Adaptive Microlearning closes one the data-feedback loops for job-cycle optimization.
This solves the persistent and hard-to-solve problem of lower worker efficiency and the inability to improve job-cycle performance.
Adaptive Microlearning produces the critical data sets for driving the continuous improvement of worker efficiency.
This represents immediate productivity gains, enabling you to achieve a full return on investment in three to five months.
In the process, it also solves four interrelated problems that have dogged most learning and development programs and technology providers.
Widening skills gap
Most industrial organizations report having difficulty in finding sufficient numbers of skilled technical workers.
Accelerated by Covid, older workers are retiring in record numbers, taking with institutional knowledge and tribal knowhow.
Adaptive Microlearning speeds the capture and transformation of workflow and tribal knowhow into ways of learning that younger workers prefer to consume: in real time, on the job, in ad hoc peer groups, and on concrete projects.
Long time-to-competency cycles
Traditional training, learning and worker development take weeks and months.
Known limitations of classroom education, organizations must close the know-how gap with mentoring and apprenticeship programs.
The root cause analysis of the longer time-to-competency cycle of traditional approaches reveals a large and widening gap between classrooms or courseware and the actual jobs to be done at a particular job site or production line.
Adaptive Microlearning reduces the time-to-competency of industrial workers by 50 percent while multiplying the mentoring interactions by a 3X to 5X.
Rising costs of trained workers
The cost to produce a productive worker will continue to increase.
New software and equipment upgrades, larger backlogs due to Covid-deferred maintenance, retrofitting production lines with sensors and robotics, etc. all add cost: new worker or supplemental training, mentoring, real-time support, documentation and traceability.
Adaptive Microlearning reduces the cost to train new workers by 30 percent, harnessing the efficiencies just-in-time training while on the job and implicit learning (doing the job, teaches the job).
Amidst all the changes taking place, what remains the same?
The demand to produce more economic value with less waste and cost.
In a phrase, this means how to achieve greater workforce agility, operational resilience, and traceability
Adaptive Microlearning provides a scalable means for achieving these ends: continuous micro-scale improvement of the industrial job-cycle and worker efficiency.