Impacts of forecast, inventory policy, and lead time on supply chain inventory--A numerical study

This paper first proposes the use of metaheuristic, to combine with exponential smoothing methods, in forecasting future demands and in determining the optimal inventory policy values for each node in a supply chain network based on historical demand or order streams without the need of any prior knowledge about the demand distribution or distribution fitting. The effects of five demand forecasting methods, two inventory policies, and three lead times on the total inventory cost of a 3-echelon serial supply chain system are then investigated. The effect of sharing the demand information for planning the inventories is also compared with that of no sharing. For testing, 15 quarterly and 15 monthly time series were taken from the M3 Competition and are considered as the multi-item demand streams to be fulfilled in the supply chain. The results indicate that: (1) the damped Pegel forecasting method is the best in terms of prediction errors because it outperforms others in three of five measures, followed by the simple exponential smoothing that wins one of the remaining two and ties the damped Pegel in one; (2) the supply chain inventory cost increases with increasing lead time and echelon level of the supply chain when the (s, S) policy is used, but not the (r, Q) policy; (3) the (r, Q) inventory policy generally incurs lower supply chain inventory cost than the (s, S) policy; (4) sharing demand information reduces inventory cost and the reduction is higher for (s, S) than for (r, Q); (5) the best demand forecasting method for minimizing inventory cost varies with the inventory policy used and lead time; and (6) the correlation between forecasting errors and inventory costs is either negligible or minimal.