Article2011

Forecasting of Optimum Raw Material Inventory Level using Artificial Neural Network

Syed Mithun Ali · Sanjoy Kumar Paul · Kamrul Ahsan · Abdullahil Azeem

15 citations0/0 files storedVictoria University Research Repository (Victoria University)

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Supply Chain and Inventory Management Forecasting Techniques and Applications Impact of AI and Big Data on Business and Society

Abstract

This paper develops an artificial neural network (ANN) model to forecast the optimum level of raw materials inventory as a function of product demand, manufacturing lead-time, supplier reliability, material holding cost, and material cost. The model selects a feed-forward back-propagation ANN with twelve hidden neurons as the optimum network. We test the model with pharmaceutical company data. The results show that the model can be useful to forecast raw material inventory level in response to different parameters. We also compare the model with fuzzy inference system (FIS) and simple economic order quantity (EOQ). It can be seen that ANN model outperforms others. Overall, the model can be applied for forecasting of raw materials inventory for any manufacturing enterprise in a competitive business environment.

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References

2005Optimal inventory model for items with imperfect quality and shortage backordering 2002Classifying inventory using an artificial neural network approach 2003An application of fuzzy sets theory to the EOQ model with imperfect quality items 2003Determining the optimal lot size for the finite production model with random defective rate, the rework process, and backlogging 2002An optimal ordering and recovery policy for reusable items 2005Fuzzy inventory model with two warehouses under possibility constraints 2008Optimising replenishment policy for an integrated production inventory deteriorating model considering green component-value design and remanufacturing

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