Driving Visibility, Velocity and Versatility: The Role of Mobile Technologies in Supply Chain Management
Bibliographic record
Abstract
Over the last decade, researchers and practitioners have recommended a myriad of information and communication technologies to operationalize the notion of a virtual enterprise. Among the business process transformations enabled through these new technologies, companies are using wireline and wireless technologies in redefining their interactions with suppliers, customers, and business partners. It is hoped that this move towards a truly collaborative supply chain environment will help the firm achieve a sustainable competitive advantage in the marketplace. Towards that end, supply chain planners are emphasizing the need for information exchange anytime, anywhere as a compelling driver for organizations to adopt mobile technologies in their intra- and inter-organizational business practices. This paper discusses the applications of mobile technologies in various areas of supply chain management, and the potential benefits along the dimensions of reduced replenishment times, and transactions and billing cycles. Among other applications, we highlight the role of these technologies in improving processes such as inventory management, product identification, mobile procurement, package tracking, and sales force and field service. To substantiate the basis for adopting mobile technologies for supply chain management, different market drivers for mobile applications are exemplified and applied to the three macro level processes of supplier relationship management, internal supply chain management, and customer relationship management. Several types of legacy, third generation (3G) and emerging mobile services and applications are discussed, and a resulting taxonomy of bearer technologies is presented to explicate their use in upstream and downstream supply chain processes.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".