Products Inventory and Order Planning at PT XYZ

Yolanda Putri, Nicky (2025) Products Inventory and Order Planning at PT XYZ. S1 thesis, Universitas Andalas.

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Abstract

One of the most attractive retail business sectors today is the retail business engaged in Fast Moving Consumer Goods (FMCG). FMCG products are defined as products that are consumed directly by many people in their daily needs and have a fast turnover cycle. One of the retail businesses engaged in Fast Moving Consumer Goods (FMCG) is PT XYZ which distributes products from PT KAO Indonesia. The types of products distributed consist of three categories, namely Health & Beauty, Hygiene and Living, and Baby Diapers. PT XYZ's procurement of products is carried out by ordering products directly to PT KAO Indonesia. The amount of ordering products is done by looking at the difference between the amount of inventory should be and the current inventory. However, this policy is not appropriate, because it causes problems such as overstock and lost sales, and non-optimal delivery capacity. Inventory planning and delivery planning need to be done in order to minimize inventory costs and optimize delivery capacity. PT XYZ needs a new ordering policy to minimize inventory costs and optimize shipping capacity. The policy is needed to determine the order quantity, reorder time, order allocation on the day and truck, and the number of trucks needed for delivery. Control is done by classifying each type of product using FSN analysis and ABC analysis. Furthermore, the data distribution test is carried out and inventory is planned using the Periodic Review (R, s, S) and (R, S) methods and average product sales. Delivery of products resulting from inventory planning with Periodic Review will be arranged with Linear Programming. Through inventory planning and order planning, PT XYZ found the right delivery plan. The delivery plan was conducted for 139 items for 365 days, using a linear programming model, with six constraints. The linear programming model resulted in 278 trucks needed throughout the year. This value was reduced from the actual number of delivery trucks, which was 332 trucks, which meant a saving of 54 trucks or 16%. Based on the sensitivity analysis results, changing the demand to 0.95 and 0.9 times the initial demand reduces the number of trucks and the number of delivery days from 80 days, to 74 and 72 days and 264, 250 trucks, respectively. Changes in delivery intervals to 0.9, 0.95, 1.05, and 1.1 times the initial interval led to changes in delivery days to 76, 88, 88, and 84 days. Changing the minimum truck capacity to 42, 43, 44, 46, and 47 m3 causes the delivery days to change to 80, 82, 82, 80, and 89 days. Changing the maximum truck load to 16,000, 17,000, 19,000, and 20,000 kg causes the delivery days to change to 90, 93, 85, and 91 days. Meanwhile, the number of trucks remains at 278 trucks for all conditions except changes in demand.

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