Malika Implementation of the SMART Method for Web-Based Determination of Bangor Burger Stock Priorities

Malika Azzahra; Asep Erlan Maulana

doi:10.55537/cosie.v5i4.1988

Authors

Malika Azzahra Universitas Pamulang
Asep Erlan Maulana Universitas Pamulang

DOI:

https://doi.org/10.55537/cosie.v5i4.1988

Keywords:

Agile Kanban, Inventory of Goods, Decision Support System, SMART, Stock of Goods

Abstract

Inventory of goods is an important factor in maintaining smooth operations at fast food outlets. Burger Bangor Goldfinch faces problems in determining stock priorities due to variations in consumer demand, limited storage capacity, and the absence of a system that supports structured decision-making. This research aims to design and build a web-based Decision Support System (SPK) to determine the priority of stock of goods in an objective and measurable manner. The research uses the Research and Development (R&D) method, while the system development applies the Agile method with a Kanban approach. The system uses the Simple Multi Attribute Rating Technique (SMART) method to determine priorities based on seven criteria, namely sales rate, available stock, demand frequency, procurement lead time, shelf life, stock vacancy rate, and supply availability. The study involved 55 alternative goods that were processed through the stages of criterion weighting, weight normalization, utility value calculation, and alternative ranking. The results of the study showed that the developed system could perform calculations using the SMART method and produce procurement priority ratings from 55 alternative goods based on seven predetermined criteria. Functional testing was also carried out to check the suitability of the system's function with the designed needs. The results showed that the system can be used to provide stock priority recommendations in a structured manner. The application of this system has the potential to help the procurement decision-making process and support more targeted inventory management. However, the operational impact in the form of reducing recording errors, out-of-stock events, overstock, and procurement costs has not been directly measured in this study.

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