Recursive Estimation of Inventory Quality Classes Using Sampling (original) (raw)
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Filtering and predicting the cost of hidden perished items in an inventory model
Journal of Applied Mathematics and Stochastic Analysis, 2002
This paper is concerned with a discrete time, discrete state inventory model for items of changing quality. Items are assumed to be in one of a finite number,M, of quality classes that are ordered in such a way that Class1contains the best quality and the last class contains the pre-perishable quality. The changes of items' quality are dependent on the state of the ambient environment. Furthermore, at each epoch time, items of different classes may be sold or moved to a lower quality class or stay in the same class. These items are priced according to their quality, and costs are incurred as items lose quality. Based on observing the history of the inventory level and prices, we propose recursive estimators as well as predictors for the joint distribution of the accumulated losses and the state of the environment.
Annals of Operations Research
To safeguard the livelihood of consumers, food producers are required, either by law or regulatory bodies, to inspect their products for quality before selling the products to consumers. This is because food processing, as is the case with most production systems, is not perfect and there is a possibility that some of the processed products do not meet the required quality standard. Likewise, the inspection process is seldom perfect, meaning that it is subject to errors and thus, some of the processed products might be incorrectly classified. In light of this, an inventory model for a four-echelon food processing supply chain is developed. The supply chain has a farming echelon where live items are grown with the possibility that some of them might not survive; a processing echelon where the live items are transformed into processed inventory; an inspection echelon where the processed inventory is classified into good and poorer quality classes under the assumption that the inspecti...
The fundamental reason for carrying inventories is that it is physically impossible and economically impractical for each stock item to arrive exactly where it is needed exactly when it is needed. The goal of inventory management is to ensure the consistent delivery of the right product in the right quantity to the right place at the right time. Most of the researchers in inventory system were directed towards non-deteriorating products. However, there are certain substances, whose utility do not remain same with the passage of time. Deterioration of these items plays an important role and items cannot be stored for a long time. Deterioration of an item may be defined as decay, evaporation, obsolescence, loss of utility or marginal value of an item that results in the decreasing usefulness of an inventory from the original condition. When the items of the commodity are kept in stock as an inventory for fulfilling the future demand, there may be the deterioration of items in the inventory system, which may occur due to one or many factors i.e. storage conditions, weather conditions or due to humidity. INTRODUCTION Most of the inventory models were formulated in a static environment where the demand is assumed to be constant and steady. In fact, the constant demand assumption is only valid dur ing the maturity phase of time. In realistic business situations many items of inventory such as electronic products, fashionable clothes, tasty food products and domestic goods generate increasing sales after gaining consumer " s acceptance. Therefore it is more realistic if we consider demand rate as time dependent. Many businesses are not as successful as they could be simply because they lack the know-how or the will to implement sound inventory management and control practices. Successful inventory is a compromise between low inventory levels and meeting targeted fill rates. Investing in the right inventory and reducing excess will improve customer fill rates, inventory turnover and cash flow and profits. The purpose of the study is to develop and analyze some inventory models for decaying items with variable demand rates for different realistic business situations.
Inventory Models with Deteriorating Items: A Literature Review
2014
This paper presents a review of the main characteristics of the mathematical models developed by the scientific community in order to determine an optimal inventory policy for deteriorating items. Thus, a classified bibliography of 390 articles published from 2001 to 2014 in high-impact journals is submitted while considering the type of demand and deterioration, the integration of inventory and pricing decisions, the inclusion of shortage and/or the time value of money, the consideration of multiple items and/or multi-echelon systems, and the incorporation of uncertain parameters other than demand. Finally, research questions not yet addressed by the research community in the field of inventory control for deteriorating items are pointed out.
INVENTORY MODEL OF DETERIORATING ITEMS WITH
Abstract: Multi-item inventory model for deteriorating items with stock dependent demand under two-warehouse system is developed in fuzzy environment (purchase cost, investment amount and storehouse capacity are imprecise ) under inflation and time value of money. For display and storage, the retailers hire one warehouse of finite capacity at market place, treated as their own warehouse (OW), and another warehouse of imprecise capacity which may be required at some place distant from the market, treated as a rented warehouse (RW). Joint replenishment and simultaneous transfer of items from one warehouse to another is proposed using basic period (BP) policy. As some parameters are fuzzy in nature, objective (average profit) functions as well as some constraints are imprecise in nature, too. The model is formulated so to optimize the possibility/necessity measure of the fuzzy goal of the objective functions, and the constraints satisfy some pre-defined necessity. A genetic algorithm (GA) is used to solve the model, which is illustrated on a numerical example.