Challenge:
Hosokawa Micron Ltd (HML) is a global leader in powder processing equipment. The company covers a wide range of manufacturing technologies used in the pharmaceutical, chemical, mineral, and food industries.
It enjoys a competitive advantage in the marketplace as a manufacturer of machines in these industries. However, HML was inspired by a vision to go a step further and stay ahead of the game.
It was challenging for a company that has a 100-year legacy to find solutions in manufacturing that were new and digitally smart with a great ‘value add’ for its customers. They needed a partner who could help them achieve their new goal.
Iain Crosley, Managing Director, Hosokawa Micron Ltd., recalls: “Our partnership with Siemens is over two decades old and we have been using their control and instrumentation – the Siemens PLCs – in all our products. Having our own Gen4 products, we remained both competitive and digitally viable for our customer base. But we wanted to enter the next phase of digitalisation.
“This is when we found Siemens’ MindSphere, which we realised would give us the complete architecture to provide a holistic solution to our customers.”
Solution:
At HML, the machine and processes are controlled through Siemens’ programmable logic controller (PLC) and, in some cases, using supervisory control and data acquisition (SCADA) or digital combat simulator (DCS). To enhance its processes an in-house intelligence solution was being used to extract and analyse the data, and improve performance.
The management realised that to stay competitive and make deeper inroads in the fourth industrial revolution, it was important to keep up with big data and analytics demands. The solution was Siemens’ cloud-based internet of things (IoT) open operating system, MindSphere. It connects the entire environment of products, plants, systems, and machines, enabling the harnessing of a wealth of data with advanced analytics. In addition, it gives access to a growing number of apps and a dynamic development ecosystem. MindSphere works with all popular web browsers.
“Our decision to use Siemens was simple really. Just like us, they are a process engineering company and so they understand the requirements and problems in transferring process data especially, high volumes of data,” added Crosley.
“Add to this the challenge of cyber-attacks, which is an important aspect of any new technology and we were rest assured that Siemens had in place best-in-class industrial cyber security.”
‘Siemens has adopted the highest cyber security standards for its products and services such as the international IEC 62443 standard while also adopting other relevant standards that are used throughout its global business to demonstrate a consistent approach to security such as the ISO 2700* series of standards.
Siemens initiated the Charter of Trust at the 2018 Munich Security Conference. This was created with eight other partners committed to greater cyber security. This has now extended to 16 members, with the first Asian company, Mitsubishi Heavy Industries (MHI) signed up in 2019. Signatories include AES, Airbus, Allianz, Atos, Cisco, Daimler, Dell Technologies, Deutsche Telekom, IBM, NXP, SGS, Total and TÜV Süd.
The Charter has some government bodies signed up, including the German Federal Office for Information Security, the CCN National Cryptologic Center of Spain and the Graz University of Technology in Austria as associate members.
Ian Elsby, Head of Chemicals UK & Ireland, Siemens Digital Industries, said, “Our partnership with Hosokawa has always produced tremendous results and this is purely because we share best practice and learn from each other. We are constantly looking for new solutions to create a better and digitally superior product for their customers.
“Industry 4.0 is constantly evolving, and so is our alliance. We are able to bring to the table the challenges from the varied verticals we work in and share the outcomes of positioning in other industries, helping Hosokawa harness that knowledge to boost their digitalisation process.”
Data analysis is ubiquitous in any setting. Using Siemens’ MindSphere, clients get access to maximum data extraction and powerful data analysis and visualization, giving manufacturers new insights into making changes with real productivity impact.
HML set the following key performance indicators to maximise data analysis: quality, energy usage, environmental conditions (internal and external), process parameters and the factors affecting them.
“We work on three levels: understanding the data, monitoring it and controlling it,” added Crosley. “The methodology of data acquisition and the manner of using it have changed and become more refined, allowing better production output decisions.”
He further explained: “Applying the five P’s to any new technology is crucial to its success. People, plant, process, products and profit, as well as the attitude of the people and the integration of a plant’s operations and processes, enhance the end product, ultimately adding to bottom line profits.”
Outcome:
How has the deployment benefited Hosokawa Micron Ltd and its customers? Simply put, it has brought about 15 per cent improvement in uptime; energy usage has been reduced by more than 10 per cent and capacity gains of between 10 to 12 per cent have been recorded for its customers.
“Since the start of our journey of advanced digitalisation in our factory in the last three to four years, we have grown our own output significantly year-on-year,” concluded Crosley. “Not only have we digitalised our own production factory but the machines we deliver to our customers are of higher digital value, primed for better productivity.”
With the integration of its assets across the factory in Runcorn, HML was able to upgrade its existing in-house app ‘Hosokawa ReMs’ to remotely monitor the performance of Hosokawa equipment and plant, including real time values and trending for key measurements, diagnostic alerts and warnings of problems detected in machinery. App users have varied options on how they want to use it, including a servitisation model from HML on providing solutions for any data anomalies that may show in the analysis.
