Addressing key data challenges in the modern manufacturing landscape
There is so much action taking place in the modern factory! Robots have pushed the maximum production capacity to new limits and digitalization has arrived, leading to products with a new and unprecedented level of complexity.
Checkpoint 001: Stepping into a Modern Factory
Successful production is not only dependent on a correct physical assembly, but also relies on the correct combination of software and firmware modules in the product. This raises many questions:
- Have all products been flashed with the correct firmware and software?
- Have all produced units reached the final checkpoint? Were some of them removed from the production line?
- Is the data consistent across the various digital systems?
- Has the correct data been forwarded to the corresponding departments?
To answer these questions, one must dive deep into the available data that is spread across a huge range of systems — a very laborious and time-consuming task that requires extensive process knowledge.
As an alternative, would it not be better to have one central system that monitors the digital flow of a product through the assembly line?
Checkpoint 010: Overcoming complexities in the assembly line
Unfortunately, the idea of having “one system to rule them all" is just a pipe dream for now, which collapses as soon as we enter the real manufacturing world.
A modern manufacturing landscape could consist of:
- Enterprise Resource Planning (ERP) applications which focus on the business aspects like supply chain management, budgeting or managing production quantities
- Manufacturing systems that supervise the flow of the device product through the production line in real time
- Firmware distribution systems that orchestrate which firmware (versions) should be flashed to the corresponding product
- Classical PLM backbones managing the bills of materials or enabling workflows for all those involved in the development of a product
The data exchange between these systems is a business-critical element. As the produced unit moves through the production line, so does the data across different systems. Let’s look at some examples:
1. Data is sometimes added or corrected manually, and accidentally might not be propagated to downstream or upstream systems
2. Sometimes bugs and downtime can paralyze the automatic synchronization of data.
As the produced unit moves through the production line, so does the data across different systems.
Because data correctness directly influences the operational capability of the production line, defective products might be shipped out to the reseller or, in the worst case, cause the production line to come to a complete halt.
This is why an all-encompassing approach is needed to connect the data flow directly to the assembly. Unfortunately, this is where a heterogeneous system landscape can become a huge obstacle.
Checkpoint 011: The data doctor station
To solve these problems, we first need a consolidated overview of all existing production data — ideally combined with automatic validations that detect anomalies and immediately notify the factory workers responsible for the task.
At Atos, we tackle this challenge by connecting our proprietary data platform with the client’s systems and integrating them with their factory processes. By analysing these data pools, we are now able to implement validations that trigger alerts, for example, when a product is about to be flashed with a wrong firmware.
A solution like this enables production experts to investigate issues and correlate data that is stored in a wide range of systems. Furthermore, it enables real-time monitoring of the entire production data flow through different factories.
Final checkpoint 101: End of the line
Comprehensive knowledge about specific PLM processes and systems is necessary to understand the entire client environment and find a perfect way to monitor data along the assembly line. This enables you to trace thousands of orders with millions of products and their corresponding data flow in the factories that produce them.
Next generation data tools can save a lot of work that is usually performed by people involved in supervising the production line. It enables them to monitor not only the physical production itself but the entire virtual data flow across the factory floor as well.