Digital Transformation as an Enabler of Sustainability in Supply Chain and Logistics: Evidence from the Field
Bibliographic record
Abstract
Digital transformation (DT), represented by technology trends like Artificial Intelligence (AI), Big Data (BD), Internet of Things (IoT) are a reality for most business. Most companies are implementing DT strategies, and the Global Pandemic may have accelerated the digitization of businesses. Concurrently, another set of trends focus on the need for improving the sustainability of business processes. It is now widely accepted the Earth cannot tolerate a continue, average increase in global temperatures at the rate it is currently experiencing. Governments committed to carbon emission goals have tighten environment regulations. Moreover, customers and even investors have shifted their mindset and sustainability related performance indicators are now part of the decision of doing business with a company or not. Lastly, an increasing number of business leaders are implementing principled leadership approaches, including social and environmental issues in their agenda. To make the matters worse, new technology developments like AI have the reputation of being power hungry requiring increasingly larges amount of energy to run. It is arguable DT may be further worsening the environmental crisis. As a sign of hope, there are multiple industry cases of the application of digital technologies, notably AI supported by BD, as enablers of sustainable business practices have emerged in recent years. The main objective of this research is to conduct an analysis of select industry examples investigate the potential of DT as an enabler of sustainable business processes instead of an additional cause of concern. This research will more specifically focus on studying DT as an enabler sustainability in supply chain and logistics since upstream and downstream carbon emissions correspond in average to 70% of a company’s carbon emissions not directly related to their primary processes.
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How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".