Maturity Model for Assessing the Extent of Automation in Sri Lankan Warehouse Operations: A Multiple Case Study
Bibliographic record
Abstract
Warehouses are facing substantial challenges due to the COVID-19 context. In this regard, automation in the warehouse industry has become an emerging trend in the supply chain sector. However, there is no proper model to measure the maturity level of warehouse operations. This paper aims to provide a maturity scale model to measure the automation stage in the Sri Lankan warehouse context. This research uses qualitative and quantitative approaches to assess the maturity level. A refined maturity assessment model was developed using early literature and industry expert views. The study analysed data collected from five major warehouses in Sri Lanka, and those were modelled as ad-hoc, mechanisation (semi-automated), and fully automated stages of examining the overall maturity stage of the selected warehouses. The study findings reveal that the majority of selected Sri Lankan warehouses have developed soft-based automation practices. According to the study, chosen warehouses in Sri Lanka retain the stage of 1.93 in maturity scale, which means combining traditional manual processes with some part of automation. Further selected warehouse operations belong to the mature stage of ad-hoc level in the maturity scale of automation. It may dramatically move to the mechanisation stage with the globalised market dynamics. Further, the maturity model of the study provides a practical diagnostic tool that will help warehouses assess the warehouses' automation level in the Sri Lankan context. Keywords: Automation of Warehouse Operations, Maturity Scale, Warehouse Automation Practices
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".