An inventory model for multiple items assuming time-varying demands and limited storage

A model for inventory systems with multiple products is studied. Demands of items are time-dependent and follow power patterns. Shortages are allowed and fully backlogged. For this inventory system, our findings provide the efficient inventory policy that helps decision-makers to obtain the initial inventory levels and the reorder points that maximize the profit per unit time. Moreover, when it is assumed that the warehouse used for the storage of products has a limited capacity, the optimal inventory policy is also developed. The model presented here extends some inventory systems studied by other authors. Numerical examples are introduced to illustrate the applicability of the theoretical results presented.

Reinforcement Learning Approach for Efficient Inventory Policy in Multi-Echelon Supply Chain Under Various Assumptions and Constraints

Background: Inventory policy highly influences Supply Chain Management (SCM) process. Evidence suggests that almost half of SCM costs are set off by stock-related expenses.Objective: This paper aims to minimise total inventory cost in SCM by applying a multi-agent-based machine learning called Reinforcement Learning (RL).Methods: The ability of RL in finding a hidden pattern of inventory policy is run under various constraints which have not been addressed together or simultaneously in previous research. These include capacitated manufacturer and warehouse, limitation of order to suppliers, stochastic demand, lead time uncertainty and multi-sourcing supply. RL was run through Q-Learning with four experiments and 1,000 iterations to examine its result consistency. Then, RL was contrasted to the previous mathematical method to check its efficiency in reducing inventory costs.Results: After 1,000 trial-error simulations, the most striking finding is that RL can perform more efficiently than the mathematical approach by placing optimum order quantities at the right time. In addition, this result was achieved under complex constraints and assumptions which have not been simultaneously simulated in previous studies.Conclusion: Results confirm that the RL approach will be invaluable when implemented to comparable supply network environments expressed in this project. Since RL still leads to higher shortages in this research, combining RL with other machine learning algorithms is suggested to have more robust end-to-end SCM analysis. Keywords: Inventory Policy, Multi-Echelon, Reinforcement Learning, Supply Chain Management, Q-Learning

On the shortage control in a continuous review (Q, r) inventory policy using αL service-level

Popular measures of product availability in inventory systems seek to control different aspects of stock shortages. However, none of them simultaneously control all aspects of shortages, because stock shortages in inventory systems are complex random events. This paper analyzes the performance of αL service measure, defined as the probability that stockouts do not occur during a replenishment cycle, to cover different aspects of stock shortages when used to design an optimal continuous review (Q, r) policy. We show that explicitly controlling the frequency of replenishment cycle stockouts, using the αL service-level, allows to implicitly control the size of the stockouts at an arbitrary time, the size of accumulated backorders at an arbitrary time, and the duration of the replenishment cycle stockouts. However, the cost of controlling the frequency of replenishment cycle stockouts is greater than the cost of controlling the size of stockouts and the duration of the replenishment cycle stockouts.

Financial Assessment on Designing Inventory Policy by Considering Demand, Lead Time, and Defective Product Uncertainties: A Monte Carlo Simulation

Inventory is one of the main components in supply chain. However, it is not easy to design inventory policy under uncertainties. The frequent occurrence of overstocks increases the company's financial expenditure. Otherwise, stockout decreases customer satisfaction and damage the company's image. This study aims to provide monte carlo model to design inventory policy with the aim of maximizing net income with a variety of uncertainties, one of the uncertainties is defective product because of the travel from suppliers. To handle the complexity and uncertainty of problem, a Monte Carlo simulation is used with spreadsheet-based representation. To test the reliability of the model, guitar company is used as relevant use case with uncertainty adhered ‘the greater number of order quantity, the greater likely the defective guitar will be’. The verification & validation process, experimental design, and alternative selection are also done with statistical tests. Based on the simulation result, it is known that changing the reorder point to 80 and the order quantity to 90 gives the best result which can increase net income by 0.44% compared to the initial net income. In addition, the number of stockouts has decreased.

EXCHANGE CURVE-BASED INVENTORY POLICY ANALYSIS OF NON-REUSABLE INDUSTRIAL PACKAGING IN PRODUCTION

The well-chosen inventory policy has a great impact on the performance of production and logistics processes, because it can influence not only the reliability, the cost efficiency, and the sustainability of the processes and resources, but packaging system can force the quality of products and processes. Within the frame of this article an exchange curve-based analysis method of packaging related inventory policy is described. This analysis method makes it possible to highlight the problems in inventory policy and find an improve solution in both macro- and micro-level. The computation method is based on the exchange of annual order cost and average inventory investment, especially in the case of economic order quantity-based packaging order policies.