Skill Sets Required for Industry 4.0


From the factory floor to the corporate analytics group, manufacturers are trying hard to find employees who possess the unique combinations of skills that will be needed for Industry 4.0.

These aren’t always easy to find. That’s because Industry 4.0, or what some call digital transformation, is a work in progress. The basic outlines are there, with intelligent machines and processes working with – and creating – mounds of data to be analyzed. But exactly how that data will be analyzed is less clear.

Machines will need operators who can quickly assess and act on sensor data, what the Boston Consulting Group calls “robot coordinators.”

Analytics teams will need more far-reaching viewpoints, not to mention unbounded curiosity and think-out-of-the-box abilities.

Factory Skills

For factory and production/logistics workers, skills such as these will be paramount:

  • Personal flexibility – Agile manufacturing, on-demand shipping and continuous improvement programs all point to the fact that employees will need to be more flexible than ever before in how they go about doing, and thinking about, their work.
  • Collaboration and process awareness – As machines become smarter and more capable, employees will be encouraged to add higher-level process management skills. Process awareness will combine with collaboration and problem-solving to help workers manage the machines and their interactions.
  • Willingness to learn and apply soft skills – Workers will still need their specific job skills, but should be ready to add what Boston Consulting Group calls “soft” skills, from basic to advanced IT-level capabilities. Boston Consulting Group gives this example:

    “Industry 4.0’s advancements will make it possible for an operator to carry out the same types of responsibilities at several machines. Standard operating procedures for any given task will be displayed on screens or glasses. The monitoring of machine performance and product quality will be aided by quality control queries provided by an automated system.

    “Consequently, the operator will require less machine- and product-specific training but will need enhanced capabilities for utilizing digital devices and software and accessing a digital knowledge repository.”

Analytics Team Skills

To make the most of the analytics technologies now available, many manufacturers will have to leave their cultural comfort zones by handing over new levels of control and autonomy to people – data scientists, for instance – they’re not used to working with. A typical analytics team will include one or more:

Data scientists, who can create the analytics models needed to translate the incoming data, which gets “bigger” by the hour, into meaningful information;

IT professionals may include software engineers, network engineers and IT architects who can tie the analytics to underlying operational systems and databases that serve as data resources; and

Domain experts, typically process engineers or other manufacturing professionals who can translate the needs and workings of the company’s production processes for the data scientists and IT people.

As for skills, most obvious is technical expertise in the team members’ specific disciplines.

Data scientists have plenty to learn and track as new models and techniques evolve regularly; software engineers need to link together wide ranging and sometimes fast-changing technical resources, from legacy manufacturing execution and enterprise requirements planning systems – MES and ERP – to newer technologies such as Hadoop and SAP HANA. Network engineers have to stay current with major changes now underway, such as the moves to software defined networking and Internet Protocol version 6.

As a group, the analytics contingent should strive for skills in:

  • Communication and collaboration – Team members should be able to explain clearly how their technical needs and outcomes relate to other team players. Patience and understanding are vital, especially when members are under the stress of project deadlines.
  • Big-picture awareness – The process engineer should be able to translate the workings not just of the factory floor but of upper management as well, so the entire group understands how their work relates to the strategic goals of the business.
  • Ability to adapt to change – The analytics team will have to get used to the inevitability of frequent change. Team members may rotate in and out of the group; business plans can change quickly; newer technologies may appear suddenly from new or existing vendors. Team members should be aware of these possibilities when the team is formed. If they’re not willing and even eager for change, they probably don’t belong in the group.

How Technology Can Help

In many ways, dealing with the new analytics technologies will be challenging. But the newer technologies can also be helpful in buttressing team skills. For instance, the better analytics frameworks will offer modular, drag and drop application development tools to support team communication and interaction among members.

Also, the analytics framework can give the organization an ability to create a real-time visibility plane across the entire business. This helps the team see the inner workings of company operations as an aid to inter-team communication. But it’s a tool not just for the analytics team. Operations managers, production supervisors and upper management can use it, too, to diagnose outages or to plan for future equipment upgrades.

Now’s the time for manufacturers to take a proactive, rather than reactive, stance on Industry 4.0, big data and the new analytics paradigm. Most know that assembling a skillful analytics team is an important step forward, and for many it’s a step that can’t come soon enough.

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