Critical Competencies of Supply Chain Leaders During Digital Transformations
Bibliographic record
Abstract
Organizational leaders have increasingly turned to enterprise resource planning (ERP) applications, also known as decision support systems, to make their firms’ operational, tactical, and strategic processes more efficient and effective in the changing global marketplace. High failure rates in ERP systems implementations make these projects risky, however. Most prior research on critical success factors for conventional ERP implementation has been on large enterprises, resulting in a gap in knowledge on these factors in the small and medium enterprises that constitute the majority of U.S. employer firms. A qualitative modified Delphi study with an expert panel of U.S. manufacturing consultants and three iterative rounds of data collection and analysis revealed consensus on 8 critical success factors in ERP implementations, with the highest agreement on top management support and commitment, enterprise resource planning fit with the organization, quality management, and a small internal team of the best employees. In addition to furthering knowledge in the fields of leadership and enterprise applications, the study expands enterprise resource planning experts’ and scholars’ understanding of strategies to improve project success and the triple bottom line for any size enterprise in the manufacturing industry. Practitioners in the ERP industry can also apply approaches outlined during ERP implementations to mitigate risk during these engagements. Implications for positive social change include additional job opportunities and higher wages through increased efficiencies in ERP applications.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".