Improving business process performance in SMEs through predictive modeling: a comparative study of statistical and machine learning models
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose This study investigates how predictive modeling can improve business process performance in small and medium-sized enterprises (SMEs) by enhancing demand forecasting. This paper examines statistical, machine learning and hybrid models to support process improvement by forecasting business outcomes, enabling data-driven decision-making. Using real-world data from a make-to-stock SME in the manufacturing sector, the research identifies context-aware forecasting strategies that align with business triggers and can be practically implemented without requiring extensive digital infrastructure. Design/methodology/approach A quantitative, comparative modeling approach is applied to real-world demand data from make-to-stock items, with a range of forecasting models evaluated using hyperparameter tuning. These models incorporate both endogenous demand trends and exogenous variables, and the results are critically assessed through a business process lens to evaluate practical relevance, scalability and workflow integration potential. Findings Hybrid and ensemble models, particularly Random Forest Regressor and Multi-Prophet, consistently outperform statistical approaches in forecasting non-linear, event-driven demand patterns. Feature-importance analysis confirms that episodic business events are stronger demand drivers than macroeconomic indicators, especially in project-based supply chains. Originality/value Drawing on operational data from an SME, this research moves beyond accuracy to focus on practical implementation, interpretability and process alignment. It positions predictive modeling as a decision-support subprocess embedded in SME operations, offering a replicable framework for data-driven forecasting in resource-constrained, real-world environments.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 it