Sustainable resource management in a supply chain: a methodological proposal combining zero-inflated fuzzy time series and clustering techniques. Issue 5 (10th July 2020)

• Record Type: Journal Article
• Authors: Ewbank, Henrique; Frutuoso Roveda, José Arnaldo; Monteiro Masalskiene Roveda, Sandra Regina; Ribeiro, Admilson ĺrio; Bressane, Adriano; Hadi-Vencheh, Abdollah; Wanke, Peter
• Is Part Of: Journal of enterprise information management. Volume 33:Issue 5(2020)
• Page Start: 1059
• Page End: 1076
• Publication Date: 2020-07-10
• Subjects: Fuzzy sets -- Sustainability -- Supply chain -- Fuzzy time series; Management information systems -- Periodicals; Business logistics -- Periodicals; Business -- Data processing -- Periodicals; Management -- Data processing -- Periodicals; 658.05
• DOI: 10.1108/JEIM-09-2019-0289
• Languages: English
• ISSNs: 1741-0398

Abstract

Abstract : Purpose: The purpose of this paper is to analyze demand forecast strategies to support a more sustainable management in a pallet supply chain, and thus avoid environmental impacts, such as reducing the consumption of forest resources. Design/methodology/approach: Since the producer presents several uncertainties regarding its demand logs, a methodology that embed zero-inflated intelligence is proposed combining fuzzy time series with clustering techniques, in order to deal with an excessive count of zeros. Findings: A comparison with other models from literature is performed. As a result, the strategy that considered at the same time the excess of zeros and low demands provided the best performance, and thus it can be considered a promising approach, particularly for sustainable supply chains where resources consumption is significant and exist a huge variation in demand over time. Originality/value: The findings of the study contribute to the knowledge of the managers and policymakers in achieving sustainable supply chain management. The results provide the important concepts regarding the sustainability of supply chain using fuzzy time series and clustering techniques.