Operation Production Report on Solar-Heat Water Heater

 
 
 
 
OPERATION PRODUCTION REPORT: SOLAR-HEAT WATER HEATER

The purpose of this paper is to develop an Operation Production Report for a solar-heat water heater. The report justifies the need for the product, details the conditions for success, and evaluates the potential for success.

The findings of this report justified a market need for the product. The findings also defined the assumptions and performance characteristics that must hold if the venture is to be successful over the longer-term.

The value-added pricing for the solar-heat water heater is well below the pricing of competing products. It is absolutely crucial therefore, for the company to assure that its supply chain management is as efficient as it can be.

Water heating is the third largest energy expense for the typical household in the United States. Water heating typically accounts for about 14 percent of a household's utility costs (Green Building Program, 2003).

There are four ways major strategies for reducing costs associated with water heating. The four strategies are as follows:

Use less hot water (frequently more easily said than done)

Lower the heat-level of the water heater thermostat (many, perhaps most, people, are unsatisfied with lower heating levels)

Insulate an existing water heater (an effective strategy up to a point; also one that does nothing to reduce dependence on fossil fuels)

Install a new, more efficient water heater (today, the choices in suc


     
 
 
 
    

 

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o a time series is the most prevalent means of describing long-term trends. The arithmetic trend is a linear equation with a constant absolute change in level per time period. The arithmetic linear trend for a data series is obtained most frequently by finding the linear regression between a dependent and independent variable, using all prior values of the variables (Russell & Taylor, 1997). Exponential smoothing is an exponentially weighted moving average. The exponential smoothing approach makes use of all data, with appropriate weighting. In the exponential smoothing approach, the weight given to past data decreases geometrically with increasing age of the data (Tersine, 1993). Thus, while all data are represented, as is true in the arithmetic mean approach, greater weight is given to the most recent data. The exponential smoothing approach, thus, attempts to incorporate the best of each of these approaches to trend analysis in a single model. Exponential trend projection discards the assumption of some absolute change in the value of the variable being projected through the use of trend analysis, and holds, instead, that proportional changes in the value of the projected variable will occur (Tersine, 1993). The trend

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