision-Making. MCDM forms an important part of the decision analysis theory as it aims at yielding decisions to achieve optimal performance. As supply chain works differently in different industries and areas, establishing a universal model and data collection approach may be impractical. It is of importance to identify the characteristics of each kind of industries and develop applicable models and data collection approaches. The data information should be translated to suit for models, which makes a strict requirement to supply chain managers to process a good knowledge of model application and data collection. The assessment criteria should be more specialized for various industries.

The impact of collaboration on the performance improvement of supply chain is still vague due to the data limitation. Whether the high-level collaboration could positively improve the supply chain performance as moderate-level collaboration does is unknown. In practice, the collaboration between supply chain members is deficient to some extent. Some supply chain managers do not comprehensively consider other involved members or collaborate with them.

5.2. Future research agenda and implications

The deficiencies of current research concluded above could provide implications for supply chain and SCC theory development. It can be found that the deeper nature behind collaboration is resource integration. Thus, the following questions should be answered first to further explore SCC. Are the resources shared or collaborated available? Are they valuable? What is the promotion mechanism behind SCC? Whether resources can be integrated should be discussed for empirical collaboration. Still, maybe the assumptions such as resources and costs for collaboration can be more dynamic and mutable, which is more realistic (Seok & Nof, 2013).

As for performance assessment, a new open and more dynamic system could be investigated. Currently, dynamic collaboration assessment is limited, although some scholars have proposed suppliers’ collaboration in multi-period to mimic this process, how the sub-integrator and ultimate suppliers make quality decision has not been discussed. More specific quantitative analysis should be applied to assessing the factors behind SCC, since they do not improve performance equally as many studies demonstrated. The causal relationship can be investigated in this way further. Moreover, environmental protection and sustainable development should be taken into consideration from a long-term perspective.

New technologies such as blockchain, cloud computing and Internet of Things could be exploratively applied in SCC. Blockchain technologies streamline information sharing processes, which would impact collaboration between chain members and improve trust in modern supply chains. The application of cloud computing and Internet of Things to supply chain management would be helpful in the information exchange mechanism, which enhances the overall operational efficiency of supply chain and realizes supply chain business collaboration. In addition, more comprehensive research in diversified fields including upstream and downstream, interfirm, could be expanded.

Further implications for readers can be offered as follows. First, the specific environment and economic institution should be taken into account when researchers adopt the foreign theories into their research. The assumptions of the theory should be clarified based on the context of the study area so that the suggestions could be practical. For example, the Chinese culture and social network could affect the mechanism and performance of SCC. Thus, the related model of Chinese supply chain and strategies for Chinese supply chain managers are supposed to be different and satisfy the reality. Second, the technologies and methodologies from other disciplines could be adopted to delve into the causal relationship between SCC and performance improvement of supply chain. To better identify and measure the effect of the important factors, the empirical research and the data collection are inevitable and various approaches to collect and process data need to be applied in the future studies.

6. Conclusions

Collaborative decision-making by multiple stakeholders at different stages in supply chain has become an important research issue in recent years. The complexity of SCC increases with the growing number of stakeholders and involved members of supply chain. Although existing literature has attempted to analyse the performance of SCC by using various approaches and tools under different scenarios, the research on the mechanism of SCC and performance could be further improved. With the application of bibliometric analysis, this paper develops an analytical framework with five components and systematically reviews the existing studies on SCC in the past decade. The main conclusions can be drawn as follows. First, previous studies lack considerations of other different stakeholders and subjects in supply chain, which leads to limitations of the strategies provided for supply chain management. Second, various methodologies and technologies are applied in effect identification and SCC performance assessment. For example, structural equation model, game theory, compile fuzzy set theory and mathematical deduction are identified as the most common methodologies in examining the effect or performance of factors. Although different MCDM models are developed to assess the supply chain performance under specific goals, the data collection approaches of these methodologies still need replenishment. Third, while the importance of collaboration in supply chain has been clarified in existing studies, the impact of collaboration on performance and the transmission between them is still vague.

About the author

Dr. Hao Wang is a Professor, Dr. Junlin Chen is an Associate Professor, Ms. Yufan Guo is a postgraduate student and Ms. Ailan Wang is an undergraduate student from Central University of Finance and Economics. Ms. Ziyang Long is a PhD student from Renmin University of China. Dr. Hao Wang serves as a special corresponding expert of Frontiers of Engineering Management hosted by Chinese Academy of Engineering, a member of editorial board of Buildings, and a guest editor of Land Use Policy, etc. His research interests include collaborative decision-making, stakeholder analysis, and supply chain management. Dr. Junlin Chen has published in journals such as Decision Sciences, European Journal of Operational Research, Transportation Research Part E, etc. Her research interests include behavioral operations, inventory control, and supply chain risk management. They worked extensively on the collaborative decision-making in supply chain management.

Acknowledgments

This work was supported by the National Natural Science Foundation of China under Grant 72074240, 72071221; the Humanities and Social Science Fund of the Ministry of Education of China under Grant 19YJCZH154; the Beijing Social Science Foundation under Grant 17GLB030; and Program for Innovation Research in Central University of Finance and Economics.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

The work was supported by the National Natural Science Foundation of China [72074240]; National Natural Science Foundation of China [72071221]; Humanities and Social Science Fund of the Ministry of Education of China [19YJCZH154]; Beijing Municipal Social Science Foundation [17GLB030]; Program for Innovation Research in Central University of Finance and Economics.

Notes on contributors

Hao Wang

Hao Wang is a professor in the School of Management Science and Engineering at Central University of Finance and Economics. His research interests include collaborative decision-making, supply chain management, and stakeholder analysis. Ziyang Long is a PhD student in the School of Public Administration and Policy at Renmin University of China. Her research directions are stakeholder analysis and collaborative decision-making. Junlin Chen is an associate professor in the School of Management Science and Engineering at Central University of Finance and Economics. Her research interests include supply chain management, management science, behavioral operation management, and cooperative game. Yufan Guo is a postgraduate student in the School of Management Science and Engineering at Central University of Finance and Economics. Her research directions are supply chain management and management science. Ailan Wang is a college student in the School of Management Science and Engineering, Central University of Finance and Economics. Her research directions are supply chain management and management science.