Field Of Inventory Management Research Articles

A Model for Decision-making to Parameterizing Demand Driven Material Requirement Planning Using Deep Reinforcement Learning

Abstract Demand-Driven Material Requirements Planning (DDMRP) is an emerging inventory management approach that has garnered significant attention from academia and industry. Numerous recent studies have highlighted the advantages of DDMRP compared to traditional methods such as material requirement planning (MRP), Theory of constraint (TOC), and Kanban. However, the performance of DDMRP relies on several parameters that affect its effectiveness. Parameterization models and the optimization of control variables have significantly contributed to the field of inventory management and have proven to be effective and practical in addressing challenges by providing a structured approach to handling complex variables and constraints. This paper introduces an innovative parameterization model that leverages deep reinforcement learning (DRL) to parameterize a DDMRP system in the face of uncertain demand. The main objective is to dynamically determine the optimal values for the variability and lead time factors within the DDMRP framework, to maximize customer service levels and optimize inventory efficiency. The results of this study emphasize the effectiveness of DRL as an automated decision-making approach for controlling DDMRP parameters. Additionally, the findings highlight the potential for enhancing the performance of the DDMRP approach, particularly in terms of on-time delivery (OTD) and average on-hand inventory (AOHI) by adjusting the variability and lead-time factors.

Research on the Inventory Management in the Modern Business

Inventory management plays a pivotal role in modern business operations, impacting operational efficiencies, financial security, and competitive advantages. This essay explores the multidimensional field of inventory management, delving into seminal research articles. It shows how accounting methods and strategic decision-making interact, shaping operational pathways. Operations research guides inventory management strategies' effectiveness assessment. Creating a culture of continuous improvement based on empirical observations is equally crucial. Innovations, like structured Markov Decision Processes (MDPs) with convex cost functions, bridge theory and real-world applications. The essay also examines forecasting errors' implications when relying too heavily on predictions, highlighting optimal control techniques as resilience tools in volatile environments. Contemporary inventory strategies balance precision and adaptability, focusing on supply/demand synchronization. Empirical case studies exemplify efficient responses to market changes. Variability management in inventory frameworks offers insights into resource allocation across diverse product lines. Lastly, the essay deconstructs the strategic implications of asymmetric sales changes on inventory strategies. In a globalized, technologically dynamic age with shifting market forces, prudent inventory management is imperative. This essay showcases its significance with empirical wisdom and academic rigor, illuminating quantitative precision, strategic insight, and operational excellence in modern business environments.

AI and IoT: A Future Perspective on Inventory Management

Abstract: The field of inventory management stands on the brink of a transformative era, heralded by the confluence of Artificial Intelligence (AI) and the Internet of Things (IoT). This paper conducts an in-depth analysis, offering a visionary outlook by leveraging an extensive review of scholarly articles and existing literature on Artificial Intelligence (AI) and Internet of Things (IoT). The fusion of these technologies promises to revolutionize traditional practices by enabling real-time tracking, predictive analytics, and automated replenishment—ushering in unprecedented levels of efficiency and precision in inventory control. Furthermore, the study probes into the horizon of emerging trends, casting light on the progressive strides in machine learning, edge computing, and blockchain technology. Such advancements beckon a reimagined future for inventory strategies. However, this promising future is not without its hurdles. The research underscores critical impediments, including concerns surrounding data privacy, security, and technological constraints

Bibliometrix Analysis: Management Inventory dan Supply Management

Inventory and Supply Management is an important aspect of efficient business operations. Both are interrelated and have an impact on achieving competitive advantage and customer satisfaction. In the case study of PT. Berkahjaya Sentosa Technique, Bibliometrix analysis is used to evaluate the performance of inventory and supply management of companies. The results show that effective inventory and supply management can improve operational efficiency and customer satisfaction. Bibliometrics is an analytical method used to map and analyze scientific literature in a particular field. In the context of inventory management and distribution control, bibliometrics can provide valuable insight into the development and focus of study in a particular field. The purpose of this study is to analyze and summarize scientific literature related to inventory management and distribution control using bibliometric methods. The method used is descriptive analysis using analysis tools such as three-field plots and tables of main information. The results show that scientific literature related to inventory management and distribution control has experienced a decline in annual growth in the period 2018-2023. However, the average citation per document shows that this literature is still very relevant and important in the context of inventory management and distribution control. This research provides valuable insights for researchers and practitioners in the field of inventory management and distribution control

3D Position Estimation of Objects for Inventory Management Automation Using Drones

With the recent development of drone technology, drones are being used in various fields. Drones have the advantage of being equipped with various devices to move freely and perform various tasks. In the field of inventory management, many studies have been conducted into management automation based on the drone. Drones scan a marker, such as a quick response code (QR code), attached to the shelves to obtain location information of the shelves on which the inventory is loaded. At the same time, drones perform inventory management by scanning the marker attached to the inventory to obtain inventory information. However, unlike indoor warehouses, where grids or shelves are well-defined, a storage yard is not fixed in the location where the inventory is stored. It is difficult to recognize the loading position from the marker for a storage yard without shelves and grids. Furthermore, the loading position of the inventory is not fixed. For the automation of inventory management of warehouses where shelves and grids are undefined, this paper proposes a framework that estimates the inventory 3D position in the video frame based on a deep learning model. The proposed framework uses the image segmentation model to detect and decode the marker in the video frame to estimate the 3D position of a drone and inventory. In addition, the estimated inventory 3D position is corrected using the continuity of the video frame. Experiment results on the video dataset verified that the proposed framework improved the 3D position estimation performance of the inventory. Consequently, efficient inventory management based on drones can be performed through the proposed framework for the 3D position estimation of inventory in all types of warehouses.

A sustainable supply chain under VMI-CS agreement with withdrawal policies for imperfect items

Supply Chain (SC) coordination, quality requirements, and environmental issues are the main areas of interest within the field of inventory management. Few studies have integrated these concepts into an inventory problem despite its overwhelming necessity. In this direction, this paper deals with a vendor-buyer SC under the Vendor-Managed Inventory with Consignment Stock (VMI-CS) agreement. According to the agreement, the vendor bears the buyer's financial holding cost in return for the storage of products in the buyer's warehouse. Further, each shipment involves a random fraction of repairable items, which need to be withdrawn from the inventory system. Unlike the existing literature, when repair policy is evaluated not to be profitable, these items can either be salvaged or scrapped in the present problem. In other words, this study aims at providing a decision support model to find the optimal withdrawal policy as a joint quality and lot-sizing policy. To fulfill sustainability criteria, carbon emissions are incorporated into the total cost of SC by considering carbon tax and cap-and-trade regulation. The optimum solution is found through an analytical algorithm. Finally, a numerical study is conducted to illustrate the applicability of the models and gain managerial insights. The results show that the performance of VMI-CS for all withdrawal policies is superior to traditional mechanism, but its cost-saving sharing has to improve. In addition, the sensitivity analyzes reveal that the fraction of imperfect items greatly affects the optimal withdrawal policy. Finally, adopting carbon regulations does not seriously affect the optimal withdrawal policy and inventory decisions but causes a relatively much increase in the total cost.

Applying Hessian Matrix Techniques to Obtain the Efficient Optimal Order Quantity Using Fuzzy Parameters

In the field of applied mathematics, optimization techniques formulate to the maximizing and minimizing for an objective function. The purpose of the optimization problems plays a vital role in the field of inventory management. The aim is to minimize the total cost, which comprises many fluctuating costs such as shortage, ordering, and holding cost. In this paper, the defective items were under the classification synchronous and asynchronous under a rework strategy process. The rework strategy is separating and accumulating the imperfect items at the time of completion of the process. This study considered asynchronous defective items and tried to minimize the total cost incurred. The optimality of the non-linear programming was achieved by the Hessian matrix, which results in the minimization of the total cost incurred. Furthermore, the usage of hexagonal fuzzy numbers formulates many real-life problems that arise due to flawed knowledge. There might be several situations in decision-making problems where optimization techniques require six parameters or more. The inclusion of Python coding has further made numerical working simpler. Furthermore, sensitivity analysis is carried out.

A New Defuzzification Method for Cost Analysis of Economic Order Quantity under Cloud Pentagonal Fuzzy Demand

The present research work explores a pentagonal dense fuzzy set (PDFS) and a novel defuzzification method for the same based on cut. The proposed concept is then applied in the field of inventory management by considering an economic order quantity (EOQ) model. To comprehend the uncertainty associated with the EOQ model, a pentagonal fuzzy number is utilized. Based on PDFS, cloud pentagonal fuzzy number (CPFN) is defined and the model is developed by considering demand as CPFN. The developed models are being tested numerically and graphically to understand the significance of the concept of CPFN. Sensitivity analysis is performed to identify the most critical parameters. A comparative analysis is made to figure out the difference between the models.

Conservative Profitability Evaluation for Newsboy-type Products with Bootstrap Methods

ABSTRACT In the field of inventory management, a profitability evaluation for ordered products is crucial to retailer revenue. Formulating such evaluations requires determining an appropriate order quantity, while ensuring that the product has high profitability potential. The achievable capacity index (ACI) provides a simple breakdown of profitability suitable for newsboy-type products with probabilistic distributed demand under the optimal order quantity. In this study, we investigated the lower confidence bound of ACI (LCBA) to obtain a conservative evaluation of profitability. Obtaining an explicit formula for the LCBA is difficult; therefore, we adopted parametric bootstrap methods to approximate the value for the LCBA. We also created a computational algorithm using R software to simulate the performances of various parametric bootstrap methods. Coverage rates and the corresponding ratios for various sample sizes and expected ACI values were tabulated to facilitate the selection of a suitable method and decide a required sample size with a conservative estimate. An illustrative example was presented to demonstrate the applicability of the proposed method and provide managerial insights.

The static model of inventory management without a deficit with Neutrosophic logic

In this paper, we present an expansion of one of the well-known classical inventory management models, which is the static model of inventory management without a deficit and for a single substance, based on the neutrosophic logic, where we provide through this study a basis for dealing with all data, whether specific or undefined in the field of inventory management, as it provides safe environment to manage inventory without running into deficit , and give us an approximate ideal volume of inventory. Since the ideal size is affected by the rate of demand for inventory, we present in this paper a study of the rate of demand for inventory when it is not precisely defined, and we find that the indeterminate values ​​of the demand rate cannot be ignored, because they actually affect determining the ideal size of inventory and calculating its costs, thus affecting on the efficiency of the facility and achieve great profits for it.

<i>K</i>-means clustering combined with principal component analysis for material profiling in automotive supply chains

At a time where available data is rapidly increasing in both volume and variety, descriptive data mining (DM) can be an important tool to support meaningful decision-making processes in dynamic supply chain (SC) contexts. Up until now, however, scarce attention has been given to the application of DM techniques in the field of inventory management. Here, we take advantage of descriptive DM to detect and grasp important patterns among several features that coexist in a real-world automotive SC. Principal component analysis (PCA) is employed to analyse and understand the interrelations between ten quantitative and dependent variables in a multi-item/multi-supplier environment. Afterwards, the principal component scores are characterised via a K-means clustering, allowing us to classify the samples into four clusters and to derive different profiles for the multiple inventory items. This work provides evidence that descriptive DM contributes to find interesting feature-patterns, resulting in the identification of important risk profiles that may effectively leverage inventory management for improved SC performance. [Received: 5 April 2019; Revised: 1 December 2019; Revised: 22 January 2020; Accepted: 21 April 2020]

Design of a supply chain network for determining the optimal number of items at the inventory groups based on ABC analysis: a comparison of exact and meta-heuristic methods

One of the most applicable techniques in the inventory management field is inventory classification based on ABC analysis, a well-known method to set the items in a different group, according to their importance and values. In this paper, a bi-objective mathematical model is proposed to improve the inventory grouping based on ABC analysis. The first objective function maximizes the total net profit of the items in the central stock, and the second objective function maximizes the total net profit of items in different wards. The proposed model simultaneously optimizes the service level, the number of inventory groups, and the number of assigned items. To solve the model in small and large dimensions, two exact methods (LP-metric and e-constraint) and two meta-heuristic methods (NSGA-II and MOPSO) are used. Then, to compare those methods in terms of efficiency, the statistical analysis besides the AHP and VIKOR techniques is implemented. The results show the superiority of the e-constraint among the exact methods and MOPSO among meta-heuristic methods. Finally, the proposed model has been implemented in two sets of numerical examples to demonstrate its applicability.

A hybrid invasive weed optimization for an imperfect, two-warehouse, lot-sizing problem

PurposeLot-sizing is among the most important problems in the production and inventory management field. The purpose of this paper is to move one step forward in the direction of the real environment of the dynamic, multi-period, lot-sizing problem. For this purpose, a two-warehouse inventory system, imperfect quality and supplier capacity are simultaneously taken into consideration, where the aim is minimization of the system costs.Design/methodology/approachThe problem is formulated in a novel continuous nonlinear programming model. Because of the high complexity of the lot-sizing model, invasive weed optimization (IWO), as a population-based metaheuristic algorithm, is proposed to solve the problem. The designed IWO benefits from an innovative encoding–decoding procedure and a heuristic operator for dispersing seeds. Moreover, sequential unconstrained minimization technique (SUMT) is used to improve the efficiency of the IWO.FindingsTaking into consideration a two-warehouse system along with the imperfect quality items leads to model nonlinearity. Using the proposed hybrid IWO and SUMT (SUIWO) for solving small-sized instances shows that SUIWO can provide satisfactory solutions within a reasonable computational time. In comparison between SUIWO and a parameter-tuned genetic algorithm (GA), it is found that when the size of the problem increases, the superiority of SUIWO to GA to find desirable solutions becomes more tangible.Originality/valueDeveloping a continuous nonlinear model for the concerned lot-sizing problem and designing a hybrid IWO and SUMT based on a heuristic encoding–decoding procedure are two main originalities of the present study.

Personalisation of production in the era of Industry 4.0 as a challenge for inventory management in small and medium-sized production enterprises

Industry 4.0 is the result of the development of cyber-physical generation systems as a part of the fourth industrial revolution. Industry 4.0 sets new areas for change in the sphere of production and management but also influences various aspects of society. Industry 4.0 is focused on continuous improvement of production processes. This is a turnaround in the production control methodology, as the growing expectations of customers in the modern market cause, along with the increase in production efficiency, customisation of the product. In this trend, the customer decides about the product, personalising it as much as possible and in the best possible way. These are new challenges in the field of inventory management. The article aims to present a method of calculating the optimal amount of the cost of a rotating stock in a production enterprise with an unevenly distributed demand, which is the case with personalised orders.

Multi-item fuzzy economic production quantity model with multiple deliveries

Inventory control is one of the most critical issues in corporate management. Many mathematical models have been developed to optimize control strategies for the companies’ inventory. Economic production quantity (EPQ) is one of the classic models for inventory control, which is widely used. To deal with the uncertainty in the real world, we need to develop new and useful models for modeling systems of inventory management. In such cases, fuzzy models play a unique role in the field of inventory management. The main contribution of this study is to apply some well-known metaheuristic to solve an extended EPQ model based on fuzzy numbers considering multiple deliveries. This study aims to develop an EPQ model by considering demand as triangular fuzzy numbers and multiple deliveries (delivering in multiple packages) and by considering limitations in warehouse space as well as the total number of orders. Given these conditions, EPQ costs are calculated, and new modeling is presented. The obtained fuzzy model has been simplified by using the $$ \alpha{\text{-cut}} $$ and changing the variables, and finally, the most well-known metaheuristic algorithms, GA, PSO, GWO, and ICA, are applied in different problem sizes, and obtained results are analyzed in terms of minimizing cost function and CPU time. The result of this paper shows that GWO has superior performance in terms of various parameters.

Методы оценки экономической эффективности логистической системы и управления уровнем логистических затрат

The current crisis situation in coal mining forces coal companies to appeal to in-depth detailed methods of assessing the economic efficiency of business processes and identify additional reserves of optimization of technical and economic indicators. The most problematic issue remains the identification of the logistics costs arising in the processes of supply, production, and sales in the total volume of the company's accounting costs. However, the assessment of the economic efficiency of the logistics system allows coal companies to improve the activities of the functional areas of logistics and develop measures to reduce costs. The article introduces an integrated approach to the consideration of the flows of material and information resources in the technological processes of the mining enterprise and in its interactions with partners. The paper defines the main tasks of optimization of functional areas of logistics to be solved by the management when working with consumers and suppliers, in production processes, in sales activities, in warehousing, cargo handling and transportation, and in the field of inventory management. The research provides indicators of estimation of economic efficiency of logistic system, requirements to system of accounting of logistic costs, and ways of their optimization. The use of the proposed methods of assessing the economic efficiency of the logistics system and management of logistics costs will allow the management of coal companies to improve the quality and reliability of information on the effectiveness of procurement, production, and marketing logistics, as well as to optimize the cost of finished products.

Optimization of inventory costs management in the construction enterprise

During planning the execution of each construction project, the factors that may affect the effectiveness of its realization should be analyzed in detail. The costs of execution construction projects are significantly influenced by (apart from technical factors) decisions in the field of inventory management, among others, variability of environmental conditions, prices of construction products, the ability to acquire local suppliers, distance of transport, limited size of storage for construction products as well as the individual character of the project. In projects related to the buildings erection and other construction works, there are many logistical tasks in the area of supply in construction products, raw materials, equipment, as well as in the area of financial and information flow. Taking into account the large share of material costs, inventory management in the costs of projects realization and the high potential for their reduction, every enterprise operating in the construction industry should pay a great importance to the supply logistics sector. Determination of the size and delivery times of construction products, as well as the transport routes, storage and reloading locations are decision-making problems that are solved by management of both the enterprise and the construction projects. The paper presents a proposal for a methodology, which allows to determination and comparison of the inventory management costs in enterprise conducting parallel works on several construction sites. Mathematical models have been developed which simulate the functioning of supply logistics systems and allow, through their solution, to determination of the minimum logistics costs with optimal arrangements for the supply schedule (delivery volumes in subsequent periods of the project execution). This is the foundation for the selection of the optimal variant of a supply chain management. The practical application of the methodology proposed is illustrated using an example.

Optimal stocking strategies for inventory mechanism with a stochastic short-term price discount and partial backordering

Price discount is an important research topic in the field of inventory management. The existing research on this topic mainly considers fixed price discount, but ignores the situation in which stochastic short-term price discount may be involved. In this paper, we study an inventory problem considering stochastic short-term price discount and partial backordering. To address this problem, we propose an optimal replenishment and stocking model to maximise the retailers' profit. After that, a cost–benefit analysis-based heuristic method for solving the developed model is presented by considering two scenarios depending on whether a replenishment point belongs to a discount period or not. Furthermore, an algorithm is provided to elicit an optimal ordering policy from multiple solutions derived from the given heuristic solution method. Finally, a real case is offered to demonstrate the application of the proposed model, followed by a sensitivity analysis. The results indicate that a retailer can identify the optimal replenishment policy with the aim of achieving maximal profit in situations where stochastic short-term price discount and partial backordering are considered for certain inventory problems at hand. In addition, sensitivity analysis illustrates a fact that different values of the introduced parameters may influence the optimal replenishment policy.

Gap Analysis between ERP procedures and Construction procedures

Although Enterprise Resource Planning (ERP) offers many benefits to the construction industry, construction companies still hesitate to adopt ERP systems. This may be due to long-term practiced ad-hoc behaviors in the construction industry, which do not match with the standard procedures embedded in ERP systems. Therefore, through this research, it is expected to evaluate the gap between construction procedures and ERP procedures technically. Hence, to obtain indicative data for the study, a questionnaire was designed and distributed to selected 210 individuals among contractors, subcontractors, and clients in the Sri-Lankan construction industry who have used ERP. In total, 174 completed questionnaires were returned and then statistically analyzed using Chi-Square test with the Mini tab tool. It is concluded that there is a significant gap between the construction procedures and ERP procedures in identified fields related to the construction industry. The highest significant gap exists in the field of Inventory management with Chi-Square 158.766 > 9.48. And HRM (142.366), Asset Management (130.264), Finance Management (126.267), Site Operation (103.793), Project management (53.88), Purchases (34.324), Petty cash (28.337), Estimating and Tendering (22.148), Sub-Contractor management (0.492) respectively. Ultimately with the identified gaps, a framework was established to meet the organizational processes and ERP processes.

Applying machine learning to the dynamic selection of replenishment policies in fast-changing supply chain environments

Firms currently operate in highly competitive scenarios, where the environmental conditions evolve over time. Many factors intervene simultaneously and their hard-to-interpret interactions throughout the supply chain greatly complicate decision-making. The complexity clearly manifests itself in the field of inventory management, in which determining the optimal replenishment rule often becomes an intractable problem. This paper applies machine learning to help managers understand these complex scenarios and better manage the inventory flow. Building on a dynamic framework, we employ an inductive learning algorithm for setting the most appropriate replenishment policy over time by reacting to the environmental changes. This approach proves to be effective in a three-echelon supply chain where the scenario is defined by seven variables (cost structure, demand variability, three lead times, and two partners’ inventory policy). Considering four alternatives, the algorithm determines the best replenishment rule around 88% of the time. This leads to a noticeable reduction of operating costs against static alternatives. Interestingly, we observe that the nodes are much more sensitive to inventory decisions in the lower echelons than in the upper echelons of the supply chain.