Strategic Planning and Budgeting: A Single Integrated Process with Ex Ante and Ex Post Alignments
Bibliographic record
Abstract
Abstract The purpose of this explanatory research was to understand how firms align strategic planning and budgeting both ex ante and ex post. After the literature review indicated that there was a shortcoming in explaining how the alignment was done, we interviewed management accountants at 20 large, profitable, stock-market listed firms with head offices in the Toronto area of Canada. To understand practice through interviews, we used qualitative, multi-case field research to address our research question, how do firms achieve alignment between their strategic plans and budgets, both ex ante and ex post? Our findings and contribution were that, rather than multiple processes (strategy, strategic planning, budgeting, and forecasting), strategic planning and budgeting are part of a single process. Alignment of strategic planning and budgeting is undertaken prior to the beginning of the fiscal year (ex ante) and during the fiscal year (ex post). Both provide opportunities to change ineffective strategies, strategic plans, and actions to minimize financial harm. Ex ante and ex post alignments enable the accomplishment of firms’ financial objectives through explicit and verifiable decisions. With forecasting heretofore being an unclear and ambiguous subprocess, this chapter has made it transparent and manageable in assisting with accomplishing the strategy, strategic plan, and budget.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".