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

Inventory Policy Implications of On-Line Customer Purchase Behavior

In this paper we will examine some implications of online data for a classical operations management model, vis. the Economic Order Quantity model. Customer waiting behavior on individual orders (which occur during stockouts) forms the basis for evaluating the potential backorders. The potential attraction of reducing inventory holding costs must be balanced with the loss due to lost sales. We clearly delineate the conditions under which it is profitable to stock out every ordering cycle, and the conditions under which the traditional economic order quantity model still holds. In order to allow practical application of the model, we develop a number of different approaches to the problem of estimating the backorder function from available on-line transaction data

Advances in Inventory Management: Dynamic Models

In this study, we develop and analyze models incorporating some of the dynamic aspects of inventory systems. In particular, we focus on two major themes to be analyzed separately: nonstationarity in demand rate and unfixed purchasing prices. In the first part of the study, we consider an inventory system with a nonstationary demand rate. In particular, we consider critical service parts subject to obsolescence. Inventory management of such items is notoriously difficult due to their slow moving character and the high risks involved when they are not available or no more needed. In practice, there is a need for policies tailored for service parts taking these aspects into account and easy to implement. We propose an obsolescence based control policy and investigate its performance and impact on costs. We find that ignoring obsolescence in the control policy increases costs significantly and early adaptation of base stock levels can lead to important savings. In the second part of the study, we consider an inventory system where the supplier offers price discounts at random points in time. We extend the literature by assuming a more general backordering structure. That is, when the system is out of stock, an arriving customer either decides to be backlogged with a certain probability or leaves the system and becomes a lost sale. We derive equations to calculate optimal policy parameters and demonstrate that allowing backorders in face of random deal offerings can result in considerable savings

QAmplifyNet: Pushing the Boundaries of Supply Chain Backorder Prediction Using Interpretable Hybrid Quantum - Classical Neural Network

Supply chain management relies on accurate backorder prediction for optimizing inventory control, reducing costs, and enhancing customer satisfaction. However, traditional machine-learning models struggle with large-scale datasets and complex relationships, hindering real-world data collection. This research introduces a novel methodological framework for supply chain backorder prediction, addressing the challenge of handling large datasets. Our proposed model, QAmplifyNet, employs quantum-inspired techniques within a quantum-classical neural network to predict backorders effectively on short and imbalanced datasets. Experimental evaluations on a benchmark dataset demonstrate QAmplifyNet's superiority over classical models, quantum ensembles, quantum neural networks, and deep reinforcement learning. Its proficiency in handling short, imbalanced datasets makes it an ideal solution for supply chain management. To enhance model interpretability, we use Explainable Artificial Intelligence techniques. Practical implications include improved inventory control, reduced backorders, and enhanced operational efficiency. QAmplifyNet seamlessly integrates into real-world supply chain management systems, enabling proactive decision-making and efficient resource allocation. Future work involves exploring additional quantum-inspired techniques, expanding the dataset, and investigating other supply chain applications. This research unlocks the potential of quantum computing in supply chain optimization and paves the way for further exploration of quantum-inspired machine learning models in supply chain management. Our framework and QAmplifyNet model offer a breakthrough approach to supply chain backorder prediction, providing superior performance and opening new avenues for leveraging quantum-inspired techniques in supply chain management

Decentralized and centralized supply chains with trade credit option

The notion of a trade credit period is a common business practice, where a supplier allows a buyer a specified period to make a payment in full for a purchase made. The objective of this thesis is to explore the role of such a credit payment option in supply chain management. Towards this end, a two-echelon supply chain, consisting of a single supplier (e.g. manufacturer) and the cases of both a single and multiple buyers (e.g. retailers) is examined under decentralized (independent) and centralized (coordinated) decision making scenarios. The major emphasis of this research is limited to the case of a single product with price-sensitive deterministic, as well as stochastic market demand.The conditions under which a trade credit period should be offered and its appropriate length are determined from the supplier’s perspective under the decentralized case. Under the centralized decision scenario, the efficacy of a trade credit policy as a supply chain coordination mechanism is thoroughly analyzed and guidelines for pricing, production and delivery decisions are developed. The concepts developed in this study are illustrated via a number of numerical examples, in conjunction with thorough sensitivity analyses involving some selected problem parameters.The major contribution of this thesis is that we incorporate the pricing and inventory issues in supply chains with an endogenous credit payment period. This is the first study that examines the efficacy of trade credit option as a coordination mechanism. We propose a coordination mechanism that coordinates the supply chain, when a trade credit by itself is not sufficient to serve such a purpose, while preserving the benefits of a trade credit option. Also, this study is the first to examine the issues concerning trade credit under price sensitive stochastic demand. Another first for this work is the exploration of the implications of a trade credit policy in supply chains consisting of multiple competing retailers. The effects of the extent of competition and the market size on trade credit policy are evaluated. Our analyses lead to some important practical implications, to serve as managerial guidelines.Ph.D., Decision Sciences -- Drexel University, 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

Loss of customer goodwill in the uncapacitated lot-sizing problem

Abstract Loss of customer goodwill in uncapacitated single level lot-sizing is studied with a mixed integer programming model extending the well-known Wagner-Whitin (WW) model. The objective is to maximize profit from production and sales of a single good over a finite planning horizon. Demand, costs, and prices vary with time. Unsatisfied demand cannot be backordered. It leads to the immediate loss of profit from sales. Previous models augment the total cost objective by this lost profit. The difference of the proposed model is that unsatisfied demand in a given period causes the demand in the next period to shrink due to the loss of customer goodwill. A neighborhood search and restoration heuristic is developed that tries to adjust the optimal lot sizes of the original no-goodwill-loss model to the situation with goodwill loss. Its performance is compared with the Wagner-Whitin solution, and with the commercial solver CPLEX 8.1 on 360 test problems of various period lengths