The progress made in the last 20 years in the field of statistics have made it possible to develop predictive algorithms that are much more efficient, especially in terms of precision. What are the possible applications in the field of cost estimation and modeling? While traditional analytical models based on the manufacturing processes of the product or service are still widely used in our Cartesian society, statistical models are gradually imposing themselves to their formidable efficiency. But rather than an opposition, these two methods are enriched and complement each other.
Traditional costing models
As a reminder, there are now 3 main methods used to estimate the cost of a product:
Artificial Intelligence paves the way for a fourth model of cost modeling
The advances made in algorithmic and machine learning in recent years largely solve the disadvantages of traditional parametric methods and improve their performance and their field of application.
Among the recent statistical methods, the random forest algorithm, formally proposed in 2001 by Leo Breiman and Adèle Cutler (Breiman, L., Random Forests, Machine Learning, 45, 5-32 (2001) is a non-parametric approach that performs learning on multiple decision trees driven on slightly different subsets of data generated by Bootstrap techniques.
1/ What are the advantages?
The main advantages of this artificial intelligence algorithm are:
- Ability to model a very large number of parameters (“cost drivers”) and particularly qualitative or “symbolic” parameters
- Ability to process databases where the number of variables largely exceeds the number of observations
- Ability to identify and weight automatically the most important parameters, and thus the “cost drivers” that impact most the cost of the product
- Ability to manage missing values / incomplete databases
- Robustness to outliers
- Ability to identify behavioral breaks in variables
- Interpretation of the tree
- Precision increased by 30 to 40% compared to traditional methods
2/ What are the applications?
The applications of these algorithms are numerous, especially in the medical, insurance, marketing targeting (with uplift methods).
The application of random forests in the field of cost estimation solves many of the disadvantages of traditional parametric approaches and opens to new opportunities for companies interested in efficiency and competitiveness.
A precise estimate of costs is now possible, even with a limited number of observations (a few dozen), limiting the resources used to collect and capitalize the data. On the other hand, the price of complex systems can be modeled from easily accessible functional cost drivers, making encryption particularly simple and fast. Thus, for an equipment manufacturer, we were able to model the cost of an air conditioning system almost exclusively from functional or environmental parameters such as the volume to be air-conditioned, the number of openings, the time required to reach the target temperature, etc.
For this reason, random forests have begun to be used by some companies in the early phases of the product life cycle, including:
- Gain productivity on their encryption activities (saving time and resources that they can focus on technological innovation figures)
- Respond more quickly to their clients’ tenders and especially use this time saving to better optimize their proposal
- Secure and optimize their margin on new business
It is not surprising that the first users were sectors with strong encryption and product development activities (automotive, capital goods, consumer goods, etc.).
The second step was to use these algorithms to perform consistency or price inconsistency analyzes by identifying products with large discrepancies between the actual price and the estimated price. The explanatory properties of random forests (classification with similar products) make it possible to argue with suppliers during negotiations and thus to generate savings in purchases.
Finally, once the model is perfectly calibrated, it becomes a cost control tool to validate the fair price offered by the supplier. This reduces the bargaining process.
3/ What are the opportunities?
The opportunities offered by random forests in the field of cost estimation and optimization are therefore enormous and far from being fully exploited. Beyond cost optimization, the self-learning of the algorithm on the data of companies and their suppliers makes it possible to consider intelligent contributions such as the automatic preparation of negotiations (objectives, levers arguments …), the proposing optimized designs or redesigns, recommending the most adapted purchasing strategies anticipating supplier behavior …
In conclusion, the 2 approaches are complementary in their use:
In conclusion, it would be futile to oppose the analytical and statistical methods of cost estimation. They complement each other in their use and purpose. The statistical method, which is more consistent because it is based on the observation of the actual data, makes it possible to obtain a rapid and precise evaluation to make the right decisions in the product design or redesign processes. Simple to implement, it allows to model many families of products and services in a non-intrusive way and without needing to acquire an advanced technological expertise. The analytical method allows to obtain an encryption precisely reflecting the reality (or the simulation) of a manufacturing process. More tedious to implement, on the other hand it allows to define targets of cost to be reached with explanatory factors based on the observed industrial parameters and benchmarks. In this sense, it is more appropriate to quantify technological breakthroughs and to lead industrial suppliers’ progress plans to bring them to the target. It is also more relevant to quantify technological innovations on which the company does not have a history.
Nevertheless, self-learning algorithms and deep learning open new horizons and fields of application for the use of statistical models, notably through the sharing of information between companies or between them and their suppliers.