Excess Inventory Research Articles

Analysis of the Production Area Planning of Ground Fish at CV Hadori in East Lampung District

This study investigates production scale planning at CV Hadori, a fish processing company, using Break-Even Point (BEP) analysis and linear trend forecasting. The company faces significant overproduction of minced fish, leading to excess inventory and reduced profits. The research employs a descriptive quantitative approach, analyzing historical production and sales data through trend analysis and BEP calculations. Findings indicate that CV Hadori must produce and sell over 405,436 kg of processed fish in 2026 to achieve profitability. Recommendations include optimizing production and sales strategies, investing in appropriate machinery, and continuously monitoring market conditions to adjust forecasts. Effective production planning will help minimize surplus, enhance profitability, and ensure long-term sustainability.

Controlling merchandise inventory using the economic order quantity multi-item discount model method using Odoo software at UD. Ridho

UD. Ridho is a business based in Jl. Sudirman Pekan Dolok Masihul, Dolok Masihul District, Serdang Bedagai Regency, North Sumatra, deals in the sale and purchase of goods. The business had an accumulation of surplus inventory, or overstock, which damaged the goods and kept them from going bad. resulting in total losses of Rp 154,104,00 from January 2023 to June 2023. Orders are placed by UD. Ridho for goods from five vendors. The EOQ Multi Item Model Discount Method is applied in this study using the Odoo software. The EOQ Multi-Item Discount Model is a technique for optimizing inventory costs by placing orders for commodities based on economic order amounts from many suppliers. Product categories offered by UD. Ridho Sugar, rice, wheat flour, instant noodles, soy sauce, detergent, salt, water, and edible oil are the ingredients of UD. Ridho. The discount model is applied to goods that receive a discount from the seller according to the quantity of goods bought. Instant noodles, rice, detergent, edible oil, and aqua are the products that are discounted. Applying the multi-item discount model approach with economic order quantity at UD. Ridho was able to save Rp 21,893,660, or 3.02% of the total amount. The Odoo software, which has three modules—inventory, purchasing, and sales orders—can be used with this paradigm to construct an inventory management application

Pengaruh Likuiditas, Leverage, Profitabilitas dan Kepemilikan Manajerial Terhadap Nilai Perusahaan: Studi Empiris pada Perusahaan Manufaktur yang Terdaftrar di BEI Tahun 2020-2022

A company will always strive to achieve its goals, for example long-term goals such as the ability to increase company value. Indrarini (2019: 3) defines company value as an investor's vision of the manager's success rate in managing the company's assets entrusted to him which is often associated with stock prices. The sampling technique in this study used purposive sampling method. The data collection method in the study is the annual financial report documentation method (annuar report). This study uses a type of quantitative research, the data used in the research is secondary data obtained by taking through the annual report of each manufacturing company which can be accessed through the company's official website. Liquidity has no effect on firm value, the high level of liquidity causes a decrease in firm value. Leverage affects firm value, companies with low leverage levels can increase firm value. Profitability has no effect on firm value, excessive inventory may have low profitability. Managerial ownership has no effect on firm value, the results of this study contradict agency theory.

Potato Consumption Forecasting Based on a Hybrid Stacked Deep Learning Model

AbstractPotato consumption forecasting is crucial for several stakeholders in the food market. Due to the market flexibility, the farmers can manipulate the volumes planted for a given type of produce to reduce costs and improve revenue. Consequently, it means that establishing optimal inventories or inventory levels is possible and critical in that sense for the sellers to avoid either inadequate inventory or excessive inventories that may lead to wastage. In addition, governments can predict future food deficits and put measures in place to guarantee that they have a steady supply of food some of the time, especially in regions that involve the use of potatoes. Increased potato-eating anticipation has advantages for the sellers and buyers of the potatoes. The experiments of this study employed various machine learning and deep learning (DL) models that comprise stacked long short-term memory (Stacked LSTM), convolutional neural network (CNN), random forest (RF), support vector regressor (SVR), K-nearest neighbour regressor (KNN), bagging regressor (BR), and dummy regressor (DR). During the study, it was discovered that the Stacked LSTM model had superior performance compared to the other models. The Stacked LSTM model achieved a mean squared error (MSE) of 0.0081, a mean absolute error (MAE) of 0.0801, a median absolute error (MedAE) of 0.0755, and a coefficient of determination (R2) value of 98.90%. These results demonstrate that our algorithms can reliably forecast global potato consumption until the year 2030.

Analisis Manajemen Operasional Sumber Daya & Proses Produksi

PT Indofood Sukses Makmur Tbk is a company engaged in the production of various types of food and beverages. In an effort to improve the efficiency and effectiveness of its operational activities, PT Indofood has implemented various operational strategies. This analysis discusses the implementation of PT Indofood Sukses Makmur Tbk's operational strategies including the use of the Just In Time system, ABC analysis, vendor-managed inventory, and inventory management software. The results show that PT Indofood's operational strategies are effective in improving the efficiency and effectiveness of its operational activities. In this way, the company can reduce the risk of stock-outs or excess inventory and improve the quality of its products.

Leveraging Inventory Management for Enhanced Performance in Elementary School Cooperatives: Case Study from Majalengka Regency

Cooperatives play a significant role in promoting economic growth by not only increasing community income but also providing employment opportunities in the small business sector. For cooperatives to effectively support the economic development of villages, it is crucial for them to function as healthy business entities. One way to enhance their efficiency is by ensuring consistency in their managerial activities, ranging from planning to control, including inventory management. SD "X" Cooperative, located in Majalengka, engages in retail trading activities through a mini market. The cooperative deals with 19 specific types of items, which require strict inventory control. The governance process starts with determining the optimal quantity of economic orders to avoid shortages or excessive inventory of goods, thus ensuring satisfactory customer service. However, the cooperative currently faces challenges in terms of working capital shortage, hindering its ability to place orders according to the predetermined optimal quantity. Therefore, it becomes imperative to redefine the optimal order placement process by considering the available working capital. Such re-optimization will have an impact on increasing inventory costs, as the optimal order quantity decreases and the frequency of orders becomes more frequent. Despite the decrease in the optimal number of orders, the cooperative strives to fulfill customer demands while managing its limited working capital effectively

Whispered words and organizational dynamics: The nuanced evaluation of gossipers' personality and its effect on workplace advice seeking

Prior research has extensively studied workplace group dynamics within the gossip triad (i.e., sender, receiver, and target). This research shifts the focus to third-party observers outside the gossip triad, examining how they evaluate gossipers and non-gossipers, and whom they turn to for advice. Across five pre-registered experiments (N = 1400), the present work builds on an integrative definition of gossip and provides a functionalist account of observers' nuanced evaluation of gossipers' personality from a global perspective. Observers perceive gossipers as less moral and competent, but more sociable, than non-gossipers (Experiment 1). Consequently, observers are less likely to seek advice from gossipers (vs. non-gossipers) for tasks requiring high morality (e.g., enforcing ethical conduct; Experiment 2a) and high competence (e.g., managing excess inventory; Experiment 2b), yet more likely to do so for tasks requiring high sociability (e.g., organizing a welcome lunch; Experiment 2c). A moderation-of-process approach shows that incidental cues signaling morality, competence, and sociability influence observers' evaluations of and advice-seeking from gossipers (versus non-gossipers) on relevant tasks (Experiments 2a–2c). These findings remain robust in an incentive-compatible setting (Experiment 3). This research advances our understanding of observers' evaluation of gossipers and its implications for workplace advice seeking.

Pengendalian Persediaan Guna Meningkatkan Efesiensi Biaya Produksi Pada CV. Buala di Idanogawo

Companies basically have to carry out appropriate inventory management to anticipate situations or challenges in order to achieve the ultimate goal of minimizing expenditure costs. Companies must plan the supply of raw materials to avoid shortages or running out of stock. By having inventory, it is hoped that the company can carry out the production process according to needs so that the costs required are lighter and more effective. Apart from that, having sufficient supplies of materials is also expected to facilitate production activities or services to company consumers and avoid shortages of raw materials. Inventory shortages can be detrimental to the company in this case increased financing. This research uses an exploratory type of quantitative research. The research sample is the entire population, namely 3 people, namely 1 (one) Director, 1 (one) Manager, 1 (one) Manager, so this research is a population research. The research results show that good inventory control can support sustainability initiatives. By reducing excess inventory, companies can reduce the environmental impact of production. The cause of the lack of material supplies at CV. Buala is when there is rainfall for days on end so that no raw materials are transported and processed, so the supply of materials runs out of stock. To anticipate material stock shortages, CV. Buala attempts to control inventory using the lead time method. Lead time is a time lag that occurs between the time an order is placed until the goods enter the warehouse. If the Lead Time is long, the Reorder Point must be placed earlier so that stock does not run out before the new shipment arrives

Distributionally robust newsvendor problem with partial demand information under carbon emission regulations

Carbon emission schemes are widely adopted and implemented in practice to control greenhouse gas emissions. However, a firm restricted by carbon emission schemes with only partial demand information may face greater challenges. In this paper, we investigate an ordering and pricing newsvendor problem with partial demand information, which considers carbon emissions generated from the processes of ordering and disposing of the excess inventory. We propose three distributionally robust models to maximize the worst-case profit under different carbon emission regulations: carbon tax, cap-and-trade and carbon cap, respectively. We derive the implicit solution under the carbon tax scheme and design heuristic algorithms based on Lagrangian relaxation under the cap-and-trade and carbon cap regulations. Numerical analysis indicates: (i) all three regulations are effective in reducing carbon emissions; (ii) the implementation of carbon schemes leads to higher optimal pricing and lower optimal ordering quantity, both carbon tax and carbon cap regulations will always lead to lower profit, while the retailer may benefit from the cap-and-trade regulation by selling out the carbon credit; (iii) the proposed approach performs well under different distribution specific scenarios.

Reducing blood product wastage through the inter-hospital redistribution of near-outdate inventory.

Hospital transfusion services order blood products to satisfy orders and maintain inventory levels during unexpected periods of increased blood demand. Surplus inventory may outdate before being allocated to a recipient. Blood product outdating is the largest contributor to blood wastage. A province-wide redistribution program was designed and implemented to redistribute near-outdate plasma protein and related blood products from low-usage to high-usage hospitals. Program operations and details are described in this paper. Two transport container configurations were designed and validated for transport of all blood products. A cost-analysis was performed to determine the effectiveness of this redistribution program. A total of 130 hospital transfusion services contributed at least one near-outdate blood product for redistribution between January 2012 and March 2020. These services redistributed 15,499 products through 3412 shipments, preventing the outdating of $17,570,700 CAD worth of product. Program costs were $14,900 for shipping and $30,000 for staffing. Failed time limits or non-compliance with packing configurations resulted in $388,200 worth of blood products (97 shipments containing 816 products) being discarded. Courier transport delays was the most common reason (42/97; 43%) for transport failure. Redistributing near-outdate blood products between hospitals is a feasible solution to minimize outdating. Despite heterogeneity of Canadian blood product inventory, all products (each with unique storage and transport requirements) were successfully redistributed in one of two validated and simple containers. Total operation costs of this program were small in comparison to the $17.6 million in savings associated with preventing the discard of outdated products.

Research on Vegetables Sales Profit Based on Machine Learning

The sales volume of different vegetables in supermarkets is different, and the profit is different. The research on the profit of each category of vegetables can help supermarkets predict the sales revenue more accurately to improve inventory management, purchasing plans, and sales strategy. This can help the supermarket avoid excess or insufficient inventory, improve profits, and help the supermarket identify potential sales risks. The research team obtained daily sales data for various categories of vegetables from a supermarket on the Internet for some time. First, according to the "cost plus pricing" method of vegetables, regression analysis was conducted on the total cost and total profit of various categories of vegetables, and it was found that there was a strong linear relationship, and the profit rate of various categories of vegetables was relatively fixed. They were [0.6629, 0.5915, 0.4663, 0.5121, 0.5596, 0.5381]. Since the sales volume of vegetables has seasonal characteristics, this paper established the ARIMA model to forecast the sales volume of each category of vegetables; and obtained the sales volume distribution of each category of vegetables from July 1 to 7, 2023. The BP neural network model with 22 layers of hidden layers was established, and the purchase unit price data could be obtained according to the known sales data. The R square of the model was greater than 0.7, and the goodness of fit was high. According to the known correlation, considering the impact of the loss rate on the profit and the rationality of the data, a multivariate linear programming model is established to give the replenishment volume and pricing strategy from July 1 to 7, 2023. It is concluded that the maximum profit of the supermarket in these seven days is [845.1835, 870.5028,861.6210, 858.8551, 839.4595, 864.7580, 892.9897]. Through the sensitivity analysis of the profit rate of each category of vegetables, it is concluded that the profit rate increases by 1%, the income will increase by about 1.1%, and the model is robust.

Feed Friend: Fostering Community Involvement in Food Assistance

According to the Food and Agriculture Organization (FAO) of the United Nations, approximately one-third of all food produced for human consumption worldwide is wasted annually. Food wastage occurs at various stages of the supply chain, including production, processing, distribution, and consumption. FeedFriend is an innovative website which was designed to combat food-wastage by fostering a vibrant community of donors and recipients. Donor Registration Individuals or Businesses can sign up as Donors on FeedFriend, providing details about the surplus food they have available for donation. This includes restaurants with excess inventory, grocery stores with perishable items nearing expiration, or individuals with leftover food from events or meals. Food Listings Donors can create listings within the website, specifying the type and quantity of food they wish to donate, include relevant details such as expiration dates etc. In Feed Friend, Users can filter the Donation requests based on their location. This type of service is used to match donors with nearby recipients in need. Recipients may include food banks, shelters, community centers, or individuals facing food insecurity. Feed Friend facilitates communication between donors and recipients, coordinates pickup or delivery logistics directly by providing donor’s contact details. So that it enables a better communication between Donors and Receivers.

Sales forecasting and classification model and pricing research of agricultural perishables based on STL-ARIMA

The prediction of sales volume for agricultural perishable goods is crucial for small-scale merchants to formulate corresponding procurement strategies. Accurate sales volume forecasting helps merchants reduce the risk of excess inventory, establish prudent and appropriate procurement strategies, and provide suggestions for product pricing. To further accurately explore the sales volume patterns of various perishable goods, this study classifies existing data into individual products using the STL (Seasonal-Trend decomposition using Loess) algorithm, revealing the characteristics of each product's sales volume over time. Based on the existing sales volume data, the ARIMA model is used to predict the sales volume of specific categories of goods. A multivariate linear regression model related to pricing, based on existing cost and pricing data, is established to provide merchants with pricing suggestions.

Sustainable automotive supply chain in the presence of disruption and government intervention

This paper aims to develop and simulate a green automotive supply chain model (ASC) consisting of one supplier, one manufacturer, and two types of products (green and non-green) under disruption risks (DRs). The greening effort (i.e., electric vehicle production) is considered for both the supplier and the manufacturer. In our modeling, we include the local government intervention (GI) and their incentivization of manufacturers to produce greener products. Moreover, the effectiveness of centralized versus decentralized supply chain integration strategies in coping with disruption consequences was explored. A mathematical pricing model based on game theory is designed to maximize the total profit for both integrated and decentralized systems. The model examines the effects of the greening effort on the supply chain (SC) members with eight disruption scenarios, including Extra Production and Surplus Inventory. Simulating numerical examples reveals that the Extra Production type of disruption increase the profitability in different scenarios. Conversely, the Surplus Inventory disruption reduces profitability. Moreover, a channel coordination through cost sharing contract in the presence of disruption sharing was developed. GI and the cost-sharing contract increase the SC profit. The managerial implications of our findings are also discussed in this paper.

Data-Driven Algorithms for Two-Location Inventory Systems

In this paper, we consider a multiperiod, two-location inventory system with unknown demand distributions and perishable products. Products can be transshipped from the location with excess inventory to the other with excess demand to better fulfill customer demand. The demand distributions are assumed to follow a family of parametric distributions and can only be learned on the fly. To address the challenge, we propose a data-driven inventory management algorithm called DD2LI that achieves a good performance in terms of regret. This algorithm, DD2LI, employs maximum likelihood estimation to approximate the unknown parameter and determines the order quantity based on these estimations. In addition, we emphasize a key assumption that tightens regret bound. Finally, we test the effectiveness of our proposed algorithm by conducting numerical experiments for two scenarios.

Novel AI-Powered Dynamic Inventory Management Algorithm in the USA: Machine Learning Dimension

Dynamic inventory management revolves around the practice of progressively modifying inventory degrees to adapt to fluctuations in client demand, production, and supply chain dynamics. At the center, inventory management focuses on upholding enhanced levels of stock to balance consumer service via availability with the costs related to holding excess inventory. This research paper aimed to explore the dynamic inventory management activities employed by organizations in the USA, shedding light on the machine learning strategies that can be deployed and their implications. The performance of the algorithms was empirically evaluated in a Python program experiment utilizing real-world data. To facilitate the data for input into the Neural Network, feature engineering, and selection were imposed to affirm its suitability. This study proposes the Sequence-to-Sequence (Seq2Quant) algorithm, a neural network-powered technique for demand prediction in inventory management. The current experiment compared and contrasted the performance of the Neural Networks against the following baselines, most notably, Naïve Seasonal Forecast, Moving Average Forecast, ARIMA, Naïve Seasonal Forecast with Averaging over four periods, SARIMAX. From the experiment, it was evident that the Seq2Seq had the lowest MAE (17.44) and the lowest SMAPE (66.91), suggesting that it was the best-performing algorithm overall. Besides, SARIMAX and ARIMAX also performed well, with MAE values of 18.33 and 18.09, respectively.

Fuzzy Logic Optimizes Global Inventory Management

Inventory control is crucial for balancing customer demand and inventory levels, especially in make-to-order production systems like those in refrigeration manufacturing. This study addresses the challenges faced by a company producing sandwich panels, where inefficiencies in inventory processing led to overstock and outstock issues. By applying the fuzzy inventory control method using Python, the study aimed to optimize inventory levels. The results showed a 6% reduction in inventory, from 191,307 m² to 182,619.4627 m², demonstrating the method's effectiveness. This approach can improve inventory management in similar industrial settings, aligning production with demand and reducing excess inventory. Highlight: Efficiency Boost: Fuzzy logic optimizes inventory, minimizing overstock and outstock. Precise Management: Python aids accurate inventory analysis for informed decisions. Cost Savings: Aligning inventory with demand reduces excess, enhancing profitability. Keywoard: Inventory control, Fuzzy logic, Optimization, Production management, Industrial engineering

Research on Green Production of the Iron and Steel Industry Based on DDF-BML Model

Although the iron and steel industry serves as the pillar industry of national economic development, the negative effects of its high emission and high pollution characteristics on the environment cannot be ignored. In order to achieve the carbon peaking and carbon neutrality goals at an early time and implement five priority tasks, including cutting overcapacity, reducing excess inventory, deleveraging, lowering costs and strengthening areas of weakness, it is urgent to realize the green production of the iron and steel industry. This paper takes listed iron and steel companies from 2016 to 2019 as research samples, and applies the two-stage Malmquist index based on the directional distance function (DDF) to measure the green total factor productivity (GTFP) of iron and steel enterprises. From macro and micro perspectives, the empirical model is used to explore the main influential factors of gtfp. The empirical results show that the overall gtfp growth rate of iron and steel enterprises is not ideal, falling behind at the end of the manufacturing industry. The promotion effect of R&D investment is limited, and the environmental carrying capacity of the production cluster area is approaching the limit. Based on the above empirical results, this paper proposes the following workable implementation path to the realization of the green production of iron and steel industry: On the one hand, the iron and steel enterprises should pay attention to improve technical efficiency, constantly improve low-carbon production process, optimize industrial layout. On the other hand, the government should guide the digital and intelligent development of the iron and steel industry and strengthen supervision to ensure the safety and stability of the supply of resources in the iron and steel industry.

A comparative analysis of how to handle stockout scenarios: the impact of newsvendor’s risk attitude

The purpose of this paper is to evaluate contrasting approaches for handling excess demand through the lens of a retailer (newsvendor) whose risk attitude (risk-neutral versus risk-averse) is modeled explicitly. We employ representative newsvendor models and provide comparison between two well-established stockout policies when demand exceeds supply. The “while supplies last” policy avoids the need for a secondary production order to satisfy the excess demand but faces potential opportunity cost through lost sales. Conversely, the “accepting backorders” policy relies on recourse production availability which is potentially costly but meets all levels of realized demand. Across distinct parameter classes, we incorporate comparison between the two policies in terms of financial metrics including expected profit and conditional value-at-risk criteria as well as metrics that relate to inventory availability and, hence, customer service, e.g., stockout probabilities and expected excess inventory. Powerful analytical results encompass both financial and inventory metrics and reveal that the outperforming policy is simply determined through relative underage cost values. That is, our insights indicate general advantages of accepting backorders when profit margins are sufficiently large and advantages of ignoring excess demand when profit margins are smaller. Although these extensive analytical takeaways hold in general, our numerical study reveals mean order quantity deviations (decision bias) in addition to which modeling approaches and counterpart optimal solutions maintain outperformance (resiliency) or lead to underperformance (sensitivity) when evaluated across all objective function criteria.

Lean Accounting: A Decision-Making Tool

Lean accounting is a management decision-making tool available to companies involving streamlining a company’s competitive internal management strategy to minimize waste, such as excess inventory, to maximize profits. This exploration of lean accounting processes looks at how companies can maximize continuous improvement using reporting and accounting methods. Barriers to lean accounting implementation are identified together with means for their resolution. The basic understanding of lean accounting is it targets waste and non-value-adding activities especially those that do not benefit the internal or external stakeholder in the manufacturing or service setting. Lean accounting offers specific styled reports, performance measures.