Artificial intelligence for forecasting in supply chain management: a case study of White Sugar consumption rate in Thailand

Kantasa-Ard, Anirut; Bekrar, Abdelghani; Cadi, Abdessamad Ait El; Sallez, Yves

doi:10.1016/j.ifacol.2019.11.201

Cited by 12 publications

(4 citation statements)

References 8 publications

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“…Supply chain management (SCM) systems are the most crucial parts of industries and organizations; a well-defined, effective SCM can directly influence operations, productions, and profitability of organizations [ 7 , 8 , 9 ]. As the time past, to fulfill a massively growing demand of the supply chain over the world, industries, organizations, or firms, have been adopting the advanced technologies, such as radio frequency identification (RFID) and sensing solutions, artificial intelligence (AI), and cloud computing to manage the extensively large and complex operations of the modern supply chain ecosystem [ 9 , 10 , 11 , 12 , 13 ]. Thus, gathering and managing large data in the complex and large-scale supply chain, in which entities and operations involved are distributed over various locations, undoubtedly is a big challenge.…”

Section: Introductionmentioning

confidence: 99%

Intuitive Development to Examine Collaborative IoT Supply Chain System Underlying Privacy and Security Levels and Perspective Powering through Proactive Blockchain

Shahzad

Zhang

Gherbi

2020

Sensors

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Undoubtedly, the supply chain management (SCM) system is an important part of many organizations worldwide; over time, the technologies used to manage a supply chain ecosystem have, therefore, a great impact on businesses’ effectiveness. Among others, numerous developments have been made that targeted to have robust supply chain systems to efficiently manage the growing demands of various supplies, considering the underlying requirements and main challenges such as scalability, specifically privacy and security, of various business networks. Internet of things (IoT) comes with a solution to manage a complex, scalable supply chain system, but to provide and attain enough security during information exchange, along with keeping the privacy of its users, is the great inherent challenge of IoT. To fulfill these limitations, this study designs and models a scaled IoT-based supply chain (IoT-SC) system, comprising several operations and participants, and deploys mechanisms to leverage the security, mainly confidentially, integrity, authentication (CIA), and a digital signature scheme to leverage potentially secured non-repudiation security service for the worst-case scenario, and to leverage privacy to keep users sensitive personal and location information protected against adversarial entities to the IoT-SC system. Indeed, a scaled IoT-SC system certainly opens new challenges to manage privacy and security while communicating. Therefore, in the IoT-SC system, each transaction writes from edge computing nodes to the IoT-SC controller is thoroughly examined to ensure the proposed solutions in bi-directional communication, and their robustness against adversarial behaviors. Future research works, employing blockchain and its integrations, are detailed as paces to accelerate the privacy and security of the IoT-SC system, for example, migrating IoT-centric computing to an immutable, decentralized platform.

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Section: Introductionmentioning

confidence: 99%

Intuitive Development to Examine Collaborative IoT Supply Chain System Underlying Privacy and Security Levels and Perspective Powering through Proactive Blockchain

Shahzad

Zhang

Gherbi

2020

Sensors

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“…Artificial intelligence has many applications in industry 4 and increases customer satisfaction (Gonz alez Rodr ıguez et al, 2019;Tang and Liao, 2021). One of the important applications of ML is to predict the demand for goods in the supply chain (Kantasa-ard et al, 2019). In order to continuously monitor and track the smart pharmaceutical industry's drug delivery process, ML has been used (Abbas et al, 2020).…”

Section: Literature Reviewmentioning

confidence: 99%

“…, 2019; Tang and Liao, 2021). One of the important applications of ML is to predict the demand for goods in the supply chain (Kantasa-ard et al. , 2019).…”

Section: Literature Reviewmentioning

confidence: 99%

Designing a post-disaster humanitarian supply chain using machine learning and multi-criteria decision-making techniques

Shakibaei

Farhadi-Ramin

Alipour-Vaezi

et al. 2023

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PurposeEvery day, small and big incidents happen all over the world, and given the human, financial and spiritual damage they cause, proper planning should be sought to deal with them so they can be appropriately managed in times of crisis. This study aims to examine humanitarian supply chain models.Design/methodology/approachA new model is developed to pursue the necessary relations in an optimal way that will minimize human, financial and moral losses. In this developed model, in order to optimize the problem and minimize the amount of human and financial losses, the following subjects have been applied: magnitude of the areas in which an accident may occur as obtained by multiple attribute decision-making methods, the distances between relief centers, the number of available rescuers, the number of rescuers required and the risk level of each patient which is determined using previous data and machine learning (ML) algorithms.FindingsFor this purpose, a case study in the east of Tehran has been conducted. According to the results obtained from the algorithms, problem modeling and case study, the accuracy of the proposed model is evaluated very well.Originality/valueObtaining each injured person's priority using ML techniques and each area's importance or risk level, besides developing a bi-objective mathematical model and using multiple attribute decision-making methods, make this study unique among very few studies that concern ML in the humanitarian supply chain. Moreover, the findings validate the results and the model's functionality very well.

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“…When it comes to drones, drones can be used to transport goods between organizations in the supply chain or to deliver goods to the customer. It is possible to create a system in which AI manages robots to transport goods from the warehouse to the drones and then drones transport goods to the customer [12], • additive manufacturing: involves the use of a 3D printer for creating products. In other words, an organization that owns a 3D printer can print the product according to the specification that other organization provides.…”

Section: Digital Transformation Of the Supply Chain And Related Tehnologiesmentioning

confidence: 99%

Measuring Digital Transformation Maturity of Supply Chain

Buntak

Kovačić

Mutavđžija

2021

Teh. glas. (Online)

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Digital transformation signifies changes in all components and systems of the supply chain. It is also a strategic decision of the organization which, in the long run, can result in the creation of competitive advantage in the market. Digital transformation is affecting all organizations, regardless of their activity. Digital transformation of the supply chain involves the use of industry 4.0 based technologies as well as the replacement of traditional practices with new ones based on digital solutions. The implementation of digital solutions, such as artificial intelligence, IoT, cloud computing, etc., therefore, improve communication between stakeholders in the supply chain, as well as improve efficiency and effectiveness. When conducted, digital transformation must be measured by different levels of maturity. In this paper, authors research current models of measuring digital transformation maturity in supply chain and propose a new model based on identified theories and needs.

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Artificial intelligence for forecasting in supply chain management: a case study of White Sugar consumption rate in Thailand

Cited by 12 publications

References 8 publications

Intuitive Development to Examine Collaborative IoT Supply Chain System Underlying Privacy and Security Levels and Perspective Powering through Proactive Blockchain

Intuitive Development to Examine Collaborative IoT Supply Chain System Underlying Privacy and Security Levels and Perspective Powering through Proactive Blockchain

Designing a post-disaster humanitarian supply chain using machine learning and multi-criteria decision-making techniques

Measuring Digital Transformation Maturity of Supply Chain

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