Inventory Planning Research Articles

Perbandingan Metode EOQ dan Silver-Meal pada Perencanaan Persediaan Bahan Baku untuk Meminimalkan Biaya Persediaan di PT. Gandum Malang

One of the efforts in improving efficiency is to do inventory control raw materials. If inventory control is running with optimal raw materials companies can minimize the total cost of the inventory. The purpose of this research is to know method planning of raw materials by using the EOQ and Silver-Meal to maintain optimal inventory as well as to determine the scheduling reserved time on the raw material for the next period. The results of the research is that lot size calculation using the EOQ method for tobacco and clove is 2 time reservations, for ambri and etiquette reservations for 5 times bobyn 3 times reservations, for the outer 7 times reservations, for filter 4 times and reservations for the carton box as much as 10 times. As for the lot size calculation by using the method of silver-meal obtained results for all raw materials as much as 12 times the reservation, except tobacco and clove of raw materials as much as 6 times. Finally, the planning of inventory that produces more inventory costs to a minimum on the calculation using silver-meal method is undertaken.

Demand Forecasting Optimization: Advanced ML Models for Retail and Inventory Planning

Demand Forecasting Optimization: Advanced ML Models for Retail and Inventory Planning

USO DE MODELOS DE PREDIÇÃO PARA MELHORAR O PLANEJAMENTO DE ESTOQUE DE ALTA ROTAÇÃO - ESTUDO EM UMA EMPRESA NO SETOR DE BEBIDAS

This project develops a prediction model for inventory planning of high-turnover products, specifically draft beer, in a company in the beverage sector. Using neural network and time series techniques, the aim is to minimize waste and guarantee product availability, taking into account the short shelf life of draft beer and the need for advance orders. The research involved collecting sales data from previous years, processing the data and developing a user-friendly interface. The model was trained with machine learning algorithms to predict weekly sales. The results show that the model is effective at predicting sales patterns, allowing for adjustments and future integrations, such as the inclusion of climate and demographic data. This work provides a solid basis for optimizing inventory management and formulating more efficient commercial strategies.

USO DE MODELOS DE PREDIÇÃO PARA MELHORAR O PLANEJAMENTO DE ESTOQUE DE ALTA ROTAÇÃO - ESTUDO EM UMA EMPRESA NO SETOR DE BEBIDAS

This project develops a prediction model for inventory planning of high-turnover products, specifically draft beer, in a company in the beverage sector. Using neural network and time series techniques, the aim is to minimize waste and guarantee product availability, taking into account the short shelf life of draft beer and the need for advance orders. The research involved collecting sales data from previous years, processing the data and developing a user-friendly interface. The model was trained with machine learning algorithms to predict weekly sales. The results show that the model is effective at predicting sales patterns, allowing for adjustments and future integrations, such as the inclusion of climate and demographic data. This work provides a solid basis for optimizing inventory management and formulating more efficient commercial strategies.

Technical Note—Leveraging the Degree of Dynamic Substitution in Assortment and Inventory Planning

Technical Note—Leveraging the Degree of Dynamic Substitution in Assortment and Inventory Planning

A Decision Support Model for Lean Supply Chain Management in City Multifloor Manufacturing Clusters

City manufacturing has once again become one of the priority areas for the sustainable development of smart cities thanks to the use of a wide range of green technologies and, first of all, additive technologies. Shortening the supply chain between producers and consumers has significant effects on economic, social, and environmental dimensions. Zoning of city multifloor manufacturing (CMFM) in areas with a compact population in large cities in the form of clusters with their own city logistics nodes (CLNs) creates favorable conditions for promptly meeting the needs of citizens for goods of everyday demand and for passenger and freight transportation. City multifloor manufacturing clusters (CMFMCs) have been already studied quite a lot for their possible uses; nevertheless, an identified research gap is related to supply chain design efficiency concerning urban CMFMCs. Thus, the main objective of this study was to explore the possibilities of lean supply chain management (LSCM) as the integrated application of lean manufacturing (LM) approaches and I4.0 technologies for customer-centric value stream management based on eliminating all types of waste, reducing the use of natural and energy resources, and continuous improvement of processes related to logistics activities. This paper presents a decision support model for LSCM in CMFMCs, which is a mathematical deterministic model. This model justifies the minimization of the number of road transport transfers within the urban area and the amount of stock that is stored in CMFMC buildings and in CLNs, and also regulating supplier lead time. The model was verified and validated using appropriately selected test data based on the case study, which was designed as a typical CMFM manufacturing system with various parameters of CMFMCs and urban freight transport frameworks. The feasibility of using the proposed model for value stream mapping (VSM) and managing logistics processes and inventories in clusters is discussed. The findings can help decisionmakers and researchers improve the planning and management of logistics processes and inventory in clusters, even in the face of unexpected disruptions.

Forecasting Blood Availability in Pontianak City using ARIMA Models to Optimize Inventory Planning at UTD PMI

It is of utmost importance to control the blood supply in UTD PMI because if there is a requirement for blood, PMI can fulfill the necessary blood needs and keep the ideal blood availability. PMI UTD may encounter a shortfall of blood supply if increases in blood demand are not supported by an increase in the number of donors contributing blood. A forecast of the number of blood requests is essential to estimate the quantity of blood that is necessary and the number of blood donors that are required to be prepared to fulfill the needed blood requests. This study is a quantitative investigation that use the Autoregressive Integrated Moving Average (ARIMA) method in order to provide an accurate prediction regarding the quantity of blood that is required for each blood type in Pontianak City. UTD PMI Pontianak City provided the information that was used in this study. The information that was used included information on the number of blood requests for blood types A, AB, B, and O. Following this, the data was subjected to three iterative steps of Box Jenkins analysis, which included order identification, parameter estimation, and diagnostic testing. The goal was to obtain the most accurate model, which was then utilised to forecast the quantity of blood demand that will occur in the subsequent periods. Furthermore, the findings of this investigation indicate that the ARIMA (2,0,0), ARIMA (3,0,3), ARIMA (1,0,2), and ARIMA (1,0,0) models are the most accurate models for predicting the availability of blood categories A, AB, B, and O. ..UTD Pontianak City is anticipated to be able to manufacture bloodstock consisting of 73 blood bags over the next five days. The bloodstock will include 19 bags of Group A, 6 bags of Group AB, 22 bags of Group B, and 6 bags of Group O specifics. In light of the forecast results, it is envisaged that UTD PMI will be able to maximize inventory planning for blood in Pontianak City to reduce the number of instances in which there are shortages of blood availability.

ANALISIS PENGENDALIAN BAHAN BAKU BATU BATA PRESS MENGGUNAKAN METODE MATERIAL REQUIREMENT PLANNING (MRP)

Bata Merah Press Majalengka was founded in 2012, which was previously called CBL Bata Merah. At that time the production system still used manual production. As time goes by and in line with the large market demand and considering the continuous development in Majalengka, to support this large demand, in 2016 the business actor changed the manual production system to production using a press machine and changed the name of the business to `Bata Merah Press Majalengka. The goal to be achieved in this research is to understand the variables used in raw material inventory planning using the MRP method, applying the Material requirements planning (MRP) method.

Dynamic reordering and inspection for the multi-item Inventory Record Inaccuracy problem

Inventory Record Inaccuracy (IRI) is a significant challenge in inventory management, caused by discrepancies between actual stock and inventory records due to factors such as spoilage, theft, and obsolescence. Despite extensive academic focus on robust ordering and inventory inspection planning, current literature either considers these in isolation, or focuses on stylized single-item scenarios, not addressing the full dynamics of reordering and inspection in warehouses with many SKUs. We consider such a large warehouse with several heterogeneous items that are subject to shrinkage and limited inspection opportunities. Our approach jointly optimizes reordering and inspection decisions in a dynamic decision framework, incorporating both inspection and travel times. We introduce an algorithmic pipeline for dynamic reordering and inspection that combines a neural network to decide on replenishments and a mixed-integer program to determine an optimal inspection subset. Our results show that ignoring shrinkage can increase costs by up to 95.3% and inspection already becomes viable at a relatively low level of IRI. Our proposed policy saves 8.3% in total costs compared to a static reordering and inspection benchmark, and 21.8% compared to a no-inspection policy.

Using blockchain technology to achieve sustainability in the hospitality industry by reducing food waste

The hospitality industry generates significant food waste, leading to adverse environmental and economic impacts, social and ethical concerns, legal and regulatory issues, and operational challenges. The lack of traceability and trust among stakeholders in the hospitality industry exacerbates the expensive and difficult nature of food waste management. This work proposes a blockchain-based solution that aims to minimize food waste generated in hospitality outlets, thereby fostering a sustainable food ecosystem. Within this context, the term ’sustainable food ecosystem’ refers to the interconnected network of stakeholders involved in the responsible management of food resources within the hospitality industry. In this context, we categorize these stakeholders under the umbrella of ’food resource management entities,’ encompassing both food donation organizations and recycling companies, recognizing their distinct roles in the effective management of surplus food resources. With our proposed solution, businesses can track and verify the origin, movement, and disposal of food items in real-time, facilitating streamlined inventory planning, waste reduction, and heightened operational efficiency. Additionally, the proposed system ensures transparency and accountability, guaranteeing the proper and efficient disposal of food waste. Using a permissioned Ethereum network, our solution establishes seamless connectivity between food and beverage outlets and various food resource management entities. We present a generalized system for secure information sharing, integrating advanced algorithms tailored to capture and enhance trust among participating stakeholders. Furthermore, our thorough security analysis underscores the robustness of our proposed solution, effectively addressing various security vulnerabilities. Lastly, the proposed blockchain-based solution is commercially viable, enabling the hospitality industry to operate with enhanced efficiency while providing improved information connectivity for various stakeholders in the food supply chain.

Enterprise supply chain network optimization algorithm based on blockchain-distributed technology under the background of digital economy

Demand forecasting is essential for streamlining supply chain operations in the digital economy and exceeding customer expectations. On the other hand, traditional forecasting techniques cannot frequently present real-time data and respond to dynamic changes in the supply chain network, leading to less-than-ideal decision-making and higher costs. This research aims to create a technique for optimizing the supply chain network based on blockchain-distributed technology (SCN-BT) to overcome these drawbacks and fully utilize the potential of the digital economy. The suggested framework uses the hybridized LSTM network and Grey Wolf Optimization (GWO) algorithm to examine demand forecasting in the supply chain network for inventory planning. The SCN-BT framework develops a safe and productive, enabling precise and flexible demand by combining blockchain with optimization techniques. A thorough case study utilized information collected from an enterprise supply chain that operates in the digital economy to show the efficiency of the suggested framework. Compared to conventional approaches, the results show considerable gains in demand forecasting precision, responsiveness of the supply chain, and cost-effectiveness. In the context of the digital economy, demand sensing and prediction enable firms to react to changes swiftly, shorten turnaround times, optimize inventory levels, and improve overall supply chain performance. The results highlight how blockchain technology has the potential to enhance collaboration, trust, and transparency inside intricate supply chain networks working in the digital economy. The experimental results show the proposed to achieve prediction rate of demand prediction rate of 128.93, demand forecasting accuracy ratio of 92.18%, optimum efficiency of 94.25%, RMSE rate of 1841.25, MAE rate of 260.74, and sMAPE rate of 0.1002 compared to other methods.

Inventory Planning of Raw Materials for Furniture Products (Case Study: CV XYZ Furniture)

The burgeoning industrial sector in Indonesia has intensified competition among businesses vying for market share. The furniture industry is a prime example of this dynamic landscape. To expand their market reach, companies must demonstrate their ability to fulfill every consumer demand. Failure to meet these expectations will prompt consumers to seek alternatives. One crucial factor in meeting consumer demands is product availability, which is directly influenced by the availability of raw materials within the company. CV XYZ Furniture, a furniture company based in Padang City, exemplifies this challenge. The company produces a diverse range of furniture, primarily utilizing rattan as its raw material. However, CV XYZ Furniture currently lacks a structured inventory planning method for raw materials, resulting in excess inventory for each period. To address this issue, this research aims to establish an optimized inventory planning strategy for raw materials used in furniture production at CV XYZ Furniture. The continuous review system method serves as the foundation for this endeavor. The research outcomes encompass the determination of optimal demand quantity, reorder point, and safety stock. The implementation of the continuous review system model yielded a significant reduction in total inventory costs, amounting to Rp8,625,594 or 2.03%. It can be proven that the continuous review system model is suitable for calculating raw material inventory at CV XYZ Furniture for raw material planning in the following years.

Analisis Persediaan Bahan Baku Susu pada Produksi Keju British Cheddar dengan Metode Economic Order Quantity ( EOQ ) di PT. Mazaraat Lokanatura Indonesia Pasuruan

To achieve profit, companies usually process raw materials into finished or semi-finished goods. Therefore, in this company, controlling and managing raw materials becomes an important factor influencing the smooth production process to achieve desired goals. Cow's milk is one of the popular agricultural products, and cheese is a dairy product consumed globally. PT Mazaraat Lokanatura Indonesia is a company that produces local Yogyakarta cheese and creamery products. The instability of organic milk quality from local farmers often negatively impacts the production process and the final quality of the company's British Cheddar Cheese products. This instability also affects raw material inventory planning, so the company must be more careful in maintaining the quality and quantity of organic milk stock to ensure optimal production. To address this issue, an analysis of raw milk inventory using the Economic Order Quantity (EOQ) method was conducted. This research began with observations and identification of problems related to raw material inventory management that could affect production efficiency and operational costs. Then, data collection was conducted regarding the demand amount, order time, ordering cost, and milk storage cost. The research results show that the application of EOQ is proven effective in optimizing inventory costs. Using EOQ allows the company to manage the optimal order quantity, significantly reducing total ordering costs by decreasing the order frequency.

UAV-based field watermelon detection and counting using YOLOv8s with image panorama stitching and overlap partitioning

Accurate watermelon yield estimation is crucial to the agricultural value chain, as it guides the allocation of agricultural resources as well as facilitates inventory and logistics planning. The conventional method of watermelon yield estimation relies heavily on manual labor, which is both time-consuming and labor-intensive. To address this, this work proposes an algorithmic pipeline that utilizes unmanned aerial vehicle (UAV) videos for detection and counting of watermelons. This pipeline uses You Only Look Once version 8 s (YOLOv8s) with panorama stitching and overlap partitioning, which facilitates the overall number estimation of watermelons in field. The watermelon detection model, based on YOLOv8s and obtained using transfer learning, achieved a detection accuracy of 99.20 %, demonstrating its potential for application in yield estimation. The panorama stitching and overlap partitioning based detection and counting method uses panoramic images as input and effectively mitigates the duplications compared with the video tracking based detection and counting method. The counting accuracy reached over 96.61 %, proving a promising application for yield estimation. The high accuracy demonstrates the feasibility of applying this method for overall yield estimation in large watermelon fields.

Artificial Neural Network Model For Optimization of Forecasting Material Inventory

The increasing competition in the fast-moving consumer goods (FMCG) industry leads to demand fluctuations, negatively impacting the accuracy of demand forecasts and determining optimal lot sizes in material inventory planning. Many companies struggle to adopt appropriate forecasting models, resulting in poor accuracy and higher material costs. This study aims to develop an integrated model for forecasting and material planning using simulation. The artificial neural network (ANN) method is proposed to improve forecasting accuracy, with performance evaluated through mean percentage error (MAPE), mean absolute deviation (MAD), and mean squared error (MSE). The forecast results are then applied to optimize material inventory using the economic order quantity (EOQ) model, considering warehouse capacity constraints. The EOQ model is applied to adjust lot sizes under time-varying demand. The findings highlight the importance of integrating forecasting with inventory planning to provide accurate demand predictions and optimal lot sizing, ultimately minimizing material costs in the FMCG industry. This research contributes to better decision-making in supply chain management by enhancing forecasting accuracy and inventory optimization.

Application of Machine Learning and Multi-Dimensional Perception in Urban Spatial Quality Evaluation: A Case Study of Shanghai Underground Pedestrian Street

The exploitation of urban subsurface space in urban inventory planning is closely connected to the quality of urban environments. Currently, the construction of underground pedestrian streets is characterised by inefficiency and traffic congestion, making them insufficient for fulfilling the demand for well-designed and human-centred spaces. In the study of spatial quality, traditional evaluation methods, such as satellite remote sensing and street maps, often suffer from low accuracy and slow updating rates, and they frequently overlook human perceptual evaluations. Consequently, there is a pressing need to develop a set of spatial quality evaluation methods incorporating pedestrian perspectives, thereby addressing the neglect of subjective human experiences in spatial quality research. This study first quantifies and clusters the characteristics of underground pedestrian spaces using spatial syntax. It then gathers multidimensional perception data from selected locations and ultimately analyses and predicts the results employing machine learning techniques, specifically Random Forest and XGBoost. The research results indicate variability in pedestrians’ evaluations of spatial quality across different functionally oriented spaces. Key factors influencing these evaluations include Gorgeous, Warm, Good Ventilation, and Flavour indicators. The study proposes a comprehensive and applicable spatial quality evaluation model integrating spatial quantification methods, machine learning algorithms, and multidimensional perception measurements. The development of this model offers valuable scientific guidance for the planning and construction of high-quality urban public spaces.

A Data-Driven Assortment and Inventory Planning Model

A Data-Driven Assortment and Inventory Planning Model

Facility location optimization with market segmentation for additive manufacturing material supplier network using quantum annealing

The determination of the material suppliers for additive manufacturing (AM) centers is a typical facility location problem (FLP). Current methods for solving the FLP in AM network design mainly focus on improving the cost, distance, and supply chain aspects and neglect market segmentation. However, AM feedstock suppliers need to understand the market size they serve so they can better prepare their inventory plans. Therefore, in this paper, we propose a new approach for the FLP with geographic market segmentation of AM material supplier networks, in which the facility locations and market segments of AM material suppliers are optimized in a multi-objective optimization problem. In order to confirm the feasibility of the proposed method, a simplified design case for a German state has been carried out.

Correlating geodiversity and mining in the seridó UNESCO Global Geopark: Spatial and impact analysis in brazilian northeast

Mining areas can provide important information for interpreting the geodiversity and geological heritage of an area, which also results in the need for special attention to conservation in terms of inventory, impact studies, and recovery plans for degraded areas. The territory of the Seridó UNESCO Global Geopark, northeastern Brazil, has several mining districts linked to the production of scheelite, gold, gemstones, dimensional rocks, and kaolin. Thus, we aimed to assess the impact of mining on the geopark's geodiversity and geoheritage. Through cartographic analysis, mineral resources and mining areas were observed, overlapping with geodiversity mapping, geosites' proximity to exploration areas, and abiotic ecosystem services generated or interrupted by mining. The results showed a wide diversity of resources and different relationships between geodiversity hotspots, geosites, and mining areas. The mineral exploitation could be fundamental for sustainable development in the geopark, considering proper conditions of ecosystem services balancing, and adequate use of international geoheritage and geoconservation policies in active mining. Furthermore, the paper presented that abiotic nature should be considered in environmental impact assessments and mining restoration plans. The geopark management should continue to develop initiatives to take advantage of mineral resources to promote sustainability.

Optimasi Persediaan Barang dengan Pendekatan Theory Of Constraints, Heuristic Silver Meal, dan Least Unit Cost untuk Meminimalkan Biaya

The aim of this research is to carry out inventory control and determine the results of optimal inventory costs using the Theory of Constraints (TOC), Silver Meal Heuristic and Least Unit Cost approaches to minimize costs. The method used in this research, namely Theory of Constraints (TOC), is a management approach that aims to identify and overcome obstacles or constraints that limit company productivity and efficiency. The Silver Meal heuristic in selecting the optimal lot size is by looking at the minimum cost of each period, while the Least Unit Cost looks at the minimum cost of each unit, both methods for determining the number of orders and appropriate inventory planning. The Heuristic Silver Meal and Least Unit Cost methods can minimize storage costs by ordering raw materials 12 times in one year, compared to the company's previous policy of placing orders 6 times in one year so that the company can reduce storage costs by IDR. 5,750,220,504.