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Record W1983285788 · doi:10.1108/17410390610658469

Optimizing success in supply chain partnerships

2006· article· en· W1983285788 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Enterprise Information Management · 2006
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicOutsourcing and Supply Chain Management
Canadian institutionsCarleton University
Fundersnot available
KeywordsGeneral partnershipProcess managementSupply chainMaturity (psychological)Critical success factorCapability Maturity ModelProcess (computing)BusinessSupply chain managementKnowledge managementRisk analysis (engineering)Management scienceOperations managementEngineeringComputer scienceMarketingFinancePolitical science

Abstract

fetched live from OpenAlex

Purpose The purpose of this research is to take an emergent process theory perspective and model the supply chain partnering process as a series of four linked models that correspond to the phases of the partnership lifecycle, from initiation to maturity/termination, and discuss the management issues in those phases critical for optimal success of partnerships. The framework developed in this paper provides a road‐map to manage and optimize realization of partnership benefits. Design/methodology/approach The “partnership formation to business value” process is described as a series of four linked models that correspond to the phases of partnership lifecycle: foundation, implementation, shakedown, and onwards and upwards. The outcomes of one phase become starting conditions for the next. Thus, decisions and actions in a phase may subsequently increase or decrease the potential for optimal success. Findings Optimal partnership success is conceptualized and a framework for approaching optimal success in four broad phases is developed. It is believed that business organizations can considerably improve the realization of partnering benefits by focusing on the critical issues in the partnering process. Organizations cognizant of the critical issues in the various phases of supply chain partnerships can make systematic efforts to manage them better by providing training, incentives, leadership, and an overall environment that facilitates partnering and realization of partnering objectives. Research limitations/implications A natural extension of this study could be to explore empirically the critical issues which have been identified, in greater detail. Given the wide variation in organizations due to size, products, and sectors, specific studies of supply chain partnerships, which compare partnerships along these dimensions, would also be valuable for understanding specific concerns. Empirical studies would also help to clarify the use of supply chain partnerships as a means to establish and sustain competitive advantage. Practical implications The framework developed in this paper provides a road‐map to manage and optimize realization of partnership benefits. Originality/value The prime benefit of this study is that it provides valuable insight on key issues in managing supply chain partnerships. Optimal partnership success is conceptualized and a framework for approaching optimal success in four broad phases is developed.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.730
Threshold uncertainty score0.799

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.004
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.011
GPT teacher head0.211
Teacher spread0.201 · 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