The advancement of manufacturing technology and Industry 4.0 has led to the development of smart factories, most of which have an entirely new infrastructure and require a new set of employee skills. The absence of analytics expertise in many of these factories, including data architects, data engineers, data analysts, and data scientists, hinders the effective and efficient use of sensor data, batched or historian data, and engineering text notes to make quality or efficiency improvements. This results in the need for external data experts to be hired as consultants, who may not be familiar with the plant or the resident skills of the plant teams. Yet, when leveraged properly, consultants can be key to helping manufacturers transition from requiring outside skillsets to achieving the benefits of inside expertise.

The value of building internal skillsets

In the 1990s, manufacturers saw the importance of investing in their people. Six Sigma Black Belts and Master Black Belts were widely used to solve complex plant problems. These experts would then train plant employees to be Green Belt or Orange Belt certified and solve problems as needed. Over time, plants developed their own Black Belts and Master Black Belts, bringing full-time capability to the plants and integrating these skills into the plant organization. This eliminated the need for external consultants.

As an example, General Electric deployed Six Sigma training over the course of five years, effectively upskilling every employee in the company. They provided both tools and skills to enable future process improvements and resulted in company savings of $12 billion¹.

Performance pressures require new people investments

Today’s manufacturers are in a similar situation – a need to invest in staff to improve operations – but facing new technologies. As factories become smarter, so must the staffing and collaboration models that impact the profitability of a plant. Manufacturers need site-level analytics experts. The integration of them into the plant operations is crucial for the effective and efficient use of AI and ML technologies and realizing their full value.

The opportunity to upskill in-house workers with the expertise to shape ongoing improvements has never been stronger or more necessary. Doing so will improve the collaboration between functional roles and specialty roles and eliminate the need for external consultants, reducing the time and cost involved.

Take Tata Steel² for example. The company trained ~130 employees on analytical techniques to help them solve complex issues. As a result, when optimizing their super-heating process for steel production, they achieved immense financial gains and global acclaim. Their operations and teams evolved because Tata Steel embraced upskilling their in-house talent and, arguably, created their own next-gen set of Data Black Belts.

This practice is common for several leading-class manufacturers who embrace not only using new technologies and tools, but also investing in the right mix of talent to capitalize on digital transformation opportunities using data.

Making the shift from external to internal talents

Manufacturers can leverage the help of a consultant to begin making the shift toward internal advanced analytics expertise. In order to improve the chance that gains from pilot projects or multi-phase implementations can be sustained, leadership must require consultants to provide a transition plan so that the use of advanced analytics can be managed internally.

The consultants should advise on the necessary skillsets, itemizing and defining them. Then, a skillset audit of existing employees should be conducted, as an absence of necessary skills threatens ongoing success. Upon discovery of any lacking talent, training should be provided by the consultants or hired out. If necessary – due to time constraints or talent capabilities – recruiting for the right talent with the necessary analytical skills should begin.

Getting experts from outside to help certainly pays off, such as for fast proofs of value on pilot projects, but teaching plant teams “how to fish” while using relevant technologies, developed models and/or tools will make investments more impactful in the long run and likely cut out the need for consultants altogether. Additionally, a stronger and more collaborative presence of data engineers and analysts helps other roles operate more efficiently and be more empowered.

Developing skills is worth the investment

Implementation into the plant workforce, by upskilling, is the key element to make analytical tools impactful in a continuous and sustainable manner. Often with the initial assistance of external analytical resources, most companies find that investing in their own people has paid dividends. Operations will not improve without the right mix of human talent and skills pointed at the right opportunities.

Learn more about how you can realize value, internally, from your advanced analytics in this free, on-demand webinar.

1. Six Sigma Case Study: General Electric, 6Sigma.us, May 22, 2017

2. McKinsey & Company, How a steel plant in India tapped the value of data—and won global acclaim, March 8, 2021