100% screening economic order quantity model under shortage and delay in payment

It is for a long time that the Economic Order Quantity(EOQ) model has been successfully applied to inventory management. This paper studies a multiproduct EOQ problem in which the defective items will be screened out by 0 screening process and will be sold after the screening period. Delay in payment is permissible though payment should be made during the grace period and the warehouse capacity is limited. Otherwise, there will be an additional penalty cost for late payment so the retailer would not be able tobuy products at discount prices.All-units and incremental discounts are considered for the products which dependon the order’s quantity just like the permissible delay in payment. Genetic algorithm (GA) and particle swarm optimization (PSO) algorithm are used to solve the proposed model and numerical examples are provided for better illustrations

An Inventory Model Considering All Unit Discount and Carbon Emissions

Consumer satisfaction is an important factor in the ongoing business process. Companies must be able to meet consumer demands and considers customers’ concerns on price. In a supplier and customer relationship, a given discount will affect the order size. Besides, in the current developing industry, environmental factors must be considered without disturbing the business. Recently, researchers and practitioners develop environmentally-friendly industries so that the environment will be well managed and not polluted. For example, carbon emissions can be managed by optimizing the production operation and product distribution. This paper presents a study on the relationship between discount on the economic order quantity model and the total carbon emissions. This research develops a procurement model by considering an all-unit discount system and carbon emission tax. The aim is to determine the optimal order that minimizes the total cost

Optimization of Quantity Discounts Using JIT Technique under Alternate Cost Policies

In traditional economic order quantity modeling technique, as per the storage in a warehouse, the rate of demand is considered to be fixed, whereas in real world practice rate of demand may be dependent on time, price and stock. This paper studies problems based on allocation of order quantity under quantity discounts by revising mathematical models already studied in this area. For example, in a multi warehouse system like a super departmental store, the rate of demand is mostly subjective on the basis of stock demand. In industry, the maintenance of large stock of goods in warehouses has a higher probability of consumers as compared to an industry with small quantity of stock. Such procedures implied in single warehouses systems may be logical for level of stock that is dependent on demand. Hence, a good and large stock level mostly results in a higher profits and larger sales. The objective is to optimize profit under the effect of price variations in the form of quantity discounts based on an alternative cost functions, with the help of JIT inventory technique and analyzing a mathematical model based on it

Exogenous coalition formation in the e-marketplace based on geographical proximity

This paper considers a model for exogenous coalition formation in e-marketplaces. Using the informational advantage e-retailer creates coalitions of customers based on geographical proximity. Most of the literature regards this process as endogenous: a coalition leader bundles eventual purchases together in order to obtain a better bargaining position. In contrast - and in response to what is being observed in business practice - we analyse a situation in which an existing e-retailer exogenously forms customers' coalitions. Results of this study are highly encouraging. Namely, we demonstrate that even under highly imperfect warehouse management schemes leading to contagion eects, suggested combined delivery service may oer signifficant efficiency gains as well as opportunities for Pareto-improvement.Coalition formation, e-commerce, multi-agent systems, consumer satisfaction, demand planning, warehouse management.

Supply chain finance for ameliorating and deteriorating products: a systematic literature review

Ameliorating and deteriorating products, or, more generally, items that change value over time, present a high sensitiveness to the surrounding environment (e.g., temperature, humidity, and light intensity). For this reason, they should be properly stored along the supply chain to guarantee the desired quality to the consumers. Specifically, ameliorating items face an increase in value if there are stored for longer periods, which can lead to higher selling price. At the same time, the costumers’ demand is sensitive to the price (i.e., the higher the selling price the lower the final demand), sensitiveness that is related to the quality of the products (i.e., lower sensitiveness for high-quality products). On the contrary, deteriorating items lose quality and value over time which result in revenue losses due to lost sales or reduced selling price. Since these products need to be properly stored (i.e., usually in temperature- and humidity-controlled warehouses) the holding costs, which comprise also the energy costs, may be particularly relevant impacting on the economic, environmental, and social sustainability of the supply chain. Furthermore, due to the recent economic crisis, companies (especially, small and medium enterprises) face payment difficulties of customers and high volatility of resources prices. This increases the risk of insolvency and on the other hand the financing needs. In this context, supply chain finance emerged as a mean for efficiency by coordinating the financial flow and providing a set of financial schemes aiming at optimizing accounts payable and receivable along the supply chain. The aim of the present study is thus to investigate through a systematic literature review the two main themes presented (i.e., inventory management models for products that change value over time, and financial techniques and strategies to support companies in inventory management) to understand if any financial technique has been studied for supporting the management of this class of products and to verify the existing literature gap

The retail revolution and food-price mismeasurement

If a product sells for $3 this week at the local supermarket and$2 next week, what is the "real" price? What if that same product has a different price at a different store? Thanks to scanner technology, food prices differ a lot these days because they can be changed quickly and easily. How do our official statistics take these price movements into account? Not too well, according to Leonard Nakamura. In this article, he describes the retail revolution of recent years and how it has led to mismeasurement of food pricesConsumer price indexes ; Food prices

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