Demand forecasting methods and accuracy implications for company operations

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 variety of different demand forecasting methods that can be utilized, depending on the specific industry or products of a given company, the amount and type of information available, and several other factors (Armstrong & Green 2006). 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.

Plant managers and operations executives will make the most direct use of demand forecasts, though they will also be useful for marketing and sales departments and even for the operational personnel -- the individuals actually involved in the manufacturing processes. If the forecast proves to be inaccurate, there are several potential consequences. A forecast that leads to production levels that are too low would see Wilkins unable to meet demand, souring relationships with clients, while a forecast that is too high will see overproduction, potentially leading to massive price cuts and greatly reduced (if not eliminated) profitability.

Demand Forecast Differences

Without direct knowledge of current and past sales records, which Wilkins does not provide to the public, it is impossible to present an accurate demand forecast for the company or its products (Wilkins 2011). There are some changes in the company, however, that could seem to signal that a change in the demand forecast that would preclude the association of these trends with past experience as a means of gaining any sort of accurate insight into future demand levels. Specifically, Wilkins has introduced a new product that differs in its construction and performance from some the other pressure vacuum breakers and other backflow preventers/water control devices that the company sells (Wilkins 2011). This could mean that demand forecasts for its other products might decrease significantly, while demand for the new backflow preventer the company has begun to manufacture could increase significantly if its cost and performance claims are accurate (Wilkins 2011).