Demand Forecasting Wilkins Water Control Products, a Zurn Company: An Analysis of Current Demand Forecasting

Current Demand Forecasting

Any manufacturing company must try to find a way to maximize its profitability by minimizing production costs while at the same time maximizing sales potential. What this means is that a company must make sure that it produces enough units to meet demand while at the same time not over-producing and leaving itself with units -- which represent an investment of time and materials, i.e. money -- that it cannot sell (Armstrong & Green 2006). Determining optimum production levels can be a complex task, and must take into account a diverse range of considerations including materials cost, labor cost, and differences created based on economies of scale (generally speaking, the more units a firm produces, the lower the per-unit cost becomes) (Armstrong & Green 2006). Equally important for many manufacturers, especially makers of specialized products that are not widely used, is determining exactly how many units need to be produced in every production cycle (StatSoft 2011).

Efforts and methods for determining future production needs are called demand forecasting -- literally attempting to forecast or foretell the level of demand that will exist for a given product, service, company, etc. (StatSoft 2011). There are a...

Selecting the right demand forecasting method is an important part of overall forecasting.
For Wilkins, a manufacturer of various water control products and a subsidiary of the larger manufacturing company Zurn, the current demand forecast that would be most useful is a rule-based forecasting, where current demands trends can be extrapolated into the future as long as they make sense according to previous managerial experience (Armstrong & Green 2006). This demand forecasting method makes primary use of quantitative data, meaning precise figures that can be gathered over time and directly compared or compiled and analyzed, along with a fair amount of qualitative analysis to ensure the appropriateness of the data and the models or "rules" used (Armstrong & Green 2006). By utilizing this method, Wilkins would be able to establish the likely demand for its products in the near future based on demand trends in previous months and years, while at the same time noting any significant differences in these trends from previous experiences and expectations as a means of determining if other forces are at work affecting demand.

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