Optimal batch production strategies under continuous price decrease and time discounting

Single price discount in unit cost for bulk purchasing is quite common in reality as well as in inventory literature. However, in today's high-tech industries such as personal computers and mobile industries, continuous decrease in unit cost is a regular phenomenon. In the present paper, an attempt has been made to investigate the effects of continuous price decrease and time-value of money on optimal decisions for inventoried goods having time-dependent demand and production rates. The proposed models are developed over a finite time horizon considering both shortages and without shortages in inventory. Numerical examples are taken to illustrate the developed models and to examine the sensitivity of model parameters

Joint pricing and ordering policies for deteriorating item with retail price-dependent demand in response to announced supply price increase

[[abstract]]Recently, due to rapid economic development in emerging nations, the world's raw material prices have been rising. In today's unrestricted information environment, suppliers typically announce impending supply price increases at specific times. This allows retailers to replenish their stock at the present price, before the price increase takes effect. The supplier, however, will generally offer only limited quantities prior to the price increase, so as to avoid excessive orders. The retail price will usually reflect any supply price increases, as market demand is dependent on retail price. This paper considers deteriorating items and investigates (1) the possible effects of a supply price increase on retail pricing, and (2) ordering policies under the conditions that special order quantities are limited and demand is dependent on retail price. The purpose of this paper is to determine the optimal special order quantity and retail price to maximize profit. Our theoretical analysis examines the necessary and sufficient conditions for an optimal solution, and an algorithm is established to obtain the optimal solution. Furthermore, several numerical examples are given to illustrate the developed model and the solution procedure. Finally, a sensitivity analysis is conducted on the optimal solutions with respect to major parameters.[[incitationindex]]SCI[[booktype]]紙

A Single Item Lot Sizing with Backorder and a Finite Replenishment Rate in MRP

There are the following characteristics in decision on lot size in material requirements planning (MRP) systems: multiple time periods, a finite time horizon, discrete demand, and time-varying costs etc. In MRP system there are several different types of lot size techniques, such as the economic order quantity (EOQ), lot-for-lot, periodic order quantity, Wagner-Whitin algorithm, Silver-Meal algorithm and part-period algorithm. Although these lot size approaches focus on controlling the cost of holding cost and order cost, none of them, with the exception of the Wagner-Whitin algorithm, assures an optimal or minimum cost solution for time-varying demand patterns and copes with quantity discount. And Zangwill(1966), Blackburn and Kunreuther (1974) et al extended the Wagner-Whitin algorithm by following demand to go unsatisfied during some period, provided it is satisfied eventually by production in some subsequent period. R. M. Hill (1997), Stanislaw Bylka, Ryszarda Rempala (2001) give dynamic programming formulation to decide lot sizing for a finite rate input process. But the Wagner-Whitin algorithm and its extensions commonly are criticized as being difficult to explain and compute because the algorithms are complicated dynamic programming algorithms. In this paper, we propose a series of inventory models in which backorder and a finite replenishment rate are considered according to the characteristics in MRP ordering and the optimal solutions can be obtained by using general-purpose linear program solver, like EXCEL, LINDO, etc

Impact of future price increase on ordering policies for deteriorating items under quadratic demand

When a supplier announces a price increase at a certain time in the future, for each retailer it is important to choose whether to purchase supplementary stock to take benefit of the current lower price or procure at a new price. This article focuses on the possible effects of price increase on a retailer's replenishment strategy for constant deterioration of items. Here, quadratic demand is debated; which is appropriate for the products for which demand increases initially and subsequently it starts to decrease with the new version of the substitute. We discuss two scenarios in this study: (I) when the special order time coincides with the retailer's replenishment time and (II) when the special order time falls during the retailer's sales period. We determine an optimal ordering policy for each case by maximizing total cost savings between special and regular orders during the depletion time of the special order quantity. Scenarios are established and illustrated with numerical examples. Through, sensitivity analysis important inventory parameters are classified. Graphical results, in two and three dimensions, are exhibited with supervisory decision

An optimal operational policy for an integrated production-delivery system under continuous price decrease

In today’s competitive world, the unit cost of a high-tech product declines significantly over its life cycle. An integrated inventory model for products experiencing continuous decrease in unit cost is studied in this research. In this integrated model a manufacturing facility purchases raw material from outside supplier at a fixed size and supplies a fixed quantity of finished products to a buyer periodically after using its production processes. Moreover, buyers demand frequent deliveries of small lots of finished products since the price is continuously decreasing, and this emphasizes the significance of just-in-time (JIT) inventory management for successful companies in technology-related industries. The goal in this study is to minimize the total cost of the supply chain in JIT environment while the price of the high-tech product is linearly decreasing over its life cycle. A cost model composed of manufacturer’s raw materials and finished goods and buyer’s incoming goods inventory costs is developed here. An efficient algorithm is employed to determine the optimal or near-optimal lot sizes for raw material procurement, manufacturing batch and buyer’s ordering policies. It is also shown in the implemented model that the integrated total cost over the planning horizon considers the changing prices at each replenishment for both manufacturer’s and buyer’ s inventory costs. Consequently, in this article, the traditional integrated inventory model is relaxed by removing the restriction of constant unit cost. Finally, the solution technique for the developed model is illustrated with numerical examples, and compared with the previously developed integrated inventory models to test its accuracy. It is proven that the model is accurate and effective for the inventory systems with decreasing unit cost

Supply chain collaboration

In the past, research in operations management focused on single-firm analysis. Its goal was to provide managers in practice with suitable tools to improve the performance of their firm by calculating optimal inventory quantities, among others. Nowadays, business decisions are dominated by the globalization of markets and increased competition among firms. Further, more and more products reach the customer through supply chains that are composed of independent firms. Following these trends, research in operations management has shifted its focus from single-firm analysis to multi-firm analysis, in particular to improving the efficiency and performance of supply chains under decentralized control. The main characteristics of such chains are that the firms in the chain are independent actors who try to optimize their individual objectives, and that the decisions taken by a firm do also affect the performance of the other parties in the supply chain. These interactions among firms’ decisions ask for alignment and coordination of actions. Therefore, game theory, the study of situations of cooperation or conflict among heterogenous actors, is very well suited to deal with these interactions. This has been recognized by researchers in the field, since there are an ever increasing number of papers that applies tools, methods and models from game theory to supply chain problems

The effect of continuous price change in the EOQ

The sensitivity of the basic economic order quantity (EOQ) model to continuous purchase price changes is explored. The phenomenon of continuous price changes exists in several countries and it is not likely to improve. The paper shows that using the conventional EOQ can be quite costly and far from optimal, if the holding cost rate is determined erroneously by ignoring the price change. © 1992

Sustainable Inventory Management Model for High-Volume Material with Limited Storage Space under Stochastic Demand and Supply

Inventory management and control has become an important management function, which is vital in ensuring the efficiency and profitability of a company’s operations. Hence, several research studies attempted to develop models to be used to minimise the quantities of excess inventory, in order to reduce their associated costs without compromising both operational efficiency and customers’ needs. The Economic Order Quantity (EOQ) model is one of the most used of these models; however, this model has a number of limiting assumptions, which led to the development of a number of extensions for this model to increase its applicability to the modern-day business environment. Therefore, in this research study, a sustainable inventory management model is developed based on the EOQ concept to optimise the ordering and storage of large-volume inventory, which deteriorates over time, with limited storage space, such as steel, under stochastic demand, supply and backorders. Two control systems were developed and tested in this research study in order to select the most robust system: an open-loop system, based on direct control through which five different time series for each stochastic variable were generated, before an attempt to optimise the average profit was conducted; and a closed-loop system, which uses a neural network, depicting the different business and economic conditions associated with the steel manufacturing industry, to generate the optimal control parameters for each week across the entire planning horizon. A sensitivity analysis proved that the closed-loop neural network control system was more accurate in depicting real-life business conditions, and more robust in optimising the inventory management process for a large-volume, deteriorating item. Moreover, due to its advantages over other techniques, a meta-heuristic Particle Swarm Optimisation (PSO) algorithm was used to solve this model. This model is implemented throughout the research in the case of a steel manufacturing factory under different operational and extreme economic scenarios. As a result of the case study, the developed model proved its robustness and accuracy in managing the inventory of such a unique industry

A continuous review inventory system in a random environment

Ankara : The Department of Management and the Graduate School of Business Administration of Bilkent Univ., 1998.Thesis (Master's) -- Bilkent University, 1998.Includes bibliographical references leaves 66-68.In this thesis, we develop a continuous review inventory model in a random environment where holding, ordering, and purchasing cost parameters are dependent on the state of the environment. We derive the exact expressions of the operating characteristics of the model and discuss some convexity properties of the expected cost rate. A numerical analysis is provided to examine the sensitivity of the optimal policy parameters with respect to various system parameters. We compare the instantaneous shock model with our model and illustrate that ignoring finite duration of environmental states results in considerable error. Moreo\er. we compare our model with a time-average EOQ and myopic model. The results illustrate that when ordering and purchasing cost parameters change, our model performs significantly better.Bayar, AslıM.S