Digital Supply Chain and Sustainability Challenges
Bibliographic record
Abstract
Abstract The convergence of the digital supply chain (DSC) with sustainability presents promising opportunities and notable challenges for today's enterprises. This chapter explores the relationship between digital supply chain management (DSCM) and sustainability within the context of supply chain management. It begins by providing a detailed overview of the digital transformation in supply chain management, emphasizing its rapid evolution and its profound impact on sustainability. The chapter then delves into the various sustainability challenges that manifest within DSCs, including issues related to energy consumption, e-waste management, and environmental impact. It goes beyond environmental considerations, exploring social and ethical dimensions, such as the potential consequences of digitalization on socioeconomic disparities. Moreover, the chapter presents a comprehensive framework of best practices and strategies aimed at navigating these challenges. It highlights the utilization of digital technologies like artificial intelligence (AI), Internet of Things (IoT), and blockchain to enhance transparency, efficiency, and responsible sourcing. Real-world case studies are included to exemplify successful implementations of sustainable practices across diverse industries. In conclusion, the chapter emphasizes the imperative of a holistic, authentically sustainable supply chain transformation, underpinned by digital technologies, to advance sustainability objectives while adeptly addressing emerging challenges in the ever-evolving landscape of supply chain management.
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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 | 0.000 |
| 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".