Productivity improvement in an agribusiness dedicated to the export of snow peas using Lean Manufacturing and Mathematical Optimization tools

This research focuses on the situation faced by an agribusiness that processes Snow Peas in the planning of its supply chain in the phases of the crop field, transportation to the production plant, and the processing plant. The planning is carried out in the short term horizons, which deals with the allocation of the operators' tasks in order to fulfill the customers' orders; and the medium term horizon that determines the monthly planning based on the harvest forecast data provided by the crop fields. In previous research, a vehicle routing algorithm was developed to optimize the collection of raw material [1]. Once the raw material arrives at the processing plant, there is a workforce of approximately 200 people in different production areas that have an 8-hour workday, and whose production capacity is about 5.6 tons of raw material entering the plant. The principal problem detected in the research is the lack of reliability in the forecasting method for the supply of raw material; for example, on several occasions, an entry of 5 tons of material is estimated when at the end of the day only 2 tons enter the plant, which generates a very high variation in the use of the plant's capacity. Also, it was determined that there is a deficient standardization of processes and a low operational capacity in the selection, cutting, and packing areas, which is reflected in a cost of US$1.2 per kilogram processed. Therefore, this research determined a three-phase methodology to improve the processes using lean manufacturing and mathematical optimization tools. In the first phase, data was collected from the crop fields in order to create a multivariable mathematical-statistical model; the model obtained has an accuracy level of 85%. In the second phase, a value stream mapping was carried out to determine value-added times, non-value-added times, and activities that do not generate value; this was the basis for restructuring process operations and developing a new layout that maximizes the production flow. In the third phase, a mathematical S&OP model was developed to determine the number of operators per week to maximize production capacity. The application of the research resulted in an 80% increase in workers' salaries, a 150% increase in capacity, and a reduction in production cost to US$ 0.38 per kilogram processed.