Weatherproofing Supply Chains: Enable Intelligent Preparedness with Data Analytics

Coyle, Doug; Ruamsook,; Symon,

doi:10.5325/transportationj.55.2.0190

Cited by 4 publications

(2 citation statements)

References 8 publications

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“…Zagorecki, Johnson, and Ristvej (2013) review the use of data mining and machine learning in disaster management applications. Coyle, Ruamsook, and Symon (2016) note the need for intelligent preparedness to sense, capture, and analyze data (specifically weather) in disaster management, providing a case demonstration. Papadopoulos et al.…”

Section: Application Fieldsmentioning

confidence: 99%

Role of Analytics for Operational Risk Management in the Era of Big Data

et al. 2020

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Operational risk management (ORM) is critical for any organization, and in the big data era, analytical tools for operational risk management are evolving faster than ever. This paper examines recent developments in academic ORM literature from the data analytics perspective. We focus on identifying present trends in ORM related to various types of natural and man‐made disasters that have been challenging all aspects of life. Although we examine the broader operations management (OM) literature, we keep the focus on the articles published in the well‐regarded OM journals, including both empirical and analytical outlets. We highlight how the use of data analytics tools and methods have facilitated ORM. We discuss the need for data monitoring and the integration of various analytical tools into decision making processes by classifying the literature on application fields, analytics techniques, and the strategies used for implementation. We summarize our findings and propose a process to implement data‐driven ORM with future research directions.

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Section: Application Fieldsmentioning

confidence: 99%

Role of Analytics for Operational Risk Management in the Era of Big Data

et al. 2020

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“…The obtained results and study focus of big data with the DSS in performance measurement can be used for continuous improvement in forecasting demands, predicting purchasing needs, decreasing lead times, minimising production wastes, controlling inventory and stock outs. Coyle et al (2016) also found that big data can be used for predicting weather events, making better decision making and implementing operations-related strategies after a disaster. Costs are directly reduced.…”

Section: Introductionmentioning

confidence: 99%

A model-driven decision approach to collaborative planning and obsolescence for manufacturing operations

Kuik

Diong

2019

IMDS

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Purpose The purpose of this paper is to present the model-driven decision support system (DSS) for small and medium manufacturing enterprises (SMMEs) that actively participates in collaborative activities and manages the planned obsolescence in production. In dealing with the complexity of such demand and supply scenario, the optimisation models are also developed to evaluate the performance of operations practices. Design/methodology/approach The model-driven DSS for SMMEs, which uses the optimisation models for managing and coordinating planned obsolescence, is developed to determine the optimal manufacturing plan and minimise operating costs. A case application with the planned obsolescence and production scenario is also provided to demonstrate the approach and practical insights of DSS. Findings Assessing planned obsolescence in production is a challenge for manufacturing managers. A DSS for SMMEs can enable the computerised support in decision making and understand the planned obsolescence scenarios. The causal relationship of different time-varying component obsolescence and availability in production are also examined, which may have an impact on the overall operating costs for producing manufactured products. Research limitations/implications DSS can resolve and handle the complexity of production and planned obsolescence scenarios in manufacturing industry. The optimisation models used in the DSS excludes the variability in component wear-out life and technology cycle. In the future study, the optimisation models in DSS will be extended by taking into the uncertainty of different component wear-out life and technology cycle considerations. Originality/value This paper demonstrates the flexibility of DSS that facilitates the optimisation models for collaborative manufacturing in planned obsolescence and achieves cost effectiveness.

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Supply chain risk management with machine learning technology: A literature review and future research directions

Yang

Lim

et al. 2023

Computers & Industrial Engineering

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Weatherproofing Supply Chains: Enable Intelligent Preparedness with Data Analytics

Cited by 4 publications

References 8 publications

Role of Analytics for Operational Risk Management in the Era of Big Data

Role of Analytics for Operational Risk Management in the Era of Big Data

A model-driven decision approach to collaborative planning and obsolescence for manufacturing operations

Supply chain risk management with machine learning technology: A literature review and future research directions

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