Inventory Management Practices among Small and Micro Businesses during COVID-19 Pandemic
Bibliographic record
Abstract
Developing micro, small, and medium businesses (SMEs) encourages Malaysia's economic growth. The Covid-19 pandemic has greatly affected SMEs. During the Covid-19 pandemic, with material shortages and shipping delays, the ability to track the inventory is critical. Adopting digital technology in the inventory tracking process will assist the company in managing its inventory effectively. SMEs need to have relevant knowledge and technical skills to adopt digital technology. Specifically, this study explores SMEs' current inventory management practices to gauge their knowledge of inventory management. Data was collected using semi-structured interviews using a questionnaire with ten SMEs in Selangor. This study documents that SME organisation value is positively associated with best inventory management practices. In addition, results show that divergence can intensify the negative relationship between low inventory management practices and SMEs' value. Findings support the need for more scrutiny by SME owners, regulators, policymakers, and standard setters to monitor the conflict, which is crucial for SMEs to overcome the issues and challenges they face. This study will contribute to the sustainability of SMEs, consistent with the national agenda to strengthen the SMEs as highlighted in the government’s new Economic Transformation Program (ETP) through Bumiputera’s Economic Transformation Agenda.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".