In Part 1 of Big Digitalization across the Globe,  I discussed what is the Industrial Internet, what it means to become a digital industrial company and some implications. Today I’m going to address the role of IIoT in manufacturing, outline what “good” looks like in becoming a digital industrial, and steps you should consider along that journey.

In recent LNS Research surveys related to the IIoT and manufacturing operations, we asked about the most important trends impacting industrial equipment companies. We have seen a significant shift in the responses when comparing previous years with 2016: today connecting smart products to the Internet via IIoT has moved to the top spot. This is creating some new challenges and opportunities with the integration of design, engineering, business and operations systems across enterprises. Number two on the list of importance is managing ever-shortening product life-cycles and time to market, along with the opportunity to enable remote service of industrial equipment.

Some may find it surprising that we are seeing the heavy industrial equipment suppliers and end-users as early adopters of the IIoT. However, as we saw from the Caterpillar case, these companies are used to large capital investments and taking the long-term view to financial capitalization, return on investment, and quality of service. And once these IoT-enabled, smart, connected industrial equipment offerings are deployed, the payback periods should be further shortened and customer service dramatically improved.

Digital Industrial: What Does "Good" Look Like

So what will digital industrial transformation and the IIoT bring to address these heavy industry marketplace trends?

  • Smart connected heavy equipment will be able to self-diagnose and self-report on location, usage, operators, and production effectiveness.
  • Scheduling of equipment will be based on real information delivered in a timely manner.
  • Asset utilization will be maximized by giving visibility to actual production use versus equipment dead times or downtimes.
  • Safety of the operation will be improved by ensuring that only properly maintained equipment and trained operators are deployed and ensuring that there are no dangerous or conflicting work activities that are occurring simultaneously.
  • Real-time diagnostic health and equipment status information can be used to perform proactive maintenance strategies that are optimized to ensure asset availability and longevity.

 All of these capabilities can be delivered and managed globally. This will create the opportunity for new partner relationships between heavy industrial equipment providers and users for remote maintenance services and parts management, along with new business models such as “digging or transporting as a service.”

For suppliers of heavy industrial equipment, the integration of their business, engineering and production systems will be key to connecting to and providing these new services to clients. It will also be increasingly important to feedback and link actual equipment performance information from end-users into the engineering and design process. This will enable faster product  improvement  and new product  introduction. Indeed, the speed of some of these updates will be dramatically improved, as they can come in the form of digital software downloads to equipment that is already deployed in the field.

Begin With What Really Matters

LNS Research sees digitization as a business imperative for Heavy Industrial Equipment providers if they are to remain competitive. They need to enable their new offerings with IIoT connectivity, global Big Data, and on-board intelligence. Providing the information capabilities that enable the strategies discussed earlier for your end-users will soon become the norm in heavy industries. It’s also time to have discussions with your end-customers about the roles that they want to take in a joint effort towards Digital Transformation.

The success or otherwise of a digitization strategy will depend on many factors way beyond today’s short discussion. However, there are some things that really matter now: 

  • Executive ownership of the strategy; everything is affected by digitization so leadership should come from those in charge of everything.
  • Executive sponsorship of the change in process and Operational Excellence that drive implementation.
  • Global buy in, deployment and accessibility to information; the more people who can use Big Data, the more value it will have.
  • Influence beyond the enterprise; the idea is that your customers benefit from your digitization efforts. Involve them from the start.

Caterpillar has demonstrated that the message must come from the top and involve everyone in the company. It is not just Big Data we’re talking about, it’s big change delivered by digitization across the extended supply chain.

If you are an end user of heavy industrial equipment, it’s time to think about your core competencies and what differentiates your operational strategy.

You need to be able to address these points:

  • Who is in the best position to schedule, deploy, and maintain your heavy industrial equipment?
  • What financial models would serve you best going forward – capital ownership, leasing, or paying for production-as-a-service?
  • Talk to suppliers who can provide some or all of the business processes that you need to support; partnership is everything in a digital world.

Start Small, But Above All, Start!

Set some strategic objectives and define some projects now. Digitization will not wait for anyone and laggards will not benefit as much as leaders. Work with customers and suppliers to define some real scenarios that can be implemented now; a world of opportunities is opening up for companies that innovate in the Internet of “big” things.

Get a copy of LNS Research’s “Metrics that Matter” report.

Metrics that Matter in an IoT World

Metrics that Matter: LNS Research and MESA International

Measuring the Progress

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About the Author

Andrew Hughes, Principal Analyst, LNS Research

Andrew Hughes joined the LNS Research team in May of 2015 and is a Principal Analyst with his primary focus being research and analysis in the Manufacturing Operations Management (MOM) practice. Andrew has 30 years of experience in manufacturing IT, software research, sales and management across a broad spectrum of manufacturing industries.

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