Demand-driven sales and operations planning: a framework proposal
Bibliographic record
Abstract
In a period of great technological advances and social and economic changes, the competitive environment is challenging for companies, which requires them to take actions towards flexibility, collaboration, integration, agility, velocity, traceability and cost-effective operations and supply chains. Both scholars and practitioners are employing the term Demand-Driven (DD) to characterize companies able to respond to these dynamic characteristics. Additionally, a critical process which has the aim of matching the supply with the demand is the Sales and Operations Planning (S&OP) and this process requires an updated understanding and the necessity of changes considering the new competitiveness environment, which also requires customer centricity and being driven by demand. Based on this, this thesis had the objective of proposing a framework for the Demand-Driven Sales and Operations Planning (DDS&OP). To achieve this objective two systematics literature (SLR) reviews were conducted, one for DD phenomenon and other for S&OP process. Based on both SLRs, the framework for the DDS&OP was proposed and it was modelled in BPMN. The framework considered three perspectives (strategic, tactical and operational) and the governance of the process. The framework was validated with 17 experts from different areas (scholars and practitioners from different industries) through the Fuzzy-Delphi Method. The main result of the thesis was a framework for the DDS&OP proposed upon a robust theoretical method and validated by experts. Other results were: a common definition for the DD phenomenon presented, a structuring and unifying conceptual framework for the DD phenomenon, the necessity of expansion of both vertical and horizontal consistencies for the S&OP and a list of 20 approaches that should be considered in the S&OP process for the new competitiveness environment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".