Optimizing Procurement Decisions With Can-Order Policy: The Case Study Of Vietgreen Limited Company

Abstract

According to Kantar's "FMCG Monitor FY 2023" report, in terms of the macroeconomic picture, Vietnam ends 2023 with a GDP growth rate of 5.05%, lower than the target of 6% set by the state. However, in the context of current global economic challenges, this growth rate is still a significant achievement compared to other countries in the region. However, when looking at the growth rate of the retail and service industry, 2023 only recorded an increase of 9.6% over the same period last year. This number is predicted to be lower, reaching only 9% in 2024. In addition, consumers are willing to spend heavily on health care-related products. According to Kantar, 85% of customers are willing to pay more for healthy products. It is stated that experiencing continuous instability from the pandemic to the economy, consumers have gradually changed their spending priorities. Health is given top priority, meanwhile, products that do not directly affect the body will have their budgets cut. Precisely predicting demand enables organizations to strategically manage production, inventory, and logistics, resulting in decreased occurrences of stockouts and unnecessary manufacturing. Companies can optimize inventory levels, save expenses, and improve customer service by utilizing past data and other factors to forecast future demand. This study aims to provide an optimal solution relating to minimizing total procurement process cost, with relation to ordering cost, backorder cost, and holding cost, the inventory management and procurement lead time will be taken under consideration. In order to account for the association between numerous items and their integrated replenishment at regular intervals, this paper suggests a MILP model for a periodic COP, a popular multi-item replenishment approach. The input data of all the parameters had been taken directly from the performance index of the company in 2023.