A New Model for Manufacturing Innovation

By Valerie C.
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by Werner Krings

The Austrian Economist Joseph Schumpeter argued that industries must incessantly revolutionize their economic structure from within. I interpret this statement to mean that manufacturers, especially in the High Tech industry, must continually strive to innovate with better or more effective processes in order to build new products.

Innovation is a core attribute of successful High Tech manufacturers, impacting every aspect of the business–economics, business profitability, product design, technology, and engineering best-practices, not to mention overall brand value.
Innovation impacts growth

Manufacturing innovation can mean the use Lean and other cost reduction strategies. Increasingly, it means automation and digitization of manufacturing as we move toward the era of the Digital Factory and big data analytics. And, In today’s global landscape, innovation must include the ability to easily replicate processes across sites to ensure higher global quality standards and greater control, visibility and synchronization across operations.

How do you get there?

A key requirement for global innovation is a unified production environment across facilities. High Tech manufacturers that use different processes and production systems in their various facilities will have difficulty achieving innovation– effectively blocking all of the potential benefits. When different plants use different MES systems, for example, there can be little agility, as every change becomes a custom IT project.

Improve operations processes across sites

This is why High Tech manufacturing leaders have moved toward unified and standardized systems, so that process changes and manufacturing agility can be achieved faster and more easily. In such an environment, global shop floor operations can be unified through a Center of Excellence, which can then ensure comparable and measurable manufacturing standards on a global scale. As they say, you can’t improve what you can’t measure.

Measuring Innovation

Innovation can (and should) be measured on an organizational level. The implementation of manufacturing intelligence solutions is often justified by this single function, as part of a manufacturer’s quest to achieve better visibility across operations. The ability to measure is greatly enhanced when it is part of an overall innovation strategy, underpinned by unified technology.

High Tech manufacturers will want to measure several aspects of innovation, such as business measures related to profitability, innovation process efficiency, or employees’ contribution and motivation. Measured values might include new product revenue, spending in R&D, time to market, quality scores for suppliers, and growth in emerging markets.

Manufacturing Innovation

What is pivotal is that innovation must align with corporate strategy and global manufacturing performance in order to ensure continuous growth and return on investment. A well-defined innovation program, combined with an IT infrastructure that supports global agility, is essential for High Tech manufacturers that want to compete and grow in a sustainable fashion, now and in the future.

Now there’s a solution for greater visibility, control, and synchronization of operations. Visit the Flexible Production solution page and read the flyer to find out what a flexible global production platform for manufacturing can do for your High Tech enterprise.

Bringing Predictive Analytics to the Shop Floor: OK, but How?

By Christian
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Predictive Analytics can bring a lot of value to shop floor operations, especially to improve quality, yield or process sustainability, for example in composite manufacturing.

Machine learning algorithms allow to extract patterns from past production data. These patterns, which make up a model, can in turn be used to obtain predictions (“what is the risk to have a defective part?”) or even recommendations (“what can I do to reduce the risk?”).

Data Scientists wanted!

I have intentionally used the expression “machine learning algorithm” and you may think that companies that want to go in this direction need to hire a team of data scientists.

Indeed, many open source or commercial solutions require the availability of data scientist skills along with good programming skills in order to:

  • Identify the proper algorithms to use
  • Fine tune algorithms to get good results
  • Ensure scalability and performance

So it is no surprise that with the explosion of Big Data and Predictive Analytics, job postings in this field have skyrocketed since early 2012:

data_scientist_job_trends
Percentage of job offers with words “Data Scientist” or “Data Science” ©  Indeed.com.

And, as a result, salaries have soared and positions are hard to fill, which slows down the adoption of Predictive Analytics solutions.

Empowering Quality Managers and Process Experts

In order to overcome this difficulty, the DELMIA Operations Intelligence  solution for shop floor optimization (DELMIA OI) has been designed from the start for Quality Managers and Process Experts. There is no need to select or fine-tune algorithms and “correlation” is probably the most complex word used in the User Interface. Training is achieved in a few days.

shop_floor_quality
A failure analysis engineer prepares boards for corrosion testing. © Intel.

We also think that expertise is essential to obtain reliable models in the manufacturing field. For example, a process expert may identify irrelevant parameters, add relevant durations between operations, spot errors in data… And, last but not least, he may get inspiration from the model, which in the case of DELMIA OI comes in the form of human-readable rules.

Does this mean that data scientists are out of the picture? No, if you are lucky enough to have such resources, you will realize that best results are actually obtained by the collaboration between all profiles. Data scientists bring their experience on how to prepare and handle data, while quality managers and process experts can make informed decisions using their process knowledge.

The need for a Method

Even simple concepts and an intuitive user interface will not guarantee best results. You need a method to avoid pitfalls when you have to deal with potentially erroneous or incomplete data and different ways to address the problem.

Using the experience of DELMIA Operations Intelligence past projects, we have been able to build such a method, which has been recently shared in the DELMIA Enterprise Intelligence community.

The method consists in 8 steps:

understand_process
Understand process
import_curve_data
Leverage curves
 cleanup
Clean data
 prepare
Prepare data
 target
Define output
build_model
Build model
 validate
Validate model
 assess_value
Assess value

The method answers questions such as:

  • How to leverage curve data (hint: you may need BIOVIA Pipeline Pilot)?
  • Where should I put the frontier between a good and a bad yield?
  • How can I measure the reliability of the model (its ability to predict)?
  • How can I improve my model?
  • How can I evaluate the number of defective parts that could be spared if DELMIA OI recommendations were applied on the shop floor?

Discover more about how to build reliable predictive models to optimize your manufacturing operations by joining our free DELMIA Enterprise Intelligence community.

Once you are registered, it all starts with this post!

[PART 2] DELMIA Helps the Aerospace Industry Meet the Challenges of Composite Manufacturing

By Christian
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Composite wing flap at Airbus
A composite wing flap in Filton, Bristol.
Source: Department for Business, Innovation & Skills, UK.

I’m Christian Chaplais, Senior Manager of R&D DELMIA Enterprise Intelligence Applications. Welcome to the  second blog  of a two-part series on how  Operational Intelligence is helping the Aerospace & Defense Industry.

Finding a Way Around the Complexity of Composite Manufacturing

Many composite parts manufacturers have been exposed to quality issues for years. Some have used classic approaches such as simple statistics, advanced statistics or optimization consulting services to find an answer, but came up short.

So, the question remains. How do you solve composite manufacturing issues without going through all the complexities? There is a way to discover and apply an empirical (data-based) model without the complications in just a few weeks: Operations Intelligence (O/I).

With (O/I) Process Rules Discovery, for example, quality engineers or process and product experts can discover patterns (or rules) explaining whether results have been satisfactory or not. This can be done with a limited number of observations, which keeps down costs, in a ramp-up study.

Process and product experts can also understand the model with Process Rules Discovery, change it by editing the rules and immediately see the impact on the rule KPIs (Key Performance Indicators) based on facts (data).

Here’s a sample rule discovered by Process Rules Discovery:

sample rule

The rule can be interpreted as:

When the product is in the autoclave for an extended period of time (cure cycle time is high)…
…and the binding strength of the fiber is low,
…and fibers have been aging sufficiently,
then the quality is good.

Let’s take another O/I example. With Operations Advisor, shop floor workers can assess risk and take preventive or corrective action in real-time. Operations Advisor recommends values for actionable parameters (settings) without requiring any change to the process specifications or investment in new material.

operations advisor

[Operations Advisor risk assessment and proposed settings ranges (in green)]

Adopting Operations Intelligence

The DELMIA Operations Intelligence solution for Composites has been widely adopted by the Aerospace & Defense Industry from both OEMs and tier-one suppliers.

For several years, one company has been faced with an important and repetitive nonconformance issue (delamination) on the composite leading edge of wings for an aircraft manufacturer. On this family of products, the reject rate could reach 13% and the rework rate 28%. There were delays (up to 6 months of manufacturing backlog), extra internal costs, a loss of confidence from the customer and internal frustration. Multiple quality tasks including process audits, investigating new processes, SPC analysis, inspections of raw material, etc. did not solve the problem.

They then decided to use Operations Intelligence to analyze two years of production. In less than six weeks, two influent parameters, unsuspected until now, were identified (the fluidity of the resin and the time during which the part is kept under vacuum), as well as the recommended lower and higher limits for these parameters. By applying the rules discovered, they managed to instantly reduce the scrap rate to zero and the rework rate to 1%, removing any backlog shortly after.

I’d like to hear your experiences with Composite Manufacturing? What was the outcome?

Continue the technical conversation. Join the DELMIA Enterprise Intelligence Community: https://swym.3ds.com/#community:453

 



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