Assets acquisition planning

Abstract

At the 127th European Study Group with Industry an energy sector com- pany proposed an industrial challenge that consisted on the asset acquisi- tion planning for its liquefied petroleum gas (LPG) cylinder business, one of the most recent business areas in the company. This business area is still in a growing phase and to satisfy the market needs and assure a sustainable growth a very tight control of the main assets, the LPG cylinders, is of paramount importance. Therefore, a detailed planning of all the as- sets acquisition is required, taking into consideration several variables: sales growth rate, seasonality, cylinder rotation and corresponding return rate to the filling plant.
The challenge was to develop a model for the assets acquisition planning. In order to tackle this challenge, it was necessary to forecast the demand. For that purpose, time series techniques were used, in particular, moving averages and exponential smoothing. The results show that the seasonality does not explain all the variation of the demand, therefore it is necessary to use a model that would consider other possible explanatory variables.
According to several authors, gas consumption may be influenced by several aspects, such as, atmospheric temperatures, heliophany (a measure of the day luminosity), wind, relative humidity, rains, minimum and maximum temperatures, demand in previous periods, and prices.
The forecast of bottled propane gas sales and return rate was also addressed through multivariate linear regression. Regression models for the monthly number of bottles of types A and B were obtained, having pre- sented good percentages of explained variability with the variables under study.
The main goal of the challenge, the acquisition plan, was addressed u ing inventory models with reverse logistics. Several deterministic approaches have been considered to enable different aspects in the framework. A new inventory model has been developed to contemplate the three possible des- tinations of returned bottles: cleaning, requalification, or disposal. The models were implemented in Excel and can be tested, using PRIO estimates of holding costs and fixed setup costs, and the forecasts of sales and return rate computed previously.