The implementation of just-in-time (JIT) principles has been shown to be worthy of analysis due to its potential economic benefits. Yet, while several empirical studies have reported the success of adopting JIT management concepts, little work has been accomplished in offering analytical tools for assisting managers for implementing JIT strategy. This paper proposes a new inventory model to better embrace JIT purchasing. In pursuing this goal, we develop a deterministic single-setup multiple-delivery model for deteriorating items by considering the effect of the time value of money (TVM). We propose a solution procedure to determine the optimal decisions that maximize the discounted profit function of this analytical model, and compare it with some other alternatives. Here, we show the derivation of the mathematical model, the algorithm of the proposed solutions, and the application of the new approach through two numerical experiments. The study reveals that modeling the TVM effect complicates the determination of an optimal JIT inventory policy; nevertheless, we find that accounting for TVM can be decisive in terms of promoting and implementing JIT purchasing agreements.

Inflation is an important factor influencing traditional economic order quality models. Marketing strategy depends on inflation due to public demand and availability of the materials. This paper presents an optimal inventory policy for deteriorating items using exponential demand rate under permissible delay in payments. Mathematical model has been derived under two cases: case I: cycle time is greater than or equal to permissible delay period, case II: cycle time is less than permissible delay period by considering holding cost as a function of time. Numerical examples and sensitivity analysis are given to reflect the numerical results. Mathematica software is used for finding optimal solutions.

Inventories are the materials stored either waiting for processing or experiencing processing and in some cases for future delivery. Inventories are treated both as blessings and evil. As they are like money placed in a drawer, assets tied up in investments, incurring costs for the care of the stored material and also subject to spoilage and obsolescence there have been a spate of programs developed by industries, all aimed at reducing inventory levels and increasing efficiency on the shop floor. Nevertheless, they do have positive purposes such as stable source of input required for production, less replenishment and may reduce ordering costs because of economies of scale. Finished goods inventories provide for better customer service. So formulating a suitable inventory model is one of the major concerns for an industry. Again considering reliability of any process is an important trend in the current research activities. Inventory models could be both deterministic and probabilistic and both of which must account for the reliability of the associated production process. This paper discusses the major works in the field of inventory modeling driven by reliability considerations, which ranges from the very beginning to latest works just published.

This paper deals with the development of an inventory model for time varying demand and constant demand; and time dependent holding cost and constant holding cost for case 1 and case 2 respectively. Previous models incorporating that the holding cost is constant for the entire inventory cycle. Mathematical model has been developed for determining the optimal order quantity, the optimal cycle time and optimal total inventory cost for both cases. Differential calculus is used for finding optimal solution. Numerical examples are given for both cases to validate the proposed model. Sensitivity analysis is carried out to analyze the effect of changes in the optimal solution with respect to change in various parameters.

In this paper, an artificial neural network (ANN) model is developed to determine the optimum
level of finished goods inventory as a function of product demand, setup, holding, and material
costs. The model selects a feed-forward back-propagation ANN with four inputs, ten hidden
neurons and one output as the optimum network. The model is tested with a manufacturing
industry data and the results indicate that the model can be used to forecast finished goods
inventory level in response to the model parameters. Overall, the model can be applied for
optimization of finished goods inventory for any manufacturing enterprise in a competitive
business environment.