Inventory Management Problem Research Articles

OPTIMASI PERSEDIAAN BAHAN BAKU DAN PRODUKSI USAHA GANEPO PUTRI YOSE DENGAN MENGGUNAKAN ALGORITMA GREY WOLF OPTIMIZER

Putri Yose's Ganepo business, located in Padang Kandi seven koto talago, is one of the Rubik Ganepo producers that has grown to be one of the main suppliers in the Bukittingi and Riau areas. Interviews with the business owner revealed problems in inventory management that affect the profitability of this business. This study aims to assess and improve inventory management with a focus on raw material procurement and production cycle time. The model used in this study is Integrated Procurement Production (IPP) with the Grey Wolf Optimizer (GWO) optimization algorithm to determine the decision variables, namely the frequency of raw material procurement and production cycle time. GWO algorithm is chosen because it is a metaheuristic optimization method that is able to overcome real-world problems, avoid stagnation in solutions of local, and has a wide search space. The results showed that the IPP model with GWO was able to provide a new inventory policy, including the optimal procurement quantities of salt, cooking oil, garlic, turmeric, and production cycle time. The implementation of the policy increased revenue by 0.309203%.

Inventory Management Problems in Stochastic Differential Game Models: Markov Chain Approximation and Optimal Policies

Inventory Management Problems in Stochastic Differential Game Models: Markov Chain Approximation and Optimal Policies

УПРАВЛЕНИЕ ЗАПАСАМИ ЗАПАСНЫХ ЧАСТЕЙ НА МУЛЬТИБРЕНДОВЫХ АВТОСЕРВИСНЫХ ПРЕДПРИЯТИЯХ (НА ПРИМЕРЕ РЕСПУБЛИКИ КИПР)

The article presents a comprehensive study of the state and problems of spare parts inventory management at multi-brand car service enterprises on the example of the Republic of Cyprus. The general characteristics of the specifics of spare parts inventory management at multi-brand car service enterprises are presented, the foundations of the organizational model for the functioning of transport enterprises of this type, including those common to the Republic of Cyprus and the Russian Federation, are disclosed. The set of tasks for managing the inventory of spare parts at multi-brand car service enterprises has been clarified. Based on the results of a survey of executives and managers of 64 Cypriot multi-brand car service enterprises, key methods for managing spare parts stocks were identified. The results of the survey were compared with the assessment of the effectiveness of management in the subject area, typical groups of enterprises were identified according to the criterion of the main method of managing spare parts inventory and the expected management efficiency. The elements of spare parts inventory management at multi-brand car service enterprises of the Republic of Cyprus, recommended for reception into Russian practice, are identified, ways to solve the problem of improving the effectiveness of management in the subject area, common to the designated group of transport enterprises in Russia and Cyprus, including the development of automation, digitalization management, combined with the use of organizational methods (flexible supply contracts, franchising and new forms of business).

Intelligent Logistics under Artificial Intelligence and Big Data

With the development of science and technology, high-tech technologies such as artificial intelligence and big data are gradually being applied to daily life. Digitalization has become a new driving force for the transformation and upgrading of the logistics industry, and there are many problems in the current logistics market. The logistics industry has developed rapidly, but the problems of low inventory management and transportation efficiency have not been effectively solved. Research in the field of logistics should not be limited to certain aspects, but should take a holistic approach. It is very meaningful to effectively combine existing advanced technology with existing logistics, strengthen the scientific management of employees, and improve logistics efficiency. This article elaborates on the basic concept of intelligent logistics and analyzes the advantages of applying technology to logistics through scientific analysis methods, combining advanced technology with logistics can effectively avoid the shortcomings of traditional logistics, and improve management efficiency and service quality, hopes to explore and provide suggestions for the development of logistics and intelligent logistics.

Managing inventory in customizable multi-echelon assembly systems

We consider a general assembly system that produces variations on a single product. Customers can choose to customize any aspect of the product which corresponds to one or more stages of the assembly system. We assume that demand for the original product and the demand for any customization at any stage are all random with known probability distributions. We model this problem as a multi-echelon inventory management problem. This problem has not been studied before. Allowing for customization at any stage in an assembly system makes this problem significantly more complex than similar problems considered in the past. To solve this problem, we ‘split' the assembly line into two parallel assembly lines which we call uncommitted and committed lines. We show that the usual method of reducing the assembly system to a series is not possible in general. We develop a heuristic policy which can be used to then reduce the system into a series system. We also present numerical calculations which highlight the efficacy of the heuristic policy.

Data-driven dynamic pricing and inventory management of an omni-channel retailer in an uncertain demand environment

In recent years, omni-channel retailing has become immensely popular among both retailers and consumers. In this approach, retailers often leverage their brick-and-mortar stores to fulfill online orders, leading to the need for simultaneous decision-making on replenishment and inventory rationing. This inventory strategy presents significant complexities in traditional dynamic pricing and inventory management problems, particularly in unpredictable market environments. Therefore, we have developed a dynamic pricing, replenishment, and rationing model for omni-channel retailers using a two-level partially observed Markov decision process to visualize the dynamic process. We propose to use a deep reinforcement learning algorithm, called Maskable LSTM-Proximal Policy Optimization (ML-PPO), which integrates the current observations and future predictions as input to the agent and uses the invalid action mask to guarantee the allowable actions. Our simulation experiments have demonstrated the ML-PPO's efficiency in maximizing retailer profit and service level, along with its generalized ability to tackle dynamic pricing and inventory management problems.

PERANCANGAN SISTEM INFORMASI PENJUALAN MOTOR LISTRIK PT. TALABU

This journal discusses the information system that will be designed as a means of selling electric motor by PT Talabu.Traditional motor cars are being replaced by more ecologically friendly electric motors, which are growing in popularity. PT. Talabu, an electric motor sales company, faces problems in inventory management and sales processes. This study's primary goal is to develop a sales information system that can help PT. Talabu in managing inventory and its operations. This research uses system needs analysis, database design, and user interface development. At the stage of analysis of system needs, the researchers found the primary needs of PT. Talabu to conduct sales operations and inventory management. This analysis shows that a database is intended to store data about customers, electric motors, and sales transactions. In addition, the app is simple to use and user-friendly.

Combining deep reinforcement learning and multi-stage stochastic programming to address the supply chain inventory management problem

We introduce a novel heuristic designed to address the supply chain inventory management problem in the context of a two-echelon divergent supply chain. The proposed heuristic advances the current state-of-the-art by combining deep reinforcement learning with multi-stage stochastic programming. In particular, deep reinforcement learning is employed to determine the number of batches to produce, while multi-stage stochastic programming is applied to make shipping decisions. To support further research, we release a publicly available software environment that simulates a wide range of two-echelon divergent supply chain settings, allowing the manipulation of various parameter values, including those associated with seasonal demands. We then present a comprehensive set of numerical experiments considering constraints on production and warehouse capacities under fixed and variable logistic costs. The results demonstrate that the proposed heuristic significantly and consistently outperforms pure deep reinforcement learning algorithms in minimizing total costs. Moreover, it overcomes several inherent limitations of multi-stage stochastic programming models, thus underscoring its potential advantages in addressing complex supply chain scenarios.

A robust optimization approach for inventory management with limited-time discounts and service-level requirement under demand uncertainty

A multi-period inventory management problem is considered for a retailer offering limited-time discounts and having a joint service-level requirement over the discount periods under demand uncertainty. In each period, the retailer, only knowing a reference value and the bounds of the uncertain demand, has to determine the order quantity of the product before demand realizations to maximize the total profit over the planning horizon while achieving the required service level over the discount periods. A budgeted uncertainty set is adopted to accommodate demand uncertainty and a joint chance constraint is used to formulate the service-level requirement. The retailer problem is formulated as an affinely adjustable robust chance-constrained model and is transformed into a linear program. A double-layer iterative approach is proposed to solve the problem. The outer layer uses a posteriori method to update the budget coefficient. The inner layer solves the affinely adjustable robust chance-constrained model with the updated budget coefficient, and obtains the updated order quantities. A case study based on real data demonstrates that the proposed approach outperforms other approaches by better balancing the average realized profit and the realized service level. Moreover, the results show that the retailer can obtain a larger average realized profit by gradually than abruptly changing the prices.

Control of Dual-Sourcing Inventory Systems Using Recurrent Neural Networks

A key challenge in inventory management is to identify policies that optimally replenish inventory from multiple suppliers. To solve such optimization problems, inventory managers need to decide what quantities to order from each supplier given the net inventory and outstanding orders so that the expected backlogging, holding, and sourcing costs are jointly minimized. Inventory management problems have been studied extensively for more than 60 years, and yet even basic dual-sourcing problems, in which orders from an expensive supplier arrive faster than orders from a regular supplier, remain intractable in their general form. In addition, there is an emerging need to develop proactive, scalable optimization algorithms that can adjust their recommendations to dynamic demand shifts in a timely fashion. In this work, we approach dual sourcing from a neural network–based optimization lens and incorporate information on inventory dynamics and its replenishment (i.e., control) policies into the design of recurrent neural networks. We show that the proposed neural network controllers (NNCs) are able to learn near-optimal policies of commonly used instances within a few minutes of CPU time on a regular personal computer. To demonstrate the versatility of NNCs, we also show that they can control inventory dynamics with empirical, nonstationary demand distributions that are challenging to tackle effectively using alternative, state-of-the-art approaches. Our work shows that high-quality solutions of complex inventory management problems with nonstationary demand can be obtained with deep neural network optimization approaches that directly account for inventory dynamics in their optimization process. As such, our research opens up new ways of efficiently managing complex, high-dimensional inventory dynamics. History: Accepted by Ram Ramesh, Area Editor for Data Science & Machine Learning. Funding: This work was supported by Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (NCCR Automation) [Grant P2EZP2 191888] and the Army Research Office [Grant W911NF-23-1-0129]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.0136 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2022.0136 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

Inventory management of new products in retailers using model-based deep reinforcement learning

This study addresses the optimal inventory management problem for new smartphone products as an effective example of a supply chain with a short product life cycle. The determination of the optimal inventory level leads to a reduction of lost opportunities and defective inventory, which is an important issue from a profit improvement perspective. Mathematical optimization and reinforcement learning approaches have been proposed for inventory management; however, most of these approaches focus on products that are regularly sold over a long period. Thus, when the target is a new product, it is difficult to optimize inventory control from its day of release due to a lack of sufficient data for learning. To solve this problem, we focus on model-based deep reinforcement learning with high sample efficiency and propose an inventory management method for new products that combines model learning in an offline environment and planning in an online environment. Simulations using real-world historical sales datasets demonstrate that the proposed method outperforms existing methods in terms of profitability, efficiency, and customer satisfaction. In particular, the proposed method improves total rewards and inventory turnover by ¿5% each than the heuristic method while maintaining the same stock-out rate. In addition, the results demonstrate that the proposed method can maintain stable inventory control for multiproduct and multistore supply chains.

Inventory and financing decisions in cross-border e-commerce: The financing and information roles of a bonded warehouse

Cross-border retail firms are often exposed to a lack of working capital for their procurement, especially for sudden increases in demand on special holidays such as Black Friday. This paper studies a cross-border product stochastic inventory management problem where capital-constrained retailers can obtain additional working capital through bonded warehouse financing and entrusted loan financing. This study obtains an optimal inventory policy in which retailers may purchase cross-border products with financing to sell to markets with uncertain demand. The results show that entrusted loan financing helps capital-constrained retailers increase their inventory procurement. Specifically, when capital levels are low, capital-constrained retailers opt for a mixed financing option of bonded warehouse and entrusted loan. Furthermore, the increase in bonded warehouse financing limits could weaken the reliance of capital-constrained retailers on entrusted loan financing. Interestingly, bonded warehouse financing and entrusted loan financing enable the formation of a liability network among retailers. While there is no risk contagion in the liability network when they sell heterogeneous products, the risk of losses due to fire sales remains. This paper further characterizes the payment and clearing model and derives the optimal payment-clearing strategy with fire sales of stagnant products. The results suggest that managers can utilize our designed procurement system with bonded warehouse financing and entrusted loan financing to purchase products without worrying about insufficient financing.

Truncated Balancing Policy for Perishable Inventory Management: Combating High Shortage Penalties

Problem definition: Motivated by a platelet inventory management problem, we study a fixed lifetime perishable inventory management problem under a general demand process. Determining an optimal ordering policy for perishable inventory systems is particularly challenging because of the well-known “curse of dimensionality.” Approximation policies with worst-case performance bounds have been developed in the literature for perishable inventory systems. However, using real data, we observe that the existing policies tend to underorder when the unit shortage penalty is high, which is an important concern for critical perishable products, such as lifesaving blood products. We seek to address this problem in this paper. Methodology/results: We present a new approximation policy for perishable inventory systems, which we call a truncated balancing (TB) policy. In particular, we first define a new balancing ordering quantity and prove a novel lower bound on the optimal ordering quantity. We then define our TB policy such that the maximum between the balancing ordering quantity and the lower bound is ordered at each period. We prove that when first in, first out is an optimal issuing policy, (1) our proposed TB policy admits a worst-case performance bound of two, and (2) it is asymptotically optimal when the unit shortage penalty goes to infinity. Finally, we present a calibrated numerical study based on real data from our partner hospital and show that our proposed policy performs significantly better than the existing policies in practical scenarios with reasonably high shortage penalties. Managerial implications: Our analysis offers managerial insights for perishable inventory management, especially for systems with an imbalance in underage and overage cost parameters. When the unit shortage penalty is high, simply balancing the underage and overage costs can lead to underordering, whereas our proposed policy effectively addresses this drawback. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0644 .

Integrated Supplier Selection and Inventory Management Problems Considering Discounts and Uncertainty: A Fuzzy-Probabilistic Approach

Integrated Supplier Selection and Inventory Management Problems Considering Discounts and Uncertainty: A Fuzzy-Probabilistic Approach

Adaptive robust optimization for lot-sizing under yield uncertainty

In manufacturing environments, uncertain production yield directly impacts the quality and feasibility of the production planning decisions. This paper investigates the use of adaptive robust optimization to hedge against uncertain yield when determining a production plan, and to react properly when updated information unfolds. We first derive a myopic adaptive robust policy for the inventory management problem, a special case of the lot-sizing problem where the setup and the production costs are omitted. We show that the policy is optimal under mild assumptions. Second, we address a multi-period single-item lot-sizing problem with a backorder and uncertain yield via adaptive robust optimization. We formulate an adaptive robust model based on the budgeted uncertainty set, where we exploit a linear approximation to transform the quadratic constraints into a mixed-integer linear program. We also propose a column and constraint generation algorithm to solve the adaptive model exactly. Finally, we demonstrate the performances of the proposed approaches and the value of the adaptive robust solutions through extensive numerical experiments.

An end-to-end deep learning model for solving data-driven newsvendor problem with accessibility to textual review data

We investigate a data-driven single-period inventory management problem with uncertain demand, where large amounts of textual online reviews and historical data are accessible. Unlike two-step frameworks (i.e., predict-then-optimization), we propose an end-to-end (E2E) framework that directly suggests the order quantity by leveraging a deep learning model that inputs textual online reviews and other demand-related feature data, without any intermediate steps such as text sentiment analysis. The E2E model does not require any prior assumptions about the demand distribution and can automatically determine the order quantity that minimizes the newsvendor cost by employing the information from real-world data. Our experiments, using publicly available real-world data, demonstrate that our method can significantly reduce the sum of overage and underage costs, outperforming other data-driven models proposed in recent years. Specifically, the inclusion of textual online review data improves ordering decisions by a 28.7% cost reduction.

Application of the inventory theory in the company Kjantu Collection S.A.C.

The present research work shows an application of a mathematical algorithm based on a linear programming model, with which a solution is given to the raw material inventory management problem presented by the company «Kjantu Collection» in its warehouses, in the It is not known how much should be invested in a given period or how much the total merchandise is valued. The objective of the research is to apply a raw material inventory control model using linear programming that contributes to decision making within the company Kjantu Collection S.A.C using the WinQSB software. In support of the solution of the model, the WinQSB software is used, whose results managed to obtain the optimal quantities for the company Kjantu Collection S.A.C with which the downtimes in the production of the different garments of the company will be eliminated, it was also possible to determine the costs per period in the production of each garment.

Inventory Routing Problem: A Problem that is Still Relevant Today

The most important challenge of a supply chain is the control of its logistics costs. The managers who drive the various logistics flows tend to reduce its overall costs in order to achieve overall profits. Among the problems that help them achieve their goals is the inventory routing problem (IRP). In its classical definition, IRP is an integration tool that can provide a joint answer to inventory management and vehicle routing problems. In this review article, we present an overview of supply chain management with a focus on the inventory routing domain. We will start by introducing the concept of IRP and the various works done in this area since its emergence that have contributed to its development. Then, we will discuss recent research and take the most studied model to expose its impact on the development of this research area until today. This paper shows that despite the age of this tool, this field of research has maintained its importance and continues to evolve and produce considerable research articles.

A Supply Chain Inventory Management Method for Civil Aircraft Manufacturing Based on Multi-Agent Reinforcement Learning

Effective supply chain inventory management is crucial for large-scale manufacturing industries such as civil aircraft and automobile manufacturing to ensure efficient manufacturing. Generally, the main manufacturer makes the annual inventory management plan, and contacts with suppliers when some material is approaching critical inventory level according to the actual production schedule, which increases the difficulty of inventory management. In recent years, many researchers have focused on using reinforcement learning method to study inventory management problems. Current approaches were mainly designed for the supply chain with single-node multi-material or multi-node single-material mode, which are not suitable to the civil aircraft manufacturing supply chain with multi-node multi-material mode. To deal with this problem, we formulated the problem as a partially observable Markov decision process (POMDP) model and proposed a multi-agent reinforcement learning method for supply chain inventory management, in which the dual-policy and information transmission mechanism was designed to help the supply chain participant improve the global information utilization efficiency of the supply chain and the coordination efficiency with other participants. The experiment results show that our method has about 45% performance improvement on efficiency compared with current reinforcement learning-based methods.

Application Research on Zero Inventory Model

The zero inventory model is a new type of management model that has become the goal of many enterprises in their future efforts. China's market changes are becoming more and more intense, the requirements for enterprises are getting higher and higher, and inventory management problems have become a roadblock on the road to the growth of each enterprise. The article first expounds the basic concepts and main forms of zero inventory management, summarizes the literature on zero inventory mode at home and abroad, and then takes Haier Company as an example to specifically analyze the current situation of zero inventory management of the company, puts forward countermeasures for existing problems, and finally summarizes the inspiration and suggestions brought by the zero inventory model from small and large. In summary, this paper takes Haier Company, which comprehensively practices zero inventory management, as an example, analyzes Haier's zero inventory management model, and studies the advantages and disadvantages of this model in order to provide relevant enlightenment to various enterprises in other fields.