Managing Environmental and Operational Risks for Sustainable Cotton Production Logistics: System Dynamics Modelling for a Textile Company

M. Ali Ülkü, Melek Akgün, Uday Venkatadri, Claver Diallo and Simranjeet S. Chadha
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M. Ali Ülkü: Rowe School of Business, Dalhousie University, Halifax, NS B3H 4R2, Canada
Melek Akgün: Sakarya Business School, Sakarya University, Sakarya 54050, Turkey
Uday Venkatadri: Centre for Research in Sustainable Supply Chain Analytics, Dalhousie University, Halifax, NS B3H 4R2, Canada
Claver Diallo: Centre for Research in Sustainable Supply Chain Analytics, Dalhousie University, Halifax, NS B3H 4R2, Canada
Simranjeet S. Chadha: Centre for Research in Sustainable Supply Chain Analytics, Dalhousie University, Halifax, NS B3H 4R2, Canada

Logistics, 2020, vol. 4, issue 4, 1-20

Abstract: Effective management of cotton production logistics (CPL) against volatile environmental conditions while maintaining product quality and yield at acceptable costs has become challenging due to increasing global population and consumption and climate change. In CPL, the harvesting, processing, and storage of cotton are all linked, prone to various environmental risks (e.g., flooding) and operational risks (e.g., excess spraying of pesticides). Thus, it is crucial for a resilient and sustainable supply chain management to prioritize risks and chart suitable risk response strategies. For a CPL, we employ a system dynamics (SD) approach to investigate the likelihoods of environmental and operational risks and their impacts in four dimensions: variable costs, fixed costs, quality performance, and yield. Using the case of a textile company in Turkey, we demonstrate an end-to-end framework for mitigating CPL risks. SD simulation results show that increases in seed prices and machine and equipment breakdowns are the risks that most affect the unit cost, whereas pests and plant diseases most hurt cotton harvest yield. Via scenario analyses, we demonstrate that a proper risk response strategy, compared to doing nothing, may reduce variance in cotton quality by about 35% at the expense of about an 11% increase in unit cost variability.

Keywords: cost management; system dynamics; risk management; cotton production logistics; scenario analysis; supply chain (search for similar items in EconPapers)
JEL-codes: L8 L80 L81 L86 L87 L9 L90 L91 L92 L93 L98 L99 M1 M10 M11 M16 M19 R4 R40 R41 R49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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