Filling the Manufacturing Skills Gap

The manufacturing skills gap may leave a projected 2.4 million positions open in the coming decade, with a potential economic impact of $2.5 trillion. How can manufacturers prepare and fill positions with qualified individuals? What training and ongoing education strategies will enable the necessary skills?

Predictive analytics and machine learning can eliminate the guesswork in quickly finding the strongest, most qualified individuals for manufacturing positions. Coupled with the capability of the gig economy, these innovations can save industry manufacturers and suppliers costly energy and resources, ultimately impacting their bottom line. The gig economy utilizes a temporary workforce for short-term engagements, a strategy that can be applied and scaled to meet the needs of most businesses. This offers a substantial benefit to manufacturers that often require on-demand talent.

Growth of the gig economy can be attributed to the high cost of healthcare and other benefits for full-time employees, constant supply and demand fluctuation, and inflated overhead costs. Manufacturers and top-tier suppliers are no strangers to these issues, making them compatible with the use of gig economy principles and predictive analytics capabilities to more efficiently and effectively fill workforce gaps.

Here’s how we can put this two-pronged solution into action.

1. Leverage predictive analytics to improve placement and productivity. By examining the performance metrics of their existing workforce, manufacturers can assess which employee factors impact productivity most. Data-driven decision-making helps target and hire freelance workers who possess necessary skills. Companies that provide and screen large talent pools of freelance workers for manufacturers can use predictive analytics to identify the candidates most likely to excel at the unique demands of each manufacturing plant.

Looking beyond traditional talent pools that no longer yield enough workers with the right skills, manufacturers can leverage the gig economy to locate employees with the analytical, technological, and problem-solving skills required for high-demand jobs. Reviewing key performance metrics can also determine the number of freelance workers needed to meet production goals.

2. Utilize quality digital platforms to optimize outcomes. To maximize workforce operations, manufacturers should utilize a digital technology platform that aggregates all data and logistics; supports two-way employer and employee communication; and provides employees with the training, education, and work-site information they need for success.

Gig workers are best engaged through mobile solutions, which can include a training component that enables freelance employees to arrive on the job with sound comprehension of their tasks. Mobile technology can also provide employers with real-time analytics such as tenure, attendance, and performance reviews across the supply chain to inform workforce needs. Integrating these affordances into one cohesive platform is key to optimizing operations from placement to production.

Manufacturers that tap the power of the gig economy, paired with innovative predictive analytics and engaging digital platforms, will be well-positioned to improve productivity, meet customer demand, and increase earnings.