Linear Cost Functions

To understand three basic types of linear cost functions and to see the role of cost functions in business decisions, consider the negotiations between StoreBox, a technology startup, and Forest Web Services (FWS), which provides StoreBox with enterprise-class cloud computing and data analytic services.

Panel A in Exhibit 10-1 presents this variable cost for StoreBox. Under alternative 1, there is no fixed cost for cloud services. We write the cost function in Panel A of Exhibit 10-1 as

y=$0.50X

  1. where X measures the number of CPU hours used (on the x-axis) and y measures the total cost of the CPU hours used (on the y-axis), calculated using the cost function. Panel A illustrates the $0.50 slope coefficient, the amount by which total cost changes when a 1-hour change occurs in CPU usage. Throughout the chapter, uppercase letters, such as X, refer to the actual observations, and lowercase letters, such as y, represent estimates or calculations made using a cost function.

Exhibit 10-1

Examples of Linear Cost Functions

Exhibit shows three line graphs depicting the variable cost, fixed cost, and mixed cost.

Y-axis shows total cost ranging from 0 to 2,000 dollars with increments of 1,000, and X-axis shows CPU hours used ranging from 0 to 8,000 with increments of 4,000. Details of each of the graph are as follows:

  1. Panel B in Exhibit 10-1 shows the fixed-cost alternative for StoreBox. We write the cost function in Panel B as

y=$1,000

  1. The fixed cost of $1,000 is called a constant; it is the component of the total cost that does not vary with changes in the level of the activity. Graphically, the value of the constant is the point where it intersects the y-axis. Therefore, the constant is also called the intercept. In this example, the constant accounts for all the cost because there is no variable cost. Graphically, the slope coefficient of the cost function is zero.
  1. Panel C in Exhibit 10-1 plots the mixed-cost function for StoreBox. We write the cost function in Panel C of Exhibit 10-1 as

y=$300+$0.20X

Unlike the graphs for alternatives 1 and 2, Panel C has both a constant value of $300 and a slope coefficient of $0.20. The total cost in the relevant range increases as the number of CPU hours used increases. However, the total cost does not vary strictly in proportion to the number of CPU hours used within the relevant range. For example, when 4,000 hours are used, the total cost equals $1,100 [$300+(0.20 per hour×4,000 hours)], and when 8,000 hours are used, the total cost equals $1,900 [$300+($0.20  per hour×8,000  hours)]. Although the usage in terms of hours has doubled, the total cost has increased by only about 73% [($1,900−$1,100)÷1,100] because some of the costs are fixed costs, which do not increase with the number of CPU hours.

StoreBox’s managers must understand the cost-behavior patterns in the three alternatives to choose the best deal. Suppose StoreBox expects to use at least 4,000 hours of CPU time each month. Its cost for 4,000 hours under the three alternatives are

Alternative 2 is the least costly. Moreover, if StoreBox expects to use more than 4,000 hours, alternatives 1 and 3 would be even more costly. StoreBox’s managers, therefore, should choose alternative 2.

Note that the graphs in Exhibit 10-1 are linear (straight lines). We simply need to know the constant, or intercept, amount (commonly designated a) and the slope coefficient (commonly designated b) to describe and graphically plot all values within the relevant range. The general form of this linear cost function is

y = a + b X

Under alternative 1, a=$0 and b=$0.50 per CPU hour used; under alternative 2, a=$1,000 and b=$0 per hour used; and under alternative 3, a=$300 and b=$0.20 per hour used.

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