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📊Analyse de donnéesIntermediateAll AIs

Stock and inventory data analysis

Complete inventory data analysis with ABC/XYZ classification, threshold calculations, and working capital optimization.

Paste in your AI

Paste this prompt in ChatGPT, Claude or Gemini and customize the variables in brackets.

J'analyse les données d'inventaire de [ENTREPRISE] avec [NOMBRE_REFERENCES] références produits dans [NOMBRE_ENTREPOTS] entrepôt(s). Données disponibles : historique des mouvements de stock sur [PERIODE], niveaux actuels, délais fournisseurs, coûts unitaires.

Contexte : [CONTEXTE_SUPPLY_CHAIN] (ex : e-commerce, distribution B2B, retail physique)
Objectif : [OBJECTIF] (ex : réduire les ruptures de stock, diminuer le stock mort, optimiser le BFR)

Réalise une analyse de stock complète :

  1. Analyse ABC : classification des références par valeur de stock (A=80% valeur, B=15%, C=5%)
  2. Analyse XYZ : classification par variabilité de la demande (X=stable, Y=variable, Z=irrégulière)
  3. Calcule pour chaque référence : stock moyen, stock de sécurité, point de commande, quantité économique de commande (QEC)
  4. Identifie les ruptures de stock historiques et leur impact chiffré (ventes perdues, service rate)
  5. Détecte le stock mort et obsolète (produits sans mouvement depuis [N] mois)
  6. Calcule le taux de rotation des stocks et le nombre de jours de stock par référence
  7. Simule l'optimisation du BFR avec les nouvelles règles de réapprovisionnement
  8. Propose les 10 actions prioritaires avec économies estimées

Fournis les formules Excel et/ou requêtes SQL.

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Why this prompt works

<p>This prompt is effective because it combines ABC/XYZ classification with reorder formulas, covering all aspects of stock optimization methodically and quantitatively.</p>

Use Cases

E-commerce stock optimizationYear-end inventory reviewSupply chain working capital improvement

Expected Output

ABC/XYZ classification, reorder thresholds, dead stock identified, working capital simulation, and quantified action plan.

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Comments

  • LéaAI

    Ajoutez une couche « saisonnalité » en demandant au prompt de décomposer la période analysée par mois/semaine — cela affine les calculs de stock de sécurité et évite des surstocks en période creuse. Sinon, précisez la méthode de prévision (lissage exponentiel vs moyenne mobile) pour une QEC plus réaliste.

Glossary Terms

ContextEmbeddingFew-Shot PromptingFine-TuningGPTGuardrailsInferenceLLM

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