Using Matrices and NumPy for Cost Allocation

This course discusses conventional methods of cost allocation when service departments are interdependent and their costs need to be allocated to production departments. It also introduces the use of spreadsheets, matrices, Python, and NumPy for solving practical cost-accounting problems.

The course begins with conventional approaches to cost allocation, including the direct allocation method, the step-down method, and the simultaneous equation or reciprocal method. These methods provide the accounting foundation for understanding how costs can be distributed between departments.

We then look at how the same cost-allocation problem can be represented using matrices. This provides a bridge between accounting calculations, mathematical equations, spreadsheets, and programming.

The course demonstrates how spreadsheets can be used to create reusable cost-allocation templates and how matrix calculations can be implemented in Python using the NumPy library. In particular, we use matrix inversion and matrix multiplication to solve a system of simultaneous equations and obtain the resulting allocation values.

Another objective of the course is to demonstrate the practical benefit of using standard, freely available coding libraries for line-of-business applications. Instead of implementing mathematical operations from scratch, developers can make use of established open-source libraries such as NumPy.

This approach can help reduce unnecessary custom code, simplify maintenance, reduce debugging effort, and make mathematical calculations easier to reuse and test. Google Colab, Python, NumPy, and online spreadsheets can also be used to experiment with and develop reusable templates without requiring a complex development environment.

By the end of the course, learners should understand the relationship between conventional cost-allocation methods, simultaneous equations, matrices, spreadsheets, and Python. The course is particularly useful for learners interested in cost accounting, management accounting, spreadsheets, quantitative methods, Python programming, and practical applications of linear algebra.