Optimizing success in supply chain partnerships
Bibliographic record
Abstract
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.
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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 | 0.012 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
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".