Bootstrap Method in Price Analysis in Reverse Logistics of Solid Waste from Commercial Restaurants

Abstract

Reverse logistics is defined as the practices and processes established to organize the returns of products from point of sales to the manufacturer in order to repair, recycle or dispose of these items in the most economical way possible. There's a close connection between reverse logistics and environmental protection because it focuses on the management of products, components and materials that have been used and disposed of, and of which the manufacturer has some kind of responsibility. Its purpose is to reuse these products and, where possible, reduce the final amount of waste. To demonstrate the economic viability of reverse logistics, robust statistical procedures are required. Among these procedures, we can highlight the bootstrap method, which is a resampling procedure used to estimate statistics of a population by sampling a set of data with repetition. Therefore, the objective of this study was to use the bootstrap method to estimate the ideal selling price of solid waste in a network of commercial restaurants in the metropolitan region of Recife, in order to obtain information on the profit from the operation. The results showed that the bootstrap method was an efficient statistical tool in the analysis of data from reverse logistics operations and can be used in decision making on the variation of solid waste sales prices in the chain of restaurants studied. Therefore, the network of restaurants studied can certainly profit from the reverse logistics operation, in addition to sustainable business marketing that attracts a larger group of customers.

Bootstrap Analysis of the Production Processes Capability Assessment

“…The bootstrap method can be used for what-if studies. It is used in many different areas, including in simulation models analyzing medical data [7][8][9][10][11][12], in financial analyzes [13][14][15], in solving problems in the area of logistics and distribution [16][17][18], in environmental protection [19][20][21][22], safety sciences [23], automotive [24], risk management [25,26] and in classic queuing models [27].…”