Snowball effect in industry 4.0
By: Andrzej Skrodzki
Tags: Startup Lessons, Big Data, Startup, Industry 4 0, Analytics

All metrics are being stored is some kind of SQL database. Hundreds of tables are optimal from a developer point of view. Those tables were designed to easily query predefined logic. Unfortunately, if you would like to get some additional insights into the factory, it becomes more troublesome. We would like to create an interface which is more readable, more concrete. So we asked line operators, which are customers of our interface, what kind of insights are they interested in and what should be included in such an interface. And this is what we heard.

Using new manufacturing interface, I would like to optimize our lines’ production

There are many definitions of being optimal, which one are we talking about? Is it resource allocation, costs, speed?



This is the beginning of a story how we managed to improve production speed by 15% which positively impacted the Overall Equipment Effectiveness.

Day 1

We are coming to the factory with the hope that interviews with users will shed some light on a topic. After spending the first hour with users, we can come to the first obvious conclusion.

Excel — It is everywhere

Excel is their main tool. There is a gigantic stack of spreadsheets containing data extracts, custom analysis, some charts. Please don’t get us wrong, we think Excel is a great tool with endless possibilities, but it might not be an appropriate solution for certain problems. Data extracts become outdated quickly, custom analysis contains the author’s definitions instead of consistent, agreed metrics. Printed charts, put in operations room becoming nothing more than a nice picture on the wall within a week.

All right we can see some potential improvement, but we would like to identify the current biggest concern for line operators. Both sides agreed that we should focus on checking the performance of single machines. Let’s get to work.

First iteration

After spending a week or so, we find some interesting things already worth sharing.



When you are looking at the effectiveness of producing one item, nothing seems concerning. No stops, items are being delivered before the deadline. But when you take a single machine, doing exactly one thing, over a period of one year, you can see that performance is degrading over time. Machines are being utilised in various ways and some of them need to be checked more often, rather than keeping the same tied schedule for maintenance for all of them.



Moreover, to speed up development and improve maintainability we switched from Excel to R.



The first step was done in a good direction. After spending more days working we are coming with 6 metrics describing some dimensions of performance. Each of them was reviewed and agreed upon by a group of operators so everyone is on the same page, now they can compare results between each other. We calculate metrics for more than 200 machines in a matter of seconds, so users can expect fresh data any time they want with a single click.



Now time is to ask users for feedback. Proud of our accomplishment, we are entering a room. We were not disappointed, operators are happy with results, but…

Where is the issue?

If we do some math, 6 metrics multiplied by 200 machines gives us more than 1200 values with every single calculation. Way too many values to monitor. Also, the operator needs to have some luck to be exactly at the time when a problem arises to act immediately. We have something to focus on, try to make the tool more useful.

Second iteration

After spending a few weeks on prototyping, user interviews, brainstorms we come up with a solution.



When you are seeing a value it is just a number. Only insight you can derive from it, whether one is bigger or smaller than the other. But when you put that in a context you can tell if this number is good or bad. You can start comparing those values from different perspectives, like how it is situated over the trend of a sequence.



Let’s put a level of importance or badness on every single value to achieve a heatmap.

Splash some colours!

After using colours it is easier to spot an item that the operator needs to take a look at first.



Speaking of looking at the issue, we would like to enable collaboration between operators and improve visibility and understanding of what happened across the factory. For every issue, there should be a corrective action.

Every issue should correspond to a corrective action

The result of the second iteration materialised in the form of alerting system and case management tool on top of it.