#TITLE_ALTERNATIVE#

• Main Author: AKBAR HIKMATULOH (NIM : 23408064) ; Pembimbing Prof. Dr. Ir. Abdul Hakim Halim, YUSUP
• Format: Theses
• Language: Indonesia
• Online Access: https://digilib.itb.ac.id/gdl/view/19757
• Institution: Institut Teknologi Bandung

This study developed a model procurement plan and production plan in the <br /> <br /> <br /> <br /> <br /> refined sugar industry with a case study in PT. XYZ. The mathematical model was <br /> <br /> <br /> <br /> <br /> developed using the assumption that demand is only fulfilled from warehouse, raw <br /> <br /> <br /> <br /> <br /> materials are always available at the supplier and the production is done <br /> <br /> <br /> <br /> <br /> continuously without any damage to the engine. Decision variable for from the <br /> <br /> <br /> <br /> <br /> model is ordering lot size’s, ordering decision, the amount of raw material that is <br /> <br /> <br /> <br /> <br /> sent, inventory of raw materials, the total production, inventory of production. <br /> <br /> <br /> <br /> <br /> Optimal decision variable is obtained by minimizing total costs. Optimal solution <br /> <br /> <br /> <br /> <br /> search conducted simultaneously and gradually. <br /> <br /> <br /> <br /> <br /> Testing and analysis carried out to demonstrate the performance of the model <br /> <br /> <br /> <br /> <br /> developed models and then compared with the real conditions in the company. <br /> <br /> <br /> <br /> <br /> Based on model analysis concluded that the procurement raw materials plan and <br /> <br /> <br /> <br /> <br /> production plans model produces the lowest total cost compared with the real <br /> <br /> <br /> <br /> <br /> conditions in the company. <br /> <br /> <br /> <br /> <br /> Sensitivity analysis is conducted to see how changing the model parameters to <br /> <br /> <br /> <br /> <br /> total cost, and optimal solution. From the sensitivity analysis found that changes <br /> <br /> <br /> <br /> <br /> in demand, transportation costs, storage costs of raw material and product <br /> <br /> <br /> <br /> <br /> influence the decision variable values and the total cost significantly.