Authors:
Solly Aryza
1
;
Syahril Efendi
2
;
Poltak Sihombing
2
and
Sawaluddin
3
Affiliations:
1
Student Doctoral Program in Computer Science, Universitas Sumatera Utara, Medan, Indonesia
;
2
Faculty of Computer Science and Information Technology, Universitas Sumatera Utara, Medan, Indonesia}
;
3
Faculty of Mathematics and Natural Sciences Universitas Sumatera Utara, Medan, Indonesia
Keyword(s):
Business Progress, Learning Optimization, Model for Multi Product Retail.
Abstract:
The progress of the business environment is highly dependent on several things such as cost issues, service, and product quality improvement which greatly impact customer satisfaction where the supply chain is faced with high dynamics and uncertainty in the business environment, which is more obvious when end customer demands and orders are considered. The supply chain network must be able to deal with uncertain demand from all its elements including manufacturers, suppliers, and distribution centers. Therefore, this study aims to optimize the multi-product distribution system and multi-level delivery of product flow under uncertain conditions. A multi-objective mathematical model is developed that minimizes supply chain costs while maximizing customer satisfaction and different scenarios. In addition, the significant diversity of different channels in terms of product information, price, consumer experience, and service level it possible to introduce of the Internet to the business
world has offered new communication channels to facilitate shopping, making product sales by manufacturers, and product purchases by customers faster and more precise. In addition, purchases through computers, mobile phones, and various applications as well as traditional purchasing methods such as buying from a store or selecting desired items from a catalog have covered all social strata, tastes, and habits. This method of using all available means, called omnichannel, allows organizations to take greater control over pricing and product selection and to receive precise feedback from the market and customers assisting them in the best production and pricing decisions.
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