Building Innovation into the Outsourcing Relationship: A Case Study
Bibliographic record
Abstract
This teaching case has been developed for a global consumer products company. Interviews from several outsourcing managers and executives within the company have contributed to the case. This case focuses on how a company can build innovation into an outsourcing relationship when it has not been explicitly stated in the outsourcing agreement. The two fictitious companies in this case are Bentley & Brooks (B&B) (the client) and AlphaCorp (the provider). Both companies are based on, but are not accurate portrayals of, actual companies that are anonymous. The case is a composite of several actual outsourcing cases between the two companies. B&B has outsourced much of their human resource operations and IT support. They have been frustrated that their global outsource provider, AlphaCorp, has not been able to bring innovation to the relationship. In the outsourcing contract, innovation was not expressly identified, but was certainly discussed during the outsourcing proposal and negotiations. In addition, the ability of AlphaCorp to deliver consistent quality of service on a global basis has been uneven, particularly in Latin America. You will be asked to take on the role of a B&B employee who is part of a team looking into the innovation problem. The team has been asked to provide answers to three key questions and to present their recommendations to a panel of senior executives of B&B.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.006 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.004 |
| 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".