Practical Supply Chain Management Knowledge from Industry-Academia Dialogue
Bibliographic record
Abstract
Value co-creation, which can be defined as a joint initiative by two or more supply chain members to create value that cannot be created by the sole effort of one member, is a cornerstone concept in Supply Chain Management (SCM). To provide needed clarity about the concept, the invitation-only summit "World Class Supply Chain 2017: Value Co-creation", was convened on May 10th, 2017 in Milton, Ontario. The summit brought together accomplished executives, scholars, and students in the SCM field to engage in dialogue directed at uncovering actionable insights about three crucial issues: The business benefits of value co-creation The actions required for successful value co-creation The obstacles to value co-creation and ways to overcome them \nThe deliberations covered an extensive range of content that included concrete real-world examples to reinforce the insights. Those insights can be summarized in the following three major points: Information technology innovations can (a) come from an industry’s established players instead of only from new entrants and (b) significantly improve not only standard operational efficiency metrics in supply chains but also how supply chains parties interact with each other to create value. The suite of key success factors in value co-creation spans three major stages of activities for any organization: (i) preparing for its discussions with potential co-creation partners, (ii) having those discussions with an intent to find common ground on the most important partnership parameters, and (iii) managing the ongoing relationship(s) with selected partners. To be better poised for future success in value co-creation, today’s young, upcoming professionals (e.g., internship and entry-level jobs) must have jobs that are designed with a view to nurturing interpersonal skills in forming and sustaining effective inter-organizational business relationships.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.029 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.016 | 0.020 |
| Scholarly communication | 0.021 | 0.022 |
| Open science | 0.004 | 0.022 |
| Research integrity | 0.011 | 0.012 |
| Insufficient payload (model declined to judge) | 0.024 | 0.004 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".