Inventory policies and safety stock optimization for supply chain planning

Abstract

In this article, traditional supply chain planning models are extended to simultaneously optimize inventory policies. The inventory policies considered are the (r,Q) and (s,S) policies. In the (r,Q) inventory policy an order for Q units is placed every time the inventory level reaches level r, while in the s,S policy the inventory is reviewed in predefined intervals. If the inventory is found to be below level s, an order is placed to bring the level back to level S. Additionally, to address demand uncertainty four safety stock formulations are presented: (1) proportional to throughput, (2) proportional to throughput with risk-pooling effect, (3) explicit risk-pooling, and (4) guaranteed service time. The models proposed allow simultaneous optimization of safety stock, reserve, and base stock levels in tandem with material flows in supply chain planning. The formulations are evaluated using simulation.

Cost Minimization of Aircraft Critical Components for Planning and Maintenance Requirements

“…Based on the literature review, spare parts inventory level that was achieved in the previous studies usually in the range of 95% to 99% of spare parts availability with the implementations of the model proposed in the studies [8], [14]. The result obtained from this model shows that the spare parts inventory stock level had obtained 100% of the minimum 50 quantity requirement and this had improved the results that was obtained from the previous model.…”

“…Based on the result from the analysis, Total Defect is one of the major contributing factor that affected the operational availability of the aircrafts fleet as stated in the literature review [11], [12], [13], [14]. Therefore, it is crucial to focus on the spare parts availability in order to achieve higher hours flown which will improve the operational availability of the aircrafts fleet [11], [12], [13], [14], [27]. The availability of spare parts is trying to be achieved by implementing the cost minimization optimization model will be discussed in the next part of this paper.…”

“…There were several other studies that used the similar approach of estimating the future demands of spare parts in determining the inventory stock level such as H. Rezaei et al where the reorder quantity is estimated by using the frequency usage of fast moving and slow moving items, which aims to minimize the total inventory cost while satisfying the targeted service level (TSL) [13] and also B. Brunaud et al that predict the uncertainty demands of spare parts due to unscheduled maintenance which is used to determine the safety stock level of inventory with the aims to reduce stock out of spare parts [14]. Furthermore, Pleumpirom and Amorsawadwatana had developed a multi objective optimization model using goal programming in forecasting spare parts requirements for schedule maintenance with the aims to minimize cost, minimize lead time and maximize the quality of the spare parts.…”

“…Meanwhile, in order to achieve second goal, another constraint is used to ensure total cost of the component purchase or repair does not exceed the total allocated budget as represented in the Equation (14) and (15). The underachievement, and overachievement, deviations are also assigned to this constraint together with the weighted constants, in order to ensure the total cost is within the allocated budget.…”

Supply chain networks servicing upstream operations in oil and gas fields after the shale revolution

“…However, they are not proportionally higher than in minor depots due to risk-pooling effects on the demand variability. 38 The inventory cost for every storage facility is computed according to its size and the total flow managed. Overall, there is a critical tradeoff between the cost paid for stocks and the responsiveness achieved when supplying materials and services from more depots, closer to production sites.…”