Kamax Logo

€ 753 M




3.5 BN

Produced parts


Locations worldwide

Kamax Logo


Guaranteed job safety in the region of Homberg and competitive lead over the next 20 years.

KAMAX is the premier manufacturer of high-strength fasteners for the automotive industry. Starting with a workforce of three in Germany in 1935, KAMAX now employs more than 3,300 staff across 11 international locations.

Project Mission

To achieve near-time transparency in order to make better decisions, by leveraging software technology and increasing automation.


KAMAX Production Workers unable to access data for 36 hours after shift end resulting in inability to react to, analyse and manage complexities

KAMAX is under constant pressure from a highly competitive marketplace. With increasingly demanding client expectations and tough competitor pricing, over the last decade the automotive parts manufacturer found itself maxxed out in terms of time and resource. The result: an IT infrastructure that was out of date and in desperate need of refresh.

The main issue arising from the lack of IT investment centred around production. Several teams found themselves unable to access essential knowledge; information needed to understand how much of a given order had been produced, at any given time. This transparency was missing for several audiences, along with an manual process resulting in shift complexity across teams:

Firsty, Planners would plan a shift one day in advance using SAP, establishing what type of bolts would be produced on that day. The planners shift is usually from 9am - 5pm, with planning occurring once a day around 11am for the next day.

The Shift Leader would then look at the plan around 11am - already three hours into the shift - attempting to make any adjustments for the next 24 hours. Many events may occur during a day, for example a machine breakdown causing the production order to be changed. Even with Mechanics on-hand to deal with issues like this, the handover and iteration process was still highly manual and extremely error-prone from a people, communication or performance perspective.

The end result was that Production Workers were getting data through until 36 hours after their shift had ended, rendering them unable to be proactive in how they reacted to issues and managed their shift. The business was in need of a scalable, global platform, able to leverage real-time data and machine learning to increase overall equipment effectiveness (OEE). By utilising such a solution, KAMAX would be able to establish a competitive edge. It would also be able to be perceived as more attractive when it comes to talent acquisition; reassuring potential candidates that even in an uncertain economic climate - especially for the automotive industry - their brand is one of innovation and technological investment.


To save jobs, demonstrate the benefits of digital transformation, and reduce the complexities faced by Shift Leaders

Our main goal was to increase KAMAX profits, in order to save jobs in Homberg. We also wanted to demonstrate the employee benefits of digital transformation, and open access to technology innovation. Namely, how we could make jobs safer and more enjoyable; as well as how factory worker value increases when KAMAX is pro-technology: vs sticking to the same 30 year-old processes. However, rather than changing machines or the process, our aim was to increase OEE and change the way teams made decisions, by leveraging data in real-time and letting predictions run on that data.

In order to reduce the complexities faced by Shift Leaders, continuous outcome transparency would be key. We wanted to leave Shift Leaders feeling PhD qualified in Data Analytics. Or better still, in Machine Learning; either way, possessing in depth knowledge about their machines combined with expert with industry experience in order to drive efficiency and productivity.


Hackerbay IRIS manages data and build prediction algorithms, enabling product to go live in four months

After being introduced to the the Production Team by KAMAX COO Herr Steins, in January 2018 we kicked off the project with a blueprint workshop. This was to get to mission clarity and set the foundation for our collaboration. Just four months later, our product went live.

As a young startup falling outside of the traditional automotive category, Hackerbay were able to help the KAMAX team clearly think outside the box. Our partnership was built on trust; with the faith KAMAX had in our capabilities combined with our process-driven approach, we produced business value beyond expectations. The Hackerbay Factory Cloud - our own standard software platform - enabled us to manage data and build prediction algorithms. Using modern technology in this way gave KAMAX the opportunity to re-define what they ever believed was possible with their old traditional machinery. Modern networked software was used to leverage their core business, helped them realise untapped potential and see the IT department as so much more than simply a support process.

By drawing upon our experience from a previous data-driven application project, we applied a familiar approach using the same standard components:

  1. Work Order Mapping
  2. Sensor Fusion
  3. Standard parts streaming (object detection) / Interchangeable Part detection
  4. Real-Time Production Planning
  5. Operation


User-friendly iPad app gives Shift Leaders ability to easily view product information and make better, more informed decisions

The Hackerbay platform helps the Planners better understand the demands of the shift, by exchanging notes, status, data, and information about shifts. At best, a Shift Leader could previously manage the complexity of a production shift without data. He could generate an output, regardless of whether staff are on holidays or not, sick or working. This however may involve 400,000-700,000 bolts over eight hours, on 14 different machines. Our tool helps the Shift Leader manage this complexity with the push of a button; by giving him the data needed; thus simplifying and speeding up his job. The more time goes on, the more shift data we’ll acquire. This enables us to manage more vectors such as bolts and people, help with shift management, and drive better, more informed decisions.

For the Mechanics, The Hackerbay platform helps reduce the time taken for them to fix an issue. Traditionally Shift Leaders would rely heavily on intuition in the event of a machine breakdown - often wasting two hours productivity time on what should be 10 minutes figuring out an issue. With the right information displaying machine / person / bolt type, and time needed to fix - we can now predict if this problem is average in size or larger and therefore requiring Mechanic assistance.

Our end solution was an iPad application that incorporates data from machines with SAP extracts - to be used by the shift leader. Because of the difference in screen size, we decided create an app designed for use on an iPad vs a smartphone. It needed to work on a device that had a screen big enough for the shift leader to see clearly, how much of an order had been produced. It also needed to work on a something that was mobile enough to be carried around the shop floor; hence an iPad was the chosen device. On the backend we used Prisma, a database access layer to aggregate the different data sources.


Production transparency saves 135 minutes of idle time a day, decreases response time, and leads to near real-time production process

We designed the app with ease of use and maximum productivity in mind. Because the user-friendly interface, KAMAX shift leaders who don’t normally use smartphones at work or for personal use now utilise the app with great ease. As a result, not only does the app provide production line transparency, it has resulted in great time savings and therefore boosted productivity. The solution saves shift leaders and managers up to 45 minutes at the beginning of each shift. With three 3 shifts per day, a total of 135 minutes is converted from idle time to productive activity time - every day. The new transparency also decreases response time, in case of unplanned machine disruption; leading to a near-real-time production process and less wastage.

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