Sales Forecasting for Supply Chain Demand Management - A Novel Fuzzy Time Series Approach

• Published in: 2019 13th International Conference on Mathematics, Actuarial Science, Computer Science and Statistics (MACS) pp. 1 - 4
• Main Authors: Burney, S. M. Aqil, Ali, Syed Mubashir
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.12.2019
• Subjects: Dairy products; Forecasting; Fuzzy logic; fuzzy time series; Predictive models; sales prediciton; soft computing; Supply chain management; Supply chains; Time series analysis
• DOI: 10.1109/MACS48846.2019.9024810

Supply chain management has become an important area of research among researchers in the past decade. The reason for its growing importance is its ability to enable businesses to have strategic competitive advantage. There are various supply chain functions and demand management is one of them. It deals with the organization's ability to meet the customer needs by maintaining the required inventory. In order to achieve this goal, organizations needs to predict the demand by forecasting sales using sales patterns in order to efficiently meet customer demands. Fuzzy time series has been extensively used for forecasting problems. The aim of this paper is to propose and implement a new fuzzy time series model to predict sales for efficient demand management within supply chain. This method was developed by us and previously used to predict university students' enrolment which had a better accuracy compared to existing methods. This paper will first discuss what sales forecasting and demand management is along with explaining the basics of fuzzy time series. Later the proposed framework will be discussed and applied to a sample monthly time series for milk cartons sales in a super market for sales forecasting. Later future research areas will be highlighted to conclude this research study.