Title: Analysing uncertainties and their impacts on deliveries of a logging company: simulation model to foster supply chain resilience

Authors: Peter Mensah; Yuri Merkuryev; Jelena Pecerska; Francesco Longo

Addresses: Department of Modelling and Simulation, Riga Technical University, 1 Kalku Street, Riga, LV-1658, Latvia ' Department of Modelling and Simulation, Riga Technical University, 1 Kalku Street, Riga, LV-1658, Latvia ' Department of Modelling and Simulation, Riga Technical University, 1 Kalku Street, Riga, LV-1658, Latvia ' Modelling and Simulation Centre, University of Calabria, Via P. Bucci, 87036, Rende, Italy

Abstract: The supply chain in today's competitive world faces uncertainties that might disrupt any part between the upper and lower levels affecting the flow of raw materials, products as well as information and money. The lower level, consisting of the delivery of goods in the form of raw materials and or finished products to customers on time, is vital as it enhances customers' loyalty. This could boost competitive advantages yielding to higher profitability of organisations over their rivals. However, to achieve and sustain these advantages, organisations need to identify and analyse the risks and their impacts on deliveries especially during strategic and operational decision making processes. Hence, modelling and simulation may be utilised as an effective tool to support decision making when planning delivery patterns. Consequently, a research is conducted in a logging company, Company 'L', to comprehend the way uncertainties affect deliveries. This article therefore uses a simulation model as a tool to boost managerial decision making, with reference to Company 'L', by identifying and analysing uncertainties and portraying their impacts on deliveries. This will enable the organisation to be agile and flexible enough to combat uncertainties.

Keywords: supply chain; deliveries; uncertainties; risk impact; resilience; simulation model.

DOI: 10.1504/IJSPM.2019.101011

International Journal of Simulation and Process Modelling, 2019 Vol.14 No.3, pp.251 - 260

Received: 15 Feb 2018
Accepted: 03 Nov 2018

Published online: 16 Jul 2019 *

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