Digitalizing supply chains potential benefits and impact on lean operations
Bibliographic record
Abstract
Purpose New technological trends continue to emerge, and businesses adopt them in different capacity in a pursuit of improving current ways of doing things and to gain competitive advantages over rivals. One of the key business functions that is impacted by the implementation of different disruptive technologies is the supply chain management. As a result, there is a continuous need to identify where digitalizing supply chains may provide businesses with benefits to capitalize such gains. This study aims to examine potential impacts of digitalizing supply chains on five selected lean operations practices through the identification of key areas and benefits under each of these practices. Design/methodology/approach Data were collected from 74 participants mainly from the academic community and who were university scholars through the use of an online survey. The used online survey consists of six main parts in total, but three were included in this paper and these were designed to gather data about participants’ general information, level of influence of seven technological trends on supply chain performance and management and potential impact of digitalizing supply chains on five lean operations practices. Findings The authors were able to confirm the significant impact of digitalizing supply chains on the five examined lean operations practices. Most of the examined potential impacts were found to improve certain areas that directly improve the practices of the explored five lean operations practices as well as the overall supply chain and business performance. They were also able to determine the level of influence of the seven examined enabling technologies on supply chain performance and management. Originality/value To the best of the authors’ knowledge, this study is the first of its kind. Although some literature explored different aspects related to the concept of Industry 4.0 and digitalizing supply chains, no study has specifically explored potential impacts of digitalizing supply chains on lean operations. The results from this study can be beneficial to academic scholars interested in the researched themes, business professionals specializing in supply chain management and lean operations, organizations within different industrial sectors particularly manufacturing where lean thinking is adopted and any other party interested in understanding more about the impact of digitalizing supply chain on lean operations and on an overall business performance.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".