Predicting material backorders in inventory management using machine learning

Santis, Rodrigo Barbosa de; Aguiar, Eduardo Pestana de; Goliatt, Leonardo

doi:10.1109/la-cci.2017.8285684

Cited by 28 publications

(18 citation statements)

References 6 publications

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“…keep your operations running smoothly. On the basis of exemplary data, a predictive procedure (Santis, Aguiar, and Goliatt, 2017) will be presented showing the benefits of such an analysis. The data set used in the analysis contains 23 variables and 1,929,935 observations.…”

Section: Detection Of Returned Ordersmentioning

confidence: 99%

Optimization of Logistics Processes of the Supply Chain Using RFID Technology

Rymarczyk¹,

Małek²,

Nowak³

et al. 2021

ERSJ

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Purpose:The aim of the article is to develop a system for optimizing supply chain logistics processes using RFID technology. Design/Methodology/Approach: Machine learning algorithms such as Gradient Boosting, random forests, decision trees, RUS were used to solve the problem. An RFID reader and dedicated software were designed. Findings: The results of the conducted research show that the methods used, a procet application based on RFID technology, increases the company's efficiency in logistic processes. Practical Implications: The methods and system presented in the article can be used in the supply chain of logistics and manufacturing companies. Originality/Value: The novelty is the appropriate selection and use of machine learning methods for the designed system that optimizes the logistics processes of returns using RFID technology. A proprietary system consisting of a dedicated reader and IT application was designed.

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Section: Detection Of Returned Ordersmentioning

confidence: 99%

Optimization of Logistics Processes of the Supply Chain Using RFID Technology

Rymarczyk¹,

Małek²,

Nowak³

et al. 2021

ERSJ

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show abstract

“…To deal with the imbalanced class problem efficiently, ML classifiers were examined in [27] to identify a suitable forecasting model. To carry out this task, they applied different measures along with the ensemble learning.…”

Section: Literature Reviewmentioning

confidence: 99%

Prediction of probable backorder scenarios in the supply chain using Distributed Random Forest and Gradient Boosting Machine learning techniques

2020

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When a customer orders a product, which is not available in the store or temporary out of stock, and the customer decides to wait until the product is available and promised to be shipped, then this scenario is called backorder of that specific product [1, 2]. If backorders are not handled promptly, they will have high impacts on the respective company's revenue, share market price, customers' trust, and may end up losing the customer

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“…Furthermore, proposed method was used with time window service constraints to compare backorder and service level structures. Santis, Aguiar, and Goliatt (2017) proposed the application of a supervised learning model for backorder prediction in inventory control.…”

Section: Literature Reviewmentioning

confidence: 99%

Simulation Optimization Approach to Periodic Review Inventory Control System With Backorders

Dosdoğru¹,

Ipek²,

Göçken³

et al. 2020

Çukurova Üniversitesi Sosyal Bilimler Enstitüsü Dergisi

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In today's competitive world, companies should minimize cost while providing high quality goods. Companies generally try to reduce the level of inventory to minimize the cost and therefore they usually observe shortage in practice. At this point, using of the right inventory control policy is the most effective and efficient way to reduce shortage. In inventory control policies, the basic question is to specify the size and the timing of a replenishment order in supply chain members. Over the years, many advanced methods have been applied to answer these questions. Due to the difficulty of dealing with the uncertainties in supply chain environment, simulation optimization (SO) is used in this study to get the application of goals in supply chain. Although SO requires a great deal of understanding related with inventory control system, the use of SO brings such complex system within the grasp of managers. In this paper, SO is used to analyze the supplier selection and inventory control system simultaneously. The system results clearly reveals that the best values of inventory control variables and the most suitable suppliers can be determined by SO in a two echelon supply chain model with backorder.

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Predicting material backorders in inventory management using machine learning

Cited by 28 publications

References 6 publications

Optimization of Logistics Processes of the Supply Chain Using RFID Technology

Optimization of Logistics Processes of the Supply Chain Using RFID Technology

Prediction of probable backorder scenarios in the supply chain using Distributed Random Forest and Gradient Boosting Machine learning techniques

Simulation Optimization Approach to Periodic Review Inventory Control System With Backorders

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