Leveraging data-driven decisions: a framework for building intracompany capability for supply chain optimization and resilience
Bibliographic record
Abstract
Purpose Companies are turning to big data (BD) programs to help mitigate supply chain (SC) disruptions and risks that are increasing in frequency and severity. The purpose of this paper is to explore exactly how companies translate data into meaningful information used to manage SC risk and create economic value; an area not well researched. As companies are turning to big-data programs to help mitigate supply chain (SC) disruptions and risks that are increasing in frequency and severity, having the capability to internally integrate SC information is cited as the most critical risk to manage. Design/methodology/approach Information processing theory and resource-based view are applied to support capability development used to make value-based BD decisions. Semi-structured interviews were conducted with leaders in both the oil and gas industry and logistics SC partners to explore each companies’ BD transformation. Findings Findings illuminate how companies can build internal capability to more effectively manage SC risk, optimize operating assets and drive employee engagement. Research limitations/implications The oil and gas industry were early adopters of gathering BD; more studies addressing how companies translate data to create value and manage SC risk would be beneficial. Practical implications Guidance for senior leaders to proactively introduce BD to their company through a practical framework. Further, this study provides insight into where the maximum benefit may reside, as data intersects with other company resources to build an internal capability. Originality/value This study presents a framework highlighting best practices for introducing BD plus creating a culture capable of using that data to reduce risk during design, implementation and ongoing operations. The steps for producing the maximum benefit are laid out in this study.
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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 | 0.022 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.004 | 0.030 |
| Scholarly communication | 0.017 | 0.018 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.004 | 0.005 |
| 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".