Introduction

Plant design involves major business considerations, including market demand, plant location, nature of the product, construction and operation costs, production capacity, government policy, climate, and potential competitors. In particular, strategic decisions for plant location and production capacity are the key for business success in the food processing industry since these decisions in the early plant design phase predetermine most of the plant operation cost.

The plant location decision has often been formulated as a cost optimization problem by converting the associated decision attributes into monetary values. This cost optimization model usually involves decision making regarding the following attributes: market demand, production and storage capacity, production cost, and supply reliability. This optimization model has been extended by incorporating uncertainty - variation of population, changeable market trends, and unpredictable demand - into the decision-making process.

A food plant is usually located close to either customers or raw material growing regions, depending on the nature of the product. In addition, the daily production volume must be carefully planned to avoid shouldering the extra cost of excessive production. Thus, the plant location decision can be considered from a supply reliability standpoint. Although the stable supply of raw materials has often been assumed in previous production capacity analysis, this ideal assumption does not always hold in food production. In other words, there are many sources of uncertainty, including quality deterioration, seasonal variation of production quantity, unstable climate, and natural disaster. In general, a decision model for plant location selection requires a precise estimation of the production capacity of each prospective location for which several simulation-based methods have been proposed in the literature; it is not straightforward to analyze too complex food production systems by using an analytical optimization model.

This study presents a simulation-based decision support model to select the right location for a new food processing plant. In particular, the simulation model of food production accounts for the supply uncertainty of raw materials depending on the production site and the harvest season in the food processing industry. However, it is very difficult to make precise decisions in complex and uncertain problems if the acquired data is imprecise or insufficient. In order to overcome this difficulty, we define the supply vulnerability factors of raw materials such as production quantity in a food-growing region, market demand, and distance. All these factors are assessed and aggregated to determine the degree of vulnerability in the form of fuzzy rules. The evaluated vulnerability scores are then converted into raw material supply variations for food production simulation to predict the quarterly production volume of a new food processing plant. For production simulation, we conduct the probability distribution analysis to estimate the supply failure rate and the duration of failure. Finally, we simulate the daily food production volume in all prospective plant locations and select a location that guarantees the production of the target quantity, despite the unstable supply of raw materials. The simulation results, in fact, help decision stakeholders make a relative rank order, even without sufficient supply failure data, and eventually, the final selection is made based on the relative ranking. The proposed selection procedure is illustrated using a case study of semiprocessed kimchi production.