Optimization of the supplier selection process in prefabrication using BIM

Prefabrication offers substantial benefits including reduction in construction waste, material waste, energy use, labor demands, and delivery time, and an improvement in project constructability and cost certainty. As the material cost accounts for nearly 70% of the total cost of the prefabrication project, to select a suitable material supplier plays an important role in such a project. The purpose of this study is to present a method for supporting supplier selection of a prefabrication project. The proposed method consists of three parts. First, a list of assessment criteria was established to evaluate the suitability of supplier alternatives. Second, Building Information Modelling (BIM) was adopted to provide sufficient information about the project requirements and suppliers’ profiles, which facilitates the storage and sharing of information. Finally, the Analytic Hierarchy Process (AHP) was used to rank the importance of the assessment criteria and obtain the score of supplier alternatives. The suppliers were ranked based on the total scores. To illustrate how to use the proposed method, it was applied to a real prefabrication project. The proposed method facilitates the supplier selection process by providing sufficient information in an effective way and by improving the understanding of the project requirements

Supplier evaluation and selection in fuzzy environments: a review of MADM approaches

In past years, the multi-attribute decision-making (MADM) approaches have been extensively applied by researchers to the supplier evaluation and selection problem. Many of these studies were performed in an uncertain environment described by fuzzy sets. This study provides a review of applications of MADM approaches for evaluation and selection of suppliers in a fuzzy environment. To this aim, a total of 339 publications were examined, including papers in peer-reviewed journals and reputable conferences and also some book chapters over the period of 2001 to 2016. These publications were extracted from many online databases and classified in some categories and subcategories according to the MADM approaches, and then they were analysed based on the frequency of approaches, number of citations, year of publication, country of origin and publishing journals. The results of this study show that the AHP and TOPSIS methods are the most popular approaches. Moreover, China and Taiwan are the top countries in terms of number of publications and number of citations, respectively. The top three journals with highest number of publications were: Expert Systems with Applications, International Journal of Production Research and The International Journal of Advanced Manufacturing Technology

Employing dynamic fuzzy membership functions to assess environmental performance in the supplier selection process

The proposed system illustrates that logic fuzzy can be used to aid management in assessing a supplier's environmental performance in the supplier selection process. A user-centred hierarchical system employing scalable fuzzy membership functions implement human priorities in the supplier selection process, with particular focus on a supplier's environmental performance. Traditionally, when evaluating supplier performance, companies have considered criteria such as price, quality, flexibility, etc. These criteria are of varying importance to individual companies pertaining to their own specific objectives. However, with environmental pressures increasing, many companies have begun to give more attention to environmental issues and, in particular, to their suppliers’ environmental performance. The framework presented here was developed to introduce efficiently environmental criteria into the existing supplier selection process and to reflect on its relevant importance to individual companies. The system presented attempts to simulate the human preference given to particular supplier selection criteria with particular focus on environmental issues when considering supplier selection. The system considers environmental data from multiple aspects of a suppliers business, and based on the relevant impact this will have on a Buying Organization, a decision is reached on the suitability of the supplier. This enables a particular supplier's strengths and weaknesses to be considered as well as considering their significance and relevance to the Buying OrganizationPeer reviewe

A methodology to select suppliers to increase sustainability within supply chains

[EN] Sustainability practice within supply chains remains in an early development phase. Enterprises still need tools that support the integration of sustainability strategy into their activity, and to align their sustainability strategy with the supplier selection process. This paper proposes a methodology using a multi-criteria technique to support supplier selection decisions by taking two groups of inputs that integrate sustainability performance: supply chain performance and supplier assessment criteria. With the proposed methodology, organisations will have a tool to select suppliers based on their development towards sustainability and on their alignment with the supply chain strategy towards sustainability. The methodology is applied to an agri-food supply chain to assess sustainability in the supplier selection process.The authors of this publication acknowledge the contribution of Project GV/2017/065 'Development of a decision support tool for the management and improvement of sustainability in supply chains', funded by the Regional Valencian Government. Also, the authors acknowledge Project 691249, RUC-APS: Enhancing and implementing knowledge-based ICT solutions within high risk and uncertain conditions for agriculture production systems (www.ruc-aps.eu), funded by the European Union according to funding scheme H2020-MSCA-RISE-2015.Verdecho Sáez, MJ.; Alarcón Valero, F.; Pérez Perales, D.; Alfaro Saiz, JJ.; Rodríguez Rodríguez, R. (2021). A methodology to select suppliers to increase sustainability within supply chains. Central European Journal of Operations Research. 29:1231-1251. https://doi.org/10.1007/s10100-019-00668-3S1231125129Agarwal G, Vijayvargy L (2012) Green supplier assessment in environmentally responsive supply chains through analytical network process. In: Proceedings international multiconference of engineers and computer scientists, Hong KongAgeron B, Gunasekaran A, Spalanzani A (2012) Sustainable supply management: an empirical study. Int J Prod Econ 140(1):168–182Akarte MM, Surendra NV, Ravi B, Rangaraj N (2001) Web based casting supplier evaluation using analytical hierarchy process. J Oper Res Soc 52:511–522Alfaro Saiz JJ, Rodríguez R, Ortiz Bas A, Verdecho MJ (2010) An information architecture for a performance management framework by collaborating SMEs. Comput Ind 61:676–685Alfaro JJ, Ortiz A, Rodríguez R (2007) Performance measurement system for enterprise networks. Int J Prod Perform Manag 56(4):305–334Awasthi A, Govindan K, Gold S (2018) Multi-tier sustainable global supplier selection using a fuzzy AHP-VIKOR based approach. Int J Prod Econ 195:106–117Azadnia AH, Ghadimi P, Zameri M, Saman M, Wong KY, Heavey C (2013) An integrated approach for sustainable supplier selection using fuzzy logic and fuzzy AHP. Appl Mech Mater 315:206–221Azimifard A, Moosavirad SH, Ariafar S (2018) Selecting sustainable supplier countries for Iran’s steel industry at three levels by using AHP and TOPSIS methods. Resour Pol 57:30–44Bai C, Sarkis J (2010) Integrating sustainability into supplier selection with grey system and rough set methodologies. Int J Prod Econ 124:252–264Bhagwat R, Sharma MK (2007) Performance measurement of supply chain management: a balanced scorecard approach. Comput Ind Eng 53(1):43–62Bititci US, Mendibil K, Martinez V, Albores P (2005) Measuring and managing performance in extended enterprises. Int J Oper Prod Manag 25(4):333–353Brewer PC, Speh TW (2000) Using the balanced scorecard to measure supply chain performance. J Bus Logist 21(1):75–93Bullinger HJ, Kühner M, Hoof AV (2002) Analysing supply chain performance using a balanced measurement method. Int J Prod Res 40(15):3533–3543Chan FTS (2003) Interactive selection model for supplier selection process: an analytical hierarchy process approach. Int J Prod Res 41(15):3549–3579De Boer L, Labro E, Morlacchi P (2001) A review of methods supporting supplier selection. Eur J Purch Supply Manag 7(2):75–89Degraeve Z, Labro E, Roodhooft F (2000) An evaluation of supplier selection methods from a total cost of ownership perspective. Eur J Oper Res 125(1):34–58Dobos I, Vörösmarty G (2014) Green supplier selection and evaluation using DEA-type composite indicators. Int J of Prod Econ 157(11):273–278Dou Y, Sarkis J (2010) A joint location and outsourcing sustainability analysis for a strategic offshoring decision. Int J Prod Res 48(2):567–592Dyllick T, Hockerts K (2002) Beyond the business case for corporate sustainability. Bus Strategy Environ 11:130–141Falatoonitoosi E, Leman Z, Sorooshian S (2013) Modeling for green supply chain evaluation. Math Probl Eng 2013:1–9Farzad T, Rasid OM, Aidy A, Rosnah MY, Alireza E (2008) AHP approach for supplier evaluation and selection in a steel manufacturing company. JIEM 1(2):54–76Ferreira LMDF, Silva C, Garrido Azevedo S (2016) An environmental balanced scorecard for supply chain performance measurement (Env_BSC_4_SCPM). Benchmark Int J 23(6):1398–1422Figge F, Hahn T, Schaltegger S, Wagner M (2002) The sustainability balanced scorecard: linking sustainability management to business strategy. Bus Strat Env 11:269–284Folan P, Browne J (2005) Development of an extended enterprise performance measurement system. Prod Plan Control 16(6):531–544Freeman J, Chen T (2015) Green supplier selection using an AHP-entropy-TOPSIS framework. Supply Chain Manag 20:327–340Genovese A, Koh L, Bruno G, Esposito E (2013) Greener supplier selection: state of the art and some empirical evidence. Int J Prod Res 51(10):2868–2886Ghodsypour SH, O’Brien C (1998) A decision support system for supplier selection using an integrated analytic hierarchy process and linear programming. Int J Prod Econ 56–57:199–212Glock CH, Grosse EH, Ries JM (2017) Decision support models for supplier development: systematic literature review and research agenda. Int J Prod Econ 194:246–260Govindan K, Khodaverdi R, Jafarian A (2013) A fuzzy multi criteria approach for measuring sustainability performance of a supplier based on triple bottom line approach. J Clean Prod 47:345–354Govindan K, Rajendran S, Sarkis J, Murugesan P (2015) Multi criteria decision making approaches for green supplier evaluation and selection: a literature review. J Clean Prod 98:66–83Gunasekaran A, Patel C, Tirtiroglu E (2001) Performance measures and metrics in a supply chain environment. Int J Oper Prod Manag 21(1/2):71–87Ho W, Xu X, Dey PK (2010) Multi-criteria decision making approaches for supplier evaluation and selection: a literature review. Eur J Oper Res 202:16–24Hsu CW, Hu AH (2009) Applying hazardous substance management to supplier selection using analytic network process. J Clean Prod 17(2):255–264Hsu CW, Kuo TC, Chen SH, Hu AH (2013) Using DEMATEL to develop a carbon management model of supplier selection in green supply chain management. J Clean Prod 56:164–172Huan SH, Sheoran SK, Wang G (2004) A review and analysis of supply chain operations reference (SCOR) model. Supply Chain Manag Int J 9(9):23–29Hutchins M, Sutherland JH (2008) An exploration of measures of social sustainability and their application to supply chain decisions. J Clean Prod 16(15):1688–1698Igarashi M, Boer L, Magerholm Fet A (2013) What is required for greener supplier selection? A literature review and conceptual model development. J Purch Supply Manag 19(4):247–263Jimenez-Jimenez D, Martínez-Costa M, Sanchez Rodriguez C (2019) The mediating role of supply chain collaboration on the relationship between information technology and innovation. J Knowl Manag 23(3):548–567Kaplan RS, Norton DP (1992) The balanced scorecard: measures that drive performance. Harvard Bus Rev 70(1):71–79Luthra S, Govindan K, Kannan D, Kumar Mangla S, Prakash Garg C (2017) An integrated framework for sustainable supplier selection and evaluation in supply chains. J Clean Prod 140:1686–1698Maestrini V, Luzzini D, Maccarrone P, Caniato F (2017) Supply chain performance measurement systems: a systematic review and research agenda. Int J Prod Econ 183A:299–315Masella C, Rangone A (2000) A contingent approach to the design of vendor selection systems for different types of co-operative customer/supplier relationships. Int J Oper Prod Manag 20(1):70–84Miller GA (1956) The magical number seven, plus or minus two: some limits on our capacity for processing information. Psychol Rev 63:81–97Mohammed A, Harris I, Govindan K (2019) A hybrid MCDM-FMOO approach for sustainable supplier selection and order allocation. Int J Prod Econ 217:171–184Motevali-Haghighi S, Torabi SA, Ghasemi R (2016) An integrated approach for performance evaluation in sustainable supply chain networks (with a case study). J Clean Prod 137:579–597Nawaz W, Koç M (2018) Development of a systematic framework for sustainability management of organizations. J Clean Prod 171:1255–1274Nie X (2013) Green suppliers selecting based on analytic hierarchy process for biotechnology industry. In: Zhong Z (ed) Proceedings of the international conference on information engineering and applications. Springer, London, pp 253–260Nielsen IE, Banaeian N, Golińska P, Mobli H, Omid M (2014) Green supplier selection criteria: from a literature review to a flexible framework for determination of suitable criteria. In: Golinska P (ed) Logistics operations, supply chain management and sustainability. Springer, Cham, pp 79–99Noci G (1997) Designing ‘green’ vendor rating systems for the assessment of a supplier’s environmental performance. Eur J Purch Supply Manag 3(2):103–114Petersen KJ, Handfield RB, Ragatz GL (2005) Supplier integration into new product development: coordinating product, process and supply chain design. J Oper Manag 23:371–388Pishchulov G, Trautrims A, Chesney T, Gold S, Schwab L (2019) The voting analytic hierarchy process revisited: a revised method with application to sustainable supplier selection. Int J Prod Econ 211:166–179Popovic T, Kraslawski A, Barbosa-Póvoa A, Carvalho A (2017) Quantitative indicators for social sustainability assessment of society and product responsibility aspects in supply chains. J Int Stud 10(4):9–36Qorri A, Mujki Z, Kraslawski A (2018) A conceptual framework for measuring sustainability performance of supply chains. J Clean Prod 189:570–584Reefke H, Trocchi M (2013) Balanced scorecard for sustainable supply chains: design and development guidelines. Int J Prod Perform Manag 62(8):805–826Saaty TL (1980) The analytic hierarchy process. McGraw-Hill, New YorkSaaty RW (1987) The analytic hierarchy process: what it is and how it is used. Math Model 9(3–5):161–176Saaty TL (2008) Decision making with the analytic hierarchy process. Int J Serv Sci 1(1):83–98Saaty TL, Ozdemir MS (2003) Why the magic number seven plus or minus two. Math Comput Model 38(3–4):233–244Seuring S, Müller M (2008) From a literature review to a conceptual framework for sustainable supply chain management. J Clean Prod 16:1699–1710Shaik M, Abdul-Kader W (2011) Green supplier selection generic framework: a multi-attribute utility theory approach. Int J Sustain Eng 4(1):37–56Shi P, Yan B, Shi S, Ke C (2015) A decision support system to select suppliers for a sustainable supply chain based on a systematic DEA approach. Inf Technol Manag 16(1):39–49Superdecisions (2018) Tutorial on hierarchical decision models. Creative Decisions Foundation. https://www.superdecisions.com/sd_resources/v28_man03.pdf. Accessed 7 Jan 2018Thakkar J, Kanda A, Deshmukh S (2009) Supply chain performance measurement framework for small and medium scale enterprises. Benchmark Int J 16(5):702–723Theißen S, Spinler S (2014) Strategic analysis of manufacturer–supplier partnerships: an ANP model for collaborative CO2 reduction management. Eur J Oper Res 233(2):383–397Tseng ML, Lim M, Wong WP (2015) Sustainable supply chain management: a closed-loop network hierarchical approach. Ind Manag Data Syst 115(3):436–461Uysal F (2012) An integrated model for sustainable performance measurement in supply chain. Proc Soc Behav Sci 62:689–694Valenzuela L, Maturana S (2016) Designing a three-dimensional performance measurement system (SMD3D) for the wine industry: a Chilean example. Agric Syst 142:112–121Verdecho MJ, Alfaro-Saiz JJ, Rodriguez-Rodriguez R, Ortiz-Bas A (2012) A multi-criteria approach for managing inter-enterprise collaborative relationships. Omega 40:249–263Virender P, Jayant A (2014) A green supplier selection model for an agriculture-machinery industry. Int J Appl Eng Res 9(5):597–605Weber CA, Current JR, Benton WC (1991) Vendor selection criteria and methods. Eur J Oper Res 50(1):2–18Xu L, Kumar DT, Madan Shankar K, Kannan D, Chen G (2013) Analyzing criteria and sub-criteria for the corporate social responsibility-based supplier selection process using AHP. Int J Adv Manuf Technol 68(1–4):907–916Xu Z, Qin J, Liu J, Martínez L (2019) Sustainable supplier selection based on AHP Sort II in interval type-2 fuzzy environment. Inf Sci 483:273–293Zaklad A, McKnight R, Kosansky A, Piermarini J (2004) The social side of the supply chain. Ind Eng 36(2):40–44Zhe S, Wong NT, Lee LH (2013) Using data envelopment analysis for supplier evaluation with environmental considerations. In: International systems conference, OrlandoZimmer K, Fröhling M, Schultmann F (2016) Sustainable supplier management: a review of models supporting sustainable supplier selection, monitoring and development. Int J Prod Res 54(5):1412–144

Fuzzy Analytical Hierarchy Process for Supplier Selection: A Case Study in An Electronic Component Manufacturer

Supplier selection has become one of the essential effects on the entire electronic supply chain network to gain competitiveness. In the upstream supply chain, companies are able to achieve a high quality and value of products to reduce the potential risks from both internal and external stakeholders by selecting the right suppliers. The case study company produces a nano sim-card connector in which four different types of raw materials are processed into different parts. Currently, the case study company selects each raw material supplier based on its appraisal record. Nevertheless, the appraisal record is measured by the department of procurement. When candidate suppliers are categorized at the same level, the cost becomes the priority criteria to select the supplier, which increases the potential risks of, for example, the components defect rate, a penalty from clients, and a reduction in orders. This paper proposed a Fuzzy analytic hierarchy process (FAHP) model for the selection of raw material suppliers by collecting data from two of the company’s departments (procurement and engineering) and the clients to address qualitative and quantitative elements, uncertainty, and linguistic vagueness based on the company’s scenario in two parts. First, the main and sub-criteria can be weighted using a decision-maker (DM) to identify the level of importance. Second, the FAHP model also dealt with personal preferences and judgement so that the right supplier(s) for each raw material could be selected by collecting and computing the data from the respondents. Then, the sensitivity analysis is applied to observe how the decisions change when the model parameters in the top five sub-criteria change. The proposed model can offer better information and solutions for the DM in the case study company to differentiate the crucial main and sub-criteria and select the suitable raw material suppliers effectively

Optimal Supplier Selection Model with Multiple Criteria: A Case Study in the Automotive Parts Industry

This research proposes a mathematical model for supplier selection for a case-study car seat manufacturer. This research is divided into 2 parts. The first part is the raw material supplier evaluation method using Analytic Hierarchy Process. This part weights the importance of main decision criteria and sub-decision criteria, complying with part makers’ satisfaction. The result from the first part is scores for each raw material supplier resulting from multiple evaluation criteria. The second part proposes a mathematical model for supplier selection using integer programming. The scores of each supplier from the first part will be considered along with raw material consumption to select the suitable raw material suppliers that maximize overall part makers’ satisfaction. The results from the first part of this research show that the most important criterion for supplier evaluation is cost, which is about 41%. Quality, Delivery, Service, and Risk factors are approximately 24%, 14%, 12% and 9%, respectively. The result from the second part shows that the model can effectively match material suppliers to part makers according to their preferences. Comparing with current situation, the satisfaction is increased by 26% with this proposed framework. It means the proposed model can help matching the right supplier to each part maker that can increase overall satisfactions for this case-study’s supply chain

Analysis of Decision Support Systems of Industrial Relevance: Application Potential of Fuzzy and Grey Set Theories

The present work articulates few case empirical studies on decision making in industrial context. Development of variety of Decision Support System (DSS) under uncertainty and vague information is attempted herein. The study emphases on five important decision making domains where effective decision making may surely enhance overall performance of the organization. The focused territories of this work are i) robot selection, ii) g-resilient supplier selection, iii) third party logistics (3PL) service provider selection, iv) assessment of supply chain’s g-resilient index and v) risk assessment in e-commerce exercises. Firstly, decision support systems in relation to robot selection are conceptualized through adaptation to fuzzy set theory in integration with TODIM and PROMETHEE approach, Grey set theory is also found useful in this regard; and is combined with TODIM approach to identify the best robot alternative. In this work, an attempt is also made to tackle subjective (qualitative) and objective (quantitative) evaluation information simultaneously, towards effective decision making. Supplier selection is a key strategic concern for the large-scale organizations. In view of this, a novel decision support framework is proposed to address g-resilient (green and resilient) supplier selection issues. Green capability of suppliers’ ensures the pollution free operation; while, resiliency deals with unexpected system disruptions. A comparative analysis of the results is also carried out by applying well-known decision making approaches like Fuzzy- TOPSIS and Fuzzy-VIKOR. In relation to 3PL service provider selection, this dissertation proposes a novel ‘Dominance- Based’ model in combination with grey set theory to deal with 3PL provider selection, considering linguistic preferences of the Decision-Makers (DMs). An empirical case study is articulated to demonstrate application potential of the proposed model. The results, obtained thereof, have been compared to that of grey-TOPSIS approach. Another part of this dissertation is to provide an integrated framework in order to assess gresilient (ecosilient) performance of the supply chain of a case automotive company. The overall g-resilient supply chain performance is determined by computing a unique ecosilient (g-resilient) index. The concepts of Fuzzy Performance Importance Index (FPII) along with Degree of Similarity (DOS) (obtained from fuzzy set theory) are applied to rank different gresilient criteria in accordance to their current status of performance. The study is further extended to analyze, and thereby, to mitigate various risk factors (risk sources) involved in e-commerce exercises. A total forty eight major e-commerce risks are recognized and evaluated in a decision making perspective by utilizing the knowledge acquired from the fuzzy set theory. Risk is evaluated as a product of two risk quantifying parameters viz. (i) Likelihood of occurrence and, (ii) Impact. Aforesaid two risk quantifying parameters are assessed in a subjective manner (linguistic human judgment), rather than exploring probabilistic approach of risk analysis. The ‘crisp risk extent’ corresponding to various risk factors are figured out through the proposed fuzzy risk analysis approach. The risk factor possessing high ‘crisp risk extent’ score is said be more critical for the current problem context (toward e-commerce success). Risks are now categorized into different levels of severity (adverse consequences) (i.e. negligible, minor, marginal, critical and catastrophic). Amongst forty eight risk sources, top five risk sources which are supposed to adversely affect the company’s e-commerce performance are recognized through such categorization. The overall risk extent is determined by aggregating individual risks (under ‘critical’ level of severity) using Fuzzy Inference System (FIS). Interpretive Structural Modeling (ISM) is then used to obtain structural relationship amongst aforementioned five risk sources. An appropriate action requirement plan is also suggested, to control and minimize risks associated with e-commerce exercises

An integrated model for green partner selection and supply chain construction

Stricter governmental regulations and rising public awareness of environmental issues are pressurising firms to make their supply chains greener. Partner selection is a critical activity in constructing a green supply chain because the environmental performance of the whole supply chain is significantly affected by all its constituents. The paper presents a model for green partner selection and supply chain construction by combining analytic network process (ANP) and multi-objective programming (MOP) methodologies. The model offers a new way of solving the green partner selection and supply chain construction problem both effectively and efficiently as it enables decision-makers to simultaneously minimize the negative environmental impact of the supply chain whilst maximizing its business performance. The paper also develops an additional decision-making tool in the form of the environmental difference, the business difference and the eco-efficiency ratio which quantify the trade-offs between environmental and business performance. The applicability and practicability of the model is demonstrated in an illustration of its use in the Chinese electrical appliance and equipment manufacturing industry

Clustering sustainable suppliers in the plastics industry: A fuzzy equivalence relation approach

Nowadays, pure economic supply chain management is not commonly contemplated among companies (especially buyers), as recently novel dimensions of supply chains, e.g., environmental, sustainability, and risk, play significant roles. In addition, since companies prefer buying their needs from a group of suppliers, the problem of supplier selection is not solely choosing or qualifying a supplier from among others. Buyers, hence, commonly assemble a portfolio of suppliers by looking at the multi-dimensional pre-determined selection criteria. Since sustainable supplier selection criteria are often assessed by linguistic terms, an appropriate clustering approach is required. This paper presents an innovative way to implement fuzzy equivalence relation to clustering sustainable suppliers through developing a comprehensive taxonomy of sustainable supplier selection criteria, including supply chain risk. Fifteen experts participated in this study to evaluate 20 suppliers and cluster them in the plastics industry. Findings reveal that the best partitioning occurs when the suppliers are divided into two clusters, with 4 (20%) and 16 (80%) suppliers, respectively. The four suppliers in cluster one are performing better in terms of the capability of supplier/delivery, service, risk, and sustainability criteria such as environment protection/management, and green innovation. These factors are critical in clustering and selecting sustainable suppliers. The originality of this study lies in developing an all-inclusive set of criteria for clustering sustainable suppliers and adding risk factors to the conventional supplier selection criteria. In addition to partitioning the suppliers and determining the best-performing ones, this study also highlights the most influential factors by analysing the suppliers in the best cluster