Cost modeling is a mathematical process that models the cost or pricing of a product or service. Cost modeling may take many variables into account to produce the most accurate and predictive estimate of cost.
Predicting cost is a common task among procurement professionals working to raise margins and profitability through cost optimization.
Historically, cost modeling has been done using statistics or algorithms to produce a final estimate. These estimates were typically based on samples of past data. To do this data collection and build cost estimates, companies had to take a selection of their products or services, calculate the average cost per product or service, and then apply that average cost to every other product or service.
As you can imagine, there are many shortcomings to this method of cost modeling. For example, the dataset might not consider external factors like inflation. In addition, it can ignore the life cycle stage of a product, and an algorithm may not be able to handle the many intertwined variables.
Cost modeling has been taken to a whole new level with the introduction of AI, particularly machine learning and deep learning. Advanced machine learning algorithms can take into account complex, dynamic factors such as inflation and provide more accurate and predictive estimates for future costs. This is because machine learning can process more data than humans can at any given time.
These cost models unlock automation - they can be updated in real-time and can be built without the need for technical experts or data scientists, so businesses can quickly and easily predict cost information as a part of automated decision-making. As a result, this new way of cost modeling has become an essential tool for many industries and businesses, from global corporations to small businesses.
Let’s explore several specific examples and case studies to see the advantages of AI cost modeling.
Medical care costs account for a fifth of government spending. At the same time, private individuals are spending massively on healthcare as well, and one study claims that medical issues cause over three-fifths of bankruptcies.
Cost modeling medical patient cost is of interest for several reasons. First, cost modeling can optimize targeted care by anticipating the health care needs of high-cost patients, in particular. Moreover, cost modeling can improve our understanding of expensive events in healthcare. In addition, cost modeling helps to investigate whether healthcare resources are equitably allocated, as cost modeling outcomes should not vary with a patient’s socioeconomic status.
Cost modeling for healthcare is crucial on the government level. Many healthcare providers are paid via capitation fees to cover the cost of their patients, including in Austria, Bulgaria, Estonia, Ireland, Italy, England, and many other countries.
Traditionally, cost modeling medical patients was done with primitive means like taking the average cost per diagnosis for a given gender and age. For example, the average benchmark for a visit to a general practitioner in the United States is roughly $300-$600. However, the variation in costs is enormous, which means that simply applying the average is bound to be highly inaccurate.
With artificial intelligence, we can include a wide range of variables for far more accurate cost modeling, such as BMI, whether or not the patient smokes, whether or not the patient has children, and so on.
You can read our hands-on guide on machine learning-powered cost modeling and optimization for healthcare here.
When considering the cost of goods sold for retail companies, many factors need to be examined.
Traditionally, cost modeling would be based on calculating the working capital needed for operating expenses such as payroll and operational costs such as rent and utilities.
However, many metrics are harder to forecast, such as supply factors and macroeconomic factors. With machine learning, you can create predictive models that take these harder to predict metrics into account. You can then include them in forecasting activities based on more accurate cost models.
Governments have many different types of cost analysis to do. These include cost estimates for payroll, taxes, and interest on debt owed to bondholders, pension obligations, and feasibility studies for infrastructure projects. To calculate these costs accurately, it's important to take into account basic factors like the age and number of employees and the size of the workforce, as well as more complex factors like forecasted growth and the rate of hiring. For example, addressing the age question is essential when considering pension obligations because pensions paid to older employees are more expensive than pensions paid to younger employees.
With artificial intelligence, these considerations are automatically learned from the data to create accurate cost models.
Server costs are a significant part of the cost of running an online business but are typically the most overlooked component of a project cost or budget. This is often because it's difficult to understand how much money goes into a server's cost.
Forecasting server costs is inherently volatile. This is because server costs depend on the price of several resources, such as electricity and computation power, as well as demand. The price of these resources is constantly fluctuating and rarely follows a set pattern.
The first way to do server cost estimation is by looking at the upfront cost drivers. The upfront cost typically includes the hardware and software necessary to run the server. This will also include any licensing fees that may be necessary to use the software.
The second way to understand how much a server costs is by looking at ongoing costs. Ongoing costs are the cost of running the server, such as electricity, compute usage, and labor. These can also include software maintenance fees.
With AI, any of these metrics can be used to build accurate predictions for cost management with a machine learning model, giving you project management superpowers.
In the aviation industry, the cost to transport a passenger mile is a measure of multiple factors, including how much fuel an airline uses per passenger, the costs of airplane maintenance and fees, and more.
This is an important metric because it reflects the airline's profitability and indirectly reflects the quality of service. For example, for an airline to remain profitable, it needs to keep its costs low. If an airline's cost per passenger mile is too high, it will not provide a quality experience and eventually go out of business.
Airlines need to consider the different types of costs they incur per passenger mile to maximize their cost savings, including transport-related costs (e.g., fuel), other operational expenses (e.g., landing fees), and marketing costs.
To do any of this, accurate cost modeling is key. Aviation is increasingly turning to AI models to do this work.
There are several factors to consider when it comes to cost modeling, from the complexity of your data to the resources you have available. In this guide, we’ve covered a number of these factors to help you understand how artificial intelligence can be a valuable tool for cost modeling and automation.
If you’re interested in building cost models, we encourage you to try Akkio. It’s a simple, affordable, and powerful no-code solution to building machine learning models that will help you model costs more effectively than ever before -- no code or data science team needed.