Modeling Raw Material Inventory Control and Delivery of Ready Mixed Concrete to Sites in Addis Ababa
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Date
2023-06
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Addis Ababa University
Abstract
Due to the city's booming construction, ready-mixed concrete is gaining wide recognition and has
a high demand in Addis Ababa. Since these operations are performed in a highly uncertain
environment, making planning and operating difficult and complex. So a supply chain
management system is needed to form a consumption pattern for the raw materials of RMC and
delivery of RMC. Inventory and delivery are the most important among the several management
plans and decisions in RMC batching plants, such a raw material inventory control system of RMC
batching plants could be applied as an approach for optimal estimation of the reservoirs required
for storage of raw materials and to reduce the effect of uncertainties on delivery operations. This
research aims to develop an integrated raw materials inventory model with a simulation model for
the delivery of RMC to sites in Addis Ababa. The data sources used in this research are observation
and financial reports using a case study plant and delivery sites. A raw material inventory model
was developed through Economic Order Quantity (EOQ) model and the developed models are
predicted using Artificial Neural Network (ANN). As a result, the testing predictive analysis
results confirmed that the ANN predictive had higher accuracy in the prediction of the optimal
order quantity and reordering point (ROP) of raw materials with an accuracy measurement value
of 0.98 R and 0.2935 MAE and 0.9998 R and 0.2935 MAE for EOQ and ROP of Aggregate,
0.9831 R and 0.418 MAE and 0.9999 R and 0.1673 MAE for EOQ and ROP of Sand, and 0.9951
R and 1.6512 MAE and 0.9828 R and 6.1731 MAE for EOQ and ROP of Cement. Additionally,
63.35%, 76.47%, and 11.27% reductions have been obtained in the estimation of the optimal size
of the required reservoirs for aggregate, sand, and cement respectively. Discrete Event Simulation
(DES) was used to develop RMC to site delivery model. The study involved close observation of
182 concrete truck delivery cycles taken from two sample projects, which cover 50.89% of the
overall yearly (2014 E.C.) concrete delivery of the case study plant. And EOQ-based ANN
predictive analysis maximum consumption output results were used to get the maximum delivered
amount of concrete used for the analysis of DES, the predictive output result was 1307.33 m3.
Finally, an overall simulation output result, which is optimal by assigning 8 numbers of trucks,
with an overall production rate of 0.025 TL/min (10.74m3/hr.) and 0.032 TL/min. (13.75 m3/hr.)
respectively for the truck and mixer are established.
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Keywords
Ready Mixed Concrete, Inventory Control, Economic Order Quantity, Artificial Neural Network, Discrete Event Simulation