Industry 4.0 Challenges
According to Industry Week, two of every three companies piloting digital manufacturing solutions flounder when moving to large-scale rollout.
Why have so many companies struggled to grasp the potential of digital manufacturing at scale?
Gap in technical skills
The needs required of the workforce all evolving. Are your employees able to keep up? When looking to fill open positions, look for applicants who possess “digital dexterity” in that they understand both the manufacturing processes and the digital tools that support those processes. Only with the right workforce will business models be able to successfully implement new technology and maintain operations.
data sensitivity
The rise in technology has also led to increasing concerns over data and IP privacy, ownership, and management. A common example? To successfully implement an AI algorithm, data is required to train it and test it. For this to happen, the data must be shared. However, many companies are reluctant to share their data with third-party solution developers. Further, our current data governance policies for internal use within organizations are inadequate to support cross-organizational data sharing. Data is a powerful asset – make sure to keep it secure!
interoperability
Another significant issue is the lack of separation between protocols, components, products, and systems. Unfortunately, interoperability impedes companies’ ability to innovate. Further, since they cannot easily “swap out” one vendor for another or one part of the system for another, interoperability also limits options to upgrade system components.
security
Threats in terms of current and emerging vulnerabilities in the factory are another significant concern. The physical and digital systems that make up smart factories make real-time interoperability possible—however, it comes with the risk of an expanded attack surface. When numerous machines and devices are connected to single or multiple networks in a smart factory, vulnerabilities in any one of those pieces of equipment could make the system vulnerable to attack. To help combat this issue, companies need to anticipate both enterprise system vulnerabilities and machine level operational vulnerabilities. Companies are not fully prepared to deal with these security threats, with many relying on their technology and solution providers to scope out vulnerabilities.
handling data growth
As more companies become dependent on AI usage, companies will be faced with more data that is being generated at a faster pace and presented in multiple formats. To wade through these vast amounts of data, AI algorithms need to be easier to comprehend. Further, these algorithms need to be able to combine data that might be of different types and timeframes.