Due to global competition modern organizations are paying particular attention to Global Sourcing. This practice has been embraced due to the cost savings it generates, the access to technologies and higher quality products in some cases. Organizations can choose suppliers from anywhere in the world, developing countries are becoming more competitive given their low labor and operating costs. Global supplier selection is riskier than the domestic supplier selection, consequently the decision making process is strongly affected by perceived risks. Suppliers with low price products can be offset by a history of delayed deliveries, or suppliers with state of the art technology can be undermined by excessive tariffs and costs. Risk can originate from economics and political uncertainty in the supplier’s country. Natural disasters are also within the risk factor, any natural disaster can have catastrophic consequences in today’s interconnected global supply network. All of these types of disruptions can damage profitability, stock price, and market reputation for the organization with significant long lasting consequences. In [1] Sawik affirms that taking risk into consideration will allow the buyer to decide whether it should cooperate with a low cost yet risky supplier, over a more expensive but possibly more reliable supplier. There is a crucial need to identify these risk factors and take them into consideration when selecting a supplier. However the supplier selection team struggles to obtain precise, complete and up to date information [2]. Data resources are usually only available at a low frequency of monthly or quarterly levels [3]. The data is sparse through reports, external databases, ERP and MIS systems which are limited and not able to provide sufficient information regarding the risks and uncertainty from the suppliers. The supplier selection process demands more transparency and up to date information. Given the new Big Data era we are in, gathering data is not a problem anymore and we can utilize global insight and knowledge to assess the risk and uncertainty for each supplier. In regards of supply chain management some researchers have already used the data in social media to revolutionize their organizations. For instance, social media has played an important role in demand prediction for supply chain management [4,5], according to [6] social media offers insight on preferences and consumer behavior. The information in social media is updated rapidly and spreads virally at an exceptional speed; this provides us with first-hand information. We now have the opportunity to analyze this vast portfolio of information to assist the supplier selection process. In this research, we focus on social media rather than conventional online data due to its ability to be generated and diffused in a quicker manner; we mainly focus on the microblogging tool Twitter. This is because up to date and relevant information is required due to the nature of the risk and uncertainty criteria.

Introduction

Selection of supplier is very critical aspect in SCM where firms expend minimum 60 percent of its total sales on purchasing items like parts, components and raw materials [1]. Further, manufacturers procure services and goods using upto 70 percent of product cost [2]. Supplier selection is a region of enormous significance and must be considered as a tactical aspect in effectual SCM. During 1990s, manufacturers attempted to develop strategic partnerships to improve their management’s preference and competitiveness [3]. Supplier selection and evaluation are complex tasks for decision makers as they required to consider various criteria. Dickson [4] recognized 23 criteria for the selection of supplier based on which Weber et al. [5] measured supplier performance considering the criteria of price, delivery, quality, location, technical capability, productive capability, industry position, reputation, financial stability, maintainability and history. Evans [6] studied various key criteria for the selection of supplier such as price, quality and delivery. Recently, the marketing management literature has been paid considerable attention to the supplier selection process. Few important criteria are: suppliers’ profitability, technological capabilities, relationship closeness, conflict resolution and performance quality. Lin and Chang [7] emphasized that reputation, customer responsiveness, communication, relationship closeness and industry position are necessary in vendor selection. Due to inexact, imprecise, vague and uncertain nature of data, modeling of many situations may be insufficient or inexact [8]. The one of the most important business function is strategic sourcing (SS). Under the expanded heading of logistics, now SS is an essential segment of the firm program to cover the purchasing schemes. Companies are interested to find out how they can provide facilities to the customers rapidly with reasonable pricing compared to their competitors. So, managers realized that they should work in a mutual system with the best corporation in their logistics networks containing warehouses, suppliers, customers undoubtedly,production units and the distribution centers. The long-term viability of the company is determined on the selection of supplier decision [9]. Liao and Kao [10] analyzed issues related to selection of supplier in SCM. Both qualitative and quantitative criteria make multi criteria for selection of supplier problem more complex [11]. Methods are developed for decision makers in a suitable way to deal with the problem related to the selection of supplier more effectively [12]. The applications of various type fuzzy models are explored in the context of decision making problems [13]. Liao and Kao [10] presented MCGP and fuzzy TOPSIS approaches simultaneously for selection of supplier problems using trapezoidal fuzzy number. Asamoah et al. [14] reported an AHP approach in a pharmaceutical manufacturing unit in Ghana for evaluation and selection of suppliers. Kumar and Roy [15] studied factors which are qualitatively important to obtain suitable suppliers. Wang et al. [16] presented preemptive goal programming (PGP) and analytic hierarchy process (AHP) jointly for selection of supplier.

Bioenergy is considered a potential solution to reduce carbon footprint and fight against global warming. However, uncertainty in the harvest of biomass could lead to the instability of feedstock supply that has a significant impact on the sustainability of biomass supply chain. In this paper, we present a two-stage stochastic programming model dealing with supplier selection to stabilize feedstock supply of a biomass supply chain in uncertain environments. The model involves the first stage decisions for the supplier selection and the second-stage decisions for planning transportation, inventory and production operations. To reduce the computational burden for large instances, we propose an enhanced and regularized L-shaped decomposition algorithm to solve the model. The applicability of this model and the performance of the solution method are evaluated by numerical studies. Sensitivity analysis shows that the values of some parameters related to suppliers have significant impacts on the optimal expected cost and supplier selection.

Introduction

Nowadays, humanity is facing enormous challenges of global warming and the exhaustion of fossil resources according to Shafiee and Topal (2009). The use of fossil fuel is one of the major causes of global warming that affects severely human life and increases natural disaster in recent decades. Therefore, renewable energy will play a significant role in the energy transition and appears to be a very promising solution to sustainable development in the future. The various renewable resources require different technologies to capture energy such as solar, hydro-power, wind, geothermal, marine and biological resources. They are clean energy sources and do not contribute to global warming, greenhouse effect and air pollution. However, the exploitation of renewable energy sources requires much time, high costs of investment and incentive policies from the government. Bioenergy is generated from sources such as wood, agricultural products, animal and plant wastes. In contrast to wind and solar power, biomass can be stored in existing infrastructures in response to various demand according to Kaut et al. (2015). Nowadays, bioenergy industry has to deal with challenges related to low energy density, high harvest, and transportation costs of biomass to compete with fossil energy. The research in Eks¸ioglu et al. (2009) shows that an ˘ efficient supply chain design and management could reduce the biofuel price significantly.

This note discusses the possibility of fair gain sharing in cooperative situations where players optimally partition themselves across a number of alternative channels. An example is group purchasing among a set of buyers facing with a range of suppliers. We introduce channel selection games as a new class of cooperative games and give a representation of their cores. With two channels (suppliers), the game has a non-empty core if the gain functions across every individual channel is supermodular. © 2018 Elsevier B.V. All rights reserved.

Introduction

In some collaborative situations, the participants (players) organize themselves across a set of alternative channels to maximize their gain. The alternative channels may represent different suppliers, logistics service providers, or adopted technologies. As a special instance, consider buyers in a group purchasing organization who collaboratively select their suppliers for different products that they procure. An example of such a situation was recently studied in [8] where buyers optimize their purchases across two alternative channels: an intermediary and an original equipment manufacturer. The supplier selection problem has been the subject of extensive study in the literature (see for example [5,16,9] and references therein). The advantages of collaboration in this context can be intuitive—combining bargaining powers or deliveries results in savings. Yet, the mere existence of economies of scale does not necessarily grant the formation and sustenance of a collaborative organization. From a cooperative game theory point of view, the possibility of sharing the obtained savings among the players in a ‘‘fair’’ manner is a crucially important requirement. A widely adopted notion of fairness in the literature requires that each subgroup of players receives at least as much as they could accrue on their own. Accordingly, an allocation of gains among the players is called stable if it satisfies the latter condition. The core of a cooperative game [12] contains all stable allocations which divide total savings among the players. It is worth mentioning that although the literature often associates the definition of the core to Gillies [6], it was Shapley [12] who first defined the core in its current form [17]. In this note, we introduce channel selection games where coalitions of players optimally partition themselves across a set of given channels. The underlying optimization problems in these games are the same as the winner-determination problem in combinatorial auctions (see for example [10]) where a set of products are distributed among a set of bidders with different valuation functions to maximize the sum of all bidders’ valuations. However, to the best of our knowledge, previous literature does not study cooperative games with the same structure as ours. Unlike partition games (e.g. [3]), players in channel selection games choose among a fixed number of distinct options with non-homogeneous costs, and dissimilar to assignment games (e.g. [15]), here multiple players can be assigned to a single channel. We give a representation of the cores of channel selection games in terms of intersections of extended contra-polymatroids associated with gain functions across all channels (e.g. savings obtained by joint purchasing from each supplier), and provide two main observations regarding the existence of allocations in the core of channel selection games. First, if the number of channels is two and the gain function across every channel is supermodular, then the core of the associated game is always non-empty.We prove this result using the generalization of Edmond’s matroid intersection theorem [4]. This closes an open problem in [8] regarding the nonemptiness of the core of games associated with collaborative replenishment situations in the presence of intermediaries. Second, if the number of channels is more than two, then the core of a channel selection game may be empty, even under supermodularity conditions.

The dynamism of the business environment is putting firms under pressure to improve quality, delivery performance, and responsiveness while simultaneously reducing costs. In response, firms are increasingly exploring ways to leverage their supply chains, and in particular to systematically evaluate the role of suppliers in their activities (Kannan and Tan 2006). It is important for managers to realize the long-term impact of their sourcing strategies on the profits and the efficient functioning of the organization. At the operational level, buyers have the ability to benefit from developing close relationships with key suppliers in the form of improved quality, delivery, reduced cost, or some combination thereof. At a strategic level, a close relationship with key suppliers is expected to lead to sustainable improvements in product quality and innovation, enhanced competitiveness, and increased market share. It is however expected that all these relationships should, in turn, be reflected by improvements in financial performance (Kannan and Tan 2006). It is important to indicate that currently, the purchasing function is considered as a crucial activity that affects the performance of any firm (Kar and Pani, 2014). Currently, the choice of suppliers has become a strategic problem for firms (Li and Zabinsky, 2011). This means selecting the right suppliers do not only bring substantial benefits to companies but also increases customer satisfaction (Lin et al., 2011). The profitability of a business nowadays critically depends on their supplier’s ability to reduce costs, improve quality, and develop new processes, products, and services faster than their rivals’ vendors can. This dependence has made the supplier selection process one of the most important sections of any business (Liker et al, 2004). Jazemi et al, (2011), indicates that one of the most important processes in optimizing or enhancing the supply chain performance of the firm is by paying much attention to the supplier selection process. The supplier’s selection decisions are primarily based on material prices, the proposed delivery times, the location of the supplier and opinion on the market, taking into account the supply costs, the quality of materials and the condition of payment (Chai et al, 2013). Fu-jiang et al, 2006, demonstrated empirically that, there exist a positive significant relationship between customer satisfaction, business performance, and supplier selection. The industrial purchasing function especially plays a considerable role in ensuring the long-term viability of a firm with its impact on business competitiveness (Cheraghi et al., 2004; Tchokogue et al., 2016) Choosing the right suppliers, therefore, involves much more than scanning a series of price lists, choices, therefore, will depend on a wide range of issues (Ho et al., 2010). Such factors include the price offered by the supplier, lead time, quality of items, the capacity of the supplier to respond flexibly to the company’s requests and the supplier’s geographical location (Ekici, 2013). They also include warranties, production capability, technical capability, management capability, vendor reputation, financial position, labor relations and post-sales services (Kar and Pani, 2014a). According to Monczka et al. (2010), the objective of supplier selection is to choose the best supplier for a particular item, one which is reliable, with fair terms, low risks and has maximum value for the client.

The concept of sustainable supply chain management (SSCM) is to integrate economic and social thinking along with environmental awareness into the traditional supply chain management. The sustainability in supply chain comes into existence right from the product design and development to the material selection, manufacturing, packaging, transportation, warehousing, distribution, consumption, return, and disposal (Linton et al. 2007; Walker et al., 2008; Chu et al., 2009; Büyüközkan and Çifçi, 2011). Supplier selection is an important assignment in industrial context to attain the preferred level of quality and quantity at the reasonable cost of desired raw/finished material with on-time delivery. Suppliers are the dealers who provide raw materials, components and after sales service that an organization cannot self-give (Kuo et al., 2011). Earlier, in traditional supply chain management, quality, cost, and delivery were considered as common and frequently used criteria for potential supplier selection; whereas, in sustainable supply chain management, economic, social, and environmental sustainability criteria need to be considered simultaneously because they allow an organization to achieve long term economic viability and position sustainability within the broader rubric of supply chain management in sustainability perspective (Carter and Rogers, 2008). In traditional supply chain construct, social sustainability factors have not been considered yet (i.e. before 2009); however, many authors have included environmental aspects on the traditional supply chain to make it ‘green’ and to reduce the anti-environmental effects up to a certain limit (Handfield et al., 2002; Humphreys et al., 2003; Lee et al., 2009). Afterwards, the existence of green supply chain and green supplier selection have still been a need of an obligation to incorporate social sustainability factors into the entire process of green supply chain management. In order to achieve a sustainable supply chain, all of the chain members from suppliers to top managers must have affinity with sustainability (Amindoust et al., 2012). Sustainable development and sustainability are frequently interpreted as a synthesis of economic, environmental and social development well known as a triple-bottom-line approach (Gauthier, 2005). Sustainability has been a major concern for organizations as awareness about environmental degradation, natural resource depletion and climate change has increased considerably. In addition, voices raised by social organizations on various social and environmental issues in developing countries have forced organizations to focus on sustainable manufacturing practices (Mani, 2014). Hence, the study of sustainable supply chain management has gained immense momentum during past two decades. Although the studies focus on three pillars of sustainability viz. economic (profit), environment (planet) and social (people); the social aspect has not been explored much due to the “humanness” and the difficulty in getting tangible outcomes from it (Elkington, 1998; Carter and Easton, 2011; Ashby et al.2012; Mani et al. 2014).

Supplier selection and supply chain restructuring are two related phenomenon (Schniederjans et al., 2015) that strongly influence the automotive industry (Choi and Hong, 2002). In the automotive sector, with its traditionally flat hierarchy, price was the main selection criterion (Fujimoto, 2001) but, gradually, other supplier selection criteria such as quality and technical capabilities (Birchall et al., 2001; Calabrese, 2001) have been considered more consistently (Choi and Hartley, 1996). Consequently, a real hierarchy among direct and indirect suppliers has been defined (Balcet and Consoni, 2007; Castelli et al., 2011) with a growing involvement of specialised suppliers to satisfy new requirements (Caputo and Zirpoli, 2001; Sturgeon and Van Biesebroeck, 2011) and with many minor firms relegated to the role of sub-suppliers (Rachid, 2001; Volpato and Stocchetti, 2007). Two main consequences arise in the car components market: the reduction in the number of suppliers to the point of scarcity (Schiele et al., 2012), and the increasing power the so-called mega-suppliers (Chanaron, 2013; Frigant, 2013), as compared to carmakers. These firms are able to combine technical skills and logistical capabilities, as well as financial strength, good reputation and managerial competences, which are essential aspects for maintaining reliable and longterm relationship with carmakers (Cheraghi et al., 2004; Karlsson and Weimarck, 2001). There is plenty of normative literature on how buyers should select their suppliers, with a general consensus on a number of factors influencing the individual choice, ranging from price, perceived quality, delivery to other more industry specific criteria, as documented by Lin and Purchase (2006). Reputational issues receive little attention in this body of work, as already highlighted by de Boer et al. (2001). Even in the presence of relevant phenomenon like competition, globalisation and the Internet boom, most recent – and not so recent – supplier rankings (Cheraghi et al., 2004; Karsak and Dursun, 2016) continue to be mainly based on general performance evaluation. In fact, there is little literature on how buyers actually select their suppliers in practice. This paper is exactly focused on this point and analyses a database of real sourcing decisions in the automotive sector. We contribute to the literature by providing an extensive quantitative analysis of the supplier selection criteria based on contracts for components for all car models assembled in European plants during the period 2002–2014. Moreover, we investigate the effect of reputation in the purchasing decision by including different indicators of corporate reputation, defined as the “perceptual representation of firm’s overall appeal to all of its key stakeholders when compared with other leading rivals” (Fombrun, 1996), within our econometric estimates. We find two intriguing and promising results. First, suppliers that serve a diversified customer portfolio have a better chance to gain additional orders and further broaden their customer base, compared to suppliers that serve particular customers. Second, suppliers with strong exposure to premium brand customers gain more orders also from nonpremium customers in the subsequent period. For the automotive supply chain, these results connect to the customer attractiveness literature, and to social exchange theory. The former argues that buying firms may want to become a preferred customer of their suppliers; hence also buyers have to build their attractiveness and their reputation in the supply market.

In the competitive business environment, companies seek to create competitive advantages by utilizing data management, knowledge management. Supply chain management has an important role in handling this issue. Moreover, in the field of the supply chain (SC), supplier selection is a strategic decision. Supplier selection is a process of taking the best suppliers with right price and quality at the right time and quantity (Ayhan and Kilic 2015). Researchers estimated that more than 60 percent of production costs relates to purchasing raw material from the suppliers (Krajewsld and Ritzman 1996). Supplier selection has great influence on the strategic and operational performance of an organization. Furthermore, good suppliers can reduce the production and inventory costs, improve the quality, flexibility and consequently satisfy customer expectations (Çebi and Otay 2016). Many aspects such as considering qualitative and quantitate criteria for various factors such as globalization of trade, government regulation, and changing customer preferences makes the supplier selection as a complex decision (De Boer et al., 2001). Moreover, sustainability was introduced as a widespread concept to integrate environmental, social and economic issues. Sustainability is defined as “satisfy the needs of the current generation without limiting next generation” (Özdemir et al., 2011). Since the first step in production is purchasing raw materials from suppliers, therefore ranking and selecting suppliers based on the sustainability indices is one of the important and strategic decisions on the way of sustainable supply chain. The sustainable concept in the supplier selection problem was introduced in 2010 by Bai and Sarkis. To rank suppliers, researchers considered the sustainability of supplier as a positive score and proposed some multi-criteria decision making (MCDM) methods. However, risk issue is not considered by most cases in a sustainable supplier selection problem. Suppliers with acceptable performance in sustainability factors may face various risks. For instance, consider a supplier with a fair price in most of the time, however, because of an unstable situation in supply chain, the costs raise 30 percent from the nominal price. In the previous works the overall performance of a supplier is considered and risks such as increasing the selling price is not incorporated in the model. Therefore, considering the risks along with other factors is essential to obtain a widespread view about a supplier. Moreover, FMEA is one of the well-known technique for risk analysis. FMEA usually used as a risk analysis tool to design a process with better product reliability (Anleitner, 2010 and Carlson, 2012). In the competitive environment, new sales policies encourage the consumer to buy more. A common type of sales policy is quantity discount. To have more realistic model these discounts are considered in the proposed mathematical models in the literature. Mainly, there are three types of price offers from the supplier: all unit discounts, incremental discounts and fixed price. For all units discount, the constant price for an item applies to all units ordered. Fig 1 shows the calculation of total purchasing costs according to all unit discount in which ci is the price of the product and Xi is the amount of purchased items.

Cloud technologies, which enable new methods for provision of services and products by innovative and technological possibilities, are widely used all around us. Cloud service is a collection of computing; it usually refers to a dynamic and scalable virtualized resource. Te quality of cloud service supplier is an important factor afecting the development of cloud computing. Due to increasing userspecifc requirements for cloud services and diferentiated quality of cloud services from numerous cloud service suppliers, selection of an appropriate cloud service supplier, based on technology, becomes a signifcant challenge for decision-makers. Cloud service supplier uses diferent techniques such as ontologies, languages, standards, and tree structure and there is not a unifed service description that describes all cloud service types, IaaS, PaaS, and SaaS. Apart from that, dynamicity of cloud computing and constant adding of new services and applications additionally make selection of the best cloud service supplier harder under the background of cloud computing emergence. Another problem in evaluation and service supplier selection is selection of a fexible and reliable model that enables fast implementation of new information. Various mathematical techniques have been used by the researchers for selection and evaluation of suppliers. Tere are many evaluation methods in domain of cloud security engineering and cloud services security [1, 2], the quality of service [3–5], cloud storage [6], cloud manufacturing [7], and so on. Multicriteria decision-making (MCDM) allows decision-makers to face complex decision-making situations involving multiple, usually conficting decision criteria, which include quantitative and/or qualitative aspects in a decision-making process. AHP method takes a signifcant place during selection of MCDM techniques for supplier evaluation and selection [8]. Critics of this method indicate that signifcance of elements presents only some sort of arithmetic accuracy that does not refect real or objective evaluation. Also, when judgment matrix is not consistent, the adjustment is ofen with blindness and extensive calculation. Authors ofen combine this and other methods in order to overcome the stated defciencies [9–12] and more can be seen in review paper of Le Sun [13]. In realistic environment of supplier selection and evaluation, a decision-maker must take into account diferent uncertain factors. Fuzzy-logic-based approach and decisionmaking problems under linguistic environment have been widely used in all areas of science and are one of the tools used for supplier selection [14, 15]. Authors are trying to resolve the complexity of supplier selection problem by applying hybrid (integrated) models. Te research of Simic´ [16] presents how fuzzy set theory, fuzzy decision-making, and hybrid solutions based on fuzzy mathematics can be used in the various models for supplier assessment and selection in a 50-year period.

Uncertainty and vagueness are an integral part of SS process [1, 2]. In SS, the decision maker (DM) or the expert committee rates each supplier based on a set of criteria. This rating can be either linguistic, numeric, or both depending on the choice of the DM. Such rating styles introduce an implicit vagueness in the process and are hence difficult to arrive at a concrete consensus. To better deal with such issues, Atanassov [3] came up with the idea of intuitionistic fuzzy set (IFS), in which a pair consisting of both membership and nonmembership value for every instance was used to better represent fuzziness in preferences. Later, Atanassov and Gargov [4] combined interval numbers and IFS to form IVIF sets. They claim that IVIF had better scope for representing fuzziness and vagueness. From then on, researchers widely explored IVIF by proposing several new theories and concepts. Let us now discuss some of them here. A brief discussion on classical operators of IVIF is also given in [5]. Bustince and Burillo [6] introduced the idea of correlation between IVIF set and developed decomposition theorems for the same. Xu and Chen [7] proposed ordered weighted and hybrid aggregation operators for IVIF sets and applied them for group decision making (GDM). Few researchers have also used IVIFTOPSIS for solving GDM problems [8, 9]. Wang and Liu [10] proposed new Einstein aggregation operators based on sum, exponent, and product theories and demonstrated the same for selecting better propulsion systems. Following this, some researchers have also formulated new IVIF based entropy measures for solving pattern recognition and GDM problems [11–13]. Also, new distance measures under IVIF context have attracted authors that provide effective GDM process [14, 15]. Mukherjee and Das [16, 17] developed a theoretic framework of IVIF soft sets and effectively solved investor selection problem. The fusing of IVIF judgment matrix is an interesting research topic which has attracted researchers to contribute with some novel mechanisms [18, 19]. Motivated by the power of possibility theory, Wan and Dong [20] proposed a new method for comparison of IVIF values. Dadgostar and Afsari [21] further came up with a new idea for accurate edge detection in image steganography using IVIF concepts. Rashmanlou et al. [22] formulated the concept of IVIF graphs and gave a detailed description on IVIF graphs and their properties. Dymova and Sevastjanov [23] extended Dempster-Shafer theory over IVIF for solving GDM problems effectively. Another interesting area of concern for researchers in IVIF lies in consistency checks and preference completion [24, 25]. Researchers have also extended popular AHP [26] and MULTIMOORA [27] ranking schemes for IVIF domain and applied these methods for better GDM process.

مدیریت زنجیره تأمین پایدار (SSCM) توجه زیادی را از شرکت‌های بزرگ و علمی به خود معطوف ساخته است. ارزیابی و انتخاب عملکرد تأمین‌کننده پایدار، نقش مهمی در ایجاد یک SSCM کارآمد ایفا می‌کند. یکی از تکنیک‌هایی که می‌تواند برای انتخاب و ارزیابی عملکرد تأمین‌کننده پایدار بکار رود، تحلیل پوششی داده‌هاست (DEA). ورودی‌ها و خروجی‌ها ممکن است در مسائل دنیای واقعی مشخص نباشند. این مقاله یک مدل اندازه‌گیری راسل پیشرفته DEA یکپارچه را در موقعیت فازی برای انتخاب بهترین تأمین‌کننده پایدار ایجاد می‌کند. یک مطالعه موردی برای نشان دادن اثربخشی روش پیشنهادی برای مسئله انتخاب تأمین‌کننده پایدار در یک شرکت تولید رزین ارائه می‌شود. مطالعه موردی نشان می‌دهد که مدل پیشنهادی می‌تواند اثربخشی و بهره‌وری در محیط نامطمئن را با سطوح مختلف اندازه‌گیری کند. همچنین، این نشان می‌دهد که مدل پیشنهادی در بررسی عوامل اقتصادی، اجتماعی و زیست‌محیطی در زمان انتخاب تأمین‌کنندگان پایدار ، به تصمیم‌گیرندگان کمک می‌کند.

1. مقدمه
پژوهشگران و متخصصان تمرکز زیادی بر مدیریت زنجیره تأمین داشته‌اند (SCM). همان‌طور که Ageron و همکاران (1) به این موضوع پرداخته‌اند، برون‌سپاری بخش اصلی SCM است. جهانی‌شدن SCM را نه تنها به تمرکز بر معیار اقتصادی وا می‌دارد، بلکه توجه آن‌ها را نیز به شرایط کاری خوب و تولید سازگار با محیط‌زیست معطوف می‌سازد. توسعه پایدار ترکیبی از عوامل اقتصادی، زیست‌محیطی و اجتماعی است. SCM پایدار (SSCM) یک عنوان رو به رشد برای شرکت‌ها در همه زمینه‌هاست. دستیابی به معیارهای اجتماعی و زیست‌محیطی در زنجیره تأمین به دستیابی به SSCM کمک می‌کند. تولید محصولات پایدار یک پاسخ دفاعی به نیروهای دولت، مشتریان ، و سازمان‌های غیردولتی (NGOs) (51) است.
همان‌طور که توسط Dyllick و Hockerts (23) موردتوجه قرار گرفت، SSCM ترکیبی از توسعه پایدار و مدیریت زنجیره تأمین است در جاییکه توسعه پایدار به عنوان ترکیبی از عوامل زیست‌محیطی، اجتماعی ، اقتصادی تعریف می‌شود. Seuring و Müller (52) بررسی کردند که تحقیق در مورد SSCM از سال 2002 به بعد پدیدار شده است. Cetinkaya و همکاران (13) توضیح دادند که زنجیره تأمین پایدار به زنجیره تأمین سبز محدود نمی‌شود، اما آن‌ها باید موضوعات مالی را در نظر بگیرند و به جامعه کمک کنند.

مقاله سال 2016

Because of the world’s rapid economic and industrial growth, today’s global business environments necessarily focus on supply chains and operations management. Managers of traditional supply chains have had to become flexible to the dynamic, changing requirements of their customers while still maintaining profits. That new approach is called “responsive,” (Roh et al., 2014), and responsive supply chains typically include green supply chain management (GSCM) techniques, sustainable supply chain management (SSCM), reverse logistics, or other strategies. Historically, corporate social responsibility (CSR) is widely accepted as an important supply chain approach that emphasizes both competition and sustainability. In fact, CSR includes the three pillars of sustainability: economic, environmental, and social concerns. Pressures from both internal and external stakeholders have urged industrial managers to adopt CSR practices into their supply chains, and the potential benefits are noteworthy. Adopting CSR practices may increase the market share and sales revenue of a firm, and it may improve customers’ perception of the firm (Salam, 2009; Xu et al., 2013). In recent decades, the rise of CSR depends on the global political economy (Luning, 2012) and has become a popular research topic, but an exact definition of the concept is still under debate (Devinney, 2009; Mcwilliams et al., 2006). Because of the lack of a clear definition of CSR, companies emphasize and practice it differently (Oberseder et al., 2013). Thus, the next research focus has been to establish the role of CSR and to identify, in particular, what stakeholders expect from a company that adopts CSR practices.

Some current literature explores the importance of stakeholder involvement in CSR implementation. First, we must recognize that in many firms, stakeholder perspectives are not considered; decisions are left solely to managers. Researchers disagree on the value of including stakeholders’ views. For instance, Trapp (2014) argues that more benefits emerge for the company when stakeholders are involved in the decision to adopt CSR strategies. On the other hand, Costa and Menichini (2013) state that stakeholders sometimes do not know how a company might specifically implement CSR practices. We can affirm, however, that adopting CSR practices is likely to ignite relations between the firm and its stakeholders and will likely improve the company’s competitive advantage.

Supplier selection is one of the toughest decisions industrial managers make, because the amount spent for procuring materials may reach 75% of the company’s initial capital costs, and inefficiencies in the supplier selection process will damage the firm’s profit margin (Cheung et al., 2009; Xu et al., 2013). In a conventional supply chain, supplier selection is generally based on some common criteria such as quality, cost, durability, or the supplier’s environmental management system. Currently, few studies consider the role of multiple stakeholders in the supplier selection process; those that do focus on either green or economic criteria. Some work that considers the supplier selection process (Xu et al., 2013; Baskaran et al., 2011; Baskaran et al., 2012) will include a discussion of CSR perspectives, but those studies are limited to the multi-stakeholder approach and examine CSR issues, not practices. Hence, to fill this literature gap, this study considers the supplier selection process based on the CSR practices with the concern of multi-stakeholder perspectives. To address the research gap and to achieve the aim of the paper, three research questions are framed as follows: RQ1: Why are CSR practices really needed for this study? RQ2: What are the most influential CSR practices that will be involved in the supplier selection process? RQ3: How do multiple stakeholders influence the supplier selection process?