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Smart Warehousing

2025· book-chapter· en· W4411717762 on OpenAlex
Harleen Aggarwal, Parinita Malhotra

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAdvances in computational intelligence and robotics book series · 2025
Typebook-chapter
Languageen
FieldEngineering
TopicAdvanced Manufacturing and Logistics Optimization
Canadian institutionsWestern University
Fundersnot available
KeywordsComputer scienceBusiness

Abstract

fetched live from OpenAlex

Warehousing is an integral component of supply chain management, serving as a link between procurement and distribution. After witnessing a downfall during the COVID-19 pandemic, the 3PL market has experienced a rebound due to the adoption of digital solutions and technology. Gone are the days when organisations were using traditional warehouse management systems to manage and track the activities of a warehouse. Modern warehouse managing systems are software-based and are designed to be cost-effective, improve order fulfillment and delivery times, optimise inventory levels, improve lead times, reduce logistics costs and increase competitive advantage. This chapter focuses on introducing and analyzing the concept of smart warehousing. This chapter aims to enable the students to understand how the supply chain industry is embracing digital transformation by using Artificial Intelligence, IoT and analytics, advanced robotics and automation, RFID, automated guided vehicles (AGV) and Blockchain Technology.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.409
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.014
GPT teacher head0.245
Teacher spread0.231 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it