Search Results - subject:"Inventory Optimization"

An analytics-driven economic order quantity model integrating fuzzy learning for deteriorating imperfect items in sustainable supply chains

Palanivel, M.; Venkadesh, M.; Vetriselvi, S.; Suganya, M. - In: Supply chain analytics 10 (2025), pp. 1-20

This study presents an advanced Economic Order Quantity inventory model that integrates intuitionistic fuzzy sets and fuzzy learning to enhance decision-making under environmental uncertainty. The model systematically incorporates green technology adoption and accounts for the uncertain impact...

Persistent link: https://www.econbiz.de/10015445395

A closed-form solution approach for optimal reorder point in economic order quantity models with uncertain demands

Jadidi, Omid; Firouzi, Fatemeh; Sorooshian, Shahryar - 2025

The reorder point formula in the Economic Order Quantity (EOQ) model traditionally assumes that demand during the lead-time follows a normal distribution. However, this assumption presents several challenges. First, alternative probability distributions may better capture demand patterns for...

Persistent link: https://www.econbiz.de/10015506763

An artificial intelligence framework for recycling dormant and obsolete inventory in supply Chains

Raouf, Youssef; Benmamoun, Zoubida; Hachimi, Hanaa - In: Supply chain analytics 11 (2025), pp. 1-9

In the automotive sector, excess inventory increases costs and prevents progress toward Sustainable Development Goals, particularly those related to Industry, Innovation, and Infrastructure (SDG 9) and Responsible Consumption and Production (SDG 12). This study introduces an innovative approach...

Persistent link: https://www.econbiz.de/10015526511

A multi-objective analytical framework for sustainable blood supply chain optimization

Mansur, Agus; Hidayat, Taufiq; Rizky, Novrianty; … - In: Supply chain analytics 11 (2025), pp. 1-26

This study presents a multi-objective optimization model for blood supply chain (BSC) management, aiming to maximize total profit and fulfillment rate and minimize carbon emissions. The model is formulated as a mixed-integer linear program (MILP) and solved using the weighted sum method. The...

Persistent link: https://www.econbiz.de/10015519774

A comparative assessment of holt winter exponential smoothing and autoregressive integrated moving average for inventory optimization in supply chains

Kumar, Lalji; Khedlekar, Sudhakar; Khedlekar, Uttam Kumar - In: Supply chain analytics 8 (2024), pp. 1-16

Precise demand forecasting and agile pricing strategies are crucial in modern business. This study aims to enhance these strategies by evaluating the efficacy of Holt-Winters Exponential Smoothing (HWES) and Autoregressive Integrated Moving Average (ARIMA) models. The study assesses their...

Persistent link: https://www.econbiz.de/10015325094

An open innovative inventory management based demand forecasting approach for the steel industry

Sukolkit, Nonthaphat; Sirawadee Arunyanart; … - In: Journal of open innovation : technology, market, and … 10 (2024) 4, pp. 1-10

This research focuses on accurately forecasting steel wire mesh demand to ensure timely order fulfillment. Various univariate time series forecasting methods were employed, including Prophet, Support Vector Regression (SVR), 1-Dimensional Convolutional Neural Network (1D-CNN), Recurrent Neural...

Persistent link: https://www.econbiz.de/10015185080

Enhancing supply chain agility and sustainability through machine learning : optimization techniques for logistics and inventory management

Pasupuleti, Vikram; Thuraka, Bharadwaj; Kodete, Chandra … - In: Logistics 8 (2024) 3, pp. 1-16

Background: In the current global market, supply chains are increasingly complex, necessitating agile and sustainable management strategies. Traditional analytical methods often fall short in addressing these challenges, creating a need for more advanced approaches. Methods: This study leverages...

Persistent link: https://www.econbiz.de/10015081054

Exploring the Nexus of inventory optimization and operational efficiency: Data-driven insights from public sector organizations in Ethiopia

Debala, Gemachis; Bhat, Mohd Abass; Khan, Shagufta Tariq - In: Cogent Business & Management 10 (2023) 2, pp. 1-18

This study explores the link between inventory optimization and operational efficiency in public sector organizations … descriptive and inferential statistics. The results indicate a significant and positive correlation between Inventory Optimization … importance of inventory optimization in enhancing operational efficiency in public sector organizations in Ethiopia. This study …

Persistent link: https://www.econbiz.de/10014527320

Evolving hybrid deep neural network models for end-to-end inventory ordering decisions

Moraes, Thais de Castro; Qin, Jiancheng; Yuan, Xue-ming; … - In: Logistics 7 (2023) 4, pp. 1-18

Background: Over the past decade, the potential advantages of employing deep learning models and leveraging auxiliary data in data-driven end-to-end (E2E) frameworks to enhance inventory decision-making have gained recognition. However, current approaches predominantly rely on feed-forward...

Persistent link: https://www.econbiz.de/10014454535

A new key performance indicator model for demand forecasting in inventory management considering supply chain reliability and seasonality

Tadayonrad, Yasin; Ndiaye, Alassane Ballé - In: Supply chain analytics 3 (2023), pp. 1-15

Forecasting demand and determining safety stocks are key aspects of supply chain planning. Demand forecasting involves predicting future demand for a product or service using historical data and other external and internal drivers. Stockouts and excess production can be reduced by accurately...

Persistent link: https://www.econbiz.de/10014516504