Optimizing Inventory Replenishment for Seasonal Demand with Discrete Delivery Times

Alnahhal, Mohammed; Ahrens, Diane; Salah, Bashir

doi:10.3390/app112311210

Cited by 3 publications

(1 citation statement)

References 52 publications

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“…Supply chain planning assuming dynamic demand has been extensively studied. Alnahhal et al (2021) argues its necessity for balancing service levels, maintenance costs and inventory orders. In the same vein, Norazira Abd et al…”

Section: Literature Reviewmentioning

confidence: 99%

Multi-product inventory modeling with demand forecasting

Valencia Olivares,

Mendoza Bojórquez,

Taquía Gutiérrez

2023

Proceedings of the International Conference on Industrial Engineering and Operations Management

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Many wholesale companies do not use an adequate control of their inventory level, and variables such as product demand tend to vary continuously due to seasonality and market trends, generating costs that reduce profitability and eventually produce customer dissatisfaction with the service offered. In this paper we propose the application of a method to reduce the level of uncertainty and risk in sales and to simulate the implementation of a policy of continuous inventory review. Using the monthly demand of the wholesale company and considering the maximization of profit, the ARIMA time series model was chosen, developed through the R programming language, and then modeled in the Arena software the results of the forecast found. The relevance of the study is to present inventory improvement tools that encourage a favorable management in terms of inventory management costs.

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Section: Literature Reviewmentioning

confidence: 99%

Multi-product inventory modeling with demand forecasting

Valencia Olivares,

Mendoza Bojórquez,

Taquía Gutiérrez

2023

Proceedings of the International Conference on Industrial Engineering and Operations Management

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An agent-based heuristics optimisation model for production scheduling of make-to-stock connector plates manufacturing systems

Madi,

Al-Bazi,

Buckley

et al. 2023

Soft Comput

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The manufacturing systems’ success directly relates to their accurate, reliable and flexible schedules, including how production is planned and scheduled and which constraints are considered in generating the schedules. The study's objective arises from the need to generate an optimal production scheduling system in a connecting plates manufacturing company that works on a Make-To-Stock basis. This research investigates the impact of demand and operational constraints on production schedules, including the facility capacity, operators and machines availability, raw materials availability, inventory level and warehouse capacity. A multi-agent-based optimisation model is developed to face the complexity of considering demand and operational constraints and reflects their impact on generating a reliable production schedule. This model involves a proposed heuristic algorithm that considers demand and operations constraints in such a manufacturing environment and optimises the production schedule based on these restrictions/requirements. A real-life case study based on a connecting plates manufacturer company is used as a test bench of the proposed agent-based heuristic optimisation model. The proposed algorithm is compared with other related approaches to check its superiority based on key criteria, including inventory levels, missed/unsatisfied orders and total production time. Results show that the proposed heuristics algorithm reduced the number of missed orders by 34% compared with similar approaches.

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Economic Order Quantity: A State-of-the-Art in the Era of Uncertain Supply Chains

Alnahhal,

Aylak,

Al Hazza

et al. 2024

Sustainability

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Inventory management is crucial for companies to minimize unnecessary costs associated with overstocking or understocking items. Utilizing the economic order quantity (EOQ) to minimize total costs is a key decision in inventory management, particularly in achieving a sustainable supply chain. The classical EOQ formula is rarely applicable in practice. For example, suppliers may enforce a minimum order quantity (MOQ) that is much larger than the EOQ. Some conditions such as imperfect quality and growing items represent variants of EOQ. Moreover, some requirements, such as the reduction of CO2 emissions, can alter the formula. Moreover, disruptions in the supply chain, such as COVID-19, can affect the formula. This study investigates which requirements must be considered during the calculation of the EOQ. Based on a literature review, 18 requirements that could alter the EOQ formula were identified. The level of coverage for these requirements has been tracked in the literature. Research gaps were presented to be investigated in future research. The analysis revealed that, despite their importance, at least 11 requirements have seldom been explored in the literature. Among these, topics such as EOQ in Industry 4.0, practical EOQ, and resilient EOQ have been identified as promising areas for future research.

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Optimizing Inventory Replenishment for Seasonal Demand with Discrete Delivery Times

Cited by 3 publications

References 52 publications

Multi-product inventory modeling with demand forecasting

Multi-product inventory modeling with demand forecasting

An agent-based heuristics optimisation model for production scheduling of make-to-stock connector plates manufacturing systems

Economic Order Quantity: A State-of-the-Art in the Era of Uncertain Supply Chains

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