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Here is your data lake: Go fish!

Ten years into the digital revolution within warehouse automation, a surprisingly large part of our customers still doesn’t collect data. Among those who collect, many don’t analyze data. Of those who analyze, a surprisingly large part doesn’t respond to the output. Conclusion: There is still a very large potential for digitalization within warehousing.


Domain knowledge or data scientists?

Let’s look at it backwards to try to understand why those who collect large amounts of data don’t harvest the benefits, and how you can tap into the potential.


In the early days of the digital transformation, many of us – or at least I – imagined that if we just had a gigantic database, optimizations and insights would come pouring out of it. Big data was the big buzz.


Now, with the tools available to collect all that data and create a giant data lake, reality is that the sheer amount of data is too overwhelming. Especially if you don’t have the necessary domain knowledge to draw meaningful insights and actions.


Inhouse data scientists may be excellent at analyzing data, but without the domain knowledge and operational insights possessed by operators and providers of warehouse automation systems, they often search in blindness for even the most obvious insights.


Access to the data lake doesn’t put food on the table if you don’t know how to fish. To fully utilize the data, you need to apply the domain knowledge from your operators and your equipment suppliers.



Sorry, we don’t have time to save an hour a day

Those who collect data without acting on it actually often have an excuse we all recognize: lack of time.


A perfect example of this is customers of our own AI solution, the Operator Eye, that increases the uptime of Körber’s Layer Picker. It does so by using neural networks to analyze pictures in real time to avoid unplanned stops and to automatically reset the machine if an alarm is caused by a non-critical event like a loose slip sheet.


Improving uptime by 8-12% is pleasing enough for our customers. So, when we tell them that all the data the Operator Eye gathers can be used to significantly improve the operation through the entire value chain, they kind of wave it off with a sentence like: “Sounds great, but we don’t have the time or the resources right now.”


I completely understand. We are all busy, and sorting through your master data can lead to a lot of work across your operation. But the answer always reminds me of the old cartoon with the people from the stone age who say “No thanks, we are too busy” to the man offering them wheels while they are sweating pushing a cart full of heavy rocks.

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System Integration & Automation

Manufacturing and distribution organizations know that automation can improve efficiencies, maximize space and increase productivity across their supply chains.






Create a small data pond and catch value

Then there is the rest of you. Those who would willingly accept the wheel, if only you had a cart ready to be pushed. Where do you start?


First, you start by collecting data to create a baseline. Preferably data reflecting quality, operational availability of your equipment, or another output that allows you to measure progress.


Next step, if you really want value from your data, is to create projects where you match these data with master data. Way too often, warehouse master data is wrong or inadequate. If you can identify wrong master data like volume or box sizes, you can create a positive impact all through your value chain down to how many boxes fit in a truck.


This is one of the possibilities with the data from the Operator Eye and our Digital Enablement platform. When we combine the ability to identify wrong master data with insights into alarms and machine downtime, we can discover if certain types of alarms are triggered by specific products, a certain weight range, or other product parameters. As simple as it sounds, and it can create a massive impact on the amount of goods that flow through your warehouse.



Conclusion – focus on data quality and domain knowledge

Although the digital revolution is not happening as fast as expected, warehouses can still gain a huge competitive edge by utilizing their data. But we must acknowledge that it is not enough to collect large amounts of data. We must focus on the quality of our data collection, use the domain knowledge of operators and suppliers, and perhaps most importantly start acting on the insights to create business value.


Today, we are able to create a lot of insights, and it is my experience that when we start acting on these insights, we can take huge steps towards the long-awaited benefits of the digital revolution.


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