Accelerating the implementation of AI projects in the Segezha forest holding

On our artificial intelligence forum RAIF An interesting case came from Dmitry Bocharov, vice president of internal control and audit at Segezha Group. Dmitry told how machine learning tools are used in the largest woodworking holding in Russia and how obstacles to implementation are overcome. We give him the floor.

First a few words about the company

Segezha Group is one of the largest vertically integrated woodworking forest holdings in the country.

I am sure that many of you have heard about our company. In the end, if you saw a paper bag in IKEA, “ABC of Taste” or “Auchan”, then it was produced, including, by our company.

Now I want to convey, on the one hand, the value of artificial intelligence in solving specific business problems, and on the other, tell about our experience and even the “project pain” that we encountered when we were dealing with this case.

Harvesting process

First, a little about how logging is done:

Forest is cut using special equipment – harvesters. Then the billets are taken out to the warehouses by timber carriers with manipulators, so that from there by rail or by road they can be delivered to mills where pulp, paper, plywood, lumber and other paper products are produced.

Timber measuring mechanism

One of the key problems not even of Segezha, but of the entire industry is the process of measuring this very forest product, or rather, logs.

How is this happening now?

With the help of a special ruler, the height, length and width of the stack are measured, which is multiplied by various coefficients, prescribed even in the USSR in various state standards and industry standards. The most basic coefficient is the “full wood coefficient”, that is, the indicator of the actual number of cubes in a stack minus the gaps between the logs. This is where the problem of the human factor arises – if the employee is inexperienced, he is likely to measure inaccurately.

However, the biggest problem from the point of view of the audit is, of course, deliberate violations, since the total salary of the employees delivering us the forest is less than the cost of timber in a timber truck (one cubic meter costs 4-5 thousand rubles). A little mathematics – and here you have the opportunity for various conspiracies, abuses, manipulations …. The problem is that then it is impossible to understand how much “forest” there really was. There is a car, there is even an act with the number of logs fixed in it, but if there were just so many of them – there is no evidence, except what they measured with a ruler. And here the problem is not even that we do not trust all our employees or the employees of our contractors. There is simply a critical lack of clarity in this process, first of all, real documentary evidence that something was really measured.

Modern approach

We have developed a special algorithm that, based on a photograph, using a neural network, not only determines the number of logs and the diameter of each log (also an important indicator for us), counting the same “full-wood coefficient”, but also takes it not from some GOST, but corrects it for a specific stack of forest products.

These photos are tied to the geolocation of the car and are stored in a special database. Therefore, after we can always take and verify whether this forest really was and how much it was. The plans for the next couple of months are to train the system so that it can automatically compare cars leaving and arriving by heuristic search: first, the system photographs it when it leaves the forest plot, then the second time it arrives at the plant. Further, the system automatically checks the photos and fixes whether, for example, a part of the logs has been removed from above and not replaced. Such automatic control is based on artificial intelligence. This greatly simplifies the work of, for example, security services, because we can’t run through all the forests of Russia (and we have a cutting area of ​​almost 8 million hectares!), Just like we can’t control every lumberjack, because it is expensive and inefficient.

When we tried to implement this together with the company that made the pilot project, we started with the Telegram bot to demonstrate the capabilities of this algorithm.

By the way, this Telegram bot is still there.

The main problems and their solution

We are faced with basic problems that, I think, are faced by any companies that implement artificial intelligence, or related projects. Firstly, budget issue – where to get the money. Secondly, cost justification issues. Thirdly, the biggest block of problems is procurement procedures and tenders.

We solved this problem for ourselves as follows: Segezha Group has so-called procurement procedures. “Pilot projects”. If we want to introduce something new and small, besides previously undescribed, there is no need to invent TK. We don’t yet know how this will work, therefore, writing up the appropriate TK is just a waste of time. There is a certain budget for this, and by decision of the procurement commission it is absolutely officially possible to choose one of the contractors. Thus, our company works in the spirit of a startup. We are ready to lose this money, but we can try to solve a specific problem.

My colleague, Segezha vice president of IT, at one of the forums talked about our project: it cost several million rubles, but it could bring about three hundred million. We took a chance, made a “pilot” and as a result it paid off many times – maybe not a hundred, but at least ten times for sure. Obviously, such experiments suffer losses, but you can and should try, because any completed project is a very valuable experience. The use of technologies developed in specific business problems is bearing fruit. But here it is necessary to know the measure as well – artificial intelligence and machine learning should not be introduced wherever possible, if only to introduce them.

Another internal life hack: we agreed with colleagues – with financiers, purchasers and company management – that we will reinvest part of the money that similar projects bring to us in the future – that is, we will constantly invest the money saved in new technologies and thereby promote similar stories at Segezha.

Now we are just finishing piloting the “wood” case. To make it clear on economic effects: the error in the method of measurement with a standard ruler according to GOST is 5%, but in fact, of course, it is much larger. Segezha Group annually harvests and buys timber for 15 billion rubles. Even if you take -1% of this amount, this is a significant loss. And such projects, which, of course, do not cost billions or even hundreds of millions of rubles, allow such risk zones to be closed. Maybe there is no direct economic effect (that is, we will not earn more or we will not have new production), but from the point of view of preventing possible losses in logging, we expect quite good efficiency.

I think many people are interested in the timing of the manufacture of such prototypes and I want more specific numbers. I can’t name the numbers for obvious reasons, but I’ll designate the most problematic point – getting relevant data. For example, what photo or what data to take for training a neural network? We cannot use the results of manual measurements (the same measurements with a ruler), because then the algorithm will work on incorrect data. Therefore, it is necessary to take each timber truck and make the so-called “point”: the logs are completely unloaded from the timber truck and each is measured by diameter and length – in this way you can determine the reliable volume of all wood with minimal error. Another feature: the wood is different: pine, spruce, larch – respectively, each has its own characteristics in the dimension. So, in order to measure everything, my employees had to travel to different regions – to Kirov, Arkhangelsk region, Krasnoyarsk, Karelia – and measure each timber carrier there. So the main time (about 2 weeks) was spent on collecting a sufficiently representative sample for training the model.

Author: Dmitry Bocharov, vice president of internal control and audit at Segezha Group

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