Inventory Management Policies Research Articles

Integrating IoT and blockchain for intelligent inventory management in supply chains: A multi-objective optimization approach for the insurance industry

This research aims to develop and optimize an intelligent supply chain framework tailored for the insurance industry by integrating an innovative inventory management policy supported by the Internet of Things (IoT) and blockchain technologies. Through an extensive literature review, key assumptions are established, needs are identified, and objectives are formulated. A multi-objective mathematical planning model is proposed to minimize cost and time within the supply chain, with optimization conducted under deterministic and fuzzy conditions. The model utilizes augmented epsilon constraint and weighted sum methods to address its multi-objectiveness. Noteworthy is the integration of advanced technologies such as Wireless Sensor Networks (WSN), Radio-Frequency Identification (RFID), blockchain, and online sales, distinguishing it from traditional approaches. Initial validation and testing in smaller dimensions demonstrate the model’s adaptability to precise methods, while larger dimensions necessitate the use of meta-heuristic algorithms for efficient problem resolution. Additionally, a comprehensive sensitivity analysis in the insurance industry on objective function coefficients reveals intricate relationships, offering valuable insights into optimization dynamics across diverse conditions.

Multi Objective and Multi-Product Perishable Supply Chain with Vendor-Managed Inventory and IoT-Related Technologies

With the emergence of the fourth industrial revolution, the use of intelligent technologies in supply chains is becoming increasingly common. The aim of this research is to propose an optimal design for an intelligent supply chain of multiple perishable products under a vendor-managed inventory management policy aided by IoT-related technologies to address the challenges associated with traditional supply chains. Various levels of the intelligent supply chain employ technologies such as Wireless Sensor Networks (WSNs), Radio Frequency Identification (RFID), and Blockchain. In this paper, we develop a bi-objective nonlinear integer mathematical programming model for designing a four-level supply chain consisting of suppliers, manufacturers, retailers, and customers. The model determines the optimal network nodes, production level, product distribution and sales, and optimal choice of technology for each level. The objective functions are total cost and delivery times. The GAMS 24.2.1 optimization software is employed to solve the mathematical model in small dimensions. Considering the NP-Hard nature of the problem, the Grey Wolf Optimizer (GWO) algorithm is employed, and its performance is compared with the Multi-Objective Whale Optimization Algorithm (MOWOA) and NSGA-III. The results indicate that the adoption of these technologies in the supply chain can reduce delivery times and total supply chain costs.

Forecasting and Planning for Material Control in the Medical Device Industry

PT. XYZ is a company operating in the electromedical sector that has supported many domestic medical devices in recent years. The demand for electromedical equipment seen from sales data is very volatile and difficult to predict, apart from that 70% of the requests received come from government institutions. The problem is compounded by the absence of a forecasting policy so that production activity planning is not optimal. Product inventory that is not accompanied by careful calculations will result in overstock and stockout which can increase total costs and reduce service levels. In carrying out its business PT. XYZ implements an existing inventory management policy which is carried out by ordering product components in large quantities but there is no clear reorder point. So this research will observe the product components CMS-600 PLUS, ECG-1200G, TENSIONE, ECG-300G, SON-C, SON-B, SON-PRO, USG PROMAX, PROMIST-3, MED-01, ECG- 100G, and ULTRAMIST then proposed the Croston and Syntetos-Boylan Approximation (SBA) forecasting method to estimate possible demand in the future and then continuous review inventory management policies (s, S) and (s, Q) to determine the maximum reorder point value. stock and order quantity. After that, a Monte Carlo simulation was carried out to see and compare existing policies with improvement policies based on total cost and service level variables. Next, a sensitivity analysis was carried out to determine the robustness of the simulation model to changes in uncertainty. The results obtained are recommendations for a continuous review (s, Q) policy with total cost savings of IDR 22,206,322,981 and an increase in service level of 1.36% to 99.98% compared to the existing policy.

Improving spare parts (MRO) inventory management policies after COVID-19 pandemic: a Lean Six Sigma 4.0 project

Purpose To redesign the spare parts (MRO) inventory management at Company XYZ's warehouse, considering the conditions after the COVID-19 pandemic.Design/methodology/approach To address this research project, the authors integrated three methodologies: action research, Lean Six Sigma (DMAIC) and Cross Industry Standard Process for Data Mining. These methodologies integrated the Lean Six Sigma (LSS) 4.0 framework applied in this project.Findings The spare parts inventory value was reduced by 15%, and inventory turnover increased by 120% without negatively impacting the internal service level.Practical implications Practitioners leading or participating in continuous improvement projects (CIPs) should consider data quality (data available and data trustworthiness), problem-solving approach and target area involvement to achieve CIP goals. Otherwise, the LSS 4.0 could fail or extend its duration by several weeks or months.Originality/value This project shows the importance of controlling a target area before deciding to conduct a LSS 4.0 project. To address this problem, the LSS 4.0 team implemented 5S during the measure phase of the DMAIC. Also, this project offers significant practitioner and theoretical contributions to the body of knowledge about LSS 4.0.

Forecasting and Inventory Planning: An Empirical Investigation of Classical and Machine Learning Approaches for Svanehøj’s Future Software Consolidation

Challenges related to effective supply and demand planning and inventory management impose critical planning issues for many small and medium-sized enterprises (SMEs). In recent years, data-driven methods in machine learning (ML) algorithms have provided beneficial results for many large-scale enterprises (LSE). However, ML applications have not yet been tested in SMEs, leaving a technological gap. Limited recourse capabilities and financial constraints expose the risk of implementing an insufficient enterprise resource planning (ERP) setup, which amplifies the need for additional support systems for data-driven decision-making. We found the forecasts and determination of inventory management policies in SMEs are often based on subjective decisions, which might fail to capture the complexity of achieving performance goals. Our research aims to utilize the leverage of ML models for SMEs within demand and inventory management by considering various key performance indicators (KPI). The research is based on collaboration with a Danish SME that faced issues related to forecasting and inventory planning. We implemented the following ML models: Artificial Neural Network (ANN), Long Short-Term Memory (LSTM), Support Vector Regression (SVR), Random Forest (RF), Wavelet-ANN (W-ANN), and Wavelet-LSTM (W-LSTM) for forecasting purposes and reinforcement learning approaches, namely Q-learning and Deep Q Network (DQN) for inventory management. Results demonstrate that predictive ML models perform superior concerning the statistical forecasting approaches, but not always if we focus on industrial KPIs. However, when ML models are solely considered, the results indicate careful consideration must be regarded, given that model evaluation can be perceived from an academic and managerial perspective. Secondly, Q-learning is found to yield preferable economic results in terms of inventory planning. The proposed models can serve as an extension to modern ERP systems by offering a data-driven approach to demand and supply planning decision-making.

Effects of Working Capital Management on Profitability of NBFIs in Rwanda

Working capital management is a key factor in determining a company's financial management. Inefficient or inefficient management of working capital impacts profitability and liquidity. To achieve optimal working capital management, company management must carefully manage the trade-off between profitability and liquidity. The general objective of this study was to determine the Effects of Working Capital Management on Profitability of NBFI in Rwanda. The study adopted Credit theory and Transaction Cost Theory. The study adopted a descriptive research design using both quantitative and qualitative approaches. The study assumed a target population of 96 individuals, a multistage random sample of 38 executives from various categories. Survey data were collected using a structured questionnaire. To test whether there is an impact on the profitability of the NBFIs in Rwanda, the correlation between inventory management policy and profitability and the correlation analysis between lending policy and profitability were used. Working capital has been found to have a significant relationship with profitability. Based on the research findings, it can be concluded that working capital is a positive, significant predictor of profitability. The findings of the study suggested that there is a positive and significant relationship between credit policy and profitability. A positive significant linear relationship between inventory control policies and profitability of NBFI in Rwanda was observed. The NBFI financial managers should regularly review their credit policies to ensure they are optimal. NBFI's finance managers should take precautions to help them maintain optimal inventory levels for both raw materials and finished goods.

Optimal Statistical Estimation and Dynamic Adaptive Control of Airline Seat Protection Levels for Several Nested Fare Classes under Parametric Uncertainty of Customer Demand Models

Assigning seats in the same compartment to different fare classes of passengers is a major problem of airline seat allocation. Airlines sell the same seat at different prices according to the time at which the reservation is made and other conditions. Thus the same seat can be sold at different prices. The problem is to find an optimal policy that maximizes total expected revenue. To solve the above problem, this paper presents the novel computational approach to optimization and dynamic adaptive prediction of airline seat protection levels for multiple nested fare classes of single-leg flights under parametric uncertainty. It is assumed that time T (before the flight is scheduled to depart) is divided into h periods, namely a full fare period and h-1 discounted fare periods. The fare structure is given. An airplane has a seat capacity of N. For the sake of simplicity, but without loss of generality, we consider (for illustration) the case of nonstop flight with two fare classes (business and economy). The proposed airline's inventory management policy is based on the use of the proposed computational models. These models emphasize pivotal quantities and ancillary statistics relevant for obtaining statistical predictive limits for anticipated quantities under parametric uncertainty and are applicable whenever the statistical problem is invariant under a group of transformations that acts transitively on the parameter space. The proposed technique is based on a probability transformation and pivotal quantity averaging. It is conceptually simple and easy to use. Finally, we give illustrative examples, where the proposed analytical methodology is illustrated in terms of the two-parameter exponential distribution. Applications to other log-location-scale distributions could follow directly.

New products or remanufactured products: Which is consumer-friendly under a closed-loop multi-level supply chain?

If the remanufactured and fresh products are in the market at the same price and quality, then it is difficult for the retailer to sell them all together simultaneously. There is differentiability in the market by the consumers even though they are of similar quality and price. These remanufactured products are the effect of a closed-loop supply chain. The closed-loop type supply chain problems have pulled much attention to tackling environmental and social issues for these remanufactured products. Based on the concern, a closed-loop formation of the supply chain is formulated with four players. Two types of quality are assigned for the manufactured and remanufactured products as remanufactured products maintain less quality than the newly manufactured products. The production system is imperfect, then all products are not perfect. A mathematical model with a multi-cycle closed-loop supply chain is discussed here, where the reworking process happens within the same cycle. An acceptance quality level influences the regaining rate of consumed products. The non-integrated phenomenon uses the inventory management policy operated by the vendor and the consignment-type stock policy. This mathematical problem is solved by employing algorithms, developed to achieve the local optimum (non-integrated situations) and global optimum (integrated situations). A numerical example and sensitivity analysis are provided to establish the mathematical model numerically. Results obtain that the retailer can tackle the situation in a proper way such that the closed-loop supply chain always is in profit. The profit is globally maximized, and the best results are obtained from different situations.

EOI or EOQ? A simulation study for the inventory management of a company operating in the railway sector

The present paper proposes a study carried out in an Italian company operating in the railway industry, whose aim was to simulate two inventory management policies, namely the Economic Order Quantity (EOQ) and the Economic Order Interval (EOI) and compare the relating results from a set of simulations with historical data. The simulation was carried out on 53 articles (37 raw materials and 16 work in progress). The two models were developed under Microsoft Excel™, and their effectiveness was assessed in terms of amount of effective stock and the total costs associated; specifically, total costs are composed of three cost items: (i) order/production, (ii) stock keeping and (iii) stock out. Overall, both models returned better results in terms of economic saving and amount of stock than the current management policy used by the company; more specifically, the EOQ policy turned out to be the best, and was then selected for being implemented in the whole warehouse under investigation. Moreover, from a theoretical perspective, the need for a literature review on inventory management models emerged.

Dynamics, Risk and Management Performance of Urban Real Estate Inventory in Yangtze River Delta

(1) Background: Inventory management is a key point in the achievement of the virtuous cycle and sustainable development of the real estate industry. In response to the practical needs of city-based policies, this paper constructs a new research approach of “evolution dynamics—risk analysis—performance evaluation—policy design” of real estate inventory, and conducts a case study on the Yangtze River Delta. (2) Methods: This paper studies the change characteristics, trends and spatial patterns of real estate inventory changes in the Yangtze River Delta based on Geographic Information System software, and quantitatively evaluates the risk level and management performance of real estate inventory by introducing the Boston Consulting Group Matrix for corporate management and the Super- Data Envelopment Analysis Model for operations research, providing a basis for policy design. (3) Results: First, the “destocking” policy has gained results to some extent and diversified the inventory evolution, thus alleviating or curbing the negative trend in most cities. Second, the real estate inventory in the Yangtze River Delta is divided into high, low, potential and zero pressure zones by risk levels, and the proportion of cities with increased, decreased and unchanged risk levels is essentially the same. Third, the average real estate inventory management performance index has been steadily improving, but overall, it is still unsatisfactory, with cities in an effective state accounting for 40% and below for a long time. Fourth, real estate inventory and its management performance both show significant spatial effects, with cold and hot spot cities characterized by a “center-periphery” spatial pattern in geographical distribution, and the cities in the study area are classified into four types: super-efficiency, efficiency, inefficiency, and super-inefficiency. Fifth, the real estate inventory in most cities is continuing to grow positively, and a small number of cities have been in the high-risk zone for a long time or become new members of the high-risk zone, making the government and enterprises still faced up with great pressure and challenges in inventory management with the risk level further increased but management performance growing slowly. (4) Conclusions: The study area is divided into four types of policy areas, that is, red key area, yellow important area, green auxiliary area, and path-dependent area, and suggestions for optimization are made from the perspectives of risk control, performance improvement, benchmarking recommendation, and redundancy governance, providing a basis for the government’s real estate inventory management policy design and the enterprise’s high-quality development decision.

Integrated Pricing and Inventory Control for Perishable Products, Taking into Account the Lack of Backlog and Inventory Management Policy by the Seller

AbstractRecently, utilizing appropriate inventory control policy and determining the optimal selling price for various goods has been the main topic of scientific and industrial research. Inventory management policy 1 by the seller is one solution that improves the chain’s performance by creating coordination between members of the supply chain. The current study attempts to devise an integrated model of inventory pricing and control under the inventory management policy by the seller for perishable goods with shortages is considered. The purpose of presenting the model is to determine the optimal price, the optimal repayment time, and the order size, in order to maximize the profit. To acquire those optimal values, the profit functions of the buyer and the seller are taken into account. Given the results acquired, it is demonstrated that at any cost, the repayment time is unique and optimal. It is concluded that with the optimal recovery time available, the objective function is a concave function of price, and its optimal value is available. Furthermore, utilizing the inventory management policy by the seller could be a proper means to reducing retailer costs while raising their profit.

Beneficiary-centric decision support framework for enhanced resource coordination in humanitarian logistics: A case study from ASEAN

The growing frequency, impact, and complexity of natural- and human-made- disasters have resulted in an increased attention toward humanitarian logistics management practices. With most disasters having a global scale due to the high level of interconnectedness between supply chains, the adequate provision of humanitarian assistance through streamlined logistics systems has become paramount. Correspondingly, the traditional organization- and execution-centric approaches are no longer sufficient, and greater attention to beneficiary- and ecosystem-centric supply chain networks with equal emphasis on planning and execution are necessary. Accordingly, this work proposes a multi-method decision support framework to analyze, visualize, optimize, and simulate supply networks to address the key challenge of resource coordination in disaster response operations. A real-world application to the Disaster Emergency Logistics System for ASEAN (DELSA), the network of pre-positioned stockpiles managed by the ASEAN Humanitarian Agency in ASEAN, is also presented. Results of the analyses show that with the introduction of inventory management policies with safety stock, the DELSA network improves its performance in service level by +41%. Unlike the current practice where inventory decisions are, in most cases, supply-and funds-driven, the findings of this work bring about a paradigm shift for beneficiary-centric policies. More importantly, the analyses of this study could generate practical insights for proper humanitarian logistics planning not only in ASEAN but throughout the world.

Cold Chain Energy Analysis for Sustainable Food and Beverage Supply

Perishable goods, such as chilled and frozen foods, have a short shelf life and high sensitivity to their surrounding environment (e.g., temperature, humidity, and light intensity). For this reason, they must be distributed within a specific time and require special equipment and facilities (e.g., refrigeration and dehumidification systems) throughout the entire chain from farm to fork to ensure slow deterioration and to deliver safe and high-quality products to consumers. Cold chains can last for short periods, such as a few hours, or for several months or even years (e.g., frozen food products) depending on the product and the target market. A huge amount of energy is required to preserve quality by maintaining the desired temperature level over time. The required energy is also affected by inventory management policies (e.g., warehouse filling levels affect the cooling demand per unit of a product) and the behavior of the operators (e.g., number and duration of door openings). Furthermore, waste entails the loss of energy and other resources consumed for processing and storing these products. The aim of the present study is to propose a quantitative approach in order to map the energy flows throughout the cold chain in the food and beverage sector and to evaluate the overall energy performance. The results of the energy flow mapping give decisionmakers insights into the minimum energy required by the cold chain and allow them to prioritize energy efficiency measures by detecting the most energy consuming stages of the cold chain. The implementation of a holistic approach, shifting from a single-company perspective to chain assessment, leads to a global optimum and to an increased implementation rate of energy efficiency measures due to the reduced barriers perceived by different actors of the cold chain.

Manufacturing Productivity with Worker Turnover

To maximize productivity, manufacturers must organize and equip their workforces to efficiently handle variable workloads. Their success depends on their ability to assign experienced and skilled workers to specialized tasks and coordinate work on production lines. Worker turnover may disrupt such efforts. We use staffing, productivity, and pay data from within a major consumer electronics manufacturer’s supply chain to study how firms should manage worker turnover and its effects using production decisions, wages, and inventory. We find that worker turnover impedes coordination between assembly line coworkers by weakening knowledge sharing and relationships. Publicly available unit-cost estimates imply that worker turnover accounts for $206–274 million in added direct expenses alone from defectively assembled units failing the firm’s stringent quality control. To evaluate managerial alternatives, we structurally estimate a dynamic equilibrium model (an Experience-Based Equilibrium) encompassing (1) workers’ endogenous turnover decisions and (2) the firm’s weekly planning of its production scheduling and staffing in response. In counterfactual analyses, a less turnover-prone, hence more productive, workforce significantly benefits the firm, reducing its variable production costs by 4.5%, or an estimated $928 million for the studied product. Such benefits justify paying higher efficiency wages even to less skilled workforces; furthermore, interestingly, rational inventory management policies incentivize self-interested firms to reduce rather than tolerate turnover. This paper was accepted by Vishal Gaur, operations management. Funding: The authors gratefully acknowledge support from the data sponsor, and K. Moon gratefully acknowledges support from the Wharton Dean’s Research Fund and the Claude Marion Endowed Faculty Scholar Award of the Wharton School. Supplemental Material: The online appendices are available at https://doi.org/10.1287/mnsc.2022.4476 .

Research on the Comparison between the Different Policies by Service Level and Inventory Level Performance of Auto Parts in N.A.C.C. (North Automobile Components Company)

As after sales services become more and more popular, particularly preventive or corrective maintenance, the intervention and repair of the customer’s goods in a timely and efficient manner ensure customer satisfaction and contribute to the establishment of brand image in the market of the suppliers. The availability and quality of spare parts are key elements of this strategy while ensuring minimal management costs. The reuse of spare parts retrieved from customer systems is a growing maintenance strategy practice which impacts the traditional spare parts supply chain. This reuse is primarily driven by extending the economic life of goods, initially regarded as waste and therefore without added value, by transforming them into valuable spare parts that can be reused; secondly, for environmental or regulatory reasons, demanding responsibility for the treatment of products at the end of their life; and thirdly, to improve the availability of parts for maintenance, especially parts that the organization can no longer purchase or that are impacted by other issues. It also involves the analysis of their condition and their eventual return to working order as they are retrieved from the customer’s systems in a defective condition. In this paper, we will identify and classify the different customers and spare parts by estimating the critical level of rationing policy based on forecasts, identify the thresholds of inventory management policies, and finally, compare the different policies by service level and inventory level performance for the N.A.C.C. company.

Decision support system for adaptive sourcing and inventory management in small- and medium-sized enterprises

• Proposes a data-driven approach in sourcing and inventory management for SMEs. • Exploits Observational and empirical data to generate decision support solutions. • Promote the adoption and initiation of artificial intelligence in SMEs. • Promising implementation results in a real-world case study. Elevated business uncertainties and competition over recent years have caused changes to the data-driven supply chain management of sourcing and inventories across industries. However, only large-sized enterprises have the resources to harness data for aiding their decision-making and planning. By contrast, small- and medium-sized enterprises (SMEs) commonly have limited resources and knowledge, which affects their ability to collect and utilize data. Thus, it is a challenge for them to implement advanced decision support tools to mitigate the effects of market uncertainties. This paper proposes a decision support system (DSS) for sourcing and inventory management, with the aims of helping SMEs compile and exploit data, and supporting their decisions under business ambiguities. The DSS was developed using a simulation-optimization approach by incorporating an artificial neural network and a genetic algorithm for problem representation and optimizing decision support solutions. The exploitation of observational and empirical data reduces the burden of data compilation obtained from unorganized data sources across SME operations. Further, uncertainty factors such as raw material demand, price, and supply lead time were considered. When implemented in a medium-sized food industry company, the DSS can provide decision support solutions that integrate the selection of recommended suppliers and optimal order quantities. It can also help decision-makers to shape their inventory management policies under uncertain raw material demands, while also considering service levels, sales promotions, lead times, and material availability from multiple suppliers. Consequently, implementation of the DSS helped to reduce the total purchased raw material costs by an average of 51.62% and reduced the order interval and on-hand inventory costs by an average of 54.24%.

Green strategies to reduce the environmental impact of jam production

Food operations management is the most impactful driver of global environmental challenges, including global warming, climate change, water pollution and loss of biodiversity. According to recent studies, food production systems efficiency and food loss are leading causes of environmental degradation. Although many methodologies to assess the environmental impact of food production have already been investigated, there is an urgent need to provide environmental evaluations in the fruit sector focusing on the most widespread industrial production processes and their corresponding impact. In this context, the operations management in jam production represents one of the most critical issues. Generally, in the most common jam production systems, energy-intensive treatments, including chemical substances, are adopted to extend the shelf-life of the raw materials or finished products, allowing to increase the process performance in terms of production levelling, inventory management, and market policies. In many cases, this approach compromises the sustainability of treatments, reducing the corresponding environmental performance. This paper aims to analyze the most common processes for jam production, comparing their environmental performances and the effects of the related centralized/decentralized strategies to be adopted. The potential impact categories of each process phase have been assessed. Results led to identifying the jam production from fresh fruit by adopting a decentralized production strategy, the best option considering the environmental performance under production capacity constraints

A Vendor Inventory Management Policy to Optimize Sales of Thermal Energy Using Evolutionary Algorithms

초록·키워드 목차 오류제보하기 In recent years, supply chain management with the aim of making the supply chain more efficient and reducing over-all costs, is one of the key issues addressed in every industry. In today"s energy supply chain, determining the level of sales for manufactured goods in specific time periods and to specific customer groups is very important. This becomes even more important when we have to do this for sensitive marine products that have different customer categories. This article presents a model for determining the optimal level of sales of thermal energy in two levels and according to customer demand in different time periods to determine the optimal level of sales using accurate methods and some meta-innovative methods. For this purpose, two meta-heuristic methods of particle swarm optimization (PSO) and the evolutionary algorithm CPSO have been used. #Hybrid Optimization #Two Echelon Supply Chain #PSO Algorithm #Vendor Inventory Management #Thermal industry ABSTRACT1. INTRODUCTION2. INTRODUCTION OF THERMAL ENERGY SUPPLY CHAIN3. LITERATURE REVIEW4. METHOD5. RESULTS6. CONCLUSIONREFERENCES

Aid Allocation for Camp‐Based and Urban Refugees with Uncertain Demand and Replenishments

There are 26 million refugees worldwide seeking safety from persecution, violence, conflict, and human rights violations. Camp‐based refugees are those that seek shelter in refugee camps, whereas urban refugees inhabit nearby, surrounding populations. The systems that supply aid to refugee camps may suffer from ineffective distribution due to challenges in administration, demand uncertainty and volatility in funding. Aid allocation should be carried out in a manner that properly balances the need of ensuring sufficient aid for camp‐based refugees, with the ability to share excess inventory, when available, with urban refugees that at times seek nearby camp‐based aid. We develop an inventory management policy to govern a camp's sharing of aid with urban refugee populations in the midst of uncertainties related to camp‐based and urban demands, and replenishment cycles due to funding issues. We use the policy to construct costs associated with: (i) referring urban populations elsewhere, (ii) depriving camp‐based refugee populations, and (iii) holding excess inventory in the refugee camp system. We then seek to allocate aid in a manner that minimizes the expected overall cost to the system. We propose two approaches to solve the resulting optimization problem, and conduct computational experiments on a real‐world case study as well as on synthetic data. Our results are complemented by an extensive simulation study that reveals broad support for our optimal thresholds and allocations to generalize across varied key parameters and distributions. We conclude by presenting related discussions that reveal key managerial insights into humanitarian aid allocation under uncertainty.

Analisis Pengendalian Persediaan Barang Dagang Pupuk Dengan Metode Economic Order Quantity Pada Perusahaan Maju Mapan

The Maju Mapan Company is a company engaged in trading, whose main activity is selling fertilizer types. In carrying out sales activities, it is closely related to merchandise inventory which is an important component that must be available for the smooth running of the sales process. This study aims to determine the quantity of economical fertilizer merchandise inventory using the Economic Order Quantity method. From the results of calculations that have been done by comparing the current trading goods inventory management policy in the company with the EOQ method in the procurement of merchandise, it can be concluded that by using the EOQ method, the quantity of EOQ orders is higher than the order according to the company, but if the company uses the method EOQ, the company will save an inventory cost of Rp. 48,546,480