Evidence map›Paper›PMID 35313660›Full record

ArticleComputers & industrial engineering2021

An agent-based model for supply chain recovery in the wake of the COVID-19 pandemic.

Towfique Rahman, Firouzeh Taghikhah, Sanjoy Kumar Paul, Nagesh Shukla, Renu Agarwal

Open access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Computers & industrial engineering, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
18.7field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

13 citing papers in PubMed, 109 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 2 institutions in 1 country.

Towfique RahmanUTS Business School, University of Technology Sydney, Sydney, Australia.
Firouzeh TaghikhahCrawford School of Public Policy, Australian National University, Canberra, Australia.
Sanjoy Kumar PaulUTS Business School, University of Technology Sydney, Sydney, Australia.
Nagesh ShuklaSchool of Information, Systems and Modelling, Faculty of Engineering, and Information Technology, University of Technology Sydney, Sydney, Australia.
Renu AgarwalUTS Business School, University of Technology Sydney, Sydney, Australia.
University of Technology Sydney · AUAustralian National University · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The current COVID-19 pandemic has hugely disrupted supply chains (SCs) in different sectors globally. The global demand for many essential items (e.g., facemasks, food products) has been phenomenal, resulting in supply failure. SCs could not keep up with the shortage of raw materials, and manufacturing firms could not ramp up their production capacity to meet these unparalleled demand levels. This study aimed to examine a set of congruent strategies and recovery plans to minimize the cost and maximize the availability of essential items to respond to global SC disruptions. We used facemask SCs as an example and simulated the current state of its supply and demand using the agent-based modeling method. We proposed two main recovery strategies relevant to building emergency supply and extra manufacturing capacity to mitigate SC disruptions. Our findings revealed that minimizing the risk response time and maximizing the production capacity helped essential item manufacturers meet consumers' skyrocketing demands and timely supply to consumers, reducing financial shocks to firms. Our study suggested that delayed implementation of the proposed recovery strategies could lead to supply, demand, and financial shocks for essential item manufacturers. This study scrutinized strategies to mitigate the demand-supply crisis of essential items. It further proposed congruent strategies and recovery plans to alleviate the problem in the exceptional disruptive event caused by COVID-19.

Indexed as

COVID-19 pandemicEssential itemRecovery strategyRisk and disruptionSupply chain resilience

Identifiers

PMID35313660
PMCPMC8926404
OpenAlexW3161689089

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.