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Information Sharing for improved Supply Chain Collaboration – Simulation Analysis

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Information Sharing for improved Supply Chain Collaboration – Simulation Analysis

Jayapalan, Suganya ORCID: https://orcid.org/0000-0002-2948-012X (2019) Information Sharing for improved Supply Chain Collaboration – Simulation Analysis. Masters thesis, Concordia University.

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Abstract

Collaboration among consumer good’s manufacturer and retailers is vital in order to elevate their performance. Such mutual cooperation’s, focusing beyond day to day business and transforming from a contract-based relationship to a value-based relationship is well received in the industries. Further coupling of information sharing with the collaboration is valued as an effective forward step. The advent of technologies naturally supports information sharing across the supply chain. Satisfying consumers demand is the main goal of any supply chain, so studying supply chain behaviour with demand as a shared information, makes it more beneficial. This thesis analyses demand information sharing in a two-stage supply chain. Three different collaboration scenarios (None, Partial and Full) are simulated using Discrete Event Simulation and their impact on supply chain costs analyzed. Arena software is used to simulate the inventory control scenarios. The test simulation results show that the total system costs decrease with the increase in the level of information sharing. There is 7% cost improvement when the information is partially shared and 43% improvement when the information is fully shared in comparison with the no information sharing scenario. The proposed work can assist decision makers in design and planning of information sharing scenarios between various supply chain partners to gain competitive advantage.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Concordia Institute for Information Systems Engineering
Item Type:Thesis (Masters)
Authors:Jayapalan, Suganya
Institution:Concordia University
Degree Name:M.A. Sc.
Program:Quality Systems Engineering
Date:5 June 2019
Thesis Supervisor(s):Awasthi, Anjali
ID Code:985501
Deposited By: Suganya Jayapalan
Deposited On:05 Feb 2020 14:26
Last Modified:05 Feb 2020 14:26
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