applicability of Robotic Process Automation in the Supply Chain & Logistics Industry in Sri Lanka
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Bibliographic record
Abstract
As technology transforms the business world, the supply chain and logistics sector need to embrace automation that promise rebuilding and integrating sustainable supply chains. Robotic process automation is the application of automating the business processes using software bots. These software bots can enter data into software applications, manipulate data, and communicate with other software applications. However, most robotic process automation literature focuses on sectors such as finance, but little research focuses on supply chain and logistics sector applications. Besides, robotic process automation in the Sri Lankan supply chain context is less studied. Therefore, the study's main objectives are to identify the applicability of robotic process automation in supply chain and logistics industry in Sri Lanka. Furthermore, the study aims to identify employee perception towards robotic process automation implementations in the supply chain logistics industry in Sri Lanka.
 This study uses qualitative and quantitative research methods to collect data. Initially, a case study was conducted in one Logistics Company. A comprehensive feasibility framework was developed to identify the most feasible process. The AS-IS process and data flow mapping were developed to in-depth study the applicability of robotic process automation of the selected process. Findings confirm that many tasks of the selected process can be automated using robotic process automation. Moreover, a survey was conducted to identify employee perception towards robotic process automation implementations. The findings reflect that the young employees who have higher education and income levels are more likely to view robotic process automation as a positive impact on their jobs in the supply chain and logistics industry in Sri Lanka.
 Keywords: Robotic Process Automation, Supply Chain and Logistics Industry in Sri Lanka, Employee Perception, Feasibility Framework
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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