Dec 18, 2020

How Manufacturers Can Scale Faster by Looking Externally

Manufacturing
aspentech
Technology
AI
Justin Eames, Sr. Manager, APM...
4 min
Justin Eames, Sr. Manager, APM Solution Consulting, Aspen Technology, Inc. explains how manufacturers can scale faster by looking externally.
Justin Eames, Sr. Manager, APM Solution Consulting, Aspen Technology, Inc. explains how manufacturers can scale faster by looking externally...

Many manufacturers have a propensity for going it alone with their own technology efforts in the belief that it will be faster and more cost-effective. They put in place data science models and platforms and concentrate on speed of data preparation, often bringing vast amounts of data quickly into a useable format. That’s frequently effective – but the wrong bottleneck for businesses to focus on. 

For organisations that follow the route of an in-house deployment, a key roadblock can be the time it takes to fully build, tune and initiate the process. Beyond the initial challenge of developing and deploying an in-house data science model, scaling up the solution can be a challenge to complete alone.

On top of this, data scientists are needed to work on data science implementations in businesses. While many organisations employ a core group of data scientists to manage these implementations, in many cases there are insufficient numbers of these professionals to quickly implement models and scale them accordingly.

Taking these factors into account, many companies are led to abandon in-house projects due to the strain on skills, time, and money in developing a solution, plus the effort it takes to sustain and scale it afterwards.

Considering the bigger picture

Focusing purely on in-house projects can allow manufacturers to solve specific problems, but ultimately keeps the organisation’s hands tied when it comes to scaling up implementation. Even just wrangling the data and making it usable can take a considerable amount of time. Data scientists building one-off use cases will struggle to translate this into multiple iterations across a facility.

In-house data science models are often based on a single-use case, and even just building an effective one can take 6 to 12 months. As an example, if this is scaled up to 300-600 assets, it can start to feel like a never-ending journey. It is here where a packaged outsourced solution from a third-party provider or partner can prove crucial, often bringing rapid value to the business through the ease of use, scalability and speed of deployment. 

The ability to prepare data and develop models faster sits with the potential of automated data science systems, aimed at the broad engineering base of qualified users. Furthermore, these systems allow continuous monitoring, providing alerts to engineers who can then take prescribed actions to avoid any business losses. By investing in these systems, manufacturers can speed up deployment by 100 or even 1000 times, increasing value.

Organisations are then able to significantly minimise investment in project resource and infrastructure and achieve faster time to value. By scaling up the solution in this way, it can also impact margin positively as well as a wider market perception in areas such as safety, emissions control and overall equipment effectiveness (OEE). It, therefore, enables the transition of technology benefits into business ones. 

The role of the data scientist

Despite this, it doesn’t mean that the role of the data scientist is diminished. While they may understandably feel defensive and at risk when implementations of these kinds take place, that should not be the case. System usability is key here. In third-party implementations, qualified users of mechanical systems and process engineers from the existing business can address day-to-day problems and challenges with the system, with data scientists able to be reassigned to concentrate on a higher level, more strategic range of challenges.  

Providing this opportunity to data scientists ultimately enables them to delve into wider projects where they can make a profound impact on the organisation, as opposed to dealing with the more granular day-to-day tasks. The message for manufacturers in this scenario is that as they bring on more data scientists they can ensure their involvement in higher-level data science and deliver major process improvement projects, often with the help of the third-party technology or equipment.

The importance of refocusing  

Following these processes will likely lead to a change in the business mindset and how projects are approached in the long-term. Rollouts on a proof of concept or a project scale won’t deliver the same impact in comparison to implementations that are scaled up, with larger rollouts often providing a faster return on investment. This is prevalent particularly in comparison to in-house data science-based rollouts where the value may not be seen for many months. Now is the time to move away from small scale pilots and start thinking bigger by not putting model development purely in the hands of data scientists. Remove the proverbial shackles and enable them and your base of qualified users to improve your business margins.

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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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