Wilkins, A Zurn Company: Demand Forecasting

WILKINS, A ZURN COMPANY: DEMAND FORECASTING

Submitted by Group 9 –

Shray Yadav Shashank Dev Shrivastava Raj Ratan Soren Shabhnam Kerketta Manisha Toppo Sangeeta Bori

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5C Analysis Company • Wilkins produces plumbing, municipal waterworks, fire production, and irrigation equipment • Zurn Industries acquired Wilkins in 1971 • Merged with Bath & Plumbing Products co. in 1998 • Changed name to Jacuzzi Brands in 2003 • General plumbing market represented half of its revenue • Achieved strong growth through favorable pricing, product innovation and targeted marketing program 2

Collaborators • Most common client contacts are transportation/

logistics managers, procurement managers • Account executives look for ways to partner with a customer service managers Customers • Commercial and industrial constructors • Residential and remodeling constructors • Affected by many external factors

• Cyclic nature of demand

5C Analysis Context 

Weather forms an important variable for the organization as it significantly impacts construction

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The plumbing products business is dependent upon commercial and institutional construction activities and affected by macroeconomic factors such as:

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Rate of unemployment

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Availability of financing opportunities

The company has achieved substantial growth due to favourable pricing, product innovation and targeted marketing programs

Decision Problems • Developing a new demand forecast model • Current model ignores external factors while forecasting demand • Current model cannot deal with new products properly due to its dependency on historical data • If information like the unemployment rate, bank prime loan rates and number of housing starts can be used as indicators of demand

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Recommendation s • Account for factors like seasonality and dummy variables as time increases to obtain more accurate results • Having an inaccurate forecasting method will increase the risks of increased inventory costs leading to a decrease in profits • We can calculate the accuracy of the existing method and compare it with regression for each variable to see which method provides more accuracy

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Thank You

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