Deep Learning for Distribution Channels' Management

Necula, Sabina-Cristiana

doi:10.12948/issn14531305/21.4.2017.06

Cited by 8 publications

(3 citation statements)

References 10 publications

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“…This gap might suggest that the choice of distribution channel hinges on factors like competitive dynamics, available opportunities, and organizational capabilities, which aren't always quantifiable. However, looking beyond the scope of this review, [311] introduced a deep learning framework designed to forecast sales across various distribution channels, offering potential guidance for this managerial decision.…”

Section: A Discussionmentioning

confidence: 99%

A Systematic Literature Review on Machine Learning in Shared Mobility

Teusch,

Gremmel,

Koetsier

et al. 2023

IEEE Open J. Intell. Transp. Syst.

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Shared mobility has emerged as a sustainable alternative to both private transportation and traditional public transport, promising to reduce the number of private vehicles on roads while offering users greater flexibility. Today, urban areas are home to a myriad of innovative services, including car-sharing, ride-sharing, and micromobility solutions like moped-sharing, bike-sharing, and e-scooter-sharing. Given the intense competition and the inherent operational complexities of shared mobility systems, providers are increasingly seeking specialized decision-support methodologies to boost operational efficiency. While recent research indicates that advanced machine learning methods can tackle the intricate challenges in shared mobility management decisions, a thorough evaluation of existing research is essential to fully grasp its potential and pinpoint areas needing further exploration. This paper presents a systematic literature review that specifically targets the application of Machine Learning for decision-making in Shared Mobility Systems. Our review underscores that Machine Learning offers methodological solutions to specific management challenges crucial for the effective operation of Shared Mobility Systems. We delve into the methods and datasets employed, spotlight research trends, and pinpoint research gaps. Our findings culminate in a comprehensive framework of Machine Learning techniques designed to bolster managerial decision-making in addressing challenges specific to Shared Mobility across various levels.

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Section: A Discussionmentioning

confidence: 99%

A Systematic Literature Review on Machine Learning in Shared Mobility

Teusch,

Gremmel,

Koetsier

et al. 2023

IEEE Open J. Intell. Transp. Syst.

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“…Examples in the field of SCRM include an AI approach to curtailing the bullwhip effect inclusive of internal and external risks, as cited in a study by Aggarwal and Davè (2018). Other AI studies suggest it could model the likelihood of occurrence of risks (Ojha et al, 2018), diminish the risk in distribution management due to churn (Necula, 2017), predict and assess damage attributes in the field of logistics (Gürbüz et al, 2019), and forecast the level of integration in the supply chain to minimise risks (Muñoz et al, 2020). Advances in AI techniques and the massive growth in data generated along with the exponential rise in computing power have started benefiting the field of SCRM immensely.…”

Section: Application Of Ai In Scrmmentioning

confidence: 99%

Adoption of Artificial Intelligence in Supply Chain Risk Management

Paul

Riaz²,

Das

2022

Journal of Global Information Management

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The study aims to examine factors that influence the adoption-diffusion process of Artificial Intelligence (AI) in Supply Chain Risk Management (SCRM) across manufacturing, wholesale trade, retail trade, and transportation industries in India. As part of this study, eleven constructs that influence the adoption-diffusion stages of AI in SCRM were identified and examined. A survey was conducted to collect data from supply chain executives, risk professionals, and AI consultants across the manufacturing, wholesale trade, retail trade, and transportation industries in India. Partial least squares structural equation modeling (PLS-SEM) was used to study the data. Results show that these factors have varying degrees of influence and direction on the three stages of adoption of AI in SCRM. The study will enable the leadership team in the organizations to build a roadmap for the adoption, implementation, and routinization of AI in SCRM.

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“…These advances in the field of AI stand to benefit SCRM immensely and hence the growing interest in the application of AI in SCRM. Recent studies cite the use of AI to predict the probability of occurrence of risks and costs of risks [7], reduce risk of churn in distribution management [8], predict damage parameters in logistics [9], and forecasting the level of integration of supply chain to minimize risks [10].…”

Section: Application Of Ai In Scrmmentioning

confidence: 99%

Organizational Adoption of Artificial Intelligence in Supply Chain Risk Management

Paul¹,

Riaz

Das³

2020

IFIP Advances in Information and Communication Technology

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With the growing complexity of global supply chains, geopolitical events, pandemics, and just-in-time processes, organizations can benefit immensely in managing supply chain risks by adopting artificial intelligence (AI). Building upon past research in technology adoption, we study factors influencing the adoption intention of AI in SCRM across organizations in India. Based on a qualitative study, we discuss the applications and uniqueness of AI adoption in the field of supply chain risk management (SCRM) and propose a research model on the adoption, implementation, and routinization intention of AI in SCRM at an organizational level. Secondly, we discuss the implications of the study and the benefits to decision-makers and supply chain planners in devising effective strategies when adopting AI in SCRM.

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Deep Learning for Distribution Channels' Management

Cited by 8 publications

References 10 publications

A Systematic Literature Review on Machine Learning in Shared Mobility

A Systematic Literature Review on Machine Learning in Shared Mobility

Adoption of Artificial Intelligence in Supply Chain Risk Management

Organizational Adoption of Artificial Intelligence in Supply Chain Risk Management

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