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Record W4285218177 · doi:10.5267/j.uscm.2022.2.012

The effect of amended order on firm resilience through supply chain coordination

2022· article· en· W4285218177 on OpenAlexvenueno aff
Hotlan Siagian, Sahnaz Ubud, Sautma Ronni Basana, Zeplin Jiwa Husada Tarigan

Bibliographic record

VenueUncertain Supply Chain Management · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicSupply Chain Resilience and Risk Management
Canadian institutionsnot available
Fundersnot available
KeywordsSupply chainBusinessOrder (exchange)Resilience (materials science)Industrial organizationSupply chain managementProduct (mathematics)MarketingProcess managementOperations managementFinanceEconomics

Abstract

fetched live from OpenAlex

Pandemic Covid 19 has resulted in manufacturing companies experiencing disruptions in fulfilling customer orders and changes in product forecasting results from calculation. Manufacturing companies essential in handling covid experience increased demands, while other companies not related to covid experienced a decrease even in cancellation of orders. Order changes determine the company's performance. This research is related to the effect of order changes on firm resilience through supply chain coordination. The distribution of questionnaires to manufacturing companies in Indonesia is done by sending a google form link in the social media group. Data obtained as many as 446 questionnaires and analyzed using the partial least square technique. The study results showed that amended orders positively impacted 0.728 to the internal operational coordination, the positive influence of 0.201 to coordinate with the supplier, and positive effect with a coefficient of 0.213 to coordination with the customer. Internal operational coordinated influence coordinated with the customer of 0.593 and coordinated with the customer of 0.515. Supply chain coordinated impact on the company's supply chain resilience determined by internal operational coordination by 0.371, coordinated with the supplier of 0.183 and coordinated with the customer of 0.199. The practical contribution of the research is to make managers able to build a coordinated Supply chain to overcome amended orders and increase supply chain deficiencies.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.585
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
Science and technology studies0.0020.000
Scholarly communication0.0000.001
Open science0.0020.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.009
GPT teacher head0.243
Teacher spread0.234 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations6
Published2022
Admission routes1
Has abstractyes

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