May 16, 2020

Emerging predictive analytics for the manufacturing industry

predictive analytics
Manufacturing
Admin
4 min
The field of ‘Predictive Analytics’, a field of analytics which tries to predict future events by finding patterns from historical events, is receiving much attention in manufacturing lately, due in l
The field of ‘Predictive Analytics, a field of analytics which tries to predict future events by finding patterns from historical events, is recei...

The field of ‘Predictive Analytics’, a field of analytics which tries to predict future events by finding patterns from historical events, is receiving much attention in manufacturing lately, due in large to the rise of ‘Big Data’.

The predictive analytics market is in fact growing at an unprecedented rate. ResearchMoz recently valued the market at $2.1 billion in 2012, and predicts it to see strong growth at 17.8 percent CAGR to 2019.

With this though comes the ‘hype’. Gartner’s ‘Hype Cycle’ predicts that predictive analytics and data science are approaching the ‘Peak of Inflated Expectations’ – this is when the early publicity around the technology starts. However the ‘Plateau of Productivity’, when mainstream adoption starts to take off, for data science will be reached in two to five years. Meanwhile, predictive analytics will take five to 10 years to reach the end of the hype cycle and maturity.

For an industry which traditionally suffers acutely from incomplete, disparate and dirty data and requires solutions to issues to be found as early as possible the need for such analytics in manufacturing is prominent. However, manufacturing is also a no-nonsense industry populated by practically-minded engineers and business leaders who are rightly sceptical of hyped technologies. So has the time for manufacturing predictive analytics come or is it just the big data vendors trying to persuade us? Let’s answer this by debunking the myths of manufacturing predictive analytics.

Firstly it is important to start by saying that ‘visualisation’ is not analytics despite what many vendors tell you. Being able to see and play around with data is useful, but if say an engineer wants to know what his COPQ is likely to be over the next few months and the factors which are driving the forecast, this is predictive analytics.

Secondly, there are many challenges with implementing and operating the common predictive analytics tools already on the market. Firstly it is too easy to produce a predictive model which is either naively incorrect to start with (because it requires a data scientist to build the model and/or validate) or soon becomes irrelevant (because things change).  Secondly there is a mathematical limit to what predictions can be made with a specific amount of data - if a vendor tells you he can predict the future from a just few data points, then it’s too good to be true.

Thirdly, if the analytics can’t be practically implemented on the shop floor then it is useless.

Lastly, there has to be a clear use case on which to apply the analyses to in the first place. If a problem requires an army of PhDs or the problem is not that large, then think again. Predictive analytics isn’t the answer to every prayer.

So, what kind of manufacturing problems are placed to benefit from predictive analytics? Well, there’s root cause analysis for resolving defects to lower COPQ , predictive maintenance and service on products and plant, hidden bottlenecks in processes, speeding up launch, lowering supply chain costs by understanding behaviours, improving the design of products by understanding how customers will use them better, product pricing…the list goes on.

For engineers, it’s a case of replacing ‘rear-view mirror analytics’ using statistical tools, with forward looking analytics which enable results to be understood and most importantly proven – perhaps ‘practical predictive analytics’.

So now we’ve debunked the topic, what about the technical hurdles? In the 2014 TDWI survey, the top three predictive analytics challenges across all sectors were: lack of skilled personnel; lack of understanding of predictive analytics technology; and an inability to assemble the necessary data.

Fortunately there are predictive analytics companies appearing who address these challenges by automating the data integration exercise, not needing to clean data (in fact positively refusing to clean data lest it remove early warning signals), dealing with sketchy data, and automating the analytics.

These new technologies look at all or whatever data is available. They are can run fast within the database or parallelised ‘in-memory’ to handle huge amounts of data from various disparate sources. They provide multiple solutions and the results are easy to understand and action. And no hypotheses are required.

Put simply, it means that a data scientist and IT department are no longer required to solve complex, and ever-changing problems. The technology at our company for example, Warwick Analytics, has been used to solve the root causes of complex failures at Motorola (the home of Six Sigma) by engineers, quality and production staff. Similarly the technology is used by many leading automotive aerospace and process industry manufacturers to reduce their Cost of Poor Quality by automatically resolving issues on-the-fly and increasing yield.

Adoption and deployment of these emerging predictive analytics tools will greatly enhance the ability of firms to move quicker and more cost-effectively by making predictive analytics available to everybody across all of their manufacturing processes. 

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Jun 17, 2021

Siemens: Providing the First Industrial 5G Router

Siemens
5G
IIoT
Data
3 min
Siemens’ first industrial 5G router, the Scalancer MUM856-1, is now available and will revolutionise the concept of remote control in industry

Across a number of industry sectors, there’s a growing need for both local wireless connectivity and remote access to machines and plants. In both of these cases, communication is, more often than not, over a long distance. Public wireless data networks can be used to enable this connectivity, both nationally and internationally, which makes the new 5G network mainframe an absolutely vital element of remote access and remote servicing solutions as we move into the interconnected age. 

 

Siemens Enables 5G IIoT

The eagerly awaited Scalance MUM856-1, Siemens’ very first industrial 5G router, is officially available to organisations. The device has the ability to connect all local industrial applications to the public 5G, 4G (LTE), and 3G (UMTS) mobile wireless networks ─ allowing companies to embrace the long-awaited Industrial Internet of Things (IIoT). 

Siemens presents its first industrial 5G router.
Siemens presents the Scalance MUM856-1.

The router can be used to remotely monitor and service plants, machines, as well as control elements and other industrial devices via a public 5G network ─ flexibly and with high data rates. Something that has been in incredibly high demand after being teased by the leading network providers for years.

 

Scalance MUM856-1 at a Glance

 

  • Scalance MUM856-1 connects local industrial applications to public 5G, 4G, and 3G mobile wireless networks
  • The router supports future-oriented applications such as remote access via public 5G networks or the connection of mobile devices such as automated guided vehicles in industry
  • A robust version in IP65 housing for use outside the control cabinet
  • Prototypes of Siemens 5G infrastructure for private networks already in use at several sites

 

5G Now

“To ensure the powerful connection of Ethernet-based subnetworks and automation devices, the Scalance MUM856-1 supports Release 15 of the 5G standard. The device offers high bandwidths of up to 1000 Mbps for the downlink and up to 500 Mbps for the uplink – providing high data rates for data-intensive applications such as the remote implementation of firmware updates. Thanks to IPv6 support, the devices can also be implemented in modern communication networks.

 

Various security functions are included to monitor data traffic and protect against unauthorised access: for example, an integrated firewall and authentication of communication devices and encryption of data transmission via VPN. If there is no available 5G network, the device switches automatically to 4G or 3G networks. The first release version of the router has an EU radio license; other versions with different licenses are in preparation. With the Sinema Remote Connect management platform for VPN connections, users can access remote plants or machines easily and securely – even if they are integrated in other networks. The software also offers easy management and autoconfiguration of the devices,” Siemens said. 

 

Preparing for a 5G-oriented Future

Siemens has announced that the new router can also be integrated into private 5G networks. This means that the Scalance MUM856-1 is, essentially, future-proofed when it comes to 5G adaptability; it supports future-oriented applications, including ‘mobile robots in manufacturing, autonomous vehicles in logistics or augmented reality applications for service technicians.’ 

 

And, for use on sites where conditions are a little harsher, Siemens has given the router robust IP65 housing ─ it’s “dust tight”, waterproof, and immersion-proofed.

 

The first release version of the router has an EU radio license; other versions with different licenses are in preparation. “With the Sinema Remote Connect management platform for VPN connections, users can access remote plants or machines easily and securely – even if they are integrated in other networks. The software also offers easy management and auto-configuration of the devices,” Siemens added.

 

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