Economic Disposal Quantity of Leftovers kept in storage: a Monte Carlo simulation method

R Assis,R Vidal,J Oliveira Santos,P Carmona Marques

doi:10.1515/eng-2019-0062

Abstract

Abstract This article describes how to reach an item’s threshold, or in other words, the limit time for it to be retrieved from stock and sold for a different use, as well as the remaining foreseen period for this situation to occur. Once a minimum length, or weight, is reached, left quantities are more difficult to sell, as demand often exceeds the remaining parts or leftovers. The number of unfulfilled orders increases, as time goes by, until it becomes further cost effective to dispose the leftover and sell it for a lower price and alternative use. A Monte Carlo simulation model was built in order to consider the randomness of future transactions and quantifying consequences providing this way a simple and effective decision-making framework.

Highlights

In the retail trade activity, a particular situation might occur when a piece of a material in the form of a reel or in the form of a rod or rigid tube is cut into different lengths to satisfy custom orders
A Monte Carlo simulation model was built in order to consider the randomness of future transactions and quantifying consequences providing this way a simple and effective decision-making framework
Having reviewed some literature regarding the issue, we propose the following research objective: The construct and use of a Monte Carlo simulation model for decision-making of randomness future transactions and quantifying costs

Summary

Introduction

In the retail trade activity, a particular situation might occur when a piece of a material in the form of a reel (e.g. electric cable, flexible tube, rope, wire, paper, or tape) or in the form of a rod or rigid tube is cut into different lengths to satisfy custom orders. Under an assumed additive demand function, at the beginning of each period, a (s, S) policy is optimal for replenishment, and the price value depends on the stock level after the replenishment decision. Their numerical study suggested that for a sufficiently long sales horizon, optimal policy is almost stationary. There must be a minimum length (or weight), where the expected marginal benefit from selling to the every-day market turns to be smaller than the expected marginal benefit from selling to an alternative use [10, 11] This so called minimum or ‘optimal economic equilibrium quantity’, can be deduced by simulating the upcoming future, and assuming the following defined condition proposed earlier by Assis and Figueira [1]: ERM < ERA. ERM is the Expected Revenue from the Market and ERA is the Expected Revenue from an Alternative use

Methods and Tools

Opportunity for ERA sales

Trade-off point

Results

Discussion and Conclusion