The digital revolution is already underway, and we have to accept the way it is unfolding. It consists in the fact that key information concerning business is increasingly collected and processed in digital form. But what does that actually mean? After all, it’s obvious that accounting records and processes all information digitally. The cash register also processes everything and communicates with the integrated accounting system. Warehouses are run digitally.
Low Digital Integration
In most cases our businesses are well saturated with digital systems. Phones contain call histories, integrated accounting systems communicate with warehouses. Those in turn communicate with logistics systems that issue or receive goods. Generally speaking, there is some integration of systems, but it is neither coherent nor uniform. For example, our mobile phone is not integrated with the ERP system, though it could be. Cars do not have onboard computers integrated with the main computer responsible for the enterprise’s logistics, though such correspondence between systems could exist. Grain-milling devices or loading scales are usually independent of warehouse systems. To connect all this we need a uniform nervous system—i.e., a network of digital connections enveloping the entire organization. And that will not exist for some time yet, because it would require close cooperation among various vendors, manufacturers of machines and measuring devices, and software—according to some agreed standard. And there is no sign of that for now. I mean a standard like what we have at Google, where a mobile phone, a computer, and a TV can operate under one identity and all of these devices can save history, data, and all kinds of information to a shared database in Google’s cloud. That solution is, let’s call it, a civil solution. It is hard, however, to find similar solutions in the professional world. The solutions supplied for warehouses, transport, machines, and systems are not integrated with one another—and won’t be for a long time.
W-Moszczynski (4)
Internet Access
Systems will be able to be integrated freely with each other when all these devices have internet access. Why is that so important? Because internet APIs make it possible to integrate individual elements of a system with clouds. Clouds are not only for data storage and simple calculations. Practically all analytical tools and methods can be found in the clouds. Their use will soon be necessary so that our business does not lose the competitive race.
In the grain-milling industry, a very important element is price speculation at the point of purchase and sale of grain and flour. In other words, analysis of the commodities exchange, macroeconomic assessments, agricultural market analysis, historical weather forecasts, and unforeseen weather phenomena must be carefully analyzed. Creating more advanced forecasting models requires hiring a very well-educated data analyst who should additionally be a practitioner. Such an expert is quite costly and certainly will not be physically available nearby, so they will work remotely. Since we have no way to supervise them and we are unable to evaluate their work substantively, it would be advisable to cooperate with a company specializing in forecasts—and here the difficult process begins again: finding such a company and establishing cooperation with it.
In the end, we would have to pay more than for a single data analyst, and we still would not be able to control their work.
The Cloud
The cloud differs from a data analyst in that it is predictable and it’s rather hard to expect that it will cheat us on working hours or go on sick leave. The cloud also has a kind of professional experience, because its systems are based on large language models like ChatGPT. In other words, cooperation with the cloud reduces most of the problems associated with cooperation with a human. First: using the cloud costs a fraction of an analyst’s price. Second: if we start treating the cloud like a very advanced calculator, we ourselves can become experts in forecasts and building forecasting models. In the cloud we can optimize warehouse size, analyze the movement of the flour-truck fleet, or check the efficiency of human resource utilization in production; we can compare everything, draw conclusions, and make decisions based on well-processed data.
Artificial Intelligence Will Destroy the Human Race, for Sure!
There has long been a discussion about what digitization actually is. What is this famous artificial intelligence that is supposed to enter our lives and turn us into not-very-bright people for whom machines do the thinking? Is there anything to fear? Usually after such an introduction, some laconic deliberations begin—more or less successful predictions of the future, scaremongering or raising hopes. Whenever something new and groundbreaking appears, there is no shortage of panic-mongers and critics. From today’s perspective, people’s statements from 100 years ago seem laughable.
So as not to speak without examples, I will cite a few instances of how people reacted to the emergence of the railways.
“Traveling at such a speed is impossible and will certainly cause illness.” — Dr. Dionysius Lardner, professor at the University of London, 1830. Back then, “high speed” was considered 30–40 km/h.
“The railway will be the end of civilization. It will destroy family, religion, and morals.” — The Quarterly Review (a conservative British periodical), ca. 1825.
“Traveling at such a speed can cause madness. People are not made for such a pace.” — an anonymous physician quoted in British newspapers in the 1830s. Many doctors warned that the rapid movement of the landscape could lead to disorientation or even insanity. They spoke of dizzying speeds on the order of 40 km/h.
“When people travel at 15 miles per hour, the blood will boil in their veins and they will die.” — Thomas Tredgold, engineer and early railway author, 1825.
“Horses will be useless. Many professions will collapse. This is a threat to the social order.” — a statement by a representative of the London cab-drivers’ guild, 1830s–1840s.
In other words, the fear—then as now—of job losses.
We have plenty of similar statements regarding the introduction of airplanes, electricity, and telephones. How could telephones destroy the human race?
“Telephone conversations will be demoralizing—women will speak with men they do not know!” — letter to the editor of the New York Times, 1880.
“This device disturbs the peace of the home and destroys family bonds.” — a pastor from Massachusetts, 1881.
“The telephone is too complicated to be useful to ordinary people.” — Western Union internal memo, 1876.
“Who would want to talk to someone they cannot see?” — comment attributed to a London aristocrat, late 19th century.
And electricity? How frightening it was!
“If we allow the widespread use of electricity, people will die by the hundreds. Alternating current is murder.” — ca. 1889.
“Electric lamps will blind people and ruin their eyesight. Gas light is gentler and safer.” — an anonymous physician quoted in The Times, London, ca. 1882.
Let’s imagine that we are a person who has never seen a railway. We have read various Cassandra-like statements spread by experts. How can we be convinced there’s nothing to fear? A newspaper will not handle it, in which other, better experts will praise this invention. You simply have to take a ride on that “terrible” railway. I am sure that everyone who rode the railway racing at the dizzying speed of 40 km/h understood how nice it is to get somewhere far without effort and at breathtaking speed.
Now let us take a ride on the “railway of our time.” In the end, we will understand what our weakness is and why this truth can set us free.
A Grain Purchase Price Forecast Made by Artificial Intelligence
Today everyone knows artificial intelligence that you can chat with—ask who invented the flush toilet or have it write an article for us or draw a picture. It’s like an expanded Internet, only you can do everything faster and better. Along the way, professions such as graphic designer, editor, or language teacher disappear from the market. However, ChatGPT in this version is no great breakthrough.
Real business needs very concrete things done massively at large scale. For example: analysis of surveillance video, processing gigantic amounts of data and creating forecasting systems that predict events, autonomous vehicle control, or organizing huge numbers of scanned documents. So let’s do one such example—not to learn how to do it, but to “take a ride on the railway.”
To build (in 15 minutes) a grain purchase price forecasting model, you need to click around a bit in the cloud.
It Was Not Always This Easy
In the 1990s, spreadsheets appeared with an option to create trends. Back then, building a forecast took a moment; unfortunately, they were unreliable and weak. In the second decade of the 21st century, the possibility of programming in Python and R appeared. Libraries emerged that enabled the creation of forecasting models. They were simply ready-made instructions to execute. You didn’t have to write the entire model algorithm from scratch.
In practice, when someone wanted a forecasting model for their sizeable company (e.g., a grain elevator) placed in the cloud, they hired a team of experts—data analysts (a “project team”)—who would spend months creating such a model. This is still how most such undertakings are carried out. I think companies implementing these projects deliberately do it the old way, because it’s profitable. Sometimes it’s not worth enlightening the client.
Welcome Aboard the Railway
Below are a few slides showing how this process looks in practice. I included the slides not so that everyone learns how to do it, but to make it clear that building a forecasting model is a matter of a dozen or so clicks in the cloud. For the experiment I chose GCP (Google Cloud Platform), one of the most popular on the market. Using this cloud is free; anyone can log in. After exceeding certain limits, the cloud starts charging fees—very low ones anyway.
Step 1. Log in to the cloud, create a project. In the slide this is the project “Wojtek1.” Choose the “BigQuery” tab.
Step 2. Upload data. The cloud needs them to create a model. Pasting the data is another few clicks.
Our data table contains 48 rows and looks like this:
We see a column with years and months; we have three grain elevator locations: Miłkowo, Połoć, and Stare Wolice. We added columns with temperature and precipitation levels. We have to paste this table into the cloud.
Step 3. Prepare the query.
This step may seem difficult if someone cannot write SQL. Currently the cloud helps us with artificial intelligence. I will not describe how to talk to it and ask for help. Artificial intelligence writes the query for us.
We press Enter—and we have a model.
We get a beautiful, hopeless model that is useless.
The Role of the Human in the Process
With this example, I wanted to show that using artificial intelligence relieves us of tedious work while simultaneously forcing us to make an intellectual effort. And that is the new quality. We no longer have to program sluggishly, create queries, search libraries, validate, and generally toil. New times require us to think, and we cannot excuse ourselves by saying we are not mathematicians or professional data analysts. Now everyone will have to think!
So what model did we get?
R-squared (R²) = 0.0979 — a very low value → it means the model explains only ~9.8
So we ask artificial intelligence what we can improve:
-
Add important features (feature engineering):
• e.g., baseline_monthly_trend, _in_harvest_peak, rainy_days
• encode base as a categorical feature (one-hot encoding) -
Use a more advanced model (e.g., tree-based):
• BigQuery ML is simple linear regression;
• In Vertex AI you can use XGBoost/AutoML Tables, which handle non-linearities better. -
More data:
• one year is too little—we need at least 5–10 years of history;
• data on inventories, acreage, previous yields, etc. -
Check data quality:
• whether there are random errors;
• whether variable scales are not dominating the model.
What have we learned so far?
First, the machine did all the work for us—and the result turned out to be hopeless. If we had done it “by hand,” it would have been just as bad, only it would have taken longer. The machine told us what to change.
What conclusions follow?
First: anyone can make (click together) a model.
Second: the machine will not clean up our company for us.
Third: from this example, we learned what is important and what is not in the model—that is, we learned something. This last conclusion is quite significant, because as passive observers of the classic path of model creation, lacking experience, we truly would not have learned anything.
What does the machine advise? “Point 1: Add important features (feature engineering).” The idea is that we, experienced millers or brokers on the purchase price market, must devise the features that we should place in our database. This could be the inflation rate or fuel prices, or a consumer optimism index. We can introduce any variable. The variables we introduced in the form of precipitation and temperature turned out to be irrelevant.
I would rather ignore point two proposed by the machine. It doesn’t matter which model you have—if the error is that large in scale, the data are to blame, not the model.
In points three and four, the machine tells us directly that we have a mess in the data. The data may be untrue, falsified, or simply entered incorrectly.
What, specifically, might be messy?
For example, transaction entry dates may be delayed; there may appear the phenomenon typical for high warehouse turnover—“debt shifting.” This consists in the warehouse manipulating volume over time or between warehouses, e.g., to avoid paying VAT. There are many other, non-tax factors that will render our data useless. In our example, the database contained a column with average temperature and average precipitation. These data were recorded at the moment of grain intake. Temperature and precipitation primarily concern the harvest of the grain, not the moment when the purchase took place. This is a typical error made by data analysts. The phenomenon is called shift—i.e., a displacement.
A Forceful Conclusion
Did the arrival of the car make people worse and weaker, did they lose the ability to walk? No—people became more technologically advanced. They also acquired many new, astonishing capabilities in carrying heavy things and moving through space. A typical car user, wanting to take advantage of these new superpowers, had to learn to drive a car, had to understand what the oil level is, what coolant is, or how to change a tire. If the car had not appeared, people would still be walking—significantly more slowly.
Today something has appeared again—some astonishing technology. And again we must take a step toward our development. The lack of IT education or the lack of programming skills will not excuse our lack of thinking this time. We must prepare the data; we must understand the business; we must simply start thinking. The example I cited today, relatively simple and intuitive, showed how the machine exposes our weaknesses, our laziness, and our lack of understanding of the business. The data we provided and which seemed decent turned out to be useless.