Designing an Integrated Model of Noise-Financial Risk in the Lean Supply Chain with a Foundation Data Theory Approach

Document Type : Production & Operations Management

Authors

1 Assistant professor, Department of Industrial Management & Engineering, Gachsaran Branch, Islamic Azad University, Gachsaran, Iran

2 Ph D student of industrial engineering, Masjed-Soleiman Branch, Islamic Azad University, Masjed-Soleiman, Iran

3 Assistant professor, Department of Industrial Engineering, Masjed-Soleiman Branch, Islamic Azad University, Masjed-Soleiman, Iran

Abstract

Due to the increasing importance of the issue of noise and risk in the supply chain, and cement factories in particular (nature and activity) is associated with various risks. Accordingly, the purpose of this study is to design an integrated pattern of noise - financial risk in the lean supply chain. This research is in the interpretive group in terms of the dominant paradigms of research. Strauss and Corbin method was used to discover and identify codes and categories related to the phenomenon. For this purpose, after reviewing the research literature in the study area, using purposeful and judgmental sampling, 15 experts in the lean supply chain of cement factories were interviewed in depth and theoretical saturation was obtained. After open, pivotal and selective coding based on the final paradigm model, the phenomenon of noises and financial risk in the lean supply chain were classified into six main dimensions, 28 themes and 145 final identifiers. To determine the validity and reliability of the data, two methods of reviewing participants as well as reviewing non-participating experts in the research have been used. Findings from this study showed that the main phenomenon in the paradigm model is the integrated pattern of noise - financial risk in the lean supply chain, including liquidity and activity ratios, production and operations, and human resource-improving capabilities. Therefore, the results of this study can be used in the design, development and integrated implementation of noise - financial risk in the lean supply chain.

Keywords

References

Abualfaraa, W., Salonitis, K., Al-Ashaab, A., & Ala’raj, M. (2020). Lean-Green Manufacturing Practices and Their Link with Sustainability: A Critical Review. Sustainability, 12(3), 981.
Adem, E. O. (2014). Supply chain risk management practices and disruptions control in power supply Kenya (Doctoral dissertation, University of Nairobi).
Alvim, S. L., & Oliveira, O. G. (2020). Lean Supply Chain Management: a lean approach applied to distribution–a literature review of the concepts, challenges and trends. Journal of Lean Systems, 5(1), 85-103.
Barrionuevo, A., & Deutsch, C. H. (2005). A distribution system brought to its knees. New York Times, 1, C1.
Bazargan, A. (2010). Introduction to Qualitative and Mixed Research Methods: Common Approaches in Behavioral Sciences. Didar Publications, Second Edition, Tehran. (In Persian)
Brooks, N. R., & Vogel, N. (2003). The nation—massive blackout—outdated power grid’s failure not a surprise. Los Angeles Times, August, 15, A1.
Campbell, J. Y. (2017). Financial decisions and markets: a course in asset pricing. New Jersey: Princeton University Press.
Cascio, M. A., Lee, E., Vaudrin, N., & Freedman, D. A. (2019). A team-based approach to open coding: Considerations for creating intercoder consensus. Field Methods, 31(2), 116-130.
Chopra, S., & Sodhi, M. S. (2004). Managing risk to avoid supply-chain breakdown. Mit Sloan Management Review, 46(1).
Corbin, J., & Strauss, A. (2014). Basics of qualitative research: Techniques and procedures for developing grounded theory. Sage publications.
Dey, R. K., Hossain, S. Z., & Rezaee, Z. (2018). Financial risk disclosure and financial attributes among publicly traded manufacturing companies: Evidence from Bangladesh. Journal of Risk and Financial Management, 11(3), 50.
Ekene, N. U. (2018). Application of lean tools in rolling stock procurement supply chain management (Doctoral dissertation, Stellenbosch: Stellenbosch University).
Elkins, D., Handfield, R. B., Blackhurst, J., & Craighead, C. W. (2005). 18 ways to guard against disruption. Management, 1(1), 10-11.
Esfandiari, M & Iman Khan, N. (2019). Banking Industry Customer Behavior Analysis: Foundation Data Theory Approach. Journal of Economic Modeling, 13 (45), 93-114 (In Persian).
Jayadev, M. (2006). Predictive power of financial risk factors: an empirical analysis of default companies. Vikalpa, 31(3), 45-56.
Ketikidis, P. H., Koh, S. L., Gunasekaran, A., Cucchiella, F., & Gastaldi, M. (2006). Risk management in supply chain: a real option approach. Journal of Manufacturing Technology Management, 17(6), 700-720.
Kleindorfer, P. R., & Saad, G. H. (2005). Managing disruption risks in supply chains. Production and operations management, 14(1), 53-68.
Klibi, W., Martel, A., & Guitouni, A. (2010). The design of robust value-creating supply chain networks: a critical review. European Journal of Operational Research, 203(2), 283-293.
Laniyan, T. A., & Adewumi, A. J. (2020). Evaluation of Contamination and Ecological Risk of Heavy Metals Associated with Cement Production in Ewekoro, Southwest Nigeria. Journal of Health and Pollution, 10(25), 200306.
Leung, S. C., Tsang, S. O., Ng, W. L., & Wu, Y. (2007). A robust optimization model for multi-site production planning problem in an uncertain environment. European journal of operational research, 181(1), 224-238.
Lin, C. C., & Wang, T. H. (2011). Build-to-order supply chain network design under supply and demand uncertainties. Transportation Research Part B: Methodological, 45(8), 1162-1176.
Liu, Y., & Huang, L. (2020). Supply chain finance credit risk assessment using support vector machine–based ensemble improved with noise elimination. International Journal of Distributed Sensor Networks, 16(1), 1550147720903631.
Manzouri, M., & Rahman, M. N. A. (2013). Adaptation of theories of supply chain management to the lean supply chain management. International Journal of Logistics Systems and Management, 14(1), 38-54.
Mehidi, S., Chakrabarty, N., & Mohiuddin, H. M. (2014). An Application of Artificial Neural Network (ANN) Process to Assess Risk in Cement Industries in Bangladesh. Ind Eng Manage, 3(4), 1-6.
Moeinzadeh, P., & Hajfathaliha, A. (2009). A combined fuzzy decision making approach to supply chain risk assessment. World Academy of Science, Engineering and Technology, 60(2), 519-528.
Mohammadi, A & Shojaei, P. (2016). Provide a comprehensive model of supply chain risk management components: a hybrid approach. Journal of Scientific-Research Executive Management. Eighth year, number fifteen (In Persian).
Nadri, K & Mehrabi, L. (2018). Investigating the types of risk and risk management in the Islamic banking system, Journal of Strategy Development, 54 (37), 160-174(In Persian).
Nelson, L. K. (2020). Computational grounded theory: A methodological framework. Sociological Methods & Research, 49(1), 3-42.
Olivares-Benitez, E., González-Velarde, J. L., & Ríos-Mercado, R. Z. (2012). A supply chain design problem with facility location and bi-objective transportation choices. Top, 20(3), 729-753.
Puche, J., Costas, J., Ponte, B., Pino, R., & de la Fuente, D. (2019). The effect of supply chain noise on the financial performance of Kanban and Drum-Buffer-Rope: An agent-based perspective. Expert Systems with Applications, 120, 87-102.
Rogachev, A. Y. (2008). Enterprise risk management in a pharmaceutical company. Risk Management, 10(1), 76-84.
Rogers, H., Srivastava, M., Pawar, K. S., & Shah, J. (2016). Supply chain risk management in India–practical insights. International Journal of Logistics Research and Applications, 19(4), 278-299.
Shafiee, M., Zareian, M., Zarei Matin, H., & Firoozi, M. (2019). Understanding and Modeling Industrial Marketing Managers’ “Behavioral Distress” using Grounded Theory Approach. Journal of Business Management, 11(1), 179-200 (In Persian).
Shah Hoseini. M. A., Heidari, A., Mohamad Aarabi, S., & Ghaderi Kangavari, S. (2019). Developing a Management Model for R&D Strategic Alliances in Automotive Industry in Iran. Journal of Business Management, 11(1), 25-44 (In Persian).
Spekman, R. E., & Davis, E. W. (2004). Risky business: expanding the discussion on risk and the extended enterprise. International Journal of Physical Distribution & Logistics Management, 34(5), 414-433.
Swanson, M., Bartol, N., & Moorthy, R. (2010). Piloting supply chain risk management practices for federal information systems. National Institute of Standards and Technology, 1.
Taleizadeh, A. A., Cárdenas-Barrón, L. E., & Sohani, R. (2019). Coordinating the supplier-retailer supply chain under noise effect with bundling and inventory strategies. Journal of Industrial & Management Optimization, 15(4), 1701-1727.
Tokhmehchi, N & Makoei, A. (2014). Presenting a codified framework for identifying sources of injury and supply chain disruption, 2nd International Conference on Management of Challenges and Solutions, Shiraz, Conference Center for Scientific Conference. in Persian.
Wang, F., Lai, X., & Shi, N. (2011). A multi-objective optimization for green supply chain network design. Decision Support Systems, 51(2), 262-269.
Wilding, R., Wagner, B., Colicchia, C., & Strozzi, F. (2012). Supply chain risk management: a new methodology for a systematic literature review. Supply Chain Management: An International Journal.
Yoseph, S. (2017). Assessment Of Supply Chain Risks And Supply Chain Risk Management Practices: The Case Of Ethio Telecom (Doctoral dissertation, Addis Ababa University).
ZandHessami, H., & Savoji, A. (2011). Risk management in supply chain management. International journal of economics and management sciences, 1(3), 60-72.
Zhang, C., He, W., & Hao, R. (2016). Comprehensive estimation of the financial risk of iron and steel enterprise-based on carbon emission reduction.

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History

  • Receive Date: 10 September 2020
  • Revise Date: 31 December 2020
  • Accept Date: 23 January 2021

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