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La prévision des ventes

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Leitmotiv

Nomenclatures correctes et à jour précises à 98%.

Gammes de fabrication précises à 95%

Relevés de stocks à jour précis à 95%.

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Horizon de planification

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Exemple de séries temporellesFig. 2-2

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Erreurs de prévision dans le tempsFig. 2-3

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Moyennes mobilesFig. 2-4

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Importance des poids sur les prévisions pour le lissage exponentiel

Fig. 2-5

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Exponential Smoothing for Different Values of Alpha

Fig. 2-6

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Exemple de régression linéaireFig. 2-7

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Serie saisonnièreFig. 2-8

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Série saisonnièreFig. 2-9

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Exemple PHEA

Période Quantités vendues

H 98 5500P 98 5100E 98 3500A 98 2900H 99 6500P 99 6300E 99 5000A 99 3800H 00 7800P 00 7300E 00 6000A 00 5400H 01 9200P 01 8800E 01 7000A 01 6300

Quantités vendues

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000

H98

P98

E98

A98

H99

P99

E99

A99

H00

P00

E00

A00

H01

P01

E01

A01

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Exemple

Hiver Y calculé Y réel Y réel / Y calculéH 98 4332 5500 1.270H 99 5232 6500 1.242H 00 6132 7800 1.272H 01 7032 9200 1.308

1.27310098

Indice moyenPrévision pour l'hiver

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Series saisonnières avec augmentation de la demande

Fig. 2-10

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Initialisation de la méthode de winters

Fig. 2-11

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Difficultés rencontrées avec les prévisions à long terme

Fig. 2-12

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Tracking the Mean When Lost Sales are Present

Fig. 2-13

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Tracking the Standard Deviation When Lost Sales are Present

Fig. 2-14

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Logistic Curve (for Problem 45)

Fig. 2-15