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Record W1990586219 · doi:10.1108/10662240210438416

Supply chain collaboration alternatives: understanding the expected costs and benefits

2002· article· en· W1990586219 on OpenAlexaff
Tim S. McLaren, Milena Head, Yufei Yuan

Bibliographic record

VenueInternet Research · 2002
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicOutsourcing and Supply Chain Management
Canadian institutionsMcMaster University
Fundersnot available
KeywordsSupply chainBusinessOutsourcingGeneral partnershipSupply chain managementProcess (computing)Product (mathematics)Service managementIndustrial organizationProcess managementMarketingComputer scienceFinance

Abstract

fetched live from OpenAlex

Collaboration is a recent trend in supply chain management (SCM) that focuses on joint planning, coordination, and process integration between suppliers, customers, and other partners in a supply chain. Its competitive benefits include cost reductions and increased return on assets, and increased reliability and responsiveness to market needs. Recent advances in inter‐enterprise software and communication technologies, along with a growing use of strategic partnering and outsourcing relationships, has resulted in a confusing assortment of alternative information systems approaches for supporting collaborative SCM. This paper analyzes the alternatives and presents a framework for understanding the expected costs and benefits of each type of system. These costs include not only the total cost of ownership of the system, but also the partnership opportunity cost – the cost of being tied to a partner due to system inflexibility. The benefits of collaborative SCM include process, inventory, and product cost reductions as well as increased cycle times, service levels, and market intelligence.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.016
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0010.002
Scholarly communication0.0060.011
Open science0.0020.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0120.001

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 head0.109
GPT teacher head0.311
Teacher spread0.202 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations258
Published2002
Admission routes1
Has abstractyes

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