A systematic review on supplier selection and order allocation problems

Aouadni, Sourour; Aouadni, Ismahene; Rebaï, Abdelwaheb Article A systematic review on supplier selection and order allocation problems Journal of Industrial Engineering International Provided in Cooperation with: Islamic Azad University (IAU), Tehran Suggested Citation: Aouadni, Sourour; Aouadni, Ismahene; Rebaï, Abdelwaheb (2019) : A systematic review on supplier selection and order allocation problems, Journal of Industrial Engineering International, ISSN 2251-712X, Springer, Heidelberg, Vol. 15, Iss. S1, pp. 267-289, https://doi.org/10.1007/s40092-019-00334-y This Version is available at: https://hdl.handle.net/10419/267667 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Vol.:(0123456789) 1 3 Journal of Industrial Engineering International (2019) 15 (Suppl 1):S267–S289 https://doi.org/10.1007/s40092-019-00334-y ORIGINAL RESEARCH A systematic review onsupplier selection andorder allocation problems SourourAouadni1,2· IsmaheneAouadni1,2· AbdelwahebRebaï2 Received: 12 July 2019 / Accepted: 4 November 2019 / Published online: 13 November 2019 © The Author(s) 2019 Abstract The supplier selection and order allocation are two key strategic decisions in purchasing problem. The review presented in this paper focuses on the supplier selection problems (SSP) and order allocation from year 2000 to 2017 in which a new structure and classification of the existing research streams and the different MCDM methods and mathematical models used for SSP will be presented. The review was examined in three aspects: the summaries of the existing evidence concerning the problems, the identification of gaps in the current research to help determine where further investigation might be needed and positioning new research activities. Keywords Supplier selection· Single sourcing· Order allocation· Optimization· Multi-criteria decision making· Multiple sourcing Introduction The supplier selection problem (SSP) is a procurement decision-making problem that consists of the definition of methods and the models to analyze and measure the performance of a set of suppliers in order to improve customer’s competitiveness. This decision is more complex because the diversity of quantitative and qualitative criteria assigns on evaluation and decision-making process. Various decisionmaking techniques have been proposed in the literature to remove this problem, particularly those of multi-criteria analysis which use both quantitative and qualitative data. Additionally, in the function supply, the decision of choosing the best suppliers consists in deciding whether we shall use singleor multiple-sourcing strategies during the acquisition of a given material resource. Also, the supplier selection and order allocations are two key strategic decisions in purchasing problem. Then, these decisions are more complex and various techniques have been proposed in the literature to solve these problems. Several taxonomies and review literature papers devoted to the supplier selection problem such as De Boer etal. (2001); Aissaoui etal. (2007); Ho etal. (2010); Chai etal. (2013); Wetzstein etal. (2016). In recent year, the number of researcher’s work is interesting in supplier selection with order allocation is increased, but a few of systematic reviews consider this issue. For these reasons, our intention is to present a rigorous review of scientific literature, by presenting a classification of these works for a large period (17years) which was not taken into consideration in the other surveys. This study proposes a new structure and classification of the existing research streams and the different methods used for SSP and order allocation as well as identifying gaps in the current research and delineating future research avenues with the aim of relating the existing quantitative methods to empirical research. The main contribution of this paper is to provide a comprehensive literature review of supplier selection with order allocation during the last 17years, which shows a significant increase in research work published in this field. In this study, we will apply the classification framework developed by Denyer and Tranfield (2009) in order to advance our understanding of the field of supplier selection and order allocation and to facilitate to the researchers to discover * Sourour Aouadni [email protected] 1 College ofBusiness, Department ofBusiness Administration, Bisha University, Bisha, SaudiArabia 2 Laboratory ofModeling andOptimization forDecisional, Industrial andLogistic Systems, Sfax University, Sfax, Tunisia S268 Journal of Industrial Engineering International (2019) 15 (Suppl 1):S267–S289 1 3 meaningful information concerning this subject. Also, this new classification seeks to identify any new trends in this field and highlight any gaps that would benefit from future research efforts, including MCDM methods and optimization models. Finally, this survey aims to enhance our ability to discover important knowledge in this large number of literature. Given this evolving research field, this paper analyzes research in international scientific journals that focus on this field and build a relevant framework for classifying the most papers published since 2000 until to 2017. Consequently, we provided a bibliography of 270 published papers in order to answer the following questions: which journals are mostly touched on this problem? Which multi-criteria decisionmaking methods are frequently applied in the process of supplier selection? Which mathematical models are prevalently used to formulate the supplier selection and order allocation problem? Which techniques are frequently used to solve these models? What are the limitations of these methods and models? In this paper, we present the most popular methods and models used in these problems. Firstly, we summarize the most characteristics about the supplier selection process and we tried to classify the different multi-criteria decision methods of collecting papers in two categories: single criterion and outranking synthesis approaches. Secondly, we present an extensive review and analysis of the decision models of a multiple-sourcing problem in the supplier selection. Moreover, a classification of mathematical models according to the objective function is a single-objective function or multiple-objective functions. Finally, we tried to analyze the hybrid methods used in supplier selection and order allocation problems. The remainder of this paper is organized as follows: Section“Research methodology” presents the research methodology used in the topic of supplier selection and order allocation. Section“Analyzing and reporting” concerns about analyzing and reporting. Section“Observations and remarks” presents an observation and discussion about the most prevalently used methods and journals by years. Research methodology In this study, we describe the systematic review for classifying the published papers about 2000 until to 2017 in this topic. Systematic review is defined by Denyer and Tranfield (2009) as “a specific methodology that locates the existing studies, selects and evaluates contributions, analyses and synthesizes data, and reports the evidence in such a way that allows reasonably clear conclusions to be reached about what is and is not known.” The introduction of the use of systematic evaluation processes in particular of systematic literature review will help to obtain an objective summary of research evidence concerning this topic by producing better-quality reviews and evaluations. The systematic reviews review the primary data which can be either quantitative or qualitative and synthesize the findings of previous research investigating the same or similar questions. The systematic reviews use a systematic approach to search, select and appraise the produced evidence, and they employ explicit rigorous and accountable methods to inform new research questions. In this paper, we based on the five-step procedure proposed by Denyer and Tranfield (2009) describe the systematic review: Step 1 Formulate the questions: we follow the CIMO logic proposed by Denyer etal. (2008) to formulate clear questions whose purpose is to establish the focus of the study and to frame the inclusion criteria. This logic of the CIMO is as follows: “in this class of problematic Contexts, use this Intervention type to invoke these generative Mechanism(s), to deliver these Outcome(s)” (Denyer etal. 2008). Applying the ICMO logic, we formulate the design proposition to identify the four main elements: If a buyer aims to take a decision concerning a supplier selection and order allocation problem which characterized by multiple criteria and multiple suppliers (C), it should use a multi-criteria decisionmaking method and the mathematical optimization (I) based on the opinion of one or group decisionmakers in order to rank suppliers according to their importance and the definition of the different objectives and constraints to take the optimal decision of order allocation (M) in order to select the best suppliers and distribute the order quantity between them (O). Step 2 Locating studies: This step aims to locate, select and appraise the relevant researches as much as possible. Relevant papers were targeted using electronic academic databases including science direct, IEEE Xplore, Springer link Journals, Emerald, Taylor and Francis and so on. These papers are searched in the search databases using the following keywords: “vendor selection,” “supplier selection,” “supplier evaluation,” multi-criteria supplier selection “order allocation,” “order lot-sizing,” “optimization supplier selection” and covering major journals in the fields of “Management Science,” “Operations Research,” “Purchasing S269Journal of Industrial Engineering International (2019) 15 (Suppl 1):S267–S289 1 3 Management” and “supply chain management and logistics” from 2000 to 2017 (up to September 2017). Step 3 Study selection and evaluation: this step defines the inclusion and exclusion criteria to focus on relevant papers. This survey was taken to identify papers that had been published in high-ranking journals in order to increase the validity of this systematic literature review. To ensure the relevance of the papers, we consider only English papers. We read as first research 400 abstracts on this topic. We extended the master’s thesis, doctoral dissertation, textbooks and unpublished articles and nonEnglish papers. We ended up with 300 papers in total. An article is included in this survey if it is thoroughly based on pre-selection methods, MCDM methods, order allocation models and methods and hybrid methods of the supplier selection problem. As a result, 270 articles are reviewed attempting to be as exhaustive as possible. This scope makes to researchers the most valuable information and an extensive search concerning the supplier selection problem and order allocation. In addition, we excluded researches devoted to green supplier selection and sustainable supplier selection. Step 4 Analysis and synthesis: in this step, we extract and store the important information about the studied problem from the collection of the most relevant papers. We used the designed questions proposed in Step 1 to extract the relevant information. The results obtained in this step are the description and the classification of the papers. Step 5 Reporting and using the results: in this last step, a systematic review should be structured in a similar manner to a report of empirical research which includes an introduction, methodology, findings and conclusion. The first three steps have been well discussed in research methodology. Now, we focus on analysis and reporting in the following sections. Analyzing andreporting Table1 presents the distribution of the papers based on the name of journal in which the 270 papers specific to the problem of supplier selection and order allocation. The lapse of time of these papers is between 2000 and 2017 for identifying the trends in the chronological progression of research on problem of supplier selection and order allocation. Since 2000, the number of publications in the domain of the SSP and order allocation has grown increasingly. Figure1 shows that the most number of published papers in this domain are in the period 2009–2013, especially in 2011 so that we find 40 published papers and the important method used is the TOPSIS which is a qualitative technique (see Fig.1). In addition, the most techniques used in this period for solving SSP are qualitative techniques such as TOPSIS and AHP. Overall, the multi-criteria nature of the supplier selection problem is the main reason that explain the usage of these MCDM approaches. Problem context‑based classification In the function supply, the decision of choosing the best suppliers consists in deciding whether we shall use singleor multiple-sourcing strategies during the acquisition of a given material resource. Supplier selection (single sourcing) In this decision, one supplier can satisfy all the buyer‘s needs and the buyer makes only one decision: which supplier is the best. Gary Dickson in 1966 is the first of the pioneers who became interested in the topic. He defined this problem as follows: «The vendor selection problem is associated with Table 1 Distribution of articles in major journals in the period 2000–2017 Journal Total Percentage % Expert Systems with Applications 64 23.70 International Journal of Production Economics 35 12.96 European Journal of Operational Research 26 9.62 Applied Mathematical Modelling 15 5.55 Computers and Industrial Engineering 15 5.55 International Journal of Production Research 12 4.44 Omega: The International Journal of Management Science 9 3.33 Others 94 34.81 S270 Journal of Industrial Engineering International (2019) 15 (Suppl 1):S267–S289 1 3 deciding how one vendor should be selected from a number of potential alternatives.» (Dickson 1966). In this problem, the purchasing department makes the decision about whether one or more suppliers are selected from a number of alternatives and for the completion of an activity or the provision of a product. In the case where several activities are carried out, we talk about the problem of the supplier selection if the activities are treated independently (no synchronization between activities, no precedence constraints). Order allocation problem (multiple sourcing) In purchasing problem, we talk about “order allocation problem” where the activities are subject to precedence constraints. In multiple sourcing, as no supplier can satisfy all the buyer’s requirements, more than one will be selected. In this situation, the buyer purchases the same items from more than one supplier, and the total demand is split among them. The order quantity is split among suppliers for a variety of reasons such as cost, quality and capacity. This decision consists in combining the supplier selection with the order allocation problem. In this decision, more questions arise: What order quantity should be allocated to each supplier? Which order should be assigned to each supplier? And which period, in the planning horizon, should be used?. Methodology‑based classification Pre‑selection ofpotential suppliers’ methods The aim of this phase is to reduce the number of suppliers for the final selection using the multi-criteria decision-making method. Therefore, the purpose of this phase is to rule out the inefficient candidates and reduce the set of all the suppliers to a small range of acceptable ones. It is a simple step in evaluating suppliers and developing a list of potential key suppliers based on a set of factors, such as experience, financial ability, managerial ability, reputation and work history. In the following paragraphs, we will discuss the supplier pre-qualification approaches, and Table2 summarizes the using of these methods: Categorical methods The main papers using the categorical method in the supplier selection problem are those of Khaled etal. (2011). Linear weighted average method Amid etal. (2006) solved a fuzzy multi-objective linear model supplier selection problem in a supply chain by applying an asymmetric fuzzy-decision-making technique. Ng (2008) constructed a weighted linear program for the multi-criteria supplier selection problem by using a transformation technique that could solve the problem without applying an optimizer. Data envelopment analysis (DEA) Liu etal. (2000) proposed a simplified DEA model to evaluate the supplier’s performance as the basis of 3 inputs, price index, delivery performance and distance factor, and 2 output criteria which are the supply, variety and quality. Narasimhan etal. (2001) proposed factors to apply the DEA to evaluate the suppliers of a multi-national corporation in the telecommunication Fig. 1 Distibution of papers by year 0 5 10 15 20 25 30 35 40 45 50 Table 2 Classification of pre-selection methods No. Criteria Number of articles Percentage % 1 Categorical 1 3.84 2 Linear weighted average method 2 7.69 3 DEA 13 52 4 Cluster analysis 03 11.53 5 CBR 06 23.07 Total 25 S271Journal of Industrial Engineering International (2019) 15 (Suppl 1):S267–S289 1 3 industry. Talluri and Baker (2002) used the DEA method with two input and four output factors to evaluate the potential stakeholders (suppliers, manufacturers, retailers and distributors). Talluri and Narasimhan (2004) proposed the DEA for an effective supplier sourcing based on the crossefficiencies and statistical methods in clustering the supply base. Garfamy (2006) applied the DEA by focusing on the total cost of ownership (TCO) to measure the overall performances of suppliers. Saen (2006) developed an innovative method based on the DEA model to evaluate the technology suppliers on mainly three factors, in which there is a qualitative factor—amount of know-how transfer. In the research of Seydel (2006), the DEA is used to solve the supplier selection problem. Talluri etal. (2006) proposed a chanceconstrained DEA approach to evaluate the performance of the suppliers taking into consideration the stochastic performance measures. Wu etal. (2007) proposed an augmented imprecise DEA approach for the selection of the suppliers. Wu and Blackhurst (2009) proposed a methodology called the augmented DEA to evaluate and select the best supplier. This method enhanced the discriminatory power over the basic DEA models to rank the suppliers. Songhori etal. (2011) proposed a structured framework to solve the supplier’s selection with order allocation problem for their firm using DEA. Falagario etal. (2012) proposed a tender evaluation method based on the DEA method and related concept of cross-efficiency. Karsak and Dursun (2014) proposed a new fuzzy multi-criteria group decision-making method for supplier selection combined quality function deployment (QFD) and data envelopment analysis (DEA). Cluster analysis Hong etal. (2005) applied a clustering method to find out the best suppliers on the basis of: preparation, pre-qualification and final selection, so, they could maximize the revenue while satisfying the customer’s needs. Bottani and Rizzi (2008) presented a hybrid method using the cluster analysis and the AHP. Ha and Krishnan (2008) introduced a hybrid method which incorporates multiple techniques into the supplier’s evaluation process in order to select the most competitive one(s) in the supply chains. Case-based reasoning method (CBR) Choy and Lee (2002) proposed a generic model of CBR integrating customer relationship management (CRM) and a supply chain management (SCM).The model was applied to a consumer product manufacturing company, which maintained a database of past suppliers and their attributes. Like in the work of Choy and Lee (2002, 2003) and Choy etal. (2002), Choy etal. (2003) applied the CBR-based methodology for the supplier selection problem. Humphreys etal. (2007) integrated both the case base reasoning and the decision support components including multi-attribute analysis which presents a framework to measure the supplier’s environmental performance. Multi‑criteria decision‑making methods This phase consists in determining the method for the final selection of suppliers and the allocation of final-order quantities among them. In this step, several methods have been used in the literature to evaluate and select the suppliers and many researches classified these methods in various categories. De Boer etal. (2001) positioned the qualitative methods in step formulation of the problem and criteria but the quantitative methods are involved in the final stages of the qualification and selection of the suppliers. Ho etal. (2010) classified the techniques of the supplier’s selection into individual and integrated approaches. Then, Chai etal. (2013) selected and reviewed 123 journal articles, between 2008 and 2012, which were used in the supplier’ evaluation and selection. They classified 26 techniques into three categories, namely Multi-criteria decision-making (MCDM) techniques, mathematical programming (MP) techniques and artificial intelligence (AI) techniques. In this study, we classify the MCDM approaches used to solve the supplier selection problem into two categories such as (1) single-criterion synthesis approach and (2) outranking synthesis approach (see Table3). Single‑criterion synthesis approach The key multi-criteria decision-making methods within this approach include: MAUT, SMART, UTA, TOPSIS, AHP and GP. Analytic Hierarchic Process (AHP) Akarte etal. (2001) developed a web-based AHP system to evaluate the casting suppliers on 18 criteria, six objectives and twelve subjective and divided them into four groups. Tam and Tummala (2001) applied the AHP approach to a real case study to examine its feasibility in selecting a vendor for a telecommunication system. Muralidharan etal. (2002) proposed a Table 3 Classification of MCDM methods Abbreviations Number of articles Local percentage % Global percentage % Single-criterion synthesis approach Number total of papers = 122 AHP 47 38.52 17.40 ANP 21 17.21 7.78 MAUT 2 1.63 0.74 SMART 4 3.27 1.48 TOPSIS 26 21.31 9.62 VIKOR 7 5.73 2.59 SIR 1 0.81 0.37 BWM 2 1.63 0.74 Outranking synthesis approach ELECTRE 6 4.91 2.22 PROMETHEE 6 4.91 2.22 S272 Journal of Industrial Engineering International (2019) 15 (Suppl 1):S267–S289 1 3 five-step AHP-based model which incorporated nine criteria to select the suppliers. Chan (2003) developed an interactive selection model using the AHP method to facilitate the selection of suppliers. Liu and Hai (2005) used Noguchi’s voting and ranking method to solve the supplier selection based on the AHP method. Hou and Su (2007) proposed an AHP-based decision support system to identify the appropriate suppliers of components in a mass customization environment. Chan and Chan (2010) used an AHP method to solve the supplier selection problem in the apparel industry. Peng (2012) used the AHP method to evaluate and select logistics outsourcing service suppliers and applied an actual case. Chen and Wu (2013) proposed a modified failure mode and effect analysis (MFMEA) method to select new suppliers from the supply chain of risk perspective. Deng etal. (2014) proposed a D-AHP method for the supplier selection problem, which extends the classical analytic hierarchy process (AHP) method, based D numbers, that is a new, effective and feasible representation of uncertain information. Dweiri etal. (2016) proposed a decision support model for supplier selection based on analytic hierarchy process (AHP) using a case of automotive industry in a developing country of Pakistan. The many papers integrated AHP with other approaches to evaluate the performance of suppliers and select the best supplier are those of: Chen and Huang (2007), Saen (2007b), Sevkli etal. (2007), Ha and Krishnan (2008), Çebi and Bayraktar (2003), Wang etal. (2004, 2005), Yang and Chen (2006), Xia and Wu (2007), Lin etal. (2011), Rezaei and Davoodi (2012), Kar (2015) and Segura and Maroto (2017). The proposed approaches integrated with fuzzy set in AHP method are: Kahraman etal. (2003), Chan and Kumar (2007), Bottani and Rizzi (2008), Yang etal. (2008), Chan etal. (2008), Lee (2009), Chamodrakas etal. (2010), Sen etal. (2010), Zeydan etal. (2011), Yucenur etal. (2011), Chen and Chao (2012), Shaw etal. (2012), Yu etal. (2012), Rezaei and Ortt (2013), Junior etal. (2014), Rezaei etal. (2014), Ayhan and Kilic (2015), Lee etal. (2015), Beikkhakhian etal. (2015), Bruno etal. (2016), Tavana etal. (2016), Büyüközkan and Göçer (2016, 2017). Analytic Network process (ANP) Sarkis and Talluri (2002) applied the ANP method to help decision makers select the best supplier by taking into account the organizational factors and strategic performance metrics, which consist of seven evaluating criteria. Bayazit (2006) proposed an ANP method to select the right supplier by identifying ten evaluating criteria that were categorized into supplier’s performance and capability clusters. Gencer and Gürpinar (2007) implemented an ANP model for an electronic company to evaluate and select the most appropriate supplier as the basis of various evaluating criteria. Lin etal. (2010) proposed a hybrid MCDM technique to cope with the complex and interactive supplier selection problem in determining the structural relationships and the interrelationships among all the evaluation dimensions. Wan etal. (2017) used ANP and ELECTRE II in interval 2-tuple linguistic environment for solving supplier selection problems with two-level criteria. The authors developed a 2-tuple linguistic ANP (TL-ANP) approach to derive criteria and sub-criteria weights. The papers using the fuzzy ANP are those of: Lin (2009), Amin and Razmi (2009), Razmi etal. (2009a), Onüt etal. (2009), Yucenur etal. (2011), Xiao etal. (2012) and Lin (2012). The researches combined ANP with other methods such as Demirtas and Üstün (2008, 2009), Ustun and Demirtas (2008), Tseng etal. (2009), Razmi and Rafiei (2010), Lin etal. (2011), Aouadni etal. (2013) and Ghadikolaei and Parkouhi (2017). Multiple-attribute utility theory (MAUT) There is one research that used this method in SSP, Sanayei etal. (2008) which presented an effective model using both MAUT and LP to solve the supplier selection problem. Segura and Maroto (2017) applied a multiple-criteria supplier segmentation approach based on PROMETHEE and multi-attribute utility theory (MAUT) Simple Multi-Attribute Rating Technique (SMART) Barla (2003) conducted a five-step approach based on the SMART for the supplier evaluation and selection in a glass manufacturing company. Huang and Keska (2007) presented a comprehensive set of 101 metrics collected from the literature for the supplier selection. Other papers integrated the SMART with other approaches, for example, Seydel (2005) and Chou and Chang (2008). Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) Shyur and Shih (2006) present an effective model using both ANP and modified TOPSIS, to accommodate the criteria with interdependencies in supplier selection problem. Junior etal. (2014) presented a comparing study between the fuzzy AHP and the fuzzy TOPSIS methods to solve the problem of supplier selection. Wood (2016) applied fuzzy and intuitionistic fuzzy TOPSIS with flexible entropy weighting to evaluate and select supplier considering criteria relevant to an oil and gas facilities development project. Aouadni etal. (2017) developed the cardinal data TOPSIS method (TOPSIS-CD method) and the meaningful mixed data TOPSIS method (TOPSIS-MMD method) which suggest novel reference points and extend the TOPSIS method to mixed data. These two extended methods applied to a multi-attribute supplier selection problem. Aouadni etal. (2017) developed the cardinal data TOPSIS method (TOPSIS-CD method) and the meaningful mixed data TOPSIS method (TOPSIS-MMD method) which suggest novel reference points and extend the TOPSIS method to mixed data. The different papers that used the hybrid fuzzy TOPSIS method for the supplier selection problem are those of: Chen etal. (2005, 2006), Boran etal. (2009), Onüt etal. (2009), S273Journal of Industrial Engineering International (2019) 15 (Suppl 1):S267–S289 1 3 Razmi etal. (2009b), Guneri etal. (2009), Awasthi etal. (2010), Dalalah etal. (2011), Zeydan etal. (2011), Kara (2011), Deng and Chan (2011), Chen (2011a, b), Jolai etal. (2011), Yucel and Guneri (2011), Liao and Kao (2011), Lin etal. (2011), Govindan etal. (2013), Kilic (2013), Junior etal. (2014), Roshandel etal. (2013), Lee etal. (2015), Igoulalene etal. (2015) and Wood (2016). VIKOR method Combined with fuzzy set Chen and Wang (2009) and Sanayei etal. (2010) used the fuzzy VIKOR method to evaluate the best supplier. Shemshadi etal. (2011) extended the VIKOR method with a mechanism to extract and deploy objective weights based on Shannon’s entropy concept for solving supplier selection.Hsu etal. (2012) select the best vendor for conducting the recycled material based on a hybrid MCDM model combining DEMATELbased on ANP (called DANP) with VIKOR method. You etal. (2015) proposed an extended VIKOR method for group multi-criteria supplier selection with interval 2-tuple linguistic information. Wu etal. (2016) used extended VIKOR under linguistic information for solving supplier selection in nuclear power industry. Ghadikolaei and Parkouhi (2017) used fuzzy analytic network process and grey VIKOR techniques to solve supplier selection problem. SIR method Chai etal. (2012) proposed a novel intuitionistic fuzzy SIR method to solve the uncertainty group multi-criterion decision-making problem and applied their method in the supplier selection problem. Best–worst method (BWM) Rezaei etal. (2015a, b) proposed an integrative approach that includes capabilities and willingness as two dimensions for evaluating and subsequently segmenting suppliers. The results of that segmentation are then used as the main basis for supplier development. Rezaei etal. (2016) used the best–worst method to solve supplier selection life cycle approach integrating traditional and environmental criteria. Outranking synthesis approach In the literature of evaluating and selecting the best supplier, the outranking methods are: ELECTRE and PROMETHEE. ELimination and Choice Expressing Reality (ELECTRE) Almeida (2007) applied the ELECTRE method to solve an outsourcing contract problem, when dealing with outsourcing vendor selection. Liu and Zhang (2011) proposed a novel method to deal with the supplier selection of supply chains that combines entropy weight and an improved ELECTREIII method. As an example, we mention: Montazer etal. (2009) and Sevkli (2010). Wan etal. (2017) presented an interval 2-tuple ELECTRE II (IT-ELECTRE II) approach to deal with the case of criteria being not compensated in supplier selection problem. Zhong and Yao (2017) proposed an ELECTRE I-based multi-criteria group decision-making method with interval type-2 fuzzy numbers applied to solve supplier selection problem. Preference Ranking Organisation METHod for Enrichment Evaluations (PROMETHEE) Dulmin and Mininno (2003) used PROMETHEE 1, 2 to solve the supplier selection problem. This approach would be able to deal with several conflicting performance criteria. Araz and Ozkarahan (2007) developed a new multi-criteria sorting method based on PROMETHEE for the supplier evaluation problem. Chen etal. (2011b) used group fuzzy PROMETHEE to solve the real case of the supplier selection problem. After that, PROMETHEE was utilized for ranking the alternatives. Chai etal. (2012) proposed an extended PROMETHEE approach based on superiority and inferiority method. Krishankumar etal. (2017) proposed a new extension to PROMETHEE outranking method under intuitionistic fuzzy set environment for solving supplier selection problem with linguistic preferences. Segura and Maroto (2017) used PROMETHEE method to develop portfolios with suppliers that should be partners of the company, as well as to identify other types of relationships, such as long-term contracts, market policies or to highlight those to be removed from their portfolio. Order allocation methods In the order allocation problem, the decision maker needs to make more than one decision: What product to order? What quantity to order and from which supplier(s)? In which periods?. Different from single sourcing, in the case of supplier’s unreliability or supplier’s default, multiple sourcing allows a buyer to order from others suppliers the defect quantity without the need for searching and negotiating with a new supplier. However, this process involves higher costs than those of single sourcing, owing to the need for managing more than one contract/supplier and the loss of scale economies. In the multiple-sourcing strategies, there are other models of supplier selection where the decision maker has to choose: What to buy and When. As a result, in the multiple-sourcing strategies, two other types can be made to classify the different models. The first type concerns the number of different purchased items, and the second concerns the scheduling horizon (see Table4, 5 and 6). Single‑ andmultiple‑item models The decision “what to buy” is among the most important decisions in the purchasing operation. In this case, the decision maker has to determine the number of items purchased. In this section, we particularly distinguish two important features: single or multiple products ordered and the presence of any form of quantity discount offered by the vendors. Single‑ and multiple‑period models The third important decision of the supplier selection problem is “in which peri- S274 Journal of Industrial Engineering International (2019) 15 (Suppl 1):S267–S289 1 3 ods?” when the buyer decides to define the planning horizon of procurement. Thereby, we have concluded that the techniques developed in the multiple-sourcing strategies can be classified in two models. The single-period models do not consider the inventory management over time, and the multiple-period models consider inventory management by determining an order allocation. Besides, the suppliers sometimes offer discounts, so that price reductions are designed to induce large orders. Quantity discounts have received significant attention in the literature of the supplier selection problem for decades. As a result, the decision of suppliers’ selection will be more complicated in the presence of discount schemes. The models developed in the literature which take into account the discounts quantity are as follows: Chauhan and Proth (2003), Crama etal. (2004), Goossens etal. (2007), Burke etal. (2008), Kokangul and Susuz (2009), Zhang (2010), Kang and Lee (2010), Mansini etal. (2012), Lee Table 4 Review of singleand multiple-item models in SSP Review Single-item model Ghodsypour and O’Brien (2001), Talluri and Narasimhan (2003), Kumar etal. (2004, 2006), Liu etal. (2006), Faez etal. (2009), Xia and Wu (2007), Liao and Rittscher (2007a), Demirtas and Üstün (2008), Yu and Tsai (2008), Glickman and White (2008), Ozgen etal. (2008), Ustun and Demirtas (2008), Burke etal. (2008), Sanayei etal. (2008), Kheljani etal. (2009), Guneri etal. (2009), Sawik (2010), Sanayei etal. (2010), Wang and Yang (2009), Lin (2009), Ebrahim etal. (2009), Kokangul and Susuz (2009), Demirtas and Üstün (2009), Amid etal. (2009), Wu etal. (2010), Mendoza and Ventura (2010), Yucel and Guneri (2011), Lin etal. (2011), Amid etal. (2011), Mafakheri etal. (2011), Hajji etal. (2011), Meena etal. (2011), Xu and Ding (2011), Lin (2012), Shaw etal. (2012), Mendoza and Ventura (2012), Glock (2012), Arikan (2013), Choudhary and Shankar (2013), Ruiz-Torres etal. (2013), Hammami etal. (2014), Lee etal. (2013), Sawik (2013), Ekici (2013), Mohammaditabar etal. (2015), Mazdeh etal. (2015) Multiple-item model Crama etal. (2004), Basnet and Leung (2005), Kawtummachai and Van Hop (2005), Narasimhan etal. (2006), Wang and Che (2007), Wadhwa and Ravindran (2007), Rezaei and Davoodi (2008), Bottani and Rizzi (2008), Wang (2008), Che and Wang (2008), Shen and Yu (2009), Lee etal. (2009), Onüt etal. (2009), Amin and Razmi (2009), Yücel etal. (2009), Osman and Demirli (2010), Chang and Lee (2010), Amin etal. (2011), Ozkok and Tiryaki (2011), Li and Zabinsky (2011), Kara (2011), Rezaei and Davoodi (2011), Yang etal. (2011), Jolai etal. (2011), Woo and Saghiri (2011), Bichler etal. (2011), Shi etal. (2011), Xu and Yan (2011), Mansini etal. (2012), Senyigit and Soylemez (2012), Manerba and Mansini (2012), Yu etal. (2012), Esmaeili etal. (2013), Esfandiari and Seifbarghy (2013), Sawik (2013), Gorji etal. (2014), Scott etal. (2015), Cárdenas-Barrón etal. (2015), Ayhan and Kilic (2015), Yu and Wong (2015) and Turk etal. (2017) Table 5 Review of singleand multiple-period models in SSP Review Single-period model Ghodsypour and O’Brien (2001), Talluri and Narasimhan (2003), Kumar etal. (2004), Crama etal. (2004), Kawtummachai and Van Hop (2005), Kumar etal. (2006), Faez etal. (2009), Narasimhan etal. (2006), Liao and Rittscher (2007a), Xia and Wu (2007), Wadhwa and Ravindran (2007), Wang and Che (2007), Sanayei etal. (2008), Burke etal. (2008), Ozgen etal. (2008), Glickman and White (2008), Yu and Tsai (2008), Demirtas and Üstün (2008), Wang (2008), Bottani and Rizzi (2008), Che and Wang (2008), Shen and Yu (2009), Onüt etal. (2009), Amin and Razmi (2009), Kheljani etal. (2009), Sawik (2010), Guneri etal. (2009), Wang and Yang (2009), Lin (2009), Ebrahim etal. (2009), Kokangul etal. (2009), Amid etal. (2009), Yücel etal. (2009), Wu etal. (2010), Mendoza and Ventura (2010), Chang and Lee (2010), Amin etal. (2011), Yucel and Guneri (2011), Lin etal. (2011), Amid etal. (2011), Ozkok and Tiryaki (2011), Li and Zabinsky (2011), Yang etal. (2011), Hajji etal. (2011), Woo and Saghiri (2011), Bichler etal. (2011), Meena etal. (2011), Shi etal. (2011), Xu and Yan (2011), Xu and Ding (2011), Lin (2012), Shaw etal. (2012), Mendoza and Ventura (2012), Mansini etal. (2012), Senyigit and Soylemez (2012), Glock (2012), Manerba and Mansini (2012), Yu etal. (2012), Arikan (2013), Ruiz-Torres etal. (2013), Hammami etal. (2014), Esmaeili etal. (2013), Ekici (2013), Esfandiari and Seifbarghy (2013), Sawik (2013) and Mohammaditabar etal. (2015) Multiple-period model Ghodsypour and O’Brien (2001), Basnet and Leung (2005), Liu etal. (2006), Liao and Rittscher (2007a, b), Rezaei and Davoodi (2008), Ustun and Demirtas (2008), Demirtas and Üstün (2009), Osman and Demirli (2010), Mafakheri etal. (2011), Kara (2011), Rezaei and Davoodi (2011), Jolai etal. (2011), Choudhary and Shankar (2013), Lee etal. (2013), Sawik (2013), Gorji etal. (2014), Mazdeh etal. (2015), Cárdenas-Barrón etal. (2015), Soto etal. (2017), Ghaniabadi and Mazinani (2017) and Turk etal. (2017) S281Journal of Industrial Engineering International (2019) 15 (Suppl 1):S267–S289 1 3 intelligence techniques in this field, especially in the last 5years. Since 2010, the number of articles that used the mathematical tools for solving this issue is increased. The fuzzy multiple-objective programming is the popular mathematical models used for the modeling of the SSP and order allocation. As a result, it should be noted that the use of exact and artificial methods is limited despite the tendency of authors to this research axis. The genetic algorithm (33.33%) is more implemented than the other AI method and the exact method. Overall, among the objectives of this survey is to find out the most popular method adopted in supplier selection and order allocation literature. As found in the previous sections, the MCDM approaches (122 papers or 45.18%) remain more popular than the mathematical programming (103 or 38.14%) and AI approaches (42 papers or 15.55%) as a tool for solving this problem. We summarize in Table10 the strengths and weaknesses of the different methods. It we found that 50.76% of these models have been applied in real-world cases and the most of them in manufacturing companies (40 articles) (see Fig.4). Fig. 2 Distribution of the MCDM methods by years 0 1 2 3 4 5 6 7 8 9 10 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 AHP ANP BWM MAUT SMART TOPSIS VIKOR SIR ELECTRE PROMOTHE E Fig. 3 Distribution of the mathematical models based on years 0 0. 5 1 1. 5 2 2. 5 3 3. 5 4 4.5 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 LP IP NLP DP SP FLP MOLP MOILP MONL P MODP MOSP FMOP GP BO S282 Journal of Industrial Engineering International (2019) 15 (Suppl 1):S267–S289 1 3 Conclusion andfuture directions This paper attempted to review papers published in the period 2000–2017 about supplier selection and order allocation issues. There are 270 international journals studied in this survey, which are accessible via the database system Web of Science. As can be seen, most papers have been published in journals with strong quantitative traditions. The majority of these papers appear in Expert Systems with Applications (64 articles), International Journal of Production Economics (35 articles) and European Journal of Operational Research (26 articles) and others (these are listed in Table1). The first aim of this survey was to systematically review the studies conducted based on the problem of supplier selection and order allocation since 2000. The total of 270 published articles about this field were systematically and carefully selected and summarized. In this paper, we determined firstly the main tools that have been employed in the domain of supplier selection and order allocation about the recent 17years. Secondly, we provided the most international journals published articles related to these problems. Thirdly, we demonstrated the different years when authors published articles related to these fields. The supplier selection with order allocation problem has attracted the interest of many researchers, and the number of research studies on this topic has increased in the last years. Several multi-criteria decision-making methods (MCDM) have been proposed in the literature to select suppliers which include both qualitative and quantitative criteria. According to the results obtained in this systematic review, we found that the number of pre-selection methods is limited despite this step is very important in the supplier selection process. This screening process makes identifying a suitable subset of suppliers and reduces the large set of initial suppliers to a smaller manageable set of acceptable suppliers by ranking them under a pre-defined set of criteria. Among the benefits of this pre-qualification step is that the possibility of rejecting good suppliers at an early stage is reduced. On the contrary, authors paid attention to MCDM methods and integrated methods because the supplier selection problem is multi-criteria decision-making problem which includes qualitative and quantitative criteria. These methods are very quick and easy to use, but they depend on human judgment because the different weights given to the Table 10 The strengths and weaknesses of supplier selection methods Methods Strengths Weaknesses MCDM Quick and easy to use Takes into account qualitative and quantitative criteria Considers the dependency between criteria Depends on human judgment No possibility to introduce constraints in the model pairwise comparison matrices is a complex task Non-meaningfulness of the resulting rankings in mixed data contexts Mathematical programming Mono-objective Finds optimal solution Possibility of introducing new constraints in model Doesn’t take into consideration human judgment Difficult to measure qualitative criteria Computing optimal solution might be time-consuming in the case of NP-hard problems Multi-objective Criteria do not necessarily have a common dimension, finds several solutions We can introduce new constraints Difficulty to take into account human judgment Doesn’t obtain optimal solution Difficult to analyze the result Computing near solution might be time-consuming in the case of NP-hard problems Exact methods Provides optimal solution High quality of solution Doesn’t take into consideration human judgment difficulty to solve large size instances Artificial intelligence Offers a flexible knowledge base Takes into account qualitative criteria The collection of knowledge about suppliers and access to expertise is long and difficult Very expensive 37.3% 50.76% 7.3% Applicaon of research papers from 2000 to 2017 illustrave example real case Numerical experimentaon Fig. 4 Distribution of the application models S283Journal of Industrial Engineering International (2019) 15 (Suppl 1):S267–S289 1 3 various attributes depend on the decision makers’ subjective judgment. Also, we showed that the AHP method is the most popular MCDM method used in this field area by 46 articles and the second approach is the TOPSIS. The AHP method has more several criticisms such as the rank reversal of the alternatives. Then, we found that the applied MCDM methods in this field suffer from two major shortcomings: (1) the non-meaningfulness of the resulting rankings in mixed data contexts (i.e., the rankings of alternatives may change under admissible transformations of the initial attribute values, in the measurement-theoretic sense of the term) and (2) rank reversals that the rankings of alternatives may change if a new alternative is added or an old one is deleted from it or replaced in it). In this board literature, several recent studies have pointed out to the importance of the order allocation problem in the supplier selection issue which is considered most important functions to be performed by the purchasing decision makers. For finding the best solution for the flowing decision “what to buy?”, “in which period?”, many mathematical programming models have been developed in the recent years in this context. It may also be argued that the mathematical programming models are more objective than MCDM methods because the decision maker can define explicitly the objective function. In this review, we classified the order allocation problem in two strategies: single-sourcing problem (47 articles considered the single-item models and 41 articles the multiple-item models) and multipleor dual-sourcing problems (66 articles studied the case of the single-period model and 21 papers the case of multi-period models). Moreover, we classified the mathematical models according to the objective function as either a single-objective function or multiple-objective functions. Besides, we discussed the different methods used in the literature to solve these models. Yet we found that the most popular of these models are a real practical case in the industrial sector by 137 articles. Nowadays, quantitative methods received more attention in recent researches in this domain. The authors are based in their researches on the exact method or intelligence artificial approaches to solve these mathematical programming according the complexity of the problem. There is an overall suggestion for future research that may be pursued. Firstly, we propose to make the MCDM methods able to handle properly mixed attribute values because the attribute values have different levels of measurement. Secondly, we should take into consideration the rank reversal problem in MCDM method because the final rank of alternatives in the global ranking can be reversed when a new alternative is removed or added from the initial set of alternatives. 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