If you’ve stumbled upon this article, you may already be in this position. However, what’s more likely is that this is going to become your situation in near future, and learning from someone else’s experience is now needed to prepare. While there’s a plethora of theory around business applications for data analytics; there is a significant lack of practical, real-life experience to draw on. This is largely due to the fact that adoption of these technologies, for many industries, is new and the results of pilots are just coming to light now. Drawing on our work with one of the world’s largest steel producers, here I will detail some of our most useful and practical learnings.
Decreasing steelmaking costs with Magnitogorsk Iron and Steel Works (MMK)
Machine learning technologies are successfully used in predictive and recommendation services. The basis of accurate predictions is formed by historical data which is used as a training set. The result of this work is one or more models that can predict the most likely outcome of the technical process or the set of options, among which the best is chosen.
For example, Yandex Data Factory developed a recommender service for Magnitogorsk Iron and Steel Works (MMK) that helps to reduce ferroalloy use by an average of 5% at the oxygen-converter stage of steel production. Not only it saves about 5% of ferroalloys but, more importantly, this happens with sure and steady maintenance of the high quality of resultant steel.
When you choose the task for applying machine learning technologies, you should choose the one with measurable results and economic effect. In addition to this, the availability of data is required, as well as understanding how these recommendations and predictions should be used practically.
However, as we have learnt, finding a solution is not simply reached at one giant leap. The process of creating a predictive or recommendation project consists of several stages.
Stage one – Determining objectives, metrics and constraints
The first and very important stage is determining the objectives and constraints used in the modelling process. In the case of ferroalloy optimisation service, the key constraint is the need to adhere to the target chemical composition of resultant steel using the ferroalloys that are available at this specific melting. The objectives are the minimum possible cost of ferroalloys used and the maximum ratio of recommendations that were accepted by the operator for execution. The second objective is important because if the recommendations seem to be sudden or aggressive, they are often rejected by the operator responsible for the management of this smelting. For each objective, there should be chosen a metric, and the model should be trained specifically for it – its success will be determined in terms of this specific metric. Therefore, choosing the right metric is a critical factor of success. If the metric is chosen badly, all the work on the model goes in the wrong direction.