Intelligence Networks: Evolution of Manufacturing And Industrial Asset Management

The World Economic Forum (WEF) and A.T. Kearney’s recent study of the future of production find that manufacturers are evaluating how combining emerging technologies including IoT, AI, and machine learning can improve asset tracking accuracy, supply chain visibility, and inventory optimization.

Technology like those as well as asset tracking and intelligence networks are evolving to the point where they will have a significant, growing impact on industrial supply chains.

While automation has already evolved manufacturing and assembly, rising platforms revolving around machine learning and asset intelligence are making short work of long-standing inefficiencies that have contributed greatly to rising operational costs.

McKinsey predicts machine learning is set to dramatically improve efficiency in manufacturing – manufacturers can expect to see a reduction in supply chain forecasting errors by 50% and lost sales will be reduced by a staggering 65% thanks to improvements with product availability.

What’s more is that supply chains are the lifeblood of any manufacturing business. Machine learning is predicted to reduce costs related to transport, warehousing and supply chain administration by 5 to 10% and 25 to 40%, respectively. Due to machine learning, overall inventory reductions of 20 to 50% are possible.

Items in the above chart might not appear to be new or revolutionary at first glance. Predictive maintenance, for example, is not a new concept. 28% of manufacturers and operators are already utilizing it.

At least in a way. Many companies had (and still have) passive tracking systems that rely on barcode technology and RFID for things like asset location and management as well as passive monitoring for predictive maintenance.

That’s to say… it was predictive because it was estimated when parts might break down. But it was passive because rather than having a system to notify when maintenance was due and to track those needs, staff usually just wait until something breaks.

The run-to-fire reaction response so common in manufacturing is finally seeing a solution.

The forecasted 38% growth over the next 5 years isn’t surprising given the leaps we’re seeing in technology around asset intelligence.

It’s not just asset tracking and maintenance that are getting huge improvements. The introduction of machine learning and intelligence networks is making a splash on the production floor.

Knowing in real-time how each machine’s load level impacts overall production schedule performance leads to better decisions managing each production run. Optimizing the best possible set of machines for a given production run is now possible using machine learning algorithms.

Intelligence networks provide equal value for manufacturers and operators

Symbiotic Production Systems, Real-Time Production Monitoring, Edge Analytics and AI Are Making Factories Intelligent and Agile, so it’s critical to get onboard sooner rather than later to reap the benefits – and the value is significant for both manufacturers and operators.

For the manufacturer there’s a tremendous amount of value around using an intelligence to manage assets around components:
 

  • Maintain model and equipment information in a single place to be efficiently managed for all parties in the supply chain
  • Gain transparency in equipment usage and rapid feedback from operators
  • Provides a single solution for all customers/operators
  • Expedites warranty and recall processes
  • Reduces touch points and customer contact over parts and models
  • Improves customer relationships translating to increasing customer value
  • Establishes grounds for collaboration between manufacturers and operators

 
These stem from an intelligence network where assets and information are centralized including product and service feedback, recommendations and updates, usage information around equipment and detailed specifications and part modeling.
 

  • Operators in the field see benefits from integration with an intelligence network, pairing them up with manufacturers
  • Dramatically reduces time spent searching for asset information (typically done manually)
  • Automatically receive notifications and bulletins around service work
  • Access to a streamlined, single communications channel to manufacturers, EPCs and service providers
  • Easily push alerts, trouble calls, and communications back to manufacturers and service providers
  • Reduction in the cost of the asset lifecycle
  • Efficient tracking for serialized components

 
Examples of asset intelligence in play:

The first example of this type of system is what we’ve seen with predictive asset supply. Asset intelligence allows for the use of an MES schedule ensuring that all the assets needed for a future phase of production are assembled in a specific location in advance.

Instances where assets are assembled across multiple locations or stored off site, an intelligence network reduces tracking time while also monitoring supply levels and maintenance requirements. Predictive asset supply will ensure parts are available on location before they’re needed to maintain production processes and reduce downtime.

Another example is used in health and safety with tracking. Similar trackers can map the location and environmental data for chemical containers and specific products, especially where exposure in close proximity could result in volatile conditions. The tracking ensures a minimum distance is kept between those containers. If that threshold is crossed, then warning alerts can be triggered within the facility to minimize workplace accidents.

Asset intelligence shines for industrial manufacturing

Here’s an example from SAP detailing how asset intelligence can benefit all collaborators involved:

Imagine you have three manufacturers:
 

  • Manufacturer C provides a motor
  • Manufacturer B provides a sensor
  • Manufacturer A uses the components to produce and manufacturer compressors

 
As part of the process, Manufacturers B and C submit a wealth of data to Manufacturer A including a variety of documentation (component specs, user manuals, service manuals, etc.) This information has to be extracted and compiled before being sent to Manufacturer A.

Manufacturer A typically has a team devoted to intercepting the information, cataloging it and adding it to their back-end system. Once the master component (in this case, the compressor) is completed it’s sent to the operator along with that cataloged documentation. The operator then must add the information to its back-end system used for maintenance purpose.

Any kind of maintenance at the operator level requires that data and documentation.

For many, those documents come in printed form, some digital, and some data via email. When these maintenance steps and subsequent documentation aren’t integrated you have people at every stage pulling, sending, processing, and logging documentation resulting in time delays, downtime, labor costs, and lost, missing, or corrupted data.

If the operator doesn’t maintain good data, and if manufacturers don’t push updated data out to all operators, even greater losses result due to extended downtime.

Data Segmentation without intelligence networks is a problem

The above example is extremely common, and it’s made worse when multiple departments are involved – such as one team handling installation and another team handling maintenance. They may each use different systems with documentation and data records stored differently.

Segmented systems that aren’t integrated at the operator level, with no integration to manufacturers, results in multiple versions of documentation that can create additional delays in maintenance and repair/replacement during downtime.

An asset intelligence network integrates at every stage of a products life cycle, and introduces a single platform containing data on all assets for all manufacturers involved. That means instant access and secured information exchange between various roles in asset-intensive industries, like equipment manufacturers, operators, maintenance providers, etc.

Use Cases for an intelligence network among industrial organizations

Predictive maintenance and asset supply are popular components of an intelligence network but that’s just a fraction of the applicable uses. There’s lifecycle management, regulatory and process compliance and integrity management as well.

Here are some common use cases:

For both manufacturers and operators an asset intelligence network will provide a global registry of equipment built and shared between countless business partners and used across an entire industry with stakeholders at various levels.

Beyond the benefits and uses mentioned already above, an intelligence network brings collaboration to a new level that redefines operational excellence.

BASF Ludwigshafen is currently using the SAP asset intelligence network to improve efficiency of its engineering and maintenance processes, specifically leveraging the collaborative nature of the platform.

According to a 2017 press release from BASF:

“BASF constantly works on optimizing its sites, plants and production processes,” said Andreas Wernsdörfer, Senior Vice President Technical Site Services Ludwigshafen, BASF. “SAP Asset Intelligence Network is an approach that has the potential to further improve our engineering and maintenance processes by establishing a fully integrated digital information chain between OEMs, service providers and BASF over the whole asset lifecycle. A more integrated digital approach with our business partners would allow us to easily access the latest and current information when and where needed, leading to quicker and better decision-making and, in consequence, higher asset effectiveness.”

“With SAP Asset Intelligence Network, we enable our customers to collaborate in a digital ecosystem to manage intelligent devices in the Internet of Things (IoT) and achieve their goals for operational excellence,” said Dr. Tanja Rueckert, President IoT and Digital Supply Chain, SAP. “Working with industry leaders like BASF, we aim to establish a network of real-time industrial asset information so that our customers and their partners can realize the full advantages of the IoT with the SAP Leonardo digital innovation system.”

The Four Drivers of an Intelligence Network – Why you need to adopt today

The advancement of the Internet of Things as intelligence network development provides the most value to manufacturing and industrial brands – like BASF – through four pillars.

1. Real-time connectivity – providing a wealth of information in real time include performance insights and data as well as optimization of performance and production efficiency through prediction.

2. Adaptability to digital disruption – As other technologies develop and advance, an intelligence network will grow to integrate appropriately. For example, as 3D printing is more widely adopted the models can live and evolve within the network.

3. Cloud-based – Cloud technology for intelligence networks provides access from anywhere and is ideal for a collaborative ecosystem where brands can partner together at all stages of the asset lifecycle. This base for asset management eliminates a vast amount of manual work among manufacturers and operators.

4. Scalable – Because of the integrated, connected nature of the cloud platform and adoption across all stakeholders in the asset lifecycle an intelligence network is a fully shareable and scalable solution offering the highest level of collaboration.

Conclusion

An intelligence network is the most capable platform for collaboration, creating trust as well as efficiency among manufacturers, operators, and end users. This kind of collaborative environment promises win-win scenarios for all involved, where the operator benefits with the latest information about equipment being distributed, improved product enhancements based on community experience, maintenance notifications based on real-time operations, efficient asset management and more. The manufacturer will grow exponentially thanks to access to improved market data, community feedback on products and processes, quantifiable use case data, enabling equipment as a service, product performance analysis in real time, virtual audits and more.

This pace of change we’re seeing with the growth of intelligence networks and connected manufacturing will only continue to accelerate according to data we’re seeing, like the predictions shared by McKinsey. The opportunity to optimize processes has been huge and going forward asset intelligence will be a major part of manufacturers taking control of efficiency and growth.

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By |2018-08-15T23:32:32+00:00June 9th, 2018|