Need for a Robust Asset Management Business Algorithm
Notice bibliographique
Résumé
The Sarbanes-Oxley Act created new standards for corporate accountability pertaining to all publicly-owned and traded firms. It holds top executives accountable for the accuracy of all financial data and statements, including reported tangible assets. It requires existence of auditable internal accounting control measures and specifies adherence to new internal controls and procedures designed to ensure the validity of their financial records and physical assets. The Act presents a challenge to every manufacturing firm to have a low-cost system implemented that can produce an exact physical-asset location, existence, verification and accounting on demand. Clearly, such low-cost solutions for enterprise-wide compliance would also provide verifiable and reliable data for corporate property tax, loan collateral, and audit requirements. In 2011, Chrysler LLC conducted a study for an improved and efficient process to locate, verify and track OEM-owned tooling assets at supplier sites, located across the globe. The collaborative effort focused on an innovative and comprehensive business algorithm (ALEXTM), for automotive tooling's “Acquire to Retire” asset locating, tracking and verification. A proof of concept pilot was initiated to evaluate the feasibility and effectiveness of the asset-acquire stage of the ALEXTM Business Algorithm. The initial phase of this pilot was focused on a third party tooling certification/verification and a supply-chain based annual validation alternative. Subsequent development phases will focus on the scalability of the ALEXTM Business Algorithm for new and legacy tooling, as well as capital equipment. This paper presents the current state of challenges with regards to supply chain asset management and inventory. It also outlines the requirements for a robust asset locating and management system. Introduction This is the first of three sequential papers, addressing financial and legal concerns related to the capability of automotive Original Equipment Manufacturers (OEMs) to accurately locate their physical assets and verify their existence as well as value in real-time. Part I focuses on current practice and issues related to asset location and verification as exercised in automotive and aerospace industries. Part II will discuss plausible solutions to asset location and verification capability based on existing technology. Part III will present a low-cost, effective solution proven through a pilot project that deploys a business algorithm for Asset Location and Existence verification (ALEXTM) and an Asset Management Identifier (AMITM). Having a full, accurate, and reliable account and control of physical assets has been a challenge for any mid-size to large manufacturing firm, particularly in the automotive industry, where the number of OEM-owned production tools at suppliers and their sub-contractors could range from 300,000 to more than a million. Such numbers of production tools would consist both of those tools in current production as well as legacy and service component tooling. Full, accurate, and reliable account and control of physical assets is important to the manufacturing firm's financial status and health. Additionally, public companies would have legal ramifications vis-a-vis 2002 Sarbanes-Oxley (hereafter SOX) Act1. for a Robust Asset Management Business Algorithm 2014-01-0783 Published 04/01/2014 Seyed M. Mirmiran and Vern Scott Chrysler LLC Bill Swenson and Stephen Funtig Accelero Solutions Inc. CITATION: Mirmiran, S., Scott, V., Swenson, B., and Funtig, S., Need for A Robust Asset Management Business Algorithm, SAE Technical Paper 2014-01-0783, 2014, doi:10.4271/2014-01-0783. Copyright © 2014 SAE International Downloaded from SAE International by Seyed Mirmiran, Thursday, March 06, 2014 09:03:06 AM
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,006 | 0,015 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,006 | 0,006 |
| Science ouverte | 0,003 | 0,003 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,005 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».