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Risk adjusted multicriteria supplier selection models with applications
International Journal of Production Research
Abstract
Most manufacturers are continuously seeking their supplier base around the world and look for an opportunity to significantly reduce supply chain costs. Singular emphasis on supply chain cost, however, can make the supply chain brittle and more susceptible to the risk of disruptions. The objective of this paper is to develop multicriteria supplier selection models incorporating supplier risk and apply them to a real company. We develop two different types of risk models, value-at-risk (VaR) and miss-the-target (MtT). We model the risk-adjusted supplier selection problem as a multicriteria optimisation problem and solve it in two phases. Phase 1 is the pre-qualification step, where a large set of initial suppliers is reduced to a smaller set of manageable suppliers using various multi-objective ranking methods. In Phase 2, order quantities are allocated among the short listed suppliers using a multi-objective optimisation model. In the multi-objective formulation, price, lead-time, VaR type risk of disruption due to natural event and MtT type risk of quality are explicitly considered as four conflicting objectives that have to be minimised simultaneously. We solve the multi-objective optimisation problem using four different variants of goal programming. The models are illustrated with an actual application to a global IT company.
... Considering this fact, a number of studies classify SC risks into two primary groups, but the names of these categories vary throughout the literature. Macro-risks [24,25], external risks [26], disruptions, and value-at-risk [27] are all terms used to describe the first category. The second group consists of micro-risks [25], internal risks, operational risk management [28], and miss-the-target risks [27]. ...
... Macro-risks [24,25], external risks [26], disruptions, and value-at-risk [27] are all terms used to describe the first category. The second group consists of micro-risks [25], internal risks, operational risk management [28], and miss-the-target risks [27]. In this paper, the terms 'macro-risks' and 'micro-risks' are used to describe the primary global categories of supply chain risks. ...
... Downstream firms always choose supplier(s) from a set of suppliers with different costs (Gurnani et al. 2014), supply capacity (Saputro, Figueira, and Almada-Lobo 2021), default risk (Babich 2006), detectability (Nepal and Yadav 2015), accuracy in meeting the promised delivery time, defective rate (Ravindran et al. 2010), sustainability (Alikhani, Torabi, and Altay 2019), and so on. The goals of supplier selection include maximising profits (Gurnani et al. 2014), geographic dispersion, and environmental reliability (Deane, Craighead, and Ragsdale 2009), or minimising total costs (Arslan, Richard, and Guan 2016), average lead time (Ravindran et al. 2010), and the total tardiness of projects (Chen et al. 2018). ...
... Downstream firms always choose supplier(s) from a set of suppliers with different costs (Gurnani et al. 2014), supply capacity (Saputro, Figueira, and Almada-Lobo 2021), default risk (Babich 2006), detectability (Nepal and Yadav 2015), accuracy in meeting the promised delivery time, defective rate (Ravindran et al. 2010), sustainability (Alikhani, Torabi, and Altay 2019), and so on. The goals of supplier selection include maximising profits (Gurnani et al. 2014), geographic dispersion, and environmental reliability (Deane, Craighead, and Ragsdale 2009), or minimising total costs (Arslan, Richard, and Guan 2016), average lead time (Ravindran et al. 2010), and the total tardiness of projects (Chen et al. 2018). ...
... We hereby give an additional global perspective including the impact of heat stress on short-term price changes as well as supply and trade chains. Besides a chance for "building back better" after an economic shock as suggested by other studies [57], we also identify a potential positive side effect for many less affected regions when considering shifting of demand and supply. We show that this effect, however, comes at the expense of the consumer. ...
... Risk for supply chains may arise from internal or external factors [55,56] or from operational disruptions [57,58]. Furthermore, managing decisions of firms like just-in-time-policy or lean production may increase risk for stable supply chains [59]. ...
Weather extremes pose a persistent threat to society on multiple layers. Besides an average of ~37,000 deaths per year, climate-related disasters cause destroyed properties and impaired economic activities, eroding people's livelihoods and prosperity. While global temperature rises – caused by anthropogenic greenhouse gas emissions – the direct impacts of climatic extreme events increase and will further intensify without proper adaptation measures. Additionally, weather extremes do not only have local direct effects. Resulting economic repercussions can propagate either upstream or downstream along trade chains causing indirect effects. One approach to analyze these indirect effects within the complex global supply network is the agent-based model Acclimate. Using and extending this loss-propagation model, I focus in this thesis on three aspects of the relation between weather extremes and economic repercussions. First, extreme weather events cause direct impacts on local economic performance. I compute daily local direct output loss time series of heat stress, river floods, tropical cyclones, and their consecutive occurrence using (near-future) climate projection ensembles. These regional impacts are estimated based on physical drivers and local productivity distribution. Direct effects of the aforementioned disaster categories are widely heterogeneous concerning regional and temporal distribution. As well, their intensity changes differently under future warming. Focusing on the hurricane-impacted capital, I find that long-term growth losses increase with higher heterogeneity of a shock ensemble. Second, repercussions are sectorally and regionally distributed via economic ripples within the trading network, causing higher-order effects. I use Acclimate to identify three phases of those economic ripples. Furthermore, I compute indirect impacts and analyze overall regional and global production and consumption changes. Regarding heat stress, global consumer losses double while direct output losses increase by a factor 1.5 between 2000 – 2039. In my research I identify the effect of economic ripple resonance and introduce it to climate impact research. This effect occurs if economic ripples of consecutive disasters overlap, which increases economic responses such as an enhancement of consumption losses. These loss enhancements can even be more amplified with increasing direct output losses, e.g. caused by climate crises. Transport disruptions can cause economic repercussions as well. For this, I extend the model Acclimate with a geographical transportation route and expand the decision horizon of economic agents. Using this, I show that policy-induced sudden trade restrictions (e.g. a no-deal Brexit) can significantly reduce the longer-term economic prosperity of affected regions. Analyses of transportation disruptions in typhoon seasons indicate that severely affected regions must reduce production as demand falls during a storm. Substituting suppliers may compensate for fluctuations at the beginning of the storm, which fails for prolonged disruptions. Third, possible coping mechanisms and adaptation strategies arise from direct and indirect economic responses to weather extremes. Analyzing annual trade changes due to typhoon-induced transport disruptions depict that overall exports rise. This trade resilience increases with higher network node diversification. Further, my research shows that a basic insurance scheme may diminish hurricane-induced long-term growth losses due to faster reconstruction in disasters aftermaths. I find that insurance coverage could be an economically reasonable coping scheme towards higher losses caused by the climate crisis. Indirect effects within the global economic network from weather extremes indicate further adaptation possibilities. For one, diversifying linkages reduce the hazard of sharp price increases. Next to this, close economic interconnections with regions that do not share the same extreme weather season can be economically beneficial in the medium run. Furthermore, economic ripple resonance effects should be considered while computing costs. Overall, an increase in local adaptation measures reduces economic ripples within the trade network and possible losses elsewhere. In conclusion, adaptation measures are necessary and potential present, but it seems rather not possible to avoid all direct or indirect losses. As I show in this thesis, dynamical modeling gives valuable insights into how direct and indirect economic impacts arise from different categories of weather extremes. Further, it highlights the importance of resolving individual extremes and reflecting amplifying effects caused by incomplete recovery or consecutive disasters.
... SC disruption is one form of uncertainty that is garnering attention from practitioners and researchers because of increasing globalization (Ravindran et al., 2010) and instability in the system. Research has focused on both contextual and methodological contributions. ...
... Multicriteria decision-making problems are categorized based on whether the constraints are (i) finite and known or (ii) infinite and unknown (Ravindran, 2016). SC network design studies with multiple criteria have used a variety of solution methodologies, including variants of goal programming (Ravindran et al., 2010), Benders decomposition algorithm (Garcia-Herreros et al., 2014), exact mathematical modeling (Huang & Goetschalckx, 2014; Peng et al., 2011), and network optimization (Mari et al., 2014). Melo et al. (2009) provided a detailed review of location design in SCs. ...
Supply chain disruptions compel professionals all over the world to consider alternate strategies for addressing these issues and remaining profitable in the future. In this study, we considered a four-stage global supply chain and designed the network with the objectives of maximizing profit and minimizing disruption risk. We quantified and modeled disruption risk as a function of the geographic diversification of facilities called supply density (evaluated based on the interstage distance between nodes) to mitigate the risk caused by disruptions. Furthermore, we developed a bi-criteria mixed-integer linear programming model for designing the supply chain in order to maximize profit and supply density. We propose an interactive fuzzy optimization algorithm that generates efficient frontiers by systematically taking decision-maker inputs and solves the bi-criteria model problem in the context of a realistic example. We also conducted disruption analysis using a discrete set of disruption scenarios to determine the advantages of the network design from the bi-criteria model over the traditional profit maximization model. Our study demonstrates that the network design from the bi-criteria model has a 2% higher expected profit and a 2.2% lower profit variance under disruption than the traditional profit maximization solution. We envisage that this model will help firms evaluate the trade-offs between mitigation benefits and mitigation costs.
... Trkman and McCormack (2009) used endogenous and exogenous categories to describe SCDs; endogenous categories refer to internal SCDs, including breakdowns in technology, and exogenous categories refer to disruptions external to the supply Tang and Tomlin (2008) Import-export restriction/quota Chopra and Sodhi (2004) Demand fluctuation Chopra and Sodhi (2004) Manuj and Mentzer (2008) chain, including terrorist attacks. Ravindran, Bilsel, Wadhwa, and Yang (2010) used the term "value at risk" to describe SCDs, which were based on the dimensions of labor strikes, terrorist attacks, and natural disasters. Although there was no consensus on the categorization of the disruption factors, several categorizations suggest that SCDs are multifaceted. ...
... The first category of natural factors is one of the most common types of SCDs (Ravindran et al., 2010). The literature provides many examples of natural disasters that disrupted supply chain operations. ...
The purpose of this paper is to develop a framework to identify, analyze, and to assess supply chain disruption factors and drivers. Based on an empirical analysis, four disruption factor categories including natural, human-made, system accidents, and financials with a total of sixteen disruption drivers are identified and examined in a real-world industrial setting. This research utilizes an integrated approach comprising both the Delphi method and the fuzzy analytic hierarchy process (FAHP). To test this integrated method, one of the well-known examples in industrial contexts of developing countries, the ready-made garment industry in Bangladesh is considered. To evaluate this industrial example, a sensitivity analysis is conducted to ensure the robustness and viability of the framework in practical settings. This study not only expands the literature scope of supply chain disruption risk assessment but through its application in any context or industry will reduce the impact of such disruptions and enhance the overall supply chain resilience. Consequently, these enhanced capabilities arm managers the ability to formulate relevant mitigation strategies that are robust and computationally efficient. These strategies will allow managers to take calculated decisions proactively. Finally, the results reveal that political and regulatory instability, cyclones, labor strikes, flooding, heavy rain, and factory fires are the top six disruption drivers causing disruptions to the ready-made garment industry in Bangladesh.
... In the literature, there are two different strategies for managing disruptions in the supply chain, namely the proactive and the reactive strategies [30,33]. A proactive strategy provides some level of protection before the disruption occurs but without recovery considerations [34][35][36]. Multiple sourcing and increased inventory to provide an extra buffer are examples of the proactive strategy [37,38]. ...
This study examines the supply chain network design (SCND) problem with multiple suppliers, warehouses and retailers in the presence of random disruptions using a two-stage approach. The first stage addresses the SCND problem without disruption, employing a genetic algorithm-based heuristic. This approach is integrated with the induced backorder for inventory coordination. In the second stage, simulation is used to evaluate several reactive strategies whilst introducing disruptions in the network and to adjust the inventory control parameters obtained in the first stage as part of a proactive strategy. The numerical results show that neglecting the risk of disruptions may lead to network designs with low fill rates and the proposed approach is able to make the supply chain network more resilient in the presence of disruptions. Based on the simulation results, a localized-reactive strategy is able to suppress the spread of disruptions to other supply channels.
... Lin and Zhou (2011) and Olson and Wu (2010) defined operational supply chain risks as internal risks (demand risks), and external risks (natural disasters, wars, terrorism, political instability). Ravindran et al. (2010) identified the risks as late delivery and missing quality requirements. Samvedi et al. (2013) classified risks such as supply, demand, process, and On the other hand, Pham et al. (2022) emphasized that while academic studies focus on identifying risks, there are not many studies on risk reduction. ...
Purpose of the Study: The main objective of this paper is to describe the multidimensionality of supply chain risk and evaluate supply chain risk management in association with logistics performance and innovation performance. Theoretical framework: No study was found that analyzes the impact of supply chain risk management on logistics performance and innovation performance in the literature review. The purpose of this study is to close this gap in the existing literature. Design/methodology/approach: This study leverages a data set of 30 medium-sized technology firms in Turkey. The first part of the analysis aims at finding determinants of supply chain risk management, and the second part is the analysis of supply chain risk management, innovation performance, and logistics performance. Structural equation modeling multivariate statistical technique is chosen. Findings: Consequently, it empirically proves that risk mitigation and risk control affect positively innovation performance. It concludes that only risk control is effective in increasing logistics performance. Moreover, it estimates that risk identification and risk assessment will have a significant regulatory effect on performance. However, this study explains that they don't have a positive effect on the results. Research, Practical & Social implications: Research gaps and opportunities are presented to lead further studies about supply chain risks in different sectors. This paper is hopefully purposed to contribute to sector representatives as a guide. Originality/value: This study contributes to our understanding of how and with whom to collaborate by highlighting the relationships among supply chain risk management, innovation performance, and logistics performance.
... The latter is characterised by a limited capacity, a higher unit purchasing cost and a fixed order cost compared to the unreliable supplier. Ravindran et al. (2010) used the goal programming by incorporating VaR and miss the target (MttR). VaR is used to estimate the supplier's risk. ...
... The latter is characterised by a limited capacity, a higher unit purchasing cost and a fixed order cost compared to the unreliable supplier. Ravindran et al. (2010) used the goal programming by incorporating VaR and miss the target (MttR). VaR is used to estimate the supplier's risk. ...
... These methodologies include mixed integer programming, simulation, experiment, fuzzy programming, genetic algorithm and agent technology, analytic hierarchy process (AHP), etc. Later on, Ravindran et al. (2010) summarized three clusters of supplier selection models: the first cluster is multi-objective mathematical programming methods; the second cluster is game theoretic methods and the third cluster is the applications of artificial intelligence on supplier selections. In their invited review, Ho et al. (2010) surveyed two different approaches to supplier evaluation and selection: individual approaches and integrated approaches. ...
... In general, the manufacturer selects the supplier(s) and allocates an order to each supplier considering the information gathered from selected suppliers such as technical capability and transaction cost (Ravindran et al., 2010). Motivated by this reality, we consider the ex-post policy announcement where the manufacturer allocates orders after realizing the purchase costs in accordance with whether the collaborative interactions are formed between suppliers. ...
In response to rapidly changing market conditions, manufacturers are increasingly trying to induce collaborative interactions between suppliers to facilitate the sharing of problem-solving ideas, technical advice, and managerial know-how. However, even if supplier-supplier collaboration is an effective way to enhance operational performance, it could be challenging if the suppliers compete to win the same order from the manufacturer. By employing a game-theoretical model, this study explores how a manufacturer can leverage the order allocation policy to facilitate collaboration between two competing suppliers. The results show that the timing of the order allocation policy announcement is critical. If the policy is announced after observing the behavior of the suppliers, the manufacturer cannot induce the desired outcome. However, the announcement that precedes the behavior of the suppliers can lead to collaboration between suppliers if the associated cost is affordable. The maximum affordable level that reflects each supplier’s collaboration burden becomes mild when the suppliers possess a similar capability and secure a sufficient margin. Unlike the manufacturer, who always benefits from suppliers engaged in collaboration, collaborative suppliers require more restricted conditions to generate a higher profit. We also examine the effectiveness of an additional lever, the manufacturer’s financial support or subsidy for supplier-supplier collaboration. Our result indicates that if the subsidy is inappropriately determined, it could become a waste of resources.
... Different methods have been used throughout the literature to test and optimise the selected suppliers. Multi-criteria decision-making (MCDM) (Ravindran et al. 2010), analytic hierarchy process (AHP) (Lee et al. 2001), linear programming, a technique for order of preference by similarity to ideal solution (TOPSIS), analytic network process (ANP) (Güngör 2006), data envelopment analysis, and multi-objective optimization are among the most commonly used methods for supplier selection problem (Chai et al. 2013). Since the decision requires several targets in unpredictable settings, MCDM techniques have been commonly used for supplier selection. ...
In product family design (PFD), deciding on a platform design strategy can be viewed as a multidisciplinary optimization problem that involves several factors, such as design variables, manufacturing costs, customizability, supplier reliability, and customer satisfaction. In this study, a multi-objective based differential evolution (MO-based DE) algorithm has been proposed for tackling the module-based PFD problem. The MO-based DE aims to find the best balance between many objectives, such as total production cost, diversity index, and a combination of other objectives (performance attributes). These objectives include commonality, modularity, and suppliers' reliability and all are aggregated to provide a goodness score. To effectively improve the DE's efficiency while solving such a complex optimization problem, the proposed DE integrates new elements such as (i) a novel solution representation, (ii) an improved heuristic technique for platform development, (iii) a weighted aggregation to combine different objectives, and (iv) a proposed platform-based crossover. To validate its performance, the proposed MO-based DE has been compared with (1) the standard DE to assess the effect of the incorporated new elements on DE’s performance, and (2) well-known fast non-dominant sorting genetic algorithms NSGA-II and (3) NSGA-III for solving a real case study of a family of kettles. The experimental results confirmed the efficacy of the proposed MO-based DE as follows: in terms of average cost value, MO-based DE outperformed standard DE and NSGA-II by 26.40% and 11.69%, respectively. While in terms of goodness score, it achieved 20.69% and 8.05% better scores compared to standard DE and NSGA-II, respectively. Moreover, the proposed MO-based DE attained a very competitive performance against NSGA-III as it reached a better average cost and goodness score of 1.74% and 0.82%, respectively.
... Karar verme modellerinde risk, problemde mevcut olan bilgilerle ilgili iyimserlik derecesinin göstergesi olabilir. Bazı durumlarda ise olumsuz etkilerin yoğunluğu ve olasılığı olarak yorumlanabilir [10]. Risk analizi mevcut bilgilerin sistematik kullanımıyla riskin nedenlerinin tespit edilmesine ve kontrol önlemlerinin belirlenmesine yardımcı olmaktadır. ...
Bu çalışma, bulanık analitik hiyerarşi prosesi (AHP), bulanık hata türleri ve etkileri analizi (HTEA) ve bulanık ortalama çözüm uzaklığına göre değerlendirme (EDAS) yöntemini kombine eden bir karar verme yaklaşımı ile metalik biyomalzemeleri incelemektedir. Çalışmada, paslanmaz çelik, titanyum ve kobalt-krom alaşımları altı ana kriter, otuz bir alt kriter ve üç risk faktörü kullanılarak değerlendirilmiştir. Bulanık AHP yöntemi değerlendirme kriterlerinin ve risk faktörlerinin önemini belirlemek için kullanılırken, bulanık EDAS yöntemi bulanık HTEA yönteminden elde edilen risk öncelik katsayılarını analiz etmek için kullanılmıştır. Sonuçlara göre, ilk üç önemli kriter enfeksiyon, kanserojenlik ve çekme mukavemetidir. Malzemelerin sıralaması; titanyum > paslanmaz çelik > kobalt-krom alaşımları şeklindedir. Sonuç olarak bu çalışma, mevcut malzemelerin tarafsız değerlendirilmesi ve önceliklendirilmesi için bir temel oluşturmaktadır.
... Methods applicable for the risk assessment depend mainly on the type of recognized macro-and/or micro-risks and the availability of the data to characterize them [1]. For example, the quantitative supply risks evaluation could be conducted with mathematical programming and data envelopment analysis [21], multicriteria decision-making and AHP approach [22], decision tree method [23] or failure mode and effect analysis [24]. The risk mitigation stage is focused on the creation of models and scenarios that aim at minimization of the supply chain risk and its consequences. ...
Supply chain risk management (SCRM) is an approach implemented by retailers consisting of the identification, assessment, and mitigation of risks within their end-to-end supply chain. Systematic handling of risks and preparation of response scenarios for unusual events allows minimize losses and take advantage of opportunities in the functioning of the supply chain. Besides the organizational approach to risk management within the supply chain, there is a potential for the application of modern smart packaging in the control and/or reduction of quantitative and qualitative losses during the transport and storage of goods. Active packaging enable direct influence on the condition of the packed products. Intelligent packaging systems provide information about the current condition of the products and their surroundings. The combination of both technologies in conjunction with Radio-frequency identification (RFID) systems, Internet of Things (IoT) solutions, or other IT support provides significant potential to improve supply chain risk management tools.
... Beyond contract design, another way to mitigate supply chain risk is to be deliberate and strategic with one's choice of supply chain partners. Ravindran et al. (2010) solve a riskadjusted supplier selection problem via a two-phase approach. In the first phase, they use a ranking technique to narrow down their initial supplier base, before employing multiobjective optimization to allocate order quantities among surviving suppliers in the second phase. ...
We study an expected payoff maximization problem for a risk-sensitive broker aiming to evaluate the merits of designing and underwriting an option contract on a traded commodity with geometric Brownian motion (GBM) spot price trajectories. Candidate firms for whom the contract would mitigate the commodity’s price risk, each face Poisson demands that are currently the broker’s responsibility to satisfy. Subject to a variance risk budget and a robustness requirement, the broker’s objective is jointly to (1) choose a so-called trigger price function that will fundamentally define the option contract, and (2) select a value-maximizing set of client firms to whom the broker will offer the contract. We reformulate the problem as a bilevel program whose continuous relaxation we transform into a single-level, univariate problem with a convenient property that makes it amenable to line search methods. The optimal solution for that single-level problem is then raw material for constructing the optimal solution for the original problem. Our theoretical and experimental findings indicate that the contract’s optimal value, and optimal trigger price function are both strictly monotone increasing in a cost parameter in the model, as well as in the GBM’s volatility coefficient. The findings also show that those two quantities are strictly monotone decreasing in the GBM’s drift coefficient. We conclude with a benchmarking sensitivity study which uses real-world data to study the implications of violating a certain constraint which implicitly bounds the optimal trigger price.
... Tedarikçi seçimi, alternatif seçim yöntemleri açısından da ampirik araştırmalara sıkça konu edilmiştir (Dickson, 1966;Weber, Current ve Benton, 1991;Choi ve Hartley, 1996;Yahya and Kingsman, 1999;Sevkli, 2010;Ravindran, Bilsel, Wadhwa and Yang, 2010;Luo, Kwong, Tang, Deng ve Gong, 2011;Yu ve Wong, 2015;Yoon, Talluri, Yıldız ve Ho, 2018). Alanyazın bu açıdan incelendiğinde seramik sektöründe yapılan bir çalışmaya rastlanmasa da farklı sektörlerde tedarikçi seçiminde, bu çalışmada temel alınan AHP ve GRA yöntemlerini birlikte kullanan çalışmalar bulunmaktadır. ...
Günümüz rekabet ortamında küreselleşme ve dış kaynak kullanımı, işletmelerde tedarikçi seçimini önemli bir ortaklık konusu haline getirmiştir. Rekabet üstünlüğü açısından işletmeler, iş birliği içinde olduğu tedarikçiden; gerekli hammadde ve malzemelerin zamanında ve etkili bir şekilde sağlanmasını istemektedir. Üreticilerin rekabetçi pazardaki taleplerinin artmasıyla daha da karmaşık hale gelen tedarikçi seçim süreci; çok sayıda alternatif tedarikçi ile bu tedarikçilerin performanslarının değerlendirilmesinde niteliksel ve niceliksel pek çok kriteri içermesi nedeniyle çok kriterli bir karar verme problemidir. Bunun yanında tedarikçi performansları hakkında bilgi eksikliği veya bulanıklık bulunması, bu sürecin doğasında belirsizliğe- griliğe neden olmaktadır. Bu çalışma, tedarikçi seçim süreci için analitik hiyerarşi süreci (AHP) ve gri ilişkisel analiz (GİA) yaklaşımlarını birleştirerek seramik sektöründe değerlendirmek üzere yapılandırılmış entegre bir karar modeli sunmayı amaçlamaktadır. Çalışmada, tedarikçi değerlendirme ve seçim sürecinde kullanılabilecek, sektöre özgü kriter ve alt kriterlerden oluşan bir AHP Modeli geliştirilmiştir. Modelin çözümü, AHP ile tedarikçi seçim kriterleri ağırlıklarının hesaplanması ve GİA yardımıyla da alternatif tedarikçilerin değerlendirilmesi ile gerçekleştirilmiştir. Çalışmanın temel amacı, özelikle seramik sektörü için alternatif bir tedarikçi seçim modeli geliştirmek; belirsiz ve yetersiz verilerin olduğu koşullar altında bir çözüm sunmaktır. Ayrıca tedarikçilerin değerlendirilmesi ve seçilmesi sürecinde modelin, seramik sektöründe faaliyet gösteren işletmelerde bir karar destek aracı olarak kullanılması hedeflenmektedir. Böylece satın alma sürecindeki riskleri azaltıp, tedarikçiler ile uzun vadeli, karşılıklı değer yaratmaya odaklanan ve güvenilir ilişkilerin kurulması sağlanacaktır. Çalışmanın, tedarikçi seçim sürecinde fazla vakit ve maliyete katlanmadan; güvenilir, yansız ve bilimsel sonuçlara ulaşılması adına, seramik sektöründeki işletmelere ve bu alandaki literatüre katkı sağlayacağı düşünülmektedir.
... This has many benefits, including reducing of transaction costs, the ability to focus on core skills, applying of key technologies and mutual profitability with the company's supplier partners, and on the other hand increases the dependency between companies and their suppliers. They are somewhat defenceless against the risks, that this case confronts companies with a contradictory situation (Ravindran et al., 2010). ...
Nowadays, dependence of supply chain members while has provided many benefits for organisations, increases uncertainty and risk in the planning and organising activities. The risk of disrupting or failing to communicate with the supplier due to various disasters has occurred in recent years. In this regard, this study introduces a model of mixed integer programming (MIP), to provide resiliency under the terms of a portfolio’s risk by minimising the disturbance conditional value at risk is optimised. A model with two meta-heuristic algorithm and using real data is solved and compared. The results showed that the two algorithms do not differ significantly in the value of the objective function. Also the resolution time for the harmony search algorithm was 8.87 seconds and the imperialist competition algorithm was 8.28 seconds. Based on the results in general, the performance of the imperialist competition algorithm, although slightly less, is better than a harmony search algorithm.
... For example, a combined decision method 'Fuzzy-ELECTRE technique' is employed to address fuzziness and inaccuracy in supplier selection process, which avoids construction work of evaluation index system (Mehmet et al. 2010). However, according to the complexity of evaluation analysis process, risk elements are either to be adjusted (Ravi et al. 2010) or mitigated (Jiho et al. 2018, so as to reflect the accuracy of constructed model. Overall, in order to reflect more main influencing factors regarding complex equipment production on military-civilian platform that combines some useful evaluation methods, because of the whole process not only full of many risks, but also is a decision-making process. ...
Two-sided matching is a common research topic in our everyday life like marriage problem between men and women, schools and students. However, from the industrial perspective, the matching problem for complex equipment is a special research direction that needs professional participants that involve in the complex equipment production process. In this paper, a two-sided matching model for complex equipment production on the military–civilian integration platform is constructed that considers the reference satisfaction, and the reference satisfaction is firstly proposed which originates from operation management field. Moreover, the main influencing factors for complex equipment production are analyzed based on combined evaluation methods. The combined evaluation methods utilized in this research, one is DEMATEL (Decision Making Trial and Evaluation) method and the other is Grey–TOPSIS (Technique for Order Preference by Similarity for an Ideal Solution) method, are employed to acquire the joint importance results of the main analyzed influencing factors. The analyzed influencing factors are based on the evaluation indicators established in the evaluation indicator system for complex equipment production on the military–civilian merging platform, and the evaluation indicators are collected from relevant exist research. Then, suppliers and the manufacturers involved in the production of complex equipment give preference information to each other, and preference information is given on the basis of comprehensive analysis of the analyzed influencing factors which is by the aid of the aforementioned DEMATEL–Grey–TOPSIS analysis; meanwhile, the real satisfaction value of the suppliers and the manufacturers is obtained based on the preference value; then, the comprehensive reference satisfaction value could be obtained from the combination of the real satisfaction value and the expected satisfaction (real satisfaction value and disappointed satisfaction value), through aforementioned could we build a two-sided matching model for complex equipment on the military–civilian integration platform. Finally, we use a numerical example to illustrate the whole process about supplier–manufacturer matching for complex equipment production on the military–civilian integration platform.
Supplier selection (SS) has emerged as a critical challenge for companies aiming to enhance the operational management of their supply chains, a task that has grown in complexity with the advent of Industry 4.0 and the ongoing digital transformation. Recognizing the gaps in current literature—specifically, the lack of consideration for stakeholders' expectations in guiding SS, as well as the inadequate handling of epistemic and stochastic uncertainties—this paper introduces a multiple-criteria Quality Function Deployment (QFD)-based model for SS. To address epistemic uncertainty, we put forward a novel subjective judgment representation method, which is named as linguistic term set integrated with discrete subjective probability distribution (LTS-DSPD), to enable decision-makers to express their judgments in a manner that is both simpler and more nuanced. Furthermore, we also give the elicitation methods and computing techniques for LTS-DSPD. Then, we integrate stakeholders’ requirements, along with their preferences and expectations for these requirements to inform and guide SS. To effectively operationalize this guidance, we design the QFD-based methods to transform stakeholders' inputs into the assessment criteria for SS, the weights of criteria, and the expectations for the performances of suppliers on each criterion, respectively. To address stochastic uncertainty, we have developed an innovative methodology for characterizing it, and adopt prospect theory to quantify the overall utility of alternative suppliers. The paper concludes with a case study to demonstrate its practical application and effectiveness in streamlining SS process.
The ability to exchange ideas between practitioners and academics is critical to policymakers around the world. The emergence of low-incidence, high-severity disruption risks from Covid-19 is currently driving investment in resilient mitigation and adaptation plans in low-security environments. To ensure continuity in process management, the research project seeks to model and simulate the impact of systemic risks on the resilience of supply chains that experience large differences in lead times and deliveries.
The ability to recover between practitioners and academics is of central concern to policy makers globally. Currently, the context driven by the occurrence of low occurrence, high severity disruptive risks Covid19 has accelerated investment in resilient mitigation and adaptation programs from low certainty contexts.
The resilience between practitioners and academics is a central concern of policy makers worldwide. Currently, the context driven by the occurrence of disruptive low-occurrence, high-severity hazards has accelerated investment in resilient mitigation and adaptation programmes with spillover effects on low-certainty contexts. To ensure continuity in the management of flows, research programmes are attempting to model and simulate the effect of systemic risks on the resilience of supply chains that experience large variations in their lead times and delivery. The present study looks at the capabilities to improve recovery times in the presence of systemic risks before and during the occurrence of the Covid19 context. Then 35 systemic risk events are identified and prioritised using the Fuzzy AHP method and sequentially simulated using Fuzzy TOPSIS to assess the ability of the resilient and sustainable dimensions to mitigate and reduce the propagation of disruptive risks.
The resilience between practitioners and academics is a central concern of policy makers worldwide. Currently, the context driven by the occurrence of disruptive low-occurrence, high-severity hazards has accelerated investment in resilient mitigation and adaptation programmes with spillover effects on low-certainty contexts. To ensure continuity in the management of flows, research programmes are attempting to model and simulate the effect of systemic risks on the resilience of supply chains that experience large variations in their lead times and delivery. The present study looks at the capabilities to improve recovery times in the presence of systemic risks before and during the occurrence of the Covid19 context. Then 35 systemic risk events are identified and prioritised using the Fuzzy AHP method and sequentially simulated using Fuzzy TOPSIS to assess the ability of the resilient and sustainable dimensions to mitigate and reduce the propagation of disruptive risks.
In recent years, supply chain (SC) disruptions and their severe economic and social consequences have sparked a growing interest on the part of decision makers and researchers to adequately manage risk. The perception of risk is strongly related to the possibility of occurrence of disruptive events. In the supply chain risk management (SCRM) domain, disruptions such as the 2011 Japan earthquake and Hurricane Sandy have severely affected operations and put corporate finances at risk, becoming one of the most pressing concerns faced by companies competing in today's global marketplace. Without a doubt, the COVID-19 pandemic has exposed the fragility of SCs on a global scale as has never been seen in the past, impacting SCs from multiple sectors such as agriculture, manufacturing, transportation, leisure, to name a few and causing giants 32% drop in international trade in 2020 and an estimated 12% drop in the global economy. In order to mitigate and control the adverse effects caused by disruption risks, both in academia and in professional circles, important work is carried out in the area of SCRM. In recent times, scholars have utilized a various types of multi-criteria decision-making (MCDM) methods to evaluate sustainable supply chain risks in many contexts. Due to its importance, to date, there are no studies that can guide researchers and decision makers on what would be the most appropriate methods to face the multiple challenges posed by risk management in sustainable supply chains. In this study, we intend to cover this need, and for this, we carry out a careful review of 101 articles published since 2010. This review allows us to know the current state of MCDM applications in SCRM, and also to propose future research directions that allow us to properly manage the risks in sustainable SCs. We concluded that most of the studies used a single MCDM method or at most integrated two methods to assess sustainable supply chain risk. According to our findings, we propose a new future research agenda that considers, among others, the following: (a) use of MCDM methods to link risk with mitigation strategies, (b) integrate three or more MCDM methods to manage risks in the fields of cleaner and more sustainable production, and (c) development of methodologies that integrate MCDM with other operational research approaches such as optimization, simulation and mathematical modelling. We understand that these lines of research can contribute so that decision makers can better address the multiple consequences of increasingly frequent and intense disruptive events.
In the age of economic globalization when the market is becoming more challenging and pervasive, especially after Covid-19 and the Ukrainian war, businesses are working to strengthen their management systems and strategies. One of the most important functions to be considered is procurement, since it represents a key driver of the value chain that must enhance efficiency and effectiveness. Using emerging technologies to digitalize procurement function will help companies to provide more opportunities and gain a competitive edge by focusing more on customers’ requirement and supplier relationship. However, it is crucial to implement a procurement risk management system to avoid future potential issues and ensure leadership of companies. The main purpose of this research is to define the significant role of procurement 4.0 in providing greater flexibility and control over all aspects of the purchasing process. Moreover, it aims to bring light on the procurement risk management combined to digitalization. This paper is based on two real case studies related to: Food industry & Energy infrastructure industry.
Traditional auditing has been commonly practiced by multinational companies to monitor their suppliers for sustainability violations. Based on a collaborative supplier sustainability performance improvement program at Koninklijke (Royal) Philips N.V., we introduce a framework that offers a paradigm shift to an improvement-based proactive approach that makes use of suppliers’ self-assessments. We refer to this framework as CARE, consisting of the following phases: collecting supplier sustainability data, assessing suppliers’ sustainability levels, reacting to future violations proactively, and enhancing sustainability performance. The framework integrates analytics techniques to understand the link between the general characteristics of the carefully assessed suppliers—such as location, size, and sector—and their sustainability profile, enabling large-scale supplier assessment and improvement. This information is then used to leverage machine learning techniques to predict current and future sustainability levels of suppliers and to determine best actions for sustainability improvement using mathematical programming. The utilization of analytics constitutes a pivotal element in this endeavor and notably makes CARE highly scalable because it harnesses limited supplier data—namely, only general supplier information—while there is a need to support decision making concerning thousands of suppliers. Philips makes use of this framework and reports that the overall 2021 year-on-year improvement in sustainability performance was 24% for suppliers that entered the program in 2020, indicating the efficacy of the suggested approach.
History: This paper was refereed.
Funding: The authors gratefully acknowledge the support of TKI Dinalog–Dutch Institute for Advance Logistics on the project entitled “Supplier Sustainability Improvement” [Grant 2017-2-132TKI].
Pharmaceutical systems are care settings characterized by the wide range of activities and scenarios with which they would be challenged. This causes interactions that result in unexpected scenarios associated with a variety of dangers such as variations in demand, risks associated with supplier relationships, and so on…. As a result, risk management appears to be a major issue for decision-makers. Today supply chain management needs to be dynamic and responsive to create added value, and mitigates these risks by using flexibility strategies to respond to change while remaining competitive. The goal of this research is to look into the supply and demand strategies used by pharmaceutical companies to respond to disruptions and avoid medicine interruptions for consumers. In this thesis the supply chain risk management strategies are basically connected to an empirical study done in 32 Moroccan pharmaceutical enterprises. Following that, the participants’ responses were based on a questionnaire structured through the literature seeks to bring more detailed information about these strategies. Accordingly, the results showed that Moroccan pharmaceutical firms focus on some crucial activities such as supplier selection, flexible control, supplier control, vendor verification, and inventory management to account for supply and demand risk. Empirical studies in the field of pharmaceutical risk management are rare. Thus, the goal of this work is address the scarcity of empirical studies in this field by outlining several key risk management strategies used by pharmaceutical supply chain laboratories in the context of Morocco which are putting more resources into these vital strategies. Most of the supply and demand techniques of these firms are found to be proactive .
Supplier selection is the process by which companies identify, evaluate, and contract suppliers. The supplier selection process deploys the enormous amount of financial resources of a company and plays a vital role in the success of any company. The main purpose of the supplier selection process is to reduce purchasing risk, increase overall value to the buyer and fosters close and long-term relationships between buyer and supplier. The VIKOR system changed into advanced to improve the various standards of complex systems. It determines the compromise ranking listing and the compromise solution received by means of the preliminary (given) weights. This approach is from an opportunity set in the presence of conflicting standards Focuses on ranking and choice. It introduces a multi-criterion ranking index primarily based on a particular measure of-proximity‖ to the-fine‖ solution. The purpose of this take a look at is to extend the VIKOR technique for selection-making troubles with c program language period numbers. The ranking of the prolonged VIKOR approach is acquired by comparing the interval numbers and to make comparisons between the periods, the selection maker's self-assurance is fixed. Finally, more than a few instance illustrates and clarifies the key conclusions made on this paper, which we used the Vigor method for the following paper. In this paper we used VIKOR for ranking the VIKOR method is the most ideal solution Short-distance and Alternative The solution with the longest distance from the solution Determines, but the comparison of these distances Does not consider importance.
As supply chains have become more global and complex, supply chain disruptions have become more frequent (Resilinc. Supply chain disruptions- Resioinc’s mid-year report. https://www.resilinc.com/in-the-news/supply-chain-disruptions-resilincs-mid-year-report/, 2021) and severe (Craighead et al. Decision Sciences 38(1):131–156, 2007). It is thus imperative for public and private enterprises to develop and implement strategies to prevent supply chain disruptions from occurring and recover quickly from them when they occur. Enterprises can do so by first establishing an effective supply chain risk management (SCRM) process that identifies, assesses, and proposes strategies to manage and monitor supply chain risks. In this chapter, we review the SCRM process and describe its four stages: risk identification, risk assessment, risk management, and risk monitoring. In doing so, we propose practices and strategies that help enterprises identify, assess, manage (accept, avoid, transfer, or mitigate), and monitor supply chain risks. We also provide examples of how enterprises across industries have implemented SCRM and identify key technologies employed within this process. Finally, we review recent research on behavioral influences in the context of SCRM. The chapter, overall, emphasizes the impact of continued risk for supply chains due to the COVID-19 pandemic. This chapter serves as a resource to academics, students, and practitioners into the SCRM process, actionable strategies employed within each stage of this process, and behavioral factors influencing it.
A Closed-Loop Supply Chain Network (CLSCN) includes forward and reverse supply chains. In this research, a dynamic cellphone closed-loop supply chain is considered, which contains multiple plants, retailers, demand markets, and collection centers. The returned cellphones are significant parts of electronic waste, and there is an increasing demand for this product due to the technology advancement and population growth. In this investigation, a Mixed-Integer Linear Programming (MILP) model is proposed and solved with the GAMS software to configure a CLSCN in the Peel Region in Ontario, Canada. The results of the optimization model show which facilities should be open for minimization of the total cost of the network. Then, the model is extended to a multi-objective model by adding two objectives considering CO2 emissions and the quality aspects of the CLSCN. To handle the qualitative factors, a fuzzy Quality Function Deployment (QFD) model is discussed in this paper. Then, weighted-sums and ε-constraint methods are utilized to detect efficient solutions for the CLSCN. Finally, the results are discussed and the conclusions are provided. Based on the results, the weights of the objective functions have significant effects on both the selection of the facilities and the flows of the products between them.
The risks found in project management have a spatio-temporal effect and can interact with each other. Such interactions can be presented as a risk interaction network (RIN), with the nodes as the risks and the edges as the interaction. This article devises a twofold process to assess the RIN, namely, 1) a three-step procedure for the division, weight elicitation, and aggregation to assess the edges, and 2) a two-step procedure comprising the node classification and weight elicitation to qualify the nodes. The RIN evaluation takes the assessed RIN as the input, including an index accommodating the project manager's level of risk-aversion and a simulation-based network model for computing the index. A graphical user interface is developed to highlight the data collection and processing. An international construction project is provided to validate the proposed approach of RIN assessment and evaluation. The results inform that the proposed index provides more accurate information for risk analysis.
Publication dans la Revue de Management et Cultures (REMAC)
Supply Chain Risk Management has been increasingly paid attention by most of the researchers, and industrialist. This has become more popularity area of research. This paper aims at comprehensive literature survey the paper supply chain risk management published in relevant journals between 2010 and 2019. This literature survey is classified into five categories: empirical, conceptual, case study, descriptive, and exploratory. Further this study has also focused on the supply chain risk types and various mitigation strategies. This literature review will provide the basis for the outline future research opportunities in this field.
DETERMINATION OF CYTOTOXIC ACTIVITY OF Maclura pomifera EXTRACT AGAINST Hep G2 AND MCF-7 CELL LINES
Organizations have worked over the years to develop efficiencies to their supply chains, which includes efforts to reduce waste, lower costs, consolidate suppliers and distributors, better manage costs of goods sold and inventory, develop efficiencies in packaging, storage, and shipping of product, as well as utilizing digital analytics to manage consumer choices and demands. These are all by-products of world-class manufacturing which have promoted systematic organizational and supply chain efficiencies. However, under economic shocks that are sustained over longer periods of time (e.g., Covid-19 Pandemic) and that affect supply chains from a variety of disruptions, a supply chain that is not prepared or adaptable may be broken or at a minimum weigh down the organization. Therefore, the ability to manage and control risk is a key aspect of effective supply chain management. However, the literature on pandemic risk mitigation is nascent. Thus, this paper offers a review of the extant literature, provides a strategic mitigation model covering five dimensions: leadership, preparedness, digitalization, resilience, and pivoting. These dimensions are designed to help organizations in the future to be more adaptive to events such as global pandemics and other large-scale disruptions and discuss implications for future research.
Tedarik zinciri riskleri tedarik zinciri ağının farklı noktalarda ortaya çıkabilmektedir. Tedarik zinciri üyelerinin maliyetlerini arttıracak olan risklerin ve bu riskleri oluşturan risk faktörlerinin belirlenmesi riskleri önlemek veya olumsuz etkilerini azaltmak günümüz işletmelerinin ihtiyaç duydukları bir meseledir. Bu çalışmada, otomotiv sektörü tedarik zincirini tehdit eden risklerin belirlemesi ve risklerin birbirlerine olan etkilerinin analiz edilmesi amaçlanmıştır. Çalışmada nitel araştırma yöntemi kullanılmıştır. Araştırmanın evrenini Türkiye’deki otomotiv sanayisinde faaliyet gösteren ve İSO 500 listesinde yer alan üretim işletmelerindeki tedarik zinciri yöneticileri oluşturmaktadır. Kasti örnekleme yöntemiyle 15 farklı firmadan 20 tedarik zinciri yöneticisiyle yarı yapılandırılmış derinlemesine görüşmeler gerçekleştirilmiştir. Elde edilen verilerin analiz edilmesinde betimsel yöntem kullanılmıştır. Araştırmada bazı ana bulgulara ulaşılmıştır. Bunlardan birincisi, tedarik kaynaklı risklerin lojistik maliyetlerin artmasına ve teslim sürelerinin uzamasına neden olmasıdır. İkincisi, taşıma işlemlerindeki gecikmelerin taşımacılık risk faktörlerinden en önemlisi olduğudur. Üçüncüsü ise üretim risk faktörlerinin diğer risklerle karşılaştırıldığında daha kolay kontrol edilebildiğidir. Türkiye’de otomotiv tedarik zinciri riskleri konusunda, yöneticilerden elde edilen verilerin analiz edildiği ilk araştırma olması nedeniyle bu çalışma literatüre önemli bir katkı sağlamaktadır.
Purpose
The purpose of this paper is to propose an innovative integration method based on decision-theoretic rough set and the extended VlseKriterijuska Optimizacija I Komoromisno Resenje (VIKOR) methods to address the resilient-sustainable supplier selection and order allocation (SS/OA) problem.
Design/methodology/approach
Specifically, a two-stage approach is designed in this paper. First, the decision-theoretic rough set is employed to calculate the rough number for coping with the subjective uncertainty of data and assigning the weights for a resilient-sustainable evaluation criterion. On this basis, the supplier resilient-sustainable performance is ranked in combination with the extended VIKOR method. Second, a novel multi-objective optimization model is proposed that applies an improved genetic algorithm to select the resilient-sustainable supplier and allocate the corresponding order quantity under a multi-tier supplier network.
Findings
The results reveal that joint consideration of resilience and sustainability is essential in the SS/OA process. The method proposed in this study based on decision-theoretic rough sets and the extended VIKOR method can handle imprecise information flexibly, reduce information loss and obtain acceptable solutions for decision-makers. Numerical cases validate that this integrated approach can combine resilience and sustainability for effective and efficient SS/OA.
Practical implications
This paper provides industry managers with a new perspective on SS/OA from a resilience and sustainability perspective as a basis for best practices for industry resilience and sustainability. The proposed method helps to evaluate the resilient-sustainable performance of potential suppliers, which is applicable to solving real-world SS/OA problems and has important practical implications for the resilient-sustainable development of supply chains.
Originality/value
The two interrelated priorities of resilience and sustainability have emerged as key strategic challenges in SS/OA issues. This paper is the first study of this issue that uses the proposed integrated approach.
Contemporary supply chains are facing myriad types of risks caused by unprecedented risk factors. This condition motivates us to develop a contemporary supply chain risk typology to help identify and monitor newly surfaced risks and reveal emerging topics and research collaborators to help foster impactful research in supply chain risk management (SCRM). In this paper, we applied the scholarly network analysis approach to critically analyse an extensive list of 345 SCRM journal articles published from 2011 to 2020. We address two research questions: What is the contemporary supply chain risk typology? How can SCRM research be mapped comprehensively in terms of its emerging topics and research collaborators? First, we propose a novel and holistic classification of supply chain risks based on three interconnected perspectives, namely the characteristics, the location, and the impact of risks. Second, we identify five emerging SCRM topics. In each of these emerging topics, we identify the prominent collaborators and the core author, the main research themes, commonly used approaches and theories, and potential research agendas for bridging the identified research gaps. This paper contributes to the field of SCRM by aiding scholars and practitioners in managing contemporary supply chains resiliently and sustainably.
Purpose
The purpose of this study is to explore modifications to the supply chain strategies of international manufacturers resulting from recent momentous trade disruptions, including the US-China trade war, global tariff escalations, Brexit and heightened geopolitical tensions.
Design/methodology/approach
The research methodology consisted of a series of in-depth personal interviews with senior supply chain executives of six large US international manufacturers.
Findings
The study identified several short-term reactive strategies taken, including pulling purchase orders or production forward, building inventory and applying for exceptions from tariffs, along with longer-term proactive strategies such as shifting and expanding supplier and manufacturing locations.
Research limitations/implications
The study’s limitations involve its small sample size and its findings being industry and company-specific to a limited number of firms. While the sample size was deemed sufficient for this exploratory study, larger sample sizes and subsequent industry-specific analyses are recommended.
Practical implications
The global supply chain modifications effectively used by the firms in the study can offer guidance for practitioners facing similar challenges following major trade disruptions.
Originality/value
Due to the very recent emergence of the trade disruptors examined in this study, extant literature is limited. Hence, the findings noted in the paper offer not only guidance for practitioners but also make a strong contribution to research and literature on global supply chain risk management and disruption risk mitigation.
We study the influence of limited capacity and multiple products on the optimal sourcing decisions for a manufacturer subject to supply and demand uncertainty. The manufacturer produces two products. The component of one product is sourced from two suppliers, one perfectly reliable and the other cheaper but unreliable, failing to deliver the order with some probability. We develop a mathematical model for a single-period setting and study the associated optimality conditions. Our results show that the supplier diversification strategy is significantly different when the buyer has limited capacity. Specifically, we find that single sourcing from an expensive and reliable supplier may be an optimal procurement strategy even when a cheaper option exists. In addition, when producing multiple products, the optimal sourcing strategy may hold inventory of one component to reduce the amount of capacity idling when the supply of a different component is disrupted. We use stochastic programming to validate our single-period findings in a multi-period setting. Although carrying inventory of finished products provides another tactic to mitigate supply uncertainty in a multi-period environment, we observe the key findings from single period, like single-sourcing from an expensive but reliable supplier, remain valid.
In the latest years, supply chain academics and practitioners have been increasingly concerned with the supplier selection problem. In this paper, a computational system based on 'Fuzzy AHP' was developed for supplier selection considering risks. Specifically, we aimed to assess the use of the Fuzzy AHP method in supplier selection and to analyze the support of the proposed system to supplier selection considering risks through a practical application in the Brazilian oil industry. For this, the main criteria, sub-criteria, and alternatives for supplier selection were identified and comparatively assessed by expert managers from the focal company. Through the Fuzzy AHP computational approach the priority weights of each item were calculated. After computing the final score for each supply alternative, supplier 1 was defined as the preferred supplier as it obtained the highest priority weight (0.848), followed by supplier 2 with the second highest priority weight (0.151). The results of the research raised important opportunities for improving supplier selection considering risks, such as decision rationalization, flexible modeling of decision variables, and automation of subjective judgments and evaluations by decision-makers. These benefits may contribute to lower purchasing costs and improve the overall assessment of supply risks, promoting supply chain competitiveness.
Supply chain network design (SCND) is a key strategic decision in supply chain management (SCM). One particular area of SCND is concerned with disruption risk modelling. This paper pre-sents a systematic literature review of quantitative models of SCND under disruption risks in the ar-ea of industrial SCM and logistics. More specifically, our analysis is focused on different costs in-duced by the planning of proactive investments in robustness and through parametrical/structural adaptation due to disruption risks. This review can be of value for researchers and decision-makers alike for several reasons. First, we categorise the existing knowledge based on decision-making problems, which can be instructive for a convenient association of a particular SCND problem to a modelling domain according to network-wise, supply-side and demand-side perspectives. Second, our analysis focuses on the costs specifically induced by disruption risks and robustness invest-ments. Third, we offer a dedicated section related to disruption probability formulation methods and their impact on resilience costs. Fourth, the integration of four supplementary SCM paradigms (i.e., social impact, environmental impact, responsiveness, and risk-averse measures) and the associ-ated multi-objective modelling settings are discussed along with disruption risks in SCND models. Finally, we summarize our findings as insights from a managerial perspective. Drawbacks and miss-ing aspects in the related literature are highlighted, and we lay out several research directions and open questions for future research.
Supplier risks have attracted significant attention in the supply chain risk management literature. In this article, we propose a new computational system based on the 'Fuzzy Extended Analytic Hierarchy Process (FEAHP)' method for supplier selection while considering the relevant risks. We sought to evaluate the opportunities and limitations of using the FEAHP method in supplier selection and analyzed the support of the system developed through the real case of a Brazilian oil and natural gas company. The computational approach based on FEAHP automates supplier selection by determining a hierarchy of criteria, sub-criteria, and alternatives. First, the criteria and sub-criteria specific to the selection problem were identified by the experts taking the relevant literature as a starting point. Next, the experts performed a pair-wise comparison of the predefined requirements using a linguistic scale. This evaluation was then quantified by calculating the priority weights of criteria, sub-criteria, and alternatives. The best decision alternative is the one with the highest final score. Sensitivity analysis was performed to verify the results of the proposed model. The FEAHP computer approach automated the supplier selection process in a rational, flexible, and agile way, as perceived by the focal company. From this, we hypothesized that using this system can provide helpful insights in choosing the best suppliers in an environment of risk and uncertainty, thereby maximizing supply chain performance.
This paper proposes a mixed-integer non-linear programming (MINLP) model for the integrated supplier selection and order allocation in a centralized supply chain considering the disruption risks and a risk-averse decision-maker. In order to capture a realistic scenario of considering the geographical characteristics of the suppliers, we assume that the suppliers belong to two regions: the buyer’s region (domestic suppliers) and outside of the buyer’s region (foreign suppliers). Considering this realistic feature, the supply chain might face two types of disruption risk: first, local disruption risks which might uniquely occur inside each supplier such as equipment breakdowns, and second, regional disruption risks that might occur in the region of the suppliers located in the same geographical region such as natural hazards. We formulate the problem considering a risk-neutral decision-maker as a benchmark, and then a risk-averse model is presented. In the latter case, we apply two types of risk assessment tools introduced in the finance literature to analyze the decision maker’s behavior: value-at-risk (VaR) and conditional value-at-risk (CVaR). We show that developed models are non-convex programming, and therefore, we apply the particle swarm optimization (PSO) algorithm as the solution approach. We also compare the developed PSO algorithm with the Genetic algorithm (GA) and the commercial GAMS solver to verify the efficiency of the solution method. The computational experiments indicate the impact of the decision maker’s attitude on the supplier selection and the order quantity.
While the literature related to supply-chain disruptions is informative, it has primarily focused on supply-chain disruptions from a general or high-level view of the phenomenon (e.g. supply-chain uncertainty, risk perceptions). Additionally, although most would agree that disruptions are present in all supply chains, there is a limited amount of information on how to deal with them from a practical perspective in both the short term and long term. Because of the importance of and research needs within this area, we launched a major multi-industry, multi-methodology empirical study on supply-chain disruptions. The study is multi-faceted in that it seeks insights into many issues within the broad area of global sourcing and supply-chain disruptions. Throughout our various interactions with industry, we found that several common themes and issues surfaced as being critical to successful disruption analysis and mitigation as well as resilient supply-chain design. Within this paper, we report on these key issues and discuss the needs within the supply-chain research to contribute to them.
We present formulations for the strategic design of robust and reliable supply chains with long term contracting. The inability to deliver a supply part due to unexpected events in a complex supply chain can have a significant impact on the performance of a supply chain. Reliable and robust supply chains leverage cost and risk of not obtaining a supply. Reliable chains are less likely to be disrupted, whereas robust chains still perform well in the case of disrupted supply channels. We consider an integrated inbound supply chain of a manufacturer and we embed aspects of reliability and robustness into the traditional supply chain model. We provide computational results on two instances.
This paper explores the differences between demand and supply uncertainty in multi-echelon supply chains. Our central premise is that the insights gained from the study of one type of uncertainty often do not apply to the other. In fact, the two types of uncertainty are in a sense mirror images of each other, in that the optimal strategy for coping with supply uncertainty may be exactly opposite to that for demand uncertainty. We present several studies, each involving a fundamental question of order frequency, inventory placement, or supply chain structure. In each study, we consider a simple multi-echelon supply chain, first under demand uncertainty and then under supply uncertainty in the form of disruptions. Using simulation, we demonstrate that the optimal strategy is different under the two types of uncertainty and discuss reasons for the differences. We begin with a study demonstrating that the cost of failing to plan for disruptions is greater than that of failing to plan for demand uncertainty. We conclude a with a study that demonstrates that resiliency to disruptions is often inexpensive, in that large improvements in service level can be achieved with only small increases in cost. The remaining studies identify a number of important properties of supply chains subject to disruptions that have not previously been discussed in the literature. For example, we introduce the "risk-diversification effect," which describes the benefit of having multiple, decentralized stocking locations under supply uncertainty. This is the opposite of the classical risk-pooling effect, which describes the reverse tendency under demand uncertainty.
With globalization and the emergence of the extended enterprise of interdependent organizations, there has been a steady increase in the outsourcing of parts and services. This has led firms to give more importance to the purchasing function and its associated decisions. One of those decisions which impacts all firms’ areas is the supplier selection. Since the 1950s, several works have addressed this decision by treating different aspects and instances. In this paper, we extend previous survey papers by presenting a literature review that covers the entire purchasing process, considers both parts and services outsourcing activities, and covers internet-based procurement environments such as electronic marketplaces auctions. In view of its complexity, we will focus especially on the final selection stage that consists of determining the best mixture of vendors and allocating orders among them so as to satisfy different purchasing requirements. In addition, we will concentrate mainly on works that employ operations research and computational models. Thereby, we will analyze and expose the main decision's features, and propose different classifications of the published models.
This paper is aimed to present a fuzzy decision-making approach to deal with the supplier selection problem in supply chain system. During recent years, how to determine suitable suppliers in the supply chain has become a key strategic consideration. However, the nature of these decisions usually is complex and unstructured. In general, many quantitative and qualitative factors such as quality, price, and flexibility and delivery performance must be considered to determine suitable suppliers. In this paper, linguistic values are used to assess the ratings and weights for these factors. These linguistic ratings can be expressed in trapezoidal or triangular fuzzy numbers. Then, a hierarchy multiple criteria decision-making (MCDM) model based on fuzzy-sets theory is proposed to deal with the supplier selection problems in the supply chain system. According to the concept of the TOPSIS, a closeness coefficient is defined to determine the ranking order of all suppliers by calculating the distances to the both fuzzy positive-ideal solution (FPIS) and fuzzy negative-ideal solution (FNIS) simultaneously. Finally, an example is shown to highlight the procedure of the proposed method at the end of this paper. This paper shows that the proposed model is very well suited as a decision-making tool for supplier selection decisions.
We study a single-product setting in which a firm can source from two suppliers, one that is unreliable and another that is reliable but more expensive. Suppliers are capacity constrained, but the reliable supplier may possess volume flexibility. We prove that in the special case in which the reliable supplier has no flexibility and the unreliable supplier has infinite capacity, a risk-neutral firm will pursue a single disruption-management strategy: mitigation by carrying inventory, mitigation by single-sourcing from the reliable supplier, or passive acceptance. We find that a supplier’s percentage uptime and the nature of the disruptions (frequent but short versus rare but long) are key determinants of the optimal strategy. For a given percentage uptime, sourcing mitigation is increasingly favored over inventory mitigation as disruptions become less frequent but longer. Further, we show that a mixed mitigation strategy (partial sourcing from the reliable supplier and carrying inventory) can be optimal if the unreliable supplier has finite capacity or if the firm is risk averse.
Contingent rerouting is a possible tactic if the reliable supplier can ramp up its processing capacity, that is, if it has volume flexibility. We find that contingent rerouting is often a component of the optimal disruption-management strategy, and that it can significantly reduce the firm’s costs. For a given percentage uptime, mitigation rather than contingent rerouting tends to be optimal if disruptions are rare.
This paper studies a queuing model in which a buyer sources a good or service from an single supplier chosen from a pool of suppliers. The buyer seeks to minimize the sum of her procurement and operating cost, the latter of which depends on the supplier's lead time. The selected supplier can regulate his lead time, but faster lead times are costly. Although the buyer selects the supplier to source from (possibly via an auction) and dictates the contractual terms, the buyer's bargaining power is limited by asymmetric information: the buyer only has an estimate of the suppliers' costs while the suppliers know their costs precisely. We identify a procurement mech- anism that minimizes the buyer's total cost (procurement plus operating). This mechanism is not simple: it is a numerically derived non-linear menu of contracts. Therefore, we study several simpler mechanisms: e.g., one that charges a late fee and one that specifies a fixed lead time requirement (no menus, no non-linear functions). We find that simple mechanisms are nearly optimal (generally within 1% of optimal) because asymmetric infor- mation conveys significant protection to the supplier, i.e., the supplier is able to retain most of the benefit of having a lower cost. Renegotiation is
Contracting with suppliers prone to default is an increasingly common problem in some industries, particularly automotive manufacturing. We model this phenomenon as a two-period contracting game with two identical suppliers, a single buyer, deterministic demand, and uncertain production costs. The suppliers are distressed at the start of the game and do not have access to external sources of capital; hence, revenues from the buyer are crucial in determining whether default occurs. The production cost of each supplier is the sum of two stochastic components: a common term that is identical for both suppliers (representing raw materials costs, design specifications, etc.) and an idiosyncratic term that is unique to a given supplier (representing inherent firm capability). The buyer chooses a supplier and then decides on a single- or two-period contract. Comparing models with and without the possibility of default, we find that, without the possibility of supplier failure, the buyer always prefers short-term contracts over long-term contracts, whereas this preference is typically reversed in the presence of failure. Neither of these contracts coordinates the supply chain. We also consider dynamic contracts, in which the contract price is partially tied to some index representing the common component of production costs (e.g., commodity prices of raw materials such as steel or oil), allowing the buyer to shoulder some of the risk from cost uncertainty. We find that dynamic long-term contracts allow the buyer to coordinate the supply chain in the presence of default risk. We also demonstrate that our results continue to hold under a
Enterprise risk management (ERM) has become very important. The financial world is not immune to systemic failure, as demonstrated by many stories such as Barings Bank collapse in 1995, the failure of Long-Term Capital Management in 1998, and a handful of bankruptcy cases in the current financial crisis, e.g., the federal government’s takeover of Fannie Mae and Freddie Mac and the fall of Lehman fell and Merrill Lynch. There is no doubt that risk management is an important and growing area in the uncertain world.
Supply Chain Risk impacts every organization irrespective of sector, size or location in the supply chain. At the same time, the cost of managing supply chain risk is escalating significantly, as are the consequences of not managing such risks effectively. This Handbook represents the work of 30 different authors from 11 different countries, all of whom are recognized international authorities in research, practice, and policy associated with Supply Chain Risk Management (SCRM) and the wider domain of Supply Chain Management (SCM). The first truly comprehensive collection of current research and leading-edge thinking on supply chain risk and its management, the book is structured into four main sections: 1) Risk Analysis, Assessment and Tools; 2) Supply Chain Design and Risk; 3) Supply Chain Risk Management; and 4) Supply Chain Security.
Reflecting the proactive approach SCRM demands, the contributors tackle issues associated with identifying, measuring and classifying risks; designing supply chains to address inherent risks; risk management within the broader Enterprise Risk Management standard; risk in the small and medium-sized sector; disruptive behavior; supply chain security and intentional disruption; continuity planning; and many other topics. Designed to appeal to researchers, scholars, policy makers and practitioners alike, the Handbook is well grounded in robust empirical methodologies and evidence and represents the structures, practices and processes currently employed in industry.
Supplier selection is one of the most critical decisions in a supply chain. While good suppliers can contribute to the supply chain's overall performance, incorrect selection can drive the whole supply chain into disarray. In this paper, we focus on the problem of supplier selection in a manufacturing firm. We allow each supplier to compete with each other to be selected by the buyer for procurement. The competition is modelled in an auction framework as a bidding process where a supplier cannot observe immediate actions of other suppliers but has complete knowledge of their previous actions. We allow a supplier to use this knowledge in guessing other suppliers' future actions and bid accordingly. Our model enables repeated games, which can be assumed to be more flexible compared to most game theory applications in the supplier selection literature. Reinforcement learning and fictitious play are used in the auction framework to implement repeated games.
The paper deals with the development of an integrated supplier selection and multi-echelon distribution inventory model (MEDIM) for the original equipment manufacturing company in a built-to-order supply chain environment using fuzzy analytical hierarchy process (FAHP) and a genetic algorithm. The main objective is to design the integrated qualitative decision-making of the supplier selection model using FAHP with that of the quantitative mathematical model for the distribution inventory supply chain using a genetic algorithm to the built-to-order environment. The proposed model is validated by considering the case study in a tyre industry in Southern India. The software used for programming is C++ language.
In a big organization like the Australian Post Office, where large sums of money are spent to purchase a very large number of items every financial year, the problem is that of allocating the orders of the different items to the competing tenderers at a minimum cost. We have dealt with this in two sections; one is devoted to quantity discounts, the other to order value discounts. We have shown the mathematical formulation using integer and mixed integer linear programming, the mathematical programming systems used, the benefits gained and costs incurred, and the effects of the implementation of minimum cost allocation techniques on the organizational structure of some sections of the A.P.O. We have also shown that these techniques could be used to analyse the relative economic efficiencies of the competing tenderers with the objective of formulating a suitable economic purchasing policy for the organization concerned.
We use the method of probability-weighted moments to derive estimators of the parameters and quantiles of the generalized extreme-value distribution. We investigate the properties of these estimators in large samples, via asymptotic theory, and in small and moderate samples, via computer simulation. Probability-weighted moment estimators have low variance and no severe bias, and they compare favorably with estimators obtained by the methods of maximum likelihood or sextiles. The method of probability-weighted moments also yields a convenient and powerful test of whether an extreme-value distribution is of Fisher-Tippett Type I, II, or III.
Supplier Selection Process (SSP) becomes increasingly important for most manufacturing firms as it helps to reduce directly cost to the bottom line. The selection process involves the determination of quantitative and qualitative factors so as to select the best possible suppliers. It is essential to identify the relationship with the suppliers in terms of tangible factors. Owing to subjective human judgement in determining the relative importance of those selection factors, a method called Chain of Interaction is proposed to solve the problems associated with the dynamic nature of supply chain management. Focusing on the goodwill of an Analytic Hierarchy Process, an Interactive Selection Model is suggested to systemize the earlier steps, such as the determination of buyer-supplier relationships and formation of selection criteria, before the implementation of the Analytic Hierarchy Process with the help of Multi-Criterion Decision Making software called Expert Choice. The proposed Interactive Selection Model can be applied to supplier selection through the identification of buyer-supplier interactions and the valid data-collection methods. A numerical example is presented and the pros and cons of the model examined to seek further modification.
This paper describes a new multi-criteria method to solve the general supplier selection problem. The supplier selection problem is complicated and risky, owing to a variety of qualitative and quantitative factors affecting the decision-making process. For this matter, we present a unique three-phase methodology to reduce the base of potential suppliers to a manageable number and optimize the allocation of orders by means of multi-criteria techniques, namely ideal solution approach, Analytical Hierarchy Process (AHP) and Goal Programming (GP). Finally, a real-life example is provided to illustrate how the method can be used in practice. Significance: For those companies where the number of potential suppliers is large, the procurement decision process becomes increasingly complicated. In response to this, in this paper we present an appropriate method to simplify the final selection of suppliers.
In this paper, we study the performance of a sourcing mechanism gaining popularity in industrial procurement environments; a tournament. Under a tournament, a buyer initially procures her parts from two suppliers with possibly different quality levels, for T time periods, i.e., she parallel sources. During this time, the buyer is able to observe noisy signals about the suppliers' quality. At time T, she selects the supplier with the highest observed performance and awards it the remainder of her business. We characterize the optimal duration of the tournament as a function of various market parameters, including information and investment costs. Furthermore, we demonstrate that a tournament can be more profitable for the buyer than selecting the highest quality supplier at time T = 0 and sole sourcing entirely.
Concerned with the risk of supplier default, a firm may choose to diversify its orders among multiple suppliers. Furthermore, the discrepancy in production lead-times among suppliers furnishes a firm with a valuable option to defer ordering decisions until uncertainty has been partially resolved. The suppliers also have an option: to defer their pricing decisions. Using a single-period, multi-stage model of a two-echelon supply chain with competing risky suppliers and a single manufacturer, this paper investigates how the supplier default risk and default co-dependence affect manufacturer procurement and production decisions, supplier pricing decisions, firms profits, and the deferment option value and how the introduction of the deferment option alters supplier competition. © 2006 Wiley Periodicals, Inc. Naval Research Logistics 2006
There are two broad categories of risk affecting supply chain design and management: (1) risks arising from the problems of coordinating supply and demand, and (2) risks arising from disruptions to normal activities. This paper is concerned with the second category of risks, which may arise from natural disasters, from strikes and economic disruptions, and from acts of purposeful agents, including terrorists. The paper provides a conceptual framework that reflects the joint activities of risk assessment and risk mitigation that are fundamental to disruption risk management in supply chains. We then consider empirical results from a rich data set covering the period 1995–2000 on accidents in the U. S. Chemical Industry. Based on these results and other literature, we discuss the implications for the design of management systems intended to cope with supply chain disruption risks.
In any large organization, millions of dollars are spent on outsourcing. Most large organizations are outsourcing those activities that are either not cost efficient if done in-house or not core to their businesses. One of the most critical steps in outsourcing is vendor selection, which is a strategic decision. We model the vendor selection problem as a multi-objective optimization problem, where one or more buyers order multiple products from different vendors in a multiple sourcing network. Price, lead-time and rejects (quality) are explicitly considered as three conflicting criteria that have to be minimized simultaneously. A pricing model under quantity discounts is used to represent the purchasing cost. We present and compare several multi-objective optimization methods for solving the vendor selection problem. The methods include weighted objective, goal programming and compromise programming. The multicriteria models and the methods are illustrated using a realistic example. Value path approach is used to compare the results of different models.
Supplier selection is a multi-criteria decision making problem which includes both qualitative and quantitative factors. In order to select the best suppliers it is necessary to make a trade-off between these tangible and intangible factors some of which may conflict. When business volume discounts exist, this problem becomes more complicated as, in these circumstances, buyer should decide about two problems: which suppliers are the best and how much should be purchased from each selected supplier. In this article an integrated approach of analytical hierarchy process improved by rough sets theory and multi-objective mixed integer programming is proposed to simultaneously determine the number of suppliers to employ and the order quantity allocated to these suppliers in the case of multiple sourcing, multiple products, with multiple criteria and with supplier's capacity constraints. In this context, suppliers offer price discounts on total business volume, not on the quantity or variety of products purchased from them. A solution methodology is presented to solve the multi-objective model, and the model is illustrated using two numerical examples.
This paper develops an indirect evolutionary game model with two-vertically integrated channels to study evolutionarily stable strategies (ESS) of retailers in the quantity-setting duopoly situation with homogeneous goods and analyzes the effects of the demand and raw material supply disruptions on the retailers’ strategies. Every channel consists of one manufacturer and many (a sufficiently large number of) retailers that sell products in different markets by adopting two pure marketing strategies: profit maximization and revenue maximization. We find that revenue maximization strategy may prevail and profit maximization strategy may become extinct. Two strategies may coexist, i.e., all retailers in one channel will choose profit maximization strategy and all retailers in the other will choose revenue maximization strategy. The ESS of retailers depends on the relative size of the market scale and unit cost. The supply chain disruptions affect the ESS of retailers. We also introduce a recovery model of the supply chain under disruptions and illustrate the effect of disruptions on the ESS and on the average profits of channels in a market using a numerical simulation.
In this paper we present a review of decision methods reported in the literature for supporting the supplier selection process. The review is based on an extensive search in the academic literature. We position the contributions in a framework that takes the diversity of procurement situations in terms of complexity and importance into account and covers all phases in the supplier selection process from initial problem definition, over the formulation of criteria, the qualification of potential suppliers, to the final choice among the qualified suppliers. Moreover, we propose decision methods and techniques that previously have not been suggested in a purchasing context. The proposed methods specifically accommodate for buying situations for which few or no decision models were published so far. This paper extends previous reviews by Weber et al. (Eur. J. Oper. Res. 50 (1991) 2), Holt (Int. J. Project Mange. 16 (1998) 153) and Degraeve et al. (Eur. J. Oper. Res. 125 (1) (2000a) 34) in that it classifies the models in a framework developed by De Boer (Ph. D. Thesis, University of Twente, Enschede, The Netherlands, 1998) which recognises more steps in the buying process than only the final among qualified suppliers and accommodates for the diversity of procurement situations.
Supply chain management has gained renewed interest among researchers in recent years. This is primarily due to the availability of timely information across the various stages of the supply chain, and therefore the need to effectively utilize the information for improved performance. Although information plays a major role in effective functioning of supply chains, there is a paucity of studies that deal specifically with the dynamics of supply chains and how data collected in these systems can be used to improve their performance. In this paper I develop a framework, with machine learning, for automated supply chain configuration. Supply chain configuration used to be mostly a one-shot problem. Once a supply chain is configured, researchers and practitioners were more interested in means to improve performance given that initial configuration. However, recent developments in e-commerce applications and faster communication over the Internet in general necessitates dynamic (re)configuration of supply chains over time to take advantage of better configurations. Using examples, I show performance improvements of the proposed adaptive supply chain configuration framework over static configurations.
Managing risk has become a critical component of supply chain management. The implications of supply chain failures can be costly and lead to significant customer delivery delays. Though, different types of supply chain vulnerability management methodologies have been proposed for managing supply risk, most offer only point-based solutions that deal with a limited set of risks. This research aims to reinforce inbound supply chain risk management by proposing an integrated methodology to classify, manage and assess inbound supply risks. The contributions of this paper are four-fold: (1) inbound supply risk factors are identified through both an extensive academic literature review on supply risk literature review as well as a series of industry interviews; (2) from these factors, a hierarchical risk factor classification structure is created; (3) an analytical hierarchy processing (AHP) method with enhanced consistency to rank risk factor for suppliers is created; and (4) a prototype computer implementation system is developed and tested on an industry example.
Traditionally, supplier selection models are based on cardinal data with less emphasis on ordinal data. However, with the widespread use of manufacturing philosophies such as just-in-time (JIT), emphasis has shifted to the simultaneous consideration of cardinal and ordinal data in supplier selection process. To select the best suppliers in the presence of both cardinal and ordinal data, this paper proposes an innovative method, which is based on imprecise data envelopment analysis (IDEA). A numerical example demonstrates the application of the proposed method.
To gain cost advantage and market share, many firms implemented various initiatives such as outsourced manufacturing and product variety. These initiatives are effective in a stable environment, but they could make a supply chain more vulnerable to various types of disruptions caused by uncertain economic cycles, consumer demands, and natural and man-made disasters. In this paper, we review various quantitative models for managing supply chain risks. We also relate various supply chain risk management (SCRM) strategies examined in the research literature with actual practices. The intent of this paper is three-fold. First, we develop a unified framework for classifying SCRM articles. Second, we hope this review can serve as a practical guide for some researchers to navigate through the sea of research articles in this important area. Third, by highlighting the gap between theory and practice, we hope to motivate researchers to develop new models for mitigating supply chain disruptions.
This paper empirically documents the association between supply chain glitches and operating performance. The results are based on a sample of 885 glitches announced by publicly traded firms. Changes in various operating performance metrics for the sample firms are compared against a sample of control firms of similar size and from similar industries. In the year leading up to the announcement, the control-adjusted mean percent changes in operating income, return on sales, and return on assets for the sample firms are -107%, -114%, and -92%, respectively. During this same period, the control-adjusted changes in the level of return on sales and return on assets are -13.78% and -2.32%, respectively. Relative to controls, firms that experience glitches report on average 6.92% lower sales growth, 10.66% higher growth in cost, and 13.88% higher growth in inventories. More importantly, firms do not quickly recover from the negative economic consequences of glitches. During the two-year time period after the glitch announcement, operating income, sales, total costs, and inventories do not improve. We also find that it does not matter who caused the glitch, what the reason was for the glitch, or what industry a firm belongs to--glitches are associated with negative operating performance across the board.
Strategic sourcing plays a critical role in supply chain planning. Supplier selection is one of the decisions that determine the long-term viability of a company. In this paper, a new simulation optimization methodology is presented to make decisions on supplier selection. The methodology is composed of three basic modules: A genetic algorithm (GA) optimizer, a discrete-event simulator and a supply chain modelling framework. The GA optimizer continuously search different supplier portfolio and related operation parameters. Corresponding simulation models are automatically created through an object-oriented process. After simulation runs, the fitness value of candidate supplier portfolio is derived from the estimations of key performance indicators (KPI). The fitness is returned to the GA to be utilized in searching the next prominent direction. By using the proposed methodology, the supply chain planner is able to optimize the supplier portfolio with taking uncertainties into consideration. Finally, a real-life case study is presented to illustrate the applicability of the proposed methodology. Experimental results are presented and analysed.
In today's accelerating world economy, the drive to continually cut costs and focus on core competencies has driven many to outsource some or all of their production. In this environment, improving supply chain execution and leveraging the supply base through effective supplier relationship management (SRM) has become more critical than ever in achieving competitive advantage. It was found that the use of artificial intelligence in the outsourcing function of SRM to identify appropriate suppliers to form a supply network has become a promising solution on which manufacturers depend for products, services and distribution. In this paper, an intelligent supplier relationship management system (ISRMS) using hybrid case based reasoning (CBR) and artificial neural networks (ANNs) techniques to select and benchmark potential suppliers is discussed. By using ISRMS in Honeywell Consumer Product (Hong Kong) Limited, the outsource cycle time from searching for potential suppliers to the allocation of order is greatly reduced.
This study investigates a terminal transient response of a Langevin-type PZT vibrator theoretically and experimentally to quantify an electrical shock and refine an equivalent circuit of the vibrator. The shock is induced immediately after an AC sinusoidal voltage of the vibrator is switched off. Then, the transient response involves a DC part and an AC part, which approach zero at the DC and AC times, respectively, and the vibrator is placed on a sponge in air. To do so, we should propose an open-circuit test to find the AC and DC times in addition to the maximum amplitude of the transient response. Thus the DC times exceeds the AC time, and the AC and DC times are used to estimate the resistances in the equivalent circuit presenting the real mechanical and dielectric losses, respectively. Therefore, the resistances in the equivalent circuit are sensitive to the vibration amplitude, but the inductance and capacitances are not. Furthermore, the maximum amplitude is required to cause the shock, and depends on the frequency of the source and the open-circuited time, and is about 65 times the amplitude of the source.
Purchases from vendors involve significant costs for many firms. Decisions related to these purchases include the selection of vendors and the determination of order quantities to be placed with the selected vendors. Such decisions are frequently multiobjective in nature. That is, they are evaluated by more than one criterion. At least 23 criteria for various vendor selection problems have been identified. In this article, we present a multiobjective approach to systematically analyze the inherent tradeoffs involved in multicriteria vendor selection problems. The approach is motivated by, and demonstrated with, an actual purchasing problem facing a division of a Fortune 500 company.
In all operations research applications, the problem of implementation rests on the information conveyed to the decision maker. The presentation of results is a critical link in the success of a project. An ineffective transfer of information will reduce the chance of decision maker acceptance. This final step in the analysis is particularly difficult in multiobjective analyses, where the amount of relevent information increases with each performance measure.This paper will describe an alternative to the typical graphical approach to multiobjective display, which is adaptable to any number of objectives. A real world example is given and some theoretical insights are developed.
Supply chain management involves the selection, coordination and motivation of independently operated suppliers. The central planner's perspective in operations management translates poorly to vertically separated chains, where suppliers recurrently seem to object to benevolent information sharing and centralized decision rights. Seen from the supplier's perspective, such resistance may very well be rational. A downstream assembly line disclosing reliable information on actual and forecasted sales puts itself at a disadvantage when bargaining on share of chain profits. In this paper, we use a minimal agency model to contrast known optimal mechanisms with the actual practice in the telecommunications industry. A three-stage supply chain under stochastic demand and varying coordination and information asymmetry is modeled. A two-period investment–production game addresses the information sharing and specific investment problem in the telecom industry. The observed price–quantity contracts under limited commitment are shown to be inadequate under realistic asymmetric information assumptions. More a result of gradually evolving changes in bargaining power than coordination efforts, the upstream urge to coordinate may further deteriorate performance in terms of our model.
Little attention is given in the literature to decisions on the appropriate selection of suppliers, and on assigning order quantities to these suppliers, in the case of multiple sourcing, with multiple criteria and with suppliers’ capacity constraints. Only a few mathematical programming models to analyse such decisions have been published to date, and these have tended to consider only net price as the cost of purchasing, although the costs of transportation, ordering and storage may be significantly important to the decision. In this paper a mixed integer non-linear programming model is presented to solve the multiple sourcing problem, which takes into account the total cost of logistics, including net price, storage, transportation and ordering costs. Buyer limitations on budget, quality, service, etc. can also be considered in the model. An algorithm is proposed to solve the model, and the model is illustrated using a numerical example.
Supply chain risk management: minimising disruptions in global sourcing
- R B Handfield
- K Mccormack
Handfield, R.B. and McCormack, K., 2008. Supply chain risk management: minimising disruptions in global sourcing. Boca Raton, FL: Auerbach Publications.
Protecting supply chain networks against catastrophic failures. Working paper
- W J Hopp
- Z Yin
Hopp, W.J. and Yin, Z., 2006. Protecting supply chain networks against catastrophic failures. Working paper. Northwestern University.
Multiple criteria decision making Operations research and management science handbook
- A M Masud
- A Ravindran
Masud, A.M. and Ravindran, A., 2008. Multiple criteria decision making, In: A. Ravindran, ed. Operations research and management science handbook. Boca Raton, FL: CRC Press.
Multi-objective optimisation models for managing supply risks. Thesis (PhD)
- T Yang
Yang, T., 2006. Multi-objective optimisation models for managing supply risks. Thesis (PhD).
- Haimes Y