Manufacturing’s digital transformation growth is truly impressive considering it’s delivering value with explosive growth rates. Consider that Manufacturing’s Industry Internet of Things (IIOT) was valued at $161b with an impressive 25% growth rate, or that the Connected Car market will be valued at $225b by 2027 with a 17% growth rate. But then conflicting information arrives as VentureBeat reports that around 90 percent of machine learning models never make it into production? In other words, only one in ten of a data scientist’s workdays actually end up producing something useful for the company. This conundrum is a major challenge that the Manufacturing industry wrestles with everyday that limits meaningful ROI, scalability, or success and it’s due to the challenges and approaches Manufacturing and Industrial companies are taking when approaching Industry 4.0. Clearly Industry 4.0 is real for some and an enigma for others.
When you consider the challenges that an industrial company faces planning, executing and nurturing an Industry 4.0 project, the multitude of challenges are distilled down to a few:
– Closed OT Infrastructure or the Mix of Legacy and Modern Systems
Many factories utilize both modern and legacy assets and devices from multiple vendors, with various protocols and data formats. Although the controllers and devices may be connected to an OT system, they are not usually connected in a way that they can easily share the data with IT systems as well. In order to enable Connected Manufacturing and emerging IIoT use cases, manufacturers need a solution that can handle all types of diverse data structures and schemas from the edge, normalize the data, and then share it with any type of data consumer including Big Data applications.
– Mass Data Fragmentation / Difficulty Ingesting all Data
Specialized processes (innovation platforms, QMS, MES, etc.) within the Manufacturing value chain reward disparate data sources and data management platforms that tailor to unique siloed solutions. These niche solutions limit enterprise value considering only a fraction of the insight cross-enterprise data can offer, while dividing the business and limiting collaboration opportunities. The right platform must have the ability to ingest, store, manage, analyze and process streaming data from all points in the value chain, combine it with Data Historians, ERP, MES and QMS sources, and leverage it into actionable insights. These insights will deliver dashboards, reports and predictive analytics that drive high-value manufacturing use cases.
– Lack of Clear ROI
The failure of companies to clearly define success and the data architecture needed at the beginning of the project are starting points for a failure, as many simply want to “embrace a smart manufacturing or Industry 4.0” solution without a full vision of what the solution entails and organizationally needs. The first misstep is that implementations are often prompted by a vague understanding of the point solution each new technology delivers rather than a value-led implementation prompted by a business use case that considers the entire data lifecycle.
Additionally, success is initially defined as a successful pilot instead of a measurable business goal such as improving OEE, optimizing a specific production line or reducing scrap. Without clear objectives, deployments fail because they never reach an unmeasurable goal. When shaping the goal, scalability needs to be considered during the planning stage, as this is one of the most challenging aspects of successful Industry 4.0 ROI.
– Ongoing Talent Challenges
“People” make the third leg of the organizational stool, considering Process and Technology are the other legs. Organizational challenges that bleed Industry 4.0 of its ROI are due to demographic changes of an aging workforce, the increasing demand for digital skills due to IT and OT convergence, the unspoken issue of “employee buy-in” (how can a machine be more skilled at predictive maintenance failures than someone with 35 years of experience?), and the skills gap created by less tenure in key evolutionary (not revolutionary) proprietary developed manufacturing processes.
Larger challenges are seen…
There are two other issues that loom larger and need closer inspection to understand their root causes that contribute to Industry 4.0 failure:
– Failure to scale use cases
– Mastering the data lifecycle
To explore these Manufacturing Industry 4.0 challenges, Cloudera turned to ecosystem industry experts to help distill the driving forces for success in creating an Industry 4.0. Join us for an upcoming event titled Industry 4.0 – Made Real. This digital panel style event will delve into practical solutions and insight from Cloudera, Accenture, Dell, Intel and Microsoft speaking in plain terms how challenges and roadblocks can be overcome delivering successful Industry 4.0.
Speaking at this digital event is:
- Jamie Engesser, Sr. VP, Cloud, Machine Learning and Field – Cloudera
- Darren Coil, Director Strategy Business Development, Automotive, Manufacturing, Operations and Emerging Tech – Microsoft
- Brian Irwin, Automotive and Industrial Leader – Accenture
- Dr. Florian Baumann, CTO (Specializing in Automotive & AI) – Dell
- Dr. Irene J. Petrick, Senior Director of Industrial Innovation in the Internet of Things Group – Intel
- Michael Ger, Managing Director – Manufacturing and Automotive – Cloudera
- Dominique Hollins, Director, DE&I – Cloudera
- Dinesh Chandrasekhar, Product Marketing Director – Cloudera Data Flow – Cloudera
Register here to join us February 17, 2021 and gain practical experience and knowledge from an honest unstructured conversation on pressing issues that dominate manufacturing today and tomorrow.