# Wilkins, a Zurn Company: Demand Forecasting

Topics: Regression analysis, Forecasting, Time series analysis Pages: 3 (827 words) Published: December 8, 2012
The current demand forecasting method is based on qualitative techniques more than quantitative ones. If the forecast is not accurate, the company would carry both inventory and stock out costs. It might lose customers due to shortage of supply or carry additional holding costs due to excess production. If the actual demand doesn’t match the forecast ones, and the forecast was too high, this will result in high inventories, obsolescence, asset disposals, and increased carrying costs. When a forecast is too low, the customer resorts to a competitive product or retailer. A supplier could lose both sales and shelf space at that retail location forever if their predictions continue to be inaccurate. The tolerance level of the average consumer for product outages is quite low. Forecasting for PVB:

YearPVB totalTimeD1D2D3
2001Q1275121100
2001Q2457982010
2001Q3769683001
2001Q4438584000
2002Q1305805100
2002Q2531986010
2002Q3887047001
2002Q4515908000
2003Q1353729100
2003Q25784010010
2003Q39338811001
2003Q45890612000
2004Q13938213100
2004Q27521914010
2004Q312286815001
2004Q45499616000

SUMMARY OUTPUT

Regression Statistics
Multiple R0.971266712
R Square0.943359025
Standard Error7165.900459
Observations16

ANOVA
dfSSMSFSignificance F
Regression494076362192.35E+0945.801428.58623E-07
Residual11564851423.251350129
Total159972487642

CoefficientsStandard Errort StatP-valueLower 95%Upper 95% Intercept32561.18755374.4253446.0585438.21E-0520732.1570844390.21792 Time1977.63125400.58601364.9368450.0004451095.9473792859.315121 D1-13193.10635207.618177-2.533420.027803-24654.99657-1731.215929 D29631.51255130.0040261.8774860.087206-1659.550226...

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