Performance measurement and management for manufacturing SMEs: a financial statement-based system
Bibliographic record
Abstract
Purpose The purpose of this paper is to propose a performance measurement and management system (PMMS) for small- and medium-sized enterprises (SMEs) based on an analysis of the connections between the firm’s business practices and financial results as reported in the financial statements. Design/methodology/approach Secondary data on the business practices and financial statements of 108 Canadian manufacturing SMEs were taken from a private database. Items from financial statements were used to measure the firm’s performance in specific areas such as sales and current assets management, while net profit was used to measure the overall performance. Information about the level of adoption of more than 120 business practices by the sampled firms was also used. Step-wise regression was then performed for two consecutive years to identify the business practices that had significantly influenced the items in the financial statements. Findings The findings show that an understanding of the business practice/financial statement connection can be useful in managing SME performance. The regression analyses provide rich and interesting results. They indicate that some practices influence performance quickly, while others have a deferred effect. In addition, some practices have impacts that are significant in specific areas of the organization but insignificant in terms of overall performance, while others affect the firm’s overall performance but not the specific area they are intended to improve. Research limitations/implications The main limitation of the study is the non-probabilistic sample. However, the sampled SMEs vary widely in their characteristics, which should partially mitigate the negative impacts of a non-probabilistic sample. Practical implications The paper offers a useful and low-cost PMMS for SMEs, using information that is easily available to owner-managers. It shows that SME performance can be managed using a simple system built around the firm’s financial statements. Originality/value The study is one of the first to empirically test the connection between an extensive list of SME business practices and the financial results presented in the firms’ financial statements.
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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 | 0.011 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".