No abstract

This article presents a tactical procurement plan for an aromatic coconut manufacturer located in Thailand using a mathematical model. Procurement planning is complex because many uncertainty factors are dependent upon the delivery schedule and season, which affect the price of the coconut and the quantity of the order. There were two alternatives for procurement: farmers and coconut collectors. The model used in this research study compared four scenarios: three deterministic models in which every parameter is constant under the worst-case, best-case, and average-case scenarios and a stochastic model simulation. For the deterministic models, the mixedinteger linear programming (MILP) was formulated in a spreadsheet and solved using the Frontline Premium Solver in Excel. In the second model, a Monte Carlo simulation-based MILP with three random variables-demand, price, and the number of coconuts-was solved optimally. The solution indicates how many coconuts will be purchased from farmers and collectors and which truck will be used to deliver the order to maximize total annual profit. The results were compared among four scenarios that could help decision-makers consider the range of profit. The results showed that the stochastic model resulted in less profits than the deterministic model. In the worstcase scenario, profit was lost; in the best-case scenario, profit was gained. In the stochastic model, profits were increasing, except in July. In summary, procurement planning helps factories and farmers realize the price, supply quantity, and demand uncertainties and organize to respond optimally. Proper farm management could increase the productivity of the farm and lessen the supply shortage, resulting in a higher profit. The findings of this research could be applied to assist coconut processors' supply planning efforts; moreover, proper farm management could increase the farm's productivity and lessen the supply shortage.