Optimal price and lot size for an EOQ model with full backordering under power price and time dependent demand

Producción CientíficaIn this paper, we address an inventory system where the demand rate multiplicatively combines the effects of time and selling price. It is assumed that the demand rate is the product of two power functions, one depending on the selling price and the other on the time elapsed since the last inventory replenishment. Shortages are allowed and fully backlogged. The aim is to obtain the lot sizing, the inventory cycle and the unit selling price that maximize the profit per unit time. To achieve this, two efficient algorithms are proposed to obtain the optimal solution to the inventory problem for all possible parameter values of the system. We solve several numerical examples to illustrate the theoretical results and the solution methodology. We also develop a numerical sensitivity analysis of the optimal inventory policy and the maximum profit with respect to the parameters of the demand function.Ministerio de Ciencia, Innovación y Universidades y Fondo Europeo de Desarrollo Regional (FEDER) - (Project MTM2017-84150-P

An inventory system with time-dependent demand and partial backordering under return on inventory investment maximization

Producción CientíficaIn this article, we study an inventory system for items that have a power demand pattern and where shortages are allowed. We suppose that only a fixed proportion of demand during the stock-out period is backordered. The decision variables are the inventory cycle and the ratio between the initial stock and the total quantity demanded throughout the inventory cycle. The objective is to maximize the Return on Inventory Investment (ROII) defined as the ratio of the profit per unit time over the average inventory cost. After analyzing the objective function, the optimal global solutions for all the possible cases of the inventory problem are determined. These optimal policies that maximize the ROII are, in general, different from those that minimize the total inventory cost per unit time. Finally, a numerical sensitivity analysis of the optimal inventory policy with respect to the system input parameters and some useful managerial insights derived from the results are presented.Ministerio de Ciencia, Innovación y Universidades - Fondo Europeo de Desarrollo Regional (project MTM2017-84150-P

A Study of Single-vendor and Multiple-retailers Pricing-Ordering Strategy under Group-Buying Online Auction

The supplier and buyers, with different objectives and self-interest, are separate economic entities acting independently and opportunistically to maximize their individual profits. In this paper, a GBA model in the B2B market is studied, where one supplier faces 2 different retailers, who cooperate in the order decision making. Firstly, the optimal ordering decision of the retailers was analyzed. Then, from the perspective of the supplier, the optimal pricing strategy of the supplier is also studied. Finally, it is concluded that the group buying online auction is a useful and efficient pricing mechanism in the B2B e-market, under which, all members of the supply chain will improve their payoffs

オムニチャネル小売業におけるファッション商品の品切れに対する対応のモデル化分析

筑波大学 (University of Tsukuba)201

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

Planning for shortages? Net present value analysis for a deteriorating item with partial backlogging

This paper develops inventory models to help answer strategic questions concerning whether planning for shortages offers financial benefits. A production-inventory system producing a deteriorating product in batches at a finite production rate with partial backordering is considered. Customers pay a deposit when placing a backorder. Backordered items receive a discount on the sales price. As lost sales may lead to customers not returning, the demand rate may depend on the fraction of lost sales. We develop a cash-flow based profit maximising Net Present Value (NPV) model without the inventory cost parameters commonly used in this context: unit holding cost, unit backorder cost, unit deterioration cost, and unit lost sales cost. The model finds the optimal inventory policy just like NPV models that discount the traditional parameters but has the advantage of not needing to estimate the value of the traditional parameters. It is shown that in models based on discounting the traditional parameters, the parameters are not exogenously determinable but are non-trivial functions of non-financial endogenous system parameters such as the production rate, annual demand rate, and backorder rate. Extensive numerical experiments illustrate how cash-flow NPV models provide insights into the value of planning for shortages and strategic choices about the design of the production-inventory system. It also provides insight into the classical problem of how to interpret unit backorder cost and unit lost sales cost. The study indicates that these insights cannot be reliably obtained from NPV models based on discounting unit backorder costs and unit lost sales costs.<br/