An Inventory Model for Deteriorating Commodity under Stock Dependent Selling Rate

Economic order quantity (EOQ) is one of the most important inventory policy that have to be decided in managing an inventory system. The problem addressed in this paper concerns with the decision of the optimal replenishment time for ordering an EOQ to a supplier. This Model is captured the affect of stock dependent selling rate and varying price. We developed an inventory model under varying of demand-deterioration-price of commodity when the relationship of supplier-grocery-consumer at stochastic environment. The replenishment assumed instantaneous with zero lead time. The commodity will decay of quality according to the original condition with randomize characteristics. First, the model is addressed to solve a problem phenomenon how long is the optimum length of cycle time. Then, an EOQ of commodity to be ordered by will be determined by model. To solve this problem, the first step is developed a mathematical model based on reference’s model, and then solve the model analytically. Finally, an inventory model for deteriorating commodity under stock dependent selling rate and considering selling price was derived by this research. Keywords: deterioration commodity, expected profit, optimal replenishment time stock dependent selling rate

A Finite Horizon Inventory Model: An Operational Framework

We present a simulation based decision support system to decide the inventory ordering policy in the context of a single commodity, multi pack, and finite horizon situation. The multiple objectives include (a) Minimizing the end of the season inventory, (b) Maximizing the operating profit, (c) Minimizing the peak working capital requirements during the season. Stochastic demand and positive lead time add to the complexity of the problem context. In addition multiple partners in the supply chain with distinct and conflicting set of objectives necessitate the need for a formal approach. The motivation for this model is based on a real life situation. The model addresses the decision choices faced by the distributor in a specific logistics chain. In this chain, a typical distributor has to balance between the stochastic nature of the demand and the attractive nature of financial incentives (order quantity based) proposed by the manufacturer. The problem can be formulated as a multi-period dynamic programming problem with stochastic demand with an objective to optimize the expected operating profit, subject to specific constraints on working capital requirement, service level, order fill rate and end of the season inventory. Such a formulation is hard to solve and does not lend itself to analyze several ordering policies. Based on simulation experiments, we propose an ordering policy which optimizes the overall objectives of supply chain partners and hence demonstrated the possibility of jointly managing the uncertain demand by supply chain partners. The model is simple and easy to use. It is implemented by using spreadsheet. It provides adequate flexibility to conduct what-if analysis. The model has a potential to be useful in a wide range of situations.

An EOQ model for MRO customers under stochastic price to quantify bullwhip effect for the manufacturer

Motivated by a particular multinational cutting-tools manufacturer, we extend the traditional economic order quantity (EOQ) model for maintenance-repair-and-overhaul (MRO) customers under stochastic purchase price and use it to show how price variance leads to bullwhip effect for the MRO manufacturer despite constant consumption by the customer. Our extension of the EOQ model is based on two assumptions that are reasonable for MRO customers: (a) customer consumption rate of the product is constant; and (b) the customer places each order when the inventory level drops to a pre-specified level (say, zero). We determine the customer's optimal ordering quantity in closed form expressions, which enables us to examine the impact of sales price variance on the variance in the orders the customer places on the manufacturer, thus creating a pricing-induced bullwhip effect. We then extend our analysis to multiple products and multiple customer segments and discuss ways for the manufacturer to mitigate the variance in the customer's orders

FOOD PROCESSORS' LOBBYING ACTIVITY AND FARM POLICY

This study tests the hypothesis that lobbying by food firms does not contravene United States farm policy, particularly commodity programs. The research is important in the analysis and understanding of the difficulties of designing and reforming agricultural policies. If farm programs significantly benefit downstream food firms, there is effectively no countervailing power to the farm lobby because (1) farm input supply and marketing firms have been shown to benefit from existing farm policies - and have therefore no incentive to lobby against the policies - and (2) consumers and taxpayers, two important stakeholders in agricultural policies, are known to be quite inefficient in lobbying due to their "large-group" characteristics. Information on food firms' total lobbying expenditure is combined with the behavioral assumption of profit maximization to generate an econometric model of lobbying expenditure allocation by food firms. The model is used to carry out the test. The results indicate that food firms do not lobby to influence agricultural commodity markets. The ultimate implication is that the food processing sector of the agribusiness sector has no serious incentive to act as a countervailing power to the farm lobby in the forming or reforming of agricultural policy. Thus, attempts to reform agricultural policies will be resisted by a coalition of agribusiness and the farm sector. A limitation of the study is that the hypothesis test does not separate agricultural commodities from other inputs to food firms. However, because agricultural commodities constitute almost half of the food firms' overall input costs, the test provides evidence about lobbying for agricultural policies.Agribusiness, Agricultural and Food Policy,

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 Fuzzy Economic Order Quantity (EOQ) Model with Consideration of Quality Items, Inspection Errors and Sales Return

In this paper, we develop an economic order quantity model with imperfect quality, inspection errors and sales returns, where upon the arrival of order lot, 100% screening process is performed and the items of imperfect quality are sold as a single batch at a lessen price, prior to receiving the next shipment. The screening process to remove the defective items may involve two types of errors. In this article we extend the Khan et al. (2011) model by considering demand and defective rate in fuzzy sense and also sales return in our model. The objective is to determine the optimal order lot size to maximize the total profit. We use the signed distance, a ranking method for fuzzy numbers, to find the approximate of total profit per unit time in the fuzzy sense. The impact of fuzziness of fraction of defectives and demand rate on optimal solution is showed by numerical example

Inventory models with reverse logistics for assets acquisition in a liquefied petroleum gas company

This paper addresses a case study regarding inventory models for acquiring liquefied petroleum gas (LPG) cylinders. This is an industrial challenge that was proposed at an European Study Group with Industry, by a Portuguese energy company, for which the LPG cylinder is the main asset of its LPG business. Due to the importance of this asset, an acquisition plan must be defined in order to determine the amount of LPG cylinders to acquire, and when to acquire them, in order to optimize the investment. As cylinders are returned and refilled, the reverse logistic flows of these assets must be considered. As the classical inventory models are not suitable for this case study, three new inventory models, which account for the return of LPG cylinders, are proposed in this work. The first proposed model considers deterministic constant demand and continuous returns of LPG cylinders, with discrete replenishment from the supplier. The second model is similar, but for the case when the returned cylinders cover for the demand. A third model is also proposed considering that both the demand and the returns are stochastic in nature and the replenishment from the supplier is discrete. The three models address different scenarios that the company is either currently facing or is expecting to occur in the near future.publishe

Optimal policy for multi-item systems with stochastic demands, backlogged shortages and limited storage capacity

Producción CientíficaIn this paper, an inventory model for multiple products with stochastic demands is developed. The scheduling period or inventory cycle is known and prescribed. Demands are independent random variables and they follow power patterns throughout the inventory cycle. For each product, an aggregate cycle demand is realized first and then the demand is released to the inventory system gradually according to power patterns within a cycle. These demand patterns express different ways of drawing units from inventory and can be a good approach to modelling customer demands in inventory systems. Shortages are allowed and they are fully backlogged. It is assumed that the warehouse where the items are stored has a limited capacity. For this inventory system, we determine the inventory policy that maximizes the expected profit per unit time. An efficient algorithmic approach is proposed to calculate the optimal inventory levels at the beginning of the inventory cycle and to obtain the maximum expected profit per unit time. This inventory model is applicable to on-line sales of a wide variety of products. In this type of sales, customers do not receive the products at the time of purchase, but sellers deliver goods a few days later. Also, this model can be used to represent inventories of products for in-shop sales when the withdrawal of items from the inventory is not at the purchasing time, but occurs in a period after the sale of the products. This inventory model extends various inventory systems studied by other authors. Numerical examples are introduced to illustrate the theoretical results presented in this work.Ministerio de Ciencia, Innovación y Universidades - Fondo Europeo de Desarrollo Regional (project MTM2017-84150-P