Strategic planning techniques and tools in business enterprises: A systematic literature analysis
Bibliographic record
Abstract
The purpose of this paper is threefold. First, to establish a list of the strategic planning techniques and tools utilized by organizations in business enterprises. Second, to classify these techniques and tools by industry type, i.e., enterprises from different sectors, manufacturing, banking, hospitality, and service industries. Third, to determine the ten most common strategic planning techniques and tools used by these enterprises. A systematic literature review was adopted to attain the purpose of the current research paper covering the period 2006-2021. The review was carried out using Google Scholar platform focusing on articles on business enterprises, i.e., excluding articles on public, healthcare, education, and non-profit organizations. A total of 46 articles were found and classified based on industry type. The Findings shown there are 45 strategic planning techniques and tools used by business enterprises from different sectors, and the most frequent tools are: benchmarking, SWOT analysis, CSFs analysis, TQM, balanced scorecard analysis, competitor analysis, CRM, Porter’s five-forces analysis, customer satisfaction analysis, and PEST analysis. This paper expands the extant literature on strategic planning techniques and tools through focusing on such techniques and tools in business enterprises to develop an updated business-related list for enterprises from different sectors, manufacturing, banking, hospitality, and service industries, as well as identifying the ten most common strategic techniques and tools that business enterprises use in general.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| 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".