Munich. Building a car generates massive amounts of
data throughout the value chain. The BMW Group uses its Smart Data
Analytics digitalisation cluster to analyse this data selectively and
enhance its production system. Results from intelligent data analysis
make an effective contribution towards improving quality in all areas
of production and logistics.
Data-driven improvements to processes and systems help reduce lead
times and lower costs. New solutions are being developed not only at
headquarters, but at many different points in the international
production network. In this way, the BMW Group is able to take
advantage of a wide range of innovations that open up additional
options for even more flexible production. The company uses an
access-protected intranet-of-things platform to link the large
quantity of sensor and process data from production and logistics
quickly and easily. Smart Data Analytics therefore offers completely
new opportunities that extend far beyond previous analysis
possibilities. The speed with which new solutions can be implemented
is increasing significantly. At the same time, new IoT sensors,
combined with cloud and big data technologies, are reducing the
technical complexity and implementation costs involved
Christian Patron, head of Innovation and Digitalisation in Production
System: “Smart Data Analytics is setting new standards for our
production system. By combining the experience of our staff with new
possibilities for efficient processing of large data volumes, we are
able to create accurate forecasts and proactively optimise processes.
This speeds up continuous improvement of our production system in line
with the basic principles of lean production.
Numerous use cases implemented in various manufacturing sections
demonstrate the benefits of Smart Data Analytics.
Laser-marked body parts: fine-tuning for presses; body parts
traceable at all times
Steel coils up to 40 tonnes in
weight and about three kilometres long are cut into blanks in the
press shop and then formed into body parts. However, sheet thickness,
strength, surface texture and degree of oiling are not uniform
throughout the coil. Deviations from target can lead to cracks in body
parts that are subjected to particular stress during the forming
process. This is where the Smart Data Analytics application at BMW
Group Plant Regensburg comes in. A laser is used to mark each blank
with a multi-digit code, which serves as its own ID. Going forward,
this ID will allow the presses to be fine-tuned to accommodate the
characteristics of the blank. If needed, the ID may contain a control
command, which triggers additional oiling of the blank in the press
before forming, for example.
This clear marking enables the blank to be identified at any time.
Each body part is assigned information that remains available
throughout all subsequent production steps. Since the blank stays in
its production line for marking, the ID is assigned without any cycle
downtime. The ID is designed so that it remains visible throughout car
body construction. BMW Group planning specialists already take
advantage of the traceability of all parts for further optimisation
involving additional algorithms. For example, taking into account the
characteristics measured for each individual body part, the gap
dimensions of the finished body can be further optimised, or the paint
application better matched to the surface of that particular body.
Fine-tuning of press parameters according to the properties of the
blank is already having a major impact: The number of scrapped parts
is significantly lower, with better utilisation of the coil material.
The system downtime required for fault analysis is also reduced.
Predictive maintenance for body shop robots, welding tools and
drives
Smart Data Analytics applications offer
especially high potential for increasing the availability of
production equipment and machines in highly-automated areas of
manufacturing. Maximum accuracy in predicting any risk of breakdown
largely helps avoid unplanned system downtime. Based on the forecast,
maintenance staff can plan a targeted maintenance intervention to
limit downtimes to an absolute minimum. This so-called predictive
maintenance is enabled by intelligent analysis of large quantities of
real production, sensor and process data: Targeted analysis of this
information makes it possible to determine the ideal time to replace
wearing parts used in production. If the change is made too late,
there is a risk of production stoppage; made too early, valuable
resources are wasted. Without the relevant data on which to base this
decision, the purely preventive maintenance of the past was conducted
without knowing the actual state of wear. This method required
allowing safety margins for the timing of the changeover, but could
not detect unexpected breakdowns.
Data-based solutions for predictive maintenance are used at various
stages in car body production to predict gear and brake wear in
robots. Sensors in welding tongs signal ahead of time when defects or
quality problems are likely to occur. Widescale sensor monitoring also
improves the reliability of the electrical drives used in a variety of
systems, including lifts and turntables. Robots and control technology
are fitted with the necessary sensors from the start. Maintenance
staff analyse the data and then draw the right conclusions. Recent
evaluations of predictive maintenance clearly demonstrate the benefits
for reliable operations.
Online process controls: Even more stable processes guarantee
top quality
The BMW Group received the Prix de la Technique 2017 at the
prestigious Surcar Congress in Cannes for its concept for
comprehensive paint shop digitalisation at the company’s new plant in
San Luis Potosí, which will begin series production in 2019. BMW Group
paint shops already use sensors for ongoing monitoring of automated
production processes. Intelligently networked systems enhance the
stability of process sequences, enable predictive maintenance and
ensure the highest quality for our customers. Online process control
combines the strengths of algorithm-based analysis of large data
volumes with employee experience: As a result, humans can focus more
on their role as architects of the production process, since real
production data is sorted and optimally pre-structured for them. Error
potential can be detected in time and rework avoided.
In May 2017, the BMW Group began using fully-automated quality
control for the first time at its Munich plant, with robots scanning
the entire outer vehicle surface. The system is capable of detecting
errors the human eye cannot perceive. The data obtained in this way
also provides valuable feedback on the precision of upstream painting
processes – allowing continuous optimisation and timely identification
of defect potential.
Fastener data analysis: More reliable error prevention
benefits thousands of bolted connections
Bolted
connections are fundamental to automobile production, since every
vehicle contains several hundreds of them. The BMW Group monitors and
analyses all bolted connections that are relevant to the safety of the
vehicle. Basically, bolted connections that do not, or only partly,
meet the desired specifications may require rework. As part of its
preventive quality strategy, the BMW Group has developed algorithms
that have been analysing bolted connections in more than 3,200
assembly systems at all vehicle plants since July 2017. Recording and
analysis of bolting process curves provide accurate feedback on the
quality of bolted connections. The programme can recognise different
types of fault and show possible sources of error in a
cause-and-effect diagram. The BMW Group uses this information to train
and qualify employees for preventive quality work – after all, a
mistake that is not made does not need correcting. A trainer at a
mobile training station or directly at the workplace can also provide
tips on error avoidance.
Analysis of bolting process curves also provides important insights
for systematic monitoring of bolting systems and parameters, such as
tightening torque. When implemented quickly, these findings create a
closed loop of continuous improvement.
In many cases, purely manual analysis of bolting process curves would
only result in a finding of “acceptable” or “not
acceptable”, without identifying the cause of errors or
highlighting potential for improvement.
Predictive maintenance for materials handling in
assembly
The BMW Group production system is
characterised by the highest degree of flexibility: The company
produces an especially wide range of models and variants on its
assembly lines, but is nevertheless competitive – as confirmed by
independent benchmarks. On the assembly line, a reliable supply of
materials is particularly important. A breakdown at any point could
cause the entire production area to grind to a halt.
In assembly, many conveyor systems are now equipped with a large
number of sensors that monitor various factors – especially
temperature, vibration and electrical power. These sensors are
cost-effective enough to allow them to be widely used. Data from these
sensor kits and other process data is streamed live to the BMW
internet-of-things platform, where it is visualised and analysed in
real-time. If the data detects a trend indicating deviation or
patterns from previous breakdowns, the platform notifies maintenance
staff. Staff can then decide whether the hanger should be removed for
maintenance. In this way, it is possible to ensure long-term, reliable
operation of the conveyor system over a number of years.
Every minute saved means another vehicle
built
Christian Patron: “In automotive production, every
second counts: If a part isn’t available on time or a system fails,
the production process is delayed and it disrupts the value chain.
Intelligent use of production data ensures a stable and efficient
process. We see tremendous potential in Smart Data Analytics for
incorporating feedback from our customers into development and
production even faster.”
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