Implementing Data-Driven Manufacturing in Northeast Ohio

Clarifying Terms and Dispelling Myths

For many manufacturers that are new to the realm of Smart Manufacturing, the vernacular is full of new terms that seem foreign and used inconsistently among their constituents, and myths abound regarding the implementation of Smart Manufacturing into the business. All of this inconsistency and misinformation can be intimidating and daunting for a manufacturer to overcome as they progress through their own deployment efforts. From my experience working with manufacturers in Northeast Ohio over the last 20 years, I wanted to offer some useful insights into the clarification and simplification of fundamental terms and to provide additional insights into the justification for implementation of Smart Manufacturing.

I recently read an article by Anurag Garg, Vice President and head of Analytics and Internet of Things (IoT) at Plex, titled “5 Common Myths about Implementing IIoT” where he highlights the following myths:

  • Myth #1: Implementing an Industrial IoT System is a Technical Decision
  • Myth #2: Industrial IoT Means a Long Implementation
  • Myth #3: We Don’t Need Industrial IoT—We’ve Been Doing SCADA for 20 Years
  • Myth #4: To Benefit from IIoT, We Would Need to Hire a Data Scientist (Or Also Buy an Advanced Analytics Solution)
  • Myth #5: IIoT Is Just Another Buzzword That Means the Same Thing as Industry 4.0

Anurag’s article is a great resource for those currently in the process of implementing a Smart Manufacturing solution. In this blog post, I will build on Anurag’s experience by further elaborating on the content of Myth #5 and Myth #1 (in that order).

Myth #5: IIoT Is Just Another Buzzword That Means the Same Thing as Industry 4.0 – And Other Terms of Importance

In his article, Anurag describes the distinction between IIoT and Industry 4.0, but it is also useful to add the terms of Smart Manufacturing and Data-Driven Manufacturing (DDM) to the discussion.

Smart Manufacturing / Industry 4.0 Illustration
This illustration is a popular image used in many places on the internet to define Industry 4.0 and Smart Manufacturing.

In most forums, the two terms inside the circle are interchangeable and the other items around the circle are considered technologies that comprise Smart Manufacturing/Industry 4.0 (SM/I 4.0). Note that SM/I 4.0 is an “umbrella” term and IIoT is a subset of that term. Also note, there is another term added to this illustration known as DDM which stands for Data-Driven Manufacturing. DDM is the foundational concept that underpins Smart Manufacturing and provides the infrastructure used by all the technologies around the circle to improve the manufacturing operations. And in general, the concept establishes a manufacturing environment where all the smart devices used in the production process are connected to a data network so they can talk to each other and so they can share their data with management. Management then processes the data to gain greater insights into how best to manage the manufacturing process – all to achieve better business performance, efficiency and growth.

Just a comment about smart devices – a smart device is a device that includes a level of automation that enables it to collect and share data, and to be connected to a communications network. Even if a device used in the manufacturing process is not smart, it typically can be made smart by simply implementing an add-on of sensors, comms and software that then enables the device to share data with the network.

Many of the manufacturers I interface with, find DDM is a more intuitive way to understand how Smart Manufacturing can improve one’s business and the term is a substitute for IIoT in many forums.

Myth #1: Implementing an Industrial IoT System is a Technical Decision – Operational Applications Play a Role in Business Justification

“Whenever new technology generates hype, there’s a perceived danger that companies not adopting quickly enough will be left behind. But manufacturers must avoid implementing technology for technology’s sake… Simply put, implementing IIoT is not a technical decision, but a business decision.”

Anurag Garg, in the article 5 Common Myths about Implementing IIoT

This implies that a manufacturer needs to employ an effective business-based justification process. In Northeast Ohio (NEO), most successful Small to Medium Enterprise (SME) manufacturers improve business performance by finding new solutions to address the application needs of their production process – they use application solutions as a basis for their business decision. This distinction between technology, applications and business decisions is vital to understand if a manufacturer expects to harvest the maximum ROI from implementing a DDM system.

Popular Application Areas for Data-Driven Manufacturing

  • OEE (Over-all Equipment Effectiveness) sometimes referred to as asset utilization;
  • Operational Efficiency
  • Predictive Maintenance
  • Quality Control
  • Inventory Control
  • Occupation Safety
  • Cybersecurity

The 3 most popular application areas are OEE, Operational Efficiency and Predictive Maintenance. Operational Efficiency is typically the first place to start when implementing a DDM system. Once a data network connecting all the critical production assets is established to support the Operational Efficiency application, the same infrastructure can be used for the other applications.

Typical Experience with Data-Driven Manufacturing to Improve Business - DDM Appliances
This diagram illustrates the range of typical improvements on business performance a manufacturer could experience by implementing some of the DDM popular applications.

Partnering with Solution Providers

SME’s are better at implementing and harvesting more value from their DDM system when they partner with a culturally compatible Solution Provider that has extensive experience assisting other similar businesses with DDM system implementations. The Smart Manufacturing Cluster of Northeast Ohio has partnered with Solution Providers to create a use case database to help aid manufacturers with the planning, implementation, and long-term support of the DDM system.

Conclusion

  • Data-Driven Manufacturing (DDM) is an intuitive term that describes the infrastructure need to support Smart Manufacturing.
  • The implementation and validation of value for a DDM system should be based on a focused application and not on technology alone.
  • Manufacturers experience business performance improvements when implementing popular data-driven manufacturing applications.
  • Solution providers can help accelerate the implementation of a DDM system and ensure value.

About the Author

Rick Earles, Senior Director, Industry and Innovation at Team NEO

Rick Earles identifies and facilitates product development and commercialization opportunities for regional innovation clusters. In this role, he engages value chain members and assists them in identifying and collaborating with potential partners, capturing funding, creating prototypes and attracting regional talent.

With over 25 years of experience, Rick specializes in technology innovation, technology-based business development and technology-based economic development. He is experienced in organic business growth through new product development, the implementation of strategic alliances, mergers and acquisitions, and the expansion into new market opportunities. Read more.