Cost estimation in cost accounting involves predicting and calculating the future costs associated with various business activities, projects, or products. Different methods can be used for cost estimation, and the choice of method often depends on the nature of the costs being estimated and the available data. Here are some common cost estimation methods in cost accounting:

**1) Historical Costing**

Based on historical cost data, taking into account past costs associated with similar activities or products. This method of cost estimation is suitable when historical trends are reliable indicators of future costs. One of the disadvantages of this method of cost estimation is that it may not be accurate if significant changes in technology, processes, or market conditions have occurred.

An example of historical cost could be a company that purchased a building in 1955 for a price of $20,000. In its accounting record, the asset value is $20,000. In the real market, however, this asset is valued at $875,000. Under the historical cost principle, the asset would remain in the company’s books at $20,000.

The current value of an asset like a vehicle which usually depreciates can be calculated by subtracting any accumulated depreciation that has been recorded for that asset.

**2) Account Analysis**

Account analysis is a method of cost estimation** which **involves a detailed examination of various accounts in the general ledger to estimate costs associated with specific activities. It is also known as inspection of accounts. This method is useful for estimating costs that are not easily traceable to specific products or projects. It relies on the judgment and expertise of the analyst, and the accuracy may be affected by changes in business conditions.

*Illustration: *

Management has estimated KSH 1,090 variable costs and KSH 1,430 fixed costs to make 100 units using 500 machine hours. Since machine hours drives variable costs in our example, the variable cost stated as:

VC = 1,090/500 = 2.18.

For 550 machine hours, the total cost is given as:

TC = 1,430 + 2.18(550) = 2,629.

**3) Scatter graph Method**

This method of cost estimation plots historical cost data on a scatter graph to identify trends and patterns, helping estimate future costs based on the relationship with a relevant variable (e.g., production volume). Scatter graph method is also called visual fit. Past data on cost levels and output levels is plotted on a graph called a scatter graph and then a line of best fit is drawn as shown in the diagram below.

A line of best fit is a line drawn to cover the most points possible on a scatter graph. It can also be defined as a straight line used as a best approximation of a summary of all the points in a scatter-plot. Its intersection with the vertical axis indicates the fixed cost while the gradient indicates variable cost per unit.

A scatter graph is suitable for estimating variable costs that vary with changes in activity level. Its limitation is that it assumes a linear relationship between costs and the chosen variable.

**4) High-Low Method**

The High-low method of cost estimation involves selecting the highest and lowest points of activity levels and their corresponding costs, using this information to estimate fixed and variable costs. Here, cost estimation is based on the relationship between past cost and past level of activity (output). Variable cost is based on the relationship between costs at the highest level of activity and the lowest level of activity. The difference between high and low activity level is taken to be the total variable cost from which the unit variable cost can be computed by dividing by the change in output level. The formula for calculating total variable cost using the high-low method is shown below:

Total Variable Cost = Cost at high activity level – Cost at low activity level.

**Illustration:**

Evans, the Managing Director of Mambo Company, has asked for information about the cost behavior of manufacturing overhead costs. Specifically, he wants to know how much overhead cost is fixed and how much is variable. The following data are the only records available:

Month |
Machine Hours |
Overhead Costs |

February | 1,700 | 20,500 |

March | 2,800 | 22,250 |

April | 1,000 | 19,950 |

May | 1,000 | 21,500 |

June | 3,500 | 23,950 |

**Required:**

Using the high-low method, determine the overhead cost equation. Use machine hours as your cost driver.

**Solution:**

Identify the cost driver, which in this case is machine hours. To find the fixed cost, you have to first find the variable cost per unit, which is calculated as:

Unit variable cost = variable cost/output cost

= __Cost at high level activity – Cost at low activity level__

Units at high level activity – Units at low activity level

Variable cost per unit = (Sh23,950 – sh19,950)/(3,500hrs-1000hrs)

= Sh. 4,000/2,500hrs

= *Sh. 1.6 per hr*

Fixed cost is then calculated by substituting “b” in the straight line equation as follows:

Straight line equation is given as Y = a + bX.

Given that b = Sh. 1.6 per HR, when machine hours (Y) = 1,000, overhead cost (total cost, Y) = 19,950;

Then, 19,950 = a + 1.6(1000)

19,950 = a + 1,600

a = 19,950 – 1,600

a = **18,350**

The equation is** Y = 18,350 + 1.6X**

NB: ‘a’ is the fixed cost, ‘b’ is variable cost per unit, ‘X’ is number of units (machine hours), and ‘Y’ is the total cost.

The limitation of the high-low cost method is that it assumes a linear relationship and may not be accurate if costs are influenced by factors other than the chosen variable.

**5) Regression Analysis**

This cost estimation method utilizes statistical techniques to identify relationships between costs and relevant variables, allowing for more accurate predictions. It is suitable for analyzing multiple variables and capturing complex relationships. Its limitation is that it requires advanced statistical skills, and the accuracy depends on the quality and quantity of data.

Regression analysis has a mathematical base of all regression lines that could be drawn to represent the data. The least square regression line of Y on X is that line for which the sum of squares of vertical deviations of all the points from the line is least. It involves estimating the cost function using past data or the dependent and the independent variables. The dependent variable will constitute the relevant cost, which may be service, variable cost, overhead cost, etc. The independent variable will be the cost drivers where the cost drivers will be labour hours, units of labor or raw materials, units of output, etc.

In regression analysis, a regression model of the form Y = a + bX for a simple regression is obtained. This formal model measures the average amount of deviation of the dependent variable that is associated with unit changes in the amount of the independent variable. For a multiple regression, a regression model of the form Y = a + b1X1 + b2X2 + … + bnXn is obtained;

Where a is fixed cost,

X1, X2, Xn are cost drivers, and b1, b2, bn are changes in cost with the change in value of cost driver i.e. variable cost per unit of change in X1, X2, X3.

Y is the dependent variable (total cost)

Note that a simple regression produces a cost function of the form Y = a + bX; but a multiple regression produces a cost function of the form Y = a + b1X1 + b2X2 + … + bnXn so that we have several variable costs per unit (b1, b2, b3, bn) and several independent variables X1, X2, X3, Xn). The general formulas used to compute a and b are as follows:

*Assumptions of the Regression Analysis:*

- There exists a cause and effect relationship between the variables. That is, a change in the independent variables causes a change in the dependent variable.
- There is a good evidence of correlation. In this case, linearity of costs exists. Correlation is the degree of relationship between variables which seek to determine how well linear or other equations, explain or describe, the relationship between variables.
- The historical data used covers a large level of activity.
- Only one independent variable or activity base affects costs. This is the case of simple regression analysis where only one independent variable exists.

**6) Learning Curve Analysis:**

The learning curve analysis assumes that as production levels increase, the time required to produce each unit decreases due to learning and experience. It is commonly used in industries with repetitive production processes. This method of cost estimation is applicable mainly to labor-intensive processes and may not be suitable for all types of activities.

**7) Engineering Cost Models:**

This method of cost estimation uses detailed engineering analysis to estimate costs based on the physical characteristics and specifications of a product or project. This method is suitable for complex and unique projects where detailed technical information is available. It requires specialized knowledge and may be time-consuming.

**Conclusion**

Each cost estimation method has its strengths and limitations, and the choice of method depends on factors such as the nature of the costs, the availability of data, and the specific requirements of the estimation task. Often, a combination of methods or sensitivity analysis may be used to enhance the accuracy of cost estimates.