Uncertain Multi-Criteria Optimization Problems

Most real-world search and optimization problems naturally involve multiple criteria as objectives. Generally, symmetry, asymmetry, and anti-symmetry are basic characteristics of binary relationships used when modeling optimization problems. Moreover, the notion of symmetry has appeared in many articles about uncertainty theories that are employed in multi-criteria problems. Different solutions may produce trade-offs (conflicting scenarios) among different objectives. A better solution with respect to one objective may compromise other objectives. There are various factors that need to be considered to address the problems in multidisciplinary research, which is critical for the overall sustainability of human development and activity. In this regard, in recent decades, decision-making theory has been the subject of intense research activities due to its wide applications in different areas. The decision-making theory approach has become an important means to provide real-time solutions to uncertainty problems. Theories such as probability theory, fuzzy set theory, type-2 fuzzy set theory, rough set, and uncertainty theory, available in the existing literature, deal with such uncertainties. Nevertheless, the uncertain multi-criteria characteristics in such problems have not yet been explored in depth, and there is much left to be achieved in this direction. Hence, different mathematical models of real-life multi-criteria optimization problems can be developed in various uncertain frameworks with special emphasis on optimization problems

An innovative framework for implementing lean principles in product- service system

The aim of this research project is to develop an innovative framework to implement lean principles in Product-Service System (PSS) with the capability of assessing the leanness level of the services offering process. The framework comprises three implementation phases namely: assessment of the current state, developing a future state, and stabilising the new way of operations. Additionally, the framework covers the enablers, factors, and appropriate lean tools required for the successful implementation of lean practices in Product-Service System (PSS), as well as, the challenges that may obstacle the implementation process. The proposed framework has integrated an assessment model that provides a quantifiable measure of the leanness level of Product-Service System (PSS). Five main enablers and thirty three factors emerging from these main enablers deemed to be critical for the successful implementation of lean practices in Product-Service System (PSS). Moreover, a series of eight inhibitors appeared to block the implementation process. The Product-Service System leanness assessment model was developed upon three main levels, namely: enablers, criteria, and attributes. The first level contains five enablers. These enablers are supplier relationship, management leanness, workforce leanness, process excellence, and customer relationship. In the second level there are twenty one criteria such as: supplier delivery, culture of management and process optimisation. Finally, the third level consists of seventy three attributes. By using multi-grade fuzzy approach the PSS leanness index was computed and areas for further improvement were identified. A combination of research methodology approaches has been employed in this research. Firstly, an extensive literature review related to lean and PSS was conducted. Secondly, the qualitative approach and the case study were selected as an appropriate methodology for this research, using semi-structured and structured interview techniques to gather the required data from experts who are involved in lean projects in their companies. Finally, validation of the results was carried out using real life industrial case studies and experts judgment. Case studies demonstrate that the framework provides guidelines for manufacturing companies that aim to implement lean principles in Product-Service System (PSS). The framework enables manufacturing companies to better satisfy their customers’ needs through responding quickly to their changing demands; to improve the service offering process through reducing the creation of wastes and non-value added activities; and to improve competitiveness through increasing customers’ value. Additionally, the PSS leanness index is useful for improving the service offering process. The index provides manufacturing companies with a real insight into the leanness level of their service offering, as well as, it provides managers with a quantifiable measure of how lean their PSS is. The index identifies the gap between the current state and the future state and this helps in determining areas for further improvement

Data quality issues in electronic health records for large-scale databases

Data Quality (DQ) in Electronic Health Records (EHRs) is one of the core functions that play a decisive role to improve the healthcare service quality. The DQ issues in EHRs are a noticeable trend to improve the introduction of an adaptive framework for interoperability and standards in Large-Scale Databases (LSDB) management systems. Therefore, large data communications are challenging in the traditional approaches to satisfy the needs of the consumers, as data is often not capture directly into the Database Management Systems (DBMS) in a seasonably enough fashion to enable their subsequent uses. In addition, large data plays a vital role in containing plenty of treasures for all the fields in the DBMS. EHRs technology provides portfolio management systems that allow HealthCare Organisations (HCOs) to deliver a higher quality of care to their patients than that which is possible with paper-based records. EHRs are in high demand for HCOs to run their daily services as increasing numbers of huge datasets occur every day. Efficient EHR systems reduce the data redundancy as well as the system application failure and increase the possibility to draw all necessary reports. However, one of the main challenges in developing efficient EHR systems is the inherent difficulty to coherently manage data from diverse heterogeneous sources. It is practically challenging to integrate diverse data into a global schema, which satisfies the need of users. The efficient management of EHR systems using an existing DBMS present challenges because of incompatibility and sometimes inconsistency of data structures. As a result, no common methodological approach is currently in existence to effectively solve every data integration problem. The challenges of the DQ issue raised the need to find an efficient way to integrate large EHRs from diverse heterogeneous sources. To handle and align a large dataset efficiently, the hybrid algorithm method with the logical combination of Fuzzy-Ontology along with a large-scale EHRs analysis platform has shown the results in term of improved accuracy. This study investigated and addressed the raised DQ issues to interventions to overcome these barriers and challenges, including the provision of EHRs as they pertain to DQ and has combined features to search, extract, filter, clean and integrate data to ensure that users can coherently create new consistent data sets. The study researched the design of a hybrid method based on Fuzzy-Ontology with performed mathematical simulations based on the Markov Chain Probability Model. The similarity measurement based on dynamic Hungarian algorithm was followed by the Design Science Research (DSR) methodology, which will increase the quality of service over HCOs in adaptive frameworks

Bioinspired metaheuristic algorithms for global optimization

This paper presents concise comparison study of newly developed bioinspired algorithms for global optimization problems. Three different metaheuristic techniques, namely Accelerated Particle Swarm Optimization (APSO), Firefly Algorithm (FA), and Grey Wolf Optimizer (GWO) are investigated and implemented in Matlab environment. These methods are compared on four unimodal and multimodal nonlinear functions in order to find global optimum values. Computational results indicate that GWO outperforms other intelligent techniques, and that all aforementioned algorithms can be successfully used for optimization of continuous functions

Experimental Evaluation of Growing and Pruning Hyper Basis Function Neural Networks Trained with Extended Information Filter

In this paper we test Extended Information Filter (EIF) for sequential training of Hyper Basis Function Neural Networks with growing and pruning ability (HBF-GP). The HBF neuron allows different scaling of input dimensions to provide better generalization property when dealing with complex nonlinear problems in engineering practice. The main intuition behind HBF is in generalization of Gaussian type of neuron that applies Mahalanobis-like distance as a distance metrics between input training sample and prototype vector. We exploit concept of neuron’s significance and allow growing and pruning of HBF neurons during sequential learning process. From engineer’s perspective, EIF is attractive for training of neural networks because it allows a designer to have scarce initial knowledge of the system/problem. Extensive experimental study shows that HBF neural network trained with EIF achieves same prediction error and compactness of network topology when compared to EKF, but without the need to know initial state uncertainty, which is its main advantage over EKF

World Multidisciplinary Civil Engineering- Architecture- Urban Planning symposium

We would like to express our sincere gratitude to all 900+ submissions by 600+ participants of WMCAUS 2018 from 60+ different countries all over the world for their interests and contributions in WMCAUS 2018. We wish you enjoy the World Multidisciplinary Civil Engineering-Architecture-Urban Planning Symposium – WMCAUS 2018 and have a pleasant stay in the city of romance Prague. We hope to see you again during next event WMCAUS 2019 which will be held in Prague (Czech Republic) approximately in the similar period

A Systematic Literature Review Protocol for the analysis of practices, measurement instruments and contextual factors inherent to Supply Chain Agility

[EN] Supply Chain Agility (SCA) has recently received substantial attention in the literature and in practice. The previous literature encompasses a number of different tools and constructs that are used to measure SCA, indicating the need to refine and validate a supply chain-level measurement instrument of SCA. In addition, the role of the contextual factors inherent to the implementation of SCA and the need to achieve a certain fit between SCA and other supply chain and firm strategies and orientations has received little attention by the research community. This paper represents a SLR protocol to identify definitions, practices, and measurement items of SCA, as well as the contextual factors that affect supply chain's ability to implement and measure supply chain agility. Finally, this paper will try to contribute to the theory development and advancement on SCA by clarifying the need to accomplish a certain fit between SCA and other supply chain and firm strategies and orientations.Alkurdi, MH.; Vázquez-Bustelo, D. (2021). A Systematic Literature Review Protocol for the analysis of practices, measurement instruments and contextual factors inherent to Supply Chain Agility. WPOM-Working Papers on Operations Management. 13(1):1-34. https://doi.org/10.4995/wpom.16096OJS134131Abdallah, A. B., & Nabass, I. H. (2018). Supply chain antecedents of agile manufacturing in a develop-ing country context. Journal of Manufacturing Technology Management, 29(6), 1042-1064. https://doi.org/10.1108/JMTM-01-2018-0019Abdelilah, B., Korchi, A. E., & Balambo, M. A. (2018). Flexibility and agility: evolution and relation-ship. Journal of Manufacturing Technology Management, 29(7), 1138-1162. https://doi.org/10.1108/JMTM-03-2018-0090Agarwal, A., Shankar, R., & Tiwari, M. (2007). Modeling agility of supply chain. Industrial Marketing Management, 36(4), 443-457. https://doi.org/10.1016/j.indmarman.2005.12.004Alfalla-Luque, R., Machuca, J. A. D., & Marin-Garcia, J. A. (2018). Triple-a and competitive advantage in supply chains: Empirical research in developed countries. International Journal of Production Economics, 203, 48-61. https://doi.org/10.1016/j.ijpe.2018.05.020Altay, N., Gunasekaran, A., Dubey, R., & Childe, S. J. (2018). Agility and resilience as antecedents of supply chain performance under moderating effects of organizational culture within the humanitarian setting: a dynamic capability view. Production Planning & Control, 29(14), 1158-1174. https://doi.org/10.1080/09537287.2018.1542174Aslam, H., Blome, C., Roscoe, S., & Azhar, T. M. (2018). Dynamic supply chain capabilities How market sensing, supply chain agility and adaptability affect supply chain ambidexterity. Interna-tional Journal of Operations & Production Management, 38(12), 2266-2285. https://doi.org/10.1108/IJOPM-09-2017-0555Avelar-Sosa, L., García-Alcaraz, J., Mejía-Muñoz, J., Maldonado-Macías, A., & Hernández, G. (2018). Government Support and Market Proximity: Exploring Their Relationship with Supply Chain Agil-ity and Financial performance. Sustainability, 10(7), 2441. https://doi.org/10.3390/su10072441Baramichai, M., Jr., E. W., & Marangos, C. (2007). Agile supply chain transformation matrix: a QFD-based tool for improving enterprise agility. International Journal of Value Chain Management, 1(3), 281. https://doi.org/10.1504/IJVCM.2007.013305Bidhandi, R. A., & Valmohammadi, C. (2017). Effects of supply chain agility on profitability. Business Process Management Journal, 23(5), 1064-1082. https://doi.org/10.1108/BPMJ-05-2016-0089Blome, C., Schoenherr, T., & Rexhausen, D. (2013). Antecedents and enablers of supply chain agility and its effect on performance: a dynamic capabilities perspective. International Journal of Produc-tion Research, 51(4), 1295-1318. https://doi.org/10.1080/00207543.2012.728011Braunscheidel, M. J., & Suresh, N. C. (2008). The organizational antecedents of a firm's supply chain agility for risk mitigation and response. Journal of Operations Management, 27(2), 119-140. https://doi.org/10.1016/j.jom.2008.09.006Brusset, X. (2016). Does supply chain visibility enhance agility? International Journal of Production Economics, 171, 46-59. https://doi.org/10.1016/j.ijpe.2015.10.005Carvalho, A. M., Sampaio, P., Rebentisch, E., Carvalho, J. Á., & Saraiva, P. (2017). Operational excel-lence, organisational culture and agility: the missing link? Total Quality Management & Business Excellence, 30(13-14), 1495-1514. https://doi.org/10.1080/14783363.2017.1374833Chan, A. T., Ngai, E. W., & Moon, K. K. (2017). The effects of strategic and manufacturing flexibilities and supply chain agility on firm performance in the fashion industry. European Journal of Opera-tional Research, 259(2), 486-499. https://doi.org/10.1016/j.ejor.2016.11.006Chen, C.-J. (2019). Developing a model for supply chain agility and innovativeness to enhance firms' competitive advantage. Management Decision, 57(7), 1511-1534. https://doi.org/10.1108/MD-12-2017-1236Degroote, S. E., & Marx, T. G. (2013). The impact of IT on supply chain agility and firm performance: An empirical investigation. International Journal of Information Management, 33(6), 909-916. https://doi.org/10.1016/j.ijinfomgt.2013.09.001Dhaigude, A., & Kapoor, R. (2017). The mediation role of supply chain agility on supply chain orienta-tion-supply chain performance link. Journal of Decision Systems, 26(3), 275-293. https://doi.org/10.1080/12460125.2017.1351862Du, Y., Hu, X., & Vakil, K. (2021). Systematic literature review on the supply chain agility for manu-facturer and consumer. International Journal of Consumer Studies, 45(4), 581-616. https://doi.org/10.1111/ijcs.12645Dubey, R., Gunasekaran, A., & Childe, S. J. (2019). Big data analytics capability in supply chain agili-ty. Management Decision, 57(8), 2092-2112. https://doi.org/10.1108/MD-01-2018-0119Durach, C. F., Kembro, J., & Wieland, A. (2017). A New Paradigm for Systematic Literature Reviews in Supply Chain Management. Journal of Supply Chain Management, 53(4), 67-85. https://doi.org/10.1111/jscm.12145Eckstein, D., Goellner, M., Blome, C., & Henke, M. (2014). The performance impact of supply chain agility and supply chain adaptability: the moderating effect of product complexity. International Journal of Production Research, 53(10), 3028-3046. https://doi.org/10.1080/00207543.2014.970707Faisal, M. N., Banwet, D. K., & Shankar, R. (2007). An approach to measure supply chain agility. International Journal of Industrial and Systems Engineering, 2(1), 79. https://doi.org/10.1504/IJISE.2007.011438Fayezi, S., Zutshi, A., & Oloughlin, A. (2016). Understanding and Development of Supply Chain Agil-ity and Flexibility: A Structured Literature Review. International Journal of Management Reviews, 19(4), 379-407. https://doi.org/10.1111/ijmr.12096Feizabadi, J., Maloni, M., & Gligor, D. (2019). Benchmarking the triple-A supply chain: orchestrating agility, adaptability, and alignment. Benchmarking: An International Journal, 26(1), 271-295. https://doi.org/10.1108/BIJ-03-2018-0059Geyi, D. A. G., Yusuf, Y., Menhat, M. S., Abubakar, T., & Ogbuke, N. J. (2019). Agile capabilities as necessary conditions for maximising sustainable supply chain performance: An empirical investiga-tion. International Journal of Production Economics, 107501. https://doi.org/10.1016/j.ijpe.2019.09.022Gligor, D. M. (2014). The role of demand management in achieving supply chain agility. Supply Chain Management: An International Journal, 19(5/6), 577-591. https://doi.org/10.1108/SCM-10-2013-0363Gligor, D. M., & Holcomb, M. C. (2012). Understanding the role of logistics capabilities in achieving supply chain agility: a systematic literature review. Supply Chain Management: An International Journal, 17(4), 438-453. https://doi.org/10.1108/13598541211246594Gligor, D. M., Esmark, C. L., & Holcomb, M. C. (2014). Performance outcomes of supply chain agili-ty: When should you be agile? Journal of Operations Management, 33-34(1), 71-82. https://doi.org/10.1016/j.jom.2014.10.008Gligor, D. M., Holcomb, M. C., & Stank, T. P. (2013). A Multidisciplinary Approach to Supply Chain Agility: Conceptualization and Scale Development. Journal of Business Logistics, 34(2), 94-108. https://doi.org/10.1111/jbl.12012Guner, H M., Çemberci, M., & Civelek, M. E. (2018). The effect of supply chain agility on firm per-formance. Journal of International Trade, Logistics and Law, 4(2), 25-34.Hwang, T., & Kim, S. T. (2018). Balancing in-house and outsourced logistics services: effects on supply chain agility and firm performance. Service Business, 13(3), 531-556. https://doi.org/10.1007/s11628-018-00394-xIsmail, H. S., & Sharifi, H. (2006). A balanced approach to building agile supply chains. International Journal of Physical Distribution & Logistics Management, 36(6), 431-444. https://doi.org/10.1108/09600030610677384Jain, V., Benyoucef, L., & Deshmukh, S. G. (2008). A new approach for evaluating agility in supply chains using Fuzzy Association Rules Mining. Engineering Applications of Artificial Intelligence, 21(3), 367-385. https://doi.org/10.1016/j.engappai.2007.07.004Khalili-Damghani, K., Taghavifard, M., Olfat, L., & Feizi, K. (2011). A hybrid approach based on fuzzy DEA and simulation to measure the efficiency of agility in supply chain: real case of dairy industry. International Journal of Management Science and Engineering Management, 6(3), 163-172. https://doi.org/10.1080/17509653.2011.10671160Kim, M., & Chai, S. (2017). The impact of supplier innovativeness, information sharing and strategic sourcing on improving supply chain agility: Global supply chain perspective. International Journal of Production Economics, 187, 42-52. https://doi.org/10.1016/j.ijpe.2017.02.007Li, X., Wu, Q., & Holsapple, C. W. (2015). Best-value supply chains and firms' competitive perfor-mance: empirical studies of their linkage. International Journal of Operations & Production Man-agement, 35(12), 1688-1709. https://doi.org/10.1108/IJOPM-01-2014-0014Lin, C.-T., Chiu, H., & Chu, P.-Y. (2006). Agility index in the supply chain. International Journal of Production Economics, 100(2), 285-299. https://doi.org/10.1016/j.ijpe.2004.11.013Liu, H., Ke, W., Wei, K. K., & Hua, Z. (2013). The impact of IT capabilities on firm performance: The mediating roles of absorptive capacity and supply chain agility. Decision Support Systems, 54(3), 1452-1462. https://doi.org/10.1016/j.dss.2012.12.016Machuca, J. A. D., Marin-Garcia, J. A., & Alfalla-Luque, R. (2020). The country context in Triple-A Supply CHAINS: An advanced PLS-SEM research study in emerging vs developed countries. In-dustrial Management & Data Systems, 121(2), 228-267. https://doi.org/10.1108/IMDS-09-2020-0536Margaret Weber, M. (2002). Measuring supply chain agility in the virtual organization. International Journal of Physical Distribution & Logistics Management, 32(7), 577-590. https://doi.org/10.1108/09600030210442595Martin, C., & Towill, D. R. (2000). Supply chain migration from lean and functional to agile and cus-tomised. Supply Chain Management: An International Journal, 5(4), 206-213. https://doi.org/10.1108/13598540010347334Martinez-Sanchez, A., & Lahoz-Leo, F. (2018). Supply chain agility: a mediator for absorptive capacity. Baltic Journal of Management, 13(2), 264-278. https://doi.org/10.1108/BJM-10-2017-0304Mason-Jones, R., & Towill, D. R. (1999). Total cycle time compression and the agile supply chain. International Journal of Production Economics, 62(1-2), 61-73. https://doi.org/10.1016/S0925-5273(98)00221-7Medina-Lopez, C., Marin-Garcia, J. A., & Alfalla-Luque, R. (2010). Una propuesta Metodológica para la realización de Búsquedas Sistemáticas de bibliografía (a methodological proposal for the systematic literature review). WPOM-Working Papers on Operations Management, 1(2), 13. https://doi.org/10.4995/wpom.v1i2.786Mishra, S., Samantra, C., Datta, S., & Mahapatra, S. S. (2014). Agility appraisement framework for integrated supply chain using generalised interval-valued fuzzy set. International Journal of Busi-ness Information Systems, 16(1), 89. https://doi.org/10.1504/IJBIS.2014.060838Nazempour, R., Yang, J., & Waheed, A. (2018). An Empirical Study to Understand the Effect of Supply Chain Agility on Organizational Operational Performance: SC Agility and Organizational Perfor-mance. International Journal of Information Systems and Supply Chain Management, 11(4), 1-20. https://doi.org/10.4018/IJISSCM.2018100101Patel, B.S. & Sambasivan, M. (2021). A systematic review of the literature on supply chain agility. Management Research Review, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/MRR-09-2020-0574Pantouvakis, A., & Bouranta, N. (2015). Agility, organisational learning culture and relationship quality in the port sector. Total Quality Management & Business Excellence, 28(3-4), 366-378. https://doi.org/10.1080/14783363.2015.1084871Pettit, T. J., Croxton, K. L., & Fiksel, J. (2013). Ensuring supply chain resilience: Development and implementation of an assessment tool. Journal of Business Logistics, 34(1), 46-76. https://doi.org/10.1111/jbl.12009Power, D. J., Sohal, A. S., & Rahman, S. U. (2001). Critical success factors in agile supply chain man-agement ‐ An empirical study. International Journal of Physical Distribution & Logistics Man-agement, 31(4), 247-265. https://doi.org/10.1108/09600030110394923Qi, Y., Zhao, X., & Sheu, C. (2011). The Impact of Competitive Strategy and Supply Chain Strategy on Business Performance: The Role of Environmental Uncertainty*. Decision Sciences, 42(2), 371-389. https://doi.org/10.1111/j.1540-5915.2011.00315.xQrunfleh, S., & Tarafdar, M. (2014). Supply chain information systems strategy: Impacts on supply chain performance and firm performance. International Journal of Production Economics, 147, 340-350. https://doi.org/10.1016/j.ijpe.2012.09.018Roscoe, S., Eckstein, D., Blome, C., & Goellner, M. (2019). Determining how internal and external process connectivity affect supply chain agility: a life-cycle theory perspective. Production Planning & Control, 31(1), 78-91. https://doi.org/10.1080/09537287.2019.1629704Russell, D. M., & Swanson, D. (2019). Transforming information into supply chain agility: an agility adaptation typology. The International Journal of Logistics Management, 30(1), 329-355. https://doi.org/10.1108/IJLM-09-2017-0237Sabegh, M. H. Z., Caliskan, A., Ozturkoglu, Y., & Cetiner, B. (2018). Testing the Effects of Agile and Flexible Supply Chain on the Firm Performance Through SEM. System Performance and Manage-ment Analytics Asset Analytics, 35-46. https://doi.org/10.1007/978-981-10-7323-6_3Samantra, C., Datta, S., Mishra, S., & Mahapatra, S. S. (2013). Agility appraisal for integrated supply chain using generalized trapezoidal fuzzy numbers set. The International Journal of Advanced Man-ufacturing Technology, 68(5-8), 1491-1503. https://doi.org/10.1007/s00170-013-4937-6Sanchez-Ruiz, L., Marin-Garcia, J. A., & Blanco, B. (2018). Protocol: A Meta-Review on continuous improvement to know the state of this research field. WPOM-Working Papers on Operations Man-agement, 9(2), 88. https://doi.org/10.4995/wpom.v9i2.10752Sellitto, M. A., Hermann, F. F., Blezs, A. E., & Barbosa-Póvoa, A. P. (2019). Describing and organiz-ing green practices in the context of Green supply CHAIN Management: Case studies. Resources, Conservation and Recycling, 145, 1-10. https://doi.org/10.1016/j.resconrec.2019.02.013Sharifi, H., Ismail, H. S., & Reid, I. (2006). Achieving agility in supply chain through simultaneous "design of" and "design for" supply chain. Journal of Manufacturing Technology Management, 17(8), 1078-1098. https://doi.org/10.1108/17410380610707393Sharma, N., Sahay, B. S., Shankar, R., & Sarma, P. R. S. (2017). Supply chain agility: review, classi-fication and synthesis. International Journal of Logistics Research and Applications, 20(6), 532-559. https://doi.org/10.1080/13675567.2017.1335296Shekarian, M., Nooraie, S. V. R., & Parast, M. M. (2019). An examination of the impact of flexibility and agility on mitigating supply chain disruptions. International Journal of Production Economics, 107438. https://doi.org/10.1016/j.ijpe.2019.07.011Siddhartha, N., & Sachan, A. (2016). Review of agile supply chain implementation frameworks. Interna-tional Journal of Business Performance and Supply Chain Modelling, 8(1), 27. https://doi.org/10.1504/IJBPSCM.2016.075999Swafford, P. M., Ghosh, S., & Murthy, N. (2006). The antecedents of supply chain agility of a firm: Scale development and model testing. Journal of Operations Management, 24(2), 170-188. https://doi.org/10.1016/j.jom.2005.05.002Tarafdar, M., & Qrunfleh, S. (2016). Agile supply chain strategy and supply chain performance: com-plementary roles of supply chain practices and information systems capability for agility. Interna-tional Journal of Production Research, 55(4), 925-938. https://doi.org/10.1080/00207543.2016.1203079Tranfield, D., Denyer, D., & Smart, P. (2003). Towards a Methodology for Developing Evidence-Informed Management Knowledge by Means of Systematic Review. British Journal of Manage-ment, 14(3), 207-222. https://doi.org/10.1111/1467-8551.00375Tse, Y. K., Zhang, M., Akhtar, P., & Macbryde, J. (2016). Embracing supply chain agility: an investi-gation in the electronics industry. Supply Chain Management: An International Journal, 21(1), 140-156. https://doi.org/10.1108/SCM-06-2015-0237Tuan, L. T. (2016). Organisational ambidexterity and supply chain agility: the mediating role of external knowledge sharing and moderating role of competitive intelligence. International Journal of Logis-tics Research and Applications, 19(6), 583-603. https://doi.org/10.1080/13675567.2015.1137278Um, J. (2016). The impact of supply chain agility on business performance in a high level customization environment. Operations Management Research, 10(1-2), 10-19. https://doi.org/10.1007/s12063-016-0120-1Wu, T., Jim Wu, Y.-C., Chen, Y. J., & Goh, M. (2014). Aligning supply chain strategy with corporate environmental strategy: A contingency approach. International Journal of Production Economics, 147, 220-229. https://doi.org/10.1016/j.ijpe.2013.02.027Yang, J. (2014). Supply chain agility: Securing performance for Chinese manufacturers. International Journal of Production Economics, 150, 104-113. https://doi.org/10.1016/j.ijpe.2013.12.018Yusuf, Y. Y., Gunasekaran, A., Adeleye, E. O., & Sivayoganathan, K. (2004). Agile supply chain capa-bilities: Determinants of competitive objectives. European Journal of Operational Research, 159(2), 379-392. https://doi.org/10.1016/j.ejor.2003.08.02