Order Quantity Research Articles

Comparison between the models of potential storage and simulation of the demand for electricity transformer (400/11) in Diyala General Company

Storage models are usually used to determine the optimal order quantity and reorder point by obtaining the lowest total cost of storage to balance the amount of production and the amount of demand for materials, but sometimes these models can not be applied, so the use of simulation is resorted to where accurate and flexible results can be relied upon, to achieve the goal of the study, which is to improve the company's storage system. This research dealt with two cases of the probabilistic storage model, where the first model was applied to the monthly demand data on one of the products of Diyala General Company, which is the distribution transformer (400|11) for the years 2019,2020,2021. The second is the probabilistic storage model by simulating demand and comparing the two models, whichever is better to reach optimal. Where the distribution of demand was found to find the average and standard deviation and apply the model to obtain the indicators of the models. Using the MATLAB program, the statistical analysis of the data was carried out and the indicators of the model were obtained.

Data Visualization of Bike Store Sales in Europe

This paper presents a comprehensive analysis of bike sales and customer demographics in Europe based on a dataset obtained from Kaggle.com. The dataset covers the period from 2011 to 2016 and includes information on customer age, gender, product categories, order quantities, unit costs, unit prices, profit, and revenue. The analysis utilizes data visualization techniques to identify key trends, including the top-selling products, customer purchasing power across different age groups and customer genders, and product profit margins. The findings suggest opportunities for retailers to target specific customer segments and product categories to maximize profits. Additionally, the report highlights the growing popularity of cycling as a sport and a healthy lifestyle choice, indicating a potential for increased demand in the bike retail market. Overall, the insights provided in this report aim to assist retailer chains in developing effective strategies to capitalize on the opportunities presented by the European bike sales market.

A Genetic algorithm approach for improving the probabilistic inventory model with the continuous review: A practical application in the department of pharmacy

This study investigates to shed light on artificial intelligence techniques, specifically the genetic algorithm, to improving traditional solutions, as well as finding the best policy for the problem of probabilistic inventory with continuous review by finding the optimal reorder point and the optimal economic quantity to avoid the risk of stock out (shortage), reduce total costs, and reach the optimal solution with little time and effort. The research was conducted in the stores of the department of pharmacy of the Ninawa health department for the period from January 1, 2021, to January 1, 2023, on a sample of three drugs (most demanded drugs). An analysis of the demand data for the study sample was conducted using the statistical program (SPSS Statistics Version 22) to determine the type of inventory. it was found that the type of inventory is probabilistic and the demand data follows a normal distribution. Based on this foundation, the mathematical model for the study problem was built. Given the complexity of the steps and iterations of the traditional solution, the solution steps were applied using the R programming language to reach the model's solution. Additionally, the solution steps were programmed for the genetic algorithm and applied using the R programming language. The results of the genetic algorithm showed an improvement in the traditional solution results by reducing total costs. Based on the results of the genetic algorithm, the economic order quantity, safety stock, reorder period, and safety period were found. Paper type: Research Paper

Generalized multi-manufacturer multi-retailer production-delivery supply chain model and its minimum cost solution policy

Production of a product at multiple sources and its deliveries to multiple destinations are a common practice in business. Minimization of the integrated total cost of performing operations and transportation considering the relevant factors such as the minimum order quantity contract, capacities of transport vehicles, and times of transportation in this supply chain is essential, in supplying products to customers at reasonable lesser prices. However, such a supply chain has received little attention in terms of minimizing the integrated total cost taking into account these related factors explicitly. So, there lies a research scope on this topic to fulfill the growing need of minimizing the cost of production-deliveries of products to meet customers’ demands fruitfully. Here we develop such a generalized mathematical model to minimize the integrated total cost of carrying out operations at manufacturers and retailers, and transporting batches (sub-lots) of lots from sources to destinations considering the mentioned realistic constraints. The integrated production-delivery flow is synchronized by delivering lots with batches of equal and/or unequal sizes. First without considering transportation costs, we obtain optimal batches to minimize the total cost of the model. Each of these optimal batches is proportionally distributed at manufacturers as supplies and at retailers as demands. Then the minimum transportation cost solution from manufacturers to retailers is incorporated to the earlier solution to obtain the final result. We illustrate this solution policy with numerical example problems. Sensitivity analyses are performed to see the effect of increasing values of parameters on the minimum total cost.

Inventory Management System Using Economic Order Quantity And Reorder Point

The inventory management system for car workshops is still using Excel, which makes it inefficient and inaccurate for complex stock calculations. The solution is to design an inventory application that uses the Economic Order Quantity and Reorder Point methods. The application will provide timely stock replenishment information and optimize order quantities, increasing inventory management efficiency and accuracy. The Software Development Life Cycle used in this study is Rapid Application Development, a process emphasizing short development cycles. User Acceptance Test results show that users received the inventory management system very well, with an average satisfaction level of 98.44% for Admins and 97% for general users, indicating high satisfaction with the system's features and functionality.

A Study on the Production-Inventory Problem with Omni-Channel and Advance Sales Based on the Brand Owner’s Perspective

This study explores a supply chain product-inventory problem with advance sales under the omni-channel strategies (physical and online sales channels) based on the brand owner’s business model and develops corresponding models that have not been proposed in previous studies. In addition, because the brand owner is a member of the supply chain, and has different handling methods for defective products or products returned by customers in various retail channels, defective products or returned products are included in the supply chain models to comply with actual operating conditions and fill the research gap in the handling of defective/returned products. Regarding the mathematical model’s development, we first clarify the definition of model parameters and relevant data collection, and then establish the production-inventory models with omni-channel strategies and advance sales. The primary objective is to determine the optimal production, delivery, and replenishment decisions of the manufacturer, physical agent, and online e-commerce company in order to maximize the joint total profits of the entire supply chain system. Further, this study takes the supply chain system of mobile game steering wheel products as an example, uses data consistent with the actual situation to demonstrate the optimal solutions of the models, and conducts sensitivity analysis for the proposed model. The findings reveal that increased demand shortens the replenishment cycle and raises order quantity and shipment frequency in the physical channel, similar to the online channel during normal sales. However, during the online pre-order period, higher demand reduces order quantity and cycle length but still increases shipment frequency. Rising ordering or fixed shipping costs lead to higher order quantity and cycle length in both channels, but variable shipping costs in the online channel reduce them. Market price increases boost order quantity and frequency in the online channel, while customer return rates significantly impact inventory decisions.

Inventory pledge financing decisions based on a permissioned blockchain by controlling fraudulent risk

Inventory pledge financing (IPF) is a crucial financing way for small and medium-sized enterprise (SMEs). But banks are reluctant to finance SMEs due to fraudulent risk in practice. This paper discusses the application of blockchain in IPF, particularly its impact on mitigating fraud risks. Utilizing game theory models, we illustrate how the finance and operation decisions of participants, along with supply chain efficiency, are influenced by the introduction of blockchain. Meanwhile, equilibrium outcomes are analysed and numerical study is given. Our analysis reveals that under certain conditions, blockchain integration can lead to reduced loan interest rates, lower wholesale prices, increased order quantities by buyers, and enhanced supply chain efficiency. Lastly, we develop a protocol to demonstrate the transfer of digital warehouse receipt on a permissioned blockchain to avoid fraudulent risk. This study provides a theoretical foundation, and a guidance for decisions-making in blockchain-enabled IPF schme.

Designing a resilient hydrogen hub under disruption risks and non-stationary demand distribution

Hydrogen is a viable and sustainable energy alternative. It offers a solution to mitigate greenhouse gases and fortify energy security. However, its supply chain faces many uncertainties and challenges, including demand fluctuations and sourcing disruptions. This paper introduces an innovative two-stage stochastic model to plan hydrogen hub procurement, storage, and sales. In the first stage, the model optimises the order quantities by considering real-time inventory levels. This forward-thinking strategy aims to improve operational efficiency and adaptability. In the second stage, the model refines the hub operations by incorporating supplier resilience, exploration of alternative markets, emission considerations from each source, and terminal connection planning. Integrating these elements contributes to a comprehensive framework for robust hydrogen hub scheduling. This paper adopts a Benders decomposition algorithm to address the mathematical complexity of the model. This approach is necessary to ensure a smooth and efficient computational process. Empirical testing and validation of the developed model, along with the robustness of the solution methodology, emphasise its effectiveness in handling uncertainties and disruptions. This paper contributes to the existing literature by shedding light on critical facets of disruption management, supplier resilience, and emissions reduction within the hydrogen supply chain design.

Fuzzy inventory model for breakable items, including damage costs and inspection errors in a green environment.

Abstract Conventional Economic Order Quantity (EOQ) models operate on the implicit assumption that all received products are flawless. Current EOQ models for items of defectiveness implicitly suggest that suppliers do not conduct a comprehensive assessment, even though they loosen this presumption and are more applicable to scenarios in reality.This paper presents a significant EOQ model. This model takes into account errors in determining whether an item is perfect or broken. Because things are piled high in stock, breaking of products takes place. These goods are stored and sold at once, with damage costs included. Moreover, Governments have prioritized reducing carbon emissions as their main goal. With the aim of creating a greener environment, we have taken carbon emission costs into account in order to minimize overall costs and maximize overall profit. This model is distinct because it closes the research gap by taking into account breakable items under inspection errors with damage cost, and carbon emission cost.The ideal total cost of the EOQ model is produced by the crisp version. It is important to note that this model is constructed in a fuzzy manner by using a triangular fuzzy number to represent the cost of accepting a broken object.Applications of this idea include shipping commodities. A numerical illustration of the ideal solution is given and the current study addresses how changes in a few parameters influence the ideal overall cost.

Financial hedging incentive contracts in global supply chains: A distributionally robust approach

AbstractThis paper developed a global supply chain with a supplier and a retailer in different countries. When exchange rate and demand risks are concentrated in retailer, a distributionally robust approach is used to formulate an optimized robust ordering strategy. Furthermore, the effect of financial hedging incentive contracts on the robust decisions and the profits of the global supply chain is explored. Our findings show that the correlation between exchange rate and demand does not affect the robust decisions of retailer. The effectiveness of financial hedging incentive contracts depends on the trade‐off between transaction costs of financial hedging and the degree of supplier incentives and the growth rate of order quantity.

Distributionally robust optimization of a newsvendor model under capital constraint and risk aversion

This paper proposes a capital-constrained newsvendor model with risk aversion under a partially known demand distribution with only knowledge of its mean and variance. We adopt the robust conditional value-at-risk (RCVaR) to characterize the vendor’s risk aversion. Firstly, we obtain the closed-form RCVaR optimal order quantity that depends on the demand volatility level: When demand volatility is low, the vendor has four financing-ordering strategies contingent upon different capital levels. When demand volatility is medium, the vendor does not seek bank loans and is left with two ordering strategies. When demand volatility is high, the vendor does not bother placing an order at all. Then, we investigate the impact of capital constraint, risk aversion and demand volatility on the RCVaR optimal order quantity. Finally, we demonstrate the robustness of the RCVaR optimal ordering policy by numerical experiments based on both randomly generated and real-world data.

PENGENDALIAN PERSEDIAN PAKAN AYAM PETELUR MENGGUNAKAN METODE EONOMIC ORDER QUANTITY (EOQ) PADA PETERNAKAN XYZ DI BANYUWANGI

Delays in the supply of chicken feed at XYZ Farm resulted in a decrease in chicken egg productivity, resulting in chicken death. To ensure that production runs smoothly, XYZ Farms must guarantee the availability of raw materials for chicken feed. The aim of this research is to determine the ideal number of orders for chicken feed supplies to minimize feed shortages due to delivery delays. This research applies the Economic Order Quantity (EOQ) method to help XYZ farms manage the purchase of raw materials for chicken feed, and Single Exponential Smoothing to forecast supplies that are updated continuously by considering the latest data. The forecasting calculation uses an accuracy of 0.6 for the need for katul, corn and concentrate in January 2024. The results of the Economic Order Quantity (EOQ) calculation show that the optimal order for Katul is 6,702 kg, corn 29,190 kg, concentrate 25,402 kg. Orders are made 4 times a month, and Reorder Point is made when 1,586 kg of katul remains, 6,494 kg of corn and 8,742 kg of concentrate. Reordering or Reorder Point (ROP) is needed to overcome the potential for stock out during lead time TRANSLATE with x English Arabic Hebrew Polish Bulgarian Hindi Portuguese Catalan Hmong Daw Romanian Chinese Simplified Hungarian Russian Chinese Traditional Indonesian Slovak Czech Italian Slovenian Danish Japanese Spanish Dutch Klingon Swedish English Korean Thai Estonian Latvian Turkish Finnish Lithuanian Ukrainian French Malay Urdu German Maltese Vietnamese Greek Norwegian Welsh Haitian Creole Persian // TRANSLATE with COPY THE URL BELOW Back EMBED THE SNIPPET BELOW IN YOUR SITE Enable collaborative features and customize widget: Bing Webmaster Portal Back //

The Neutrosophic Economic Order Quantity: Backlogged Shortages and Quality Issues

The Neutrosophic Economic Order Quantity: Backlogged Shortages and Quality Issues

Coordination contract design for a two‐echelon supply chain considering risk aversion and yield uncertainties

AbstractThis study establishes a two‐echelon supply chain (SC) wherein the supplier and the manufacturer suffer from yield uncertainty, and the supplier has a risk‐averse attitude. The SC members’ optimal input quantity and order quantity are investigated. On this basis, the revenue‐sharing and subsidy contracts are introduced to achieve SC coordination. The findings indicate that the two contracts are valid on SC coordination, and there exists a one‐to‐one relationship between these two contracts. Moreover, the two contracts can achieve the same utility allocation between the SC members under specific conditions. Overall, our findings help the SC members develop more efficient cooperation mechanisms.

Single-vendor single-buyer multi-product economic production quantity problem with stochastic constraints: a modified generalized elimination method

This study designs a single-buyer single-vendor multi-product Economic Production Quantity (EPQ) model to optimize the total cost in an inventory system. To do so, a Non-Linear Programming (NLP) model is developed considering a set of stochastic constraints on space or warehouse capacity, backordering cost, procurement, ordering, and obtainable budget. A modified Generalized Elimination Method (GEM) is then offered to treat the complexity of the model wherein the variables are eliminated by adding two equations. The developed method generates much more high-quality solutions compared with the classic GEM, while the number of iterations slightly increases. Three problem instances in different scales are then taken into account to assess the efficiency of the modified GEM in terms of optimality criteria. The results demonstrate that the modified GEM has an excellent performance with respect to optimal solutions, infeasibility, number of iterations, complementarity, and errors of optimality. Finally, the behavior of the objective function is analyzed against order quantity fluctuations to draw out practical implications.

Optimal product pricing and lot sizing decisions for an omni-channel supply chain

In an omni-channel supply chain, companies offer customers multiple channels to interact with their brand. This allows for greater outreach and sales, resulting in increased customer loyalty and trust. Furthermore, there has been little discussion on the simultaneous consideration of strategic and tactical decisions under return policies in omni-channel supply chains. To maximize total profit, this research proposes a two-echelon supply chain, comprising a manufacturer and multiple retailers, including traditional, electronic and omni-channels. The non-deterministic demand function takes into account the sales price, refund price, delivery time and order quantities. A heuristic algorithm based on analysis of Karush–Kuhn–Tucker optimality conditions used to solve a nonlinear programming model. This research presents an analysis of the factors influencing the decision variables and profit of the entire supply chain and offers retailers a unique way to evaluate their performance and make operational decisions regarding price, delivery time and order quantities.

Coordination of a socially responsible two-stage supply chain under random yield and demand

This study integrates corporate social responsibility (CSR) and channel coordination in a supply chain (SC) under random yield and random demand. In the SC, a supplier with random yield determines wholesale price and production input, and a producer with CSR decides order quantity and CSR investment facing CSR-related random demand. For centralized SCs with and without CSR, we prove the unimodality of the expected profits of the SCs for generic random yield and demand, and show the uniqueness of optimal ratio of order quantity to production input quantity. We disclose that the optimal expected profit of the SC with CSR is larger than that of the SC without CSR, and the profit difference increases with CSR effort and expected yield. We also find that the optimal CSR investment increases with the expected yield. Subsequently, the Nash equilibrium solutions of decentralized SCs under wholesale price and revenue-sharing contracts are analyzed. Next, we present a revenue and CSR sharing contract to realize channel coordination and win-win of SC members and related stakeholders. Lastly, we verify theoretical statements of centralized and decentralized SCs versus three numerical examples, and present managerial insights for the effect of yield uncertainty and CSR effort.

The evolution of two‐stage production and order equilibrium in a random yield supply chain with demand information updating

AbstractThe mismatch between production, order, and demand seriously affects supply chain performance. However, most research focus on the mismatch between the retailer's order and customer's demand, which ignores the influence of the supplier's random yield on supply chain members' decision‐making. This paper investigates a two‐stage optimization problem within a two‐echelon supply chain, featuring a supplier with random yield and a retailer updating demand information in real‐time. Faced with a long production lead time, the retailer can either place advance orders at the production season's onset (first‐stage advance order) or opt for instant orders at the beginning of the sales season (second‐stage instant order). To ensure timely order fulfillment, the supplier initially employs a cost‐effective regular production mode with random yield during the production season. If yields are insufficient during sales, a pricier emergency production mode with guaranteed output becomes available. Utilizing a dynamic programming approach, we formulate the two‐stage optimization problem to derive optimal production and order decisions. Our analysis uncovers how realized random yield and stochastic market signals influence emergency production and instant order quantities in the second stage. We compare expected profits in scenarios with perfect and imperfect market signals, probing the members' preferences regarding order strategies. An intriguing finding emerges: as instant wholesale prices rising, the supplier's preferred order strategy diverges from the retailer. By strategic adjustments to the instant wholesale price, we demonstrate the potential for unanimous agreement on preferred order strategies among supply chain members — a quality enhancing the chain's flexibility and performance. Moreover, we extend the model to hybrid order strategies and identify conditions for unanimous preference among the three strategies. To bolster our theoretical findings, we provide numerical examples, lending practical support to our study.

OPTIMAL DECISION-MAKING FOR NEWSPAPER INVENTORY: BALANCING DEMAND UNCERTAINTY AND PROFITABILITY

Estimating cost-benefit analysis, calculating the real value of anything before buying it is essential in order to know if this purchase is fetching or not. In the industry of selling newspapers, since demand is determined by various unpredictable features like news quality and types of reader interest, it may be difficult to estimate how many copies you should buy daily. On the other hand, this study addresses the classic Newsvendor Problem by analyzing a single-period inventory management model to find the optimal order quantity that maximizes profit. Simulating different demand scenarios up to 20 days into the future, using financial-based assumptions (e.g.: selling price, buying cost, and salvage value) on order quantities in order to investigate profits. The results show that we can identify the optimal level of order quantity in the cost-benefit analysis and profit analysis under different demand situations. This method reduces both excess waste and missed sales opportunities, making an optimal answer for inventory alignment in sought-after uncertainty-based industries. The insights of this analysis are not just relevant to newspaper sales but also to all comparable industries where accurate inventory management becomes challenging.

When should the fresh-food retailer embrace near-expired food commercialization?

Despite the potential of near-expired food commercialization (NFC) to recover value from unsold food, skepticism persists due to concern about its impact on fresh food margins. This study introduces a game-theoretic model to explore the optimal strategy and price-setting timing for the fresh-food retailer considering NFC. We find that the optimal strategy transitions from no NFC to partial and then to full NFC as the order quantity increases. Specifically, under full NFC, the fresh-food retailer with strong bargaining power and high perceived quality benefits from adopting clearance-priority pricing, which prioritizes the pricing of the near-expired food. Conversely, the retailer with limited power and lower perceived quality prefers fresh-priority pricing, which sets the retail price of fresh food first. Under partial NFC, while clearance-priority pricing generally yields profits, the high disposal cost may compel even powerful fresh-food retailers to switch to fresh-priority pricing to mitigate the risks associated with negotiation failures. We further analyze extended models to confirm the robustness of our findings. Our research contributes to the literature and guides practice by addressing common misconceptions about NFC.