Supply/demand chain modeling utilizing logistical‐based costing
Notice bibliographique
Résumé
Purpose The purpose of this research is to describe how total cost concept with logistical based costing (LBC) is developed in detail and then used to build logistical models on the Microsoft Excel™ platform that are integrated from the customer's factory to the supplier's door. Design/methodology/approach The models developed in this project are deterministic, event‐based algorithms to compare logistical conduits for bulk and containerized commodities. The demand chain approach is used to derive the pathways in reverse order from the customer to the supplier. The methodology is necessary to find all possible conduits from origin to destination, including points where product may cross over between various logistics systems. The approach is applied to the bulk and container system with disconnects (elevators, ports) serving as the demarcation points. The pathways from supplier to end‐user must be identified prior to application of classification and costing techniques. A goal of this research was to compare the per unit cost of two different logistical systems – bulk versus container – in two case studies. The first case study was for a miller in Northern China and the second was for a mill in Helsinki, Finland. Findings The spreadsheet models produced results that were within 3 percent of real world costs. Each demand chain was shown to be unique and required customized cost functions to properly configure algorithms. Research limitations/implications The paper suggests that, while a core algorithm may exist for all supply/demand chains, no one particular algorithm configuration suffices. Each supply/demand chain is unique, in terms of both costs and performance. The use of modular cost functions provides the customization necessary to address this issue. Practical implications This project verifies that successful implementation of a model is dependent on following a set of procedures that begins with a clear statement of what the model is to measure, along with what is to be included and what are the constraints imposed on the algorithm. Mapping the flow of the goods through logistical systems provides visibility as to where costs are incurred and how they are to be assigned to the supplier or customer. An improperly assigned variable in the early stages of a supply/demand chain reduces accuracy of subsequent calculations. LBC increases the precision of models by properly establishing the configuration of cost drivers for each stage of the supply/demand chain by avoiding the use of the cost averaging used in statistical analysis. Originality/value This paper provides a standardized approach for mapping, costing and building global supply/demand chain models. The ultimate customer, once thought of as the “end of the line”, now dictates the cost and performance requirements of logistical conduits. While this paper encapsulates methods for building total cost models from the customer's perspective, other configurations can be readily constructed to examine physical and performance characteristics.
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 | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,000 |
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 ».