How are employers improving OJT programs?

Our team is very fortunate to get the opportunity to speak with training teams focused on getting the most out of their OJT programs. These teams are often well-versed in the principles of continuous improvement and are therefore accustomed to constantly evaluating opportunities to update/upgrade their training processes and eliminate waste. Through these conversations, we’ve learned some constructive ways these thought-leaders are optimizing their OJT practices.

Rethinking hours as a proxy for proficiency

Let’s assume that a registered apprenticeship contains an 8,000-hour OJT component, where the 8,000 hours are broken down across a handful of high-level categories. In some ways, this implies that if the apprentice can accumulate 8,000 hours on the job, mostly shadowing more experienced workers, they should theoretically be exposed to enough of the standards of work to effectively and safely operate the machinery. While this method of tracking on-the-floor proficiency might be the standard, the 8,000 hours becomes a black box that provides limited visibility into what is truly being learned. Companies may lose out on opportunities to:

a. Preemptively intervene with trainees who are falling behind in certain skills

b. Accelerate the development of fast-learners

c. Ensure trainer or mentor consistency across the program

An increasing number of companies are therefore in the process of transitioning over to competency-based programs to combat these shortcomings. These competency-based programs have predetermined training instructions, evaluation criteria, rating systems, etc. While it takes more upfront work to structure more granular competency models, in making this adjustment, program managers can more effectively validate who has what task-specific proficiencies, drive greater process improvement, and ultimately reduce costs associated with ramping up employees on critical competencies on the floor.

Enabling peer-to-peer learning

In high-stakes manufacturing environments with rapid hiring, high turnover, or frequent rotations through different roles, many companies are exploring how to leverage their existing workforce to train each other (i.e., a more experienced worker trains a less experienced worker versus having a full-time trainer). To do this, companies first document all of the knowledge (which is often a work-in-progress) and then structure a “train-the-trainer” program to teach experienced employees and managers how to train and validate proficiencies in a consistent way. When done correctly, peer-to-peer learning enables more agile operations as you have qualified trainers and subject-matter experts accessible on the plant floor or in the field.

Collecting and leveraging better data

Industrial leaders are thinking more critically about the data they would want to collect across all manufacturing activities, including OJT programs. Capturing more granular data surrounding what, when, and how employees are trained on the floor can answer questions like: What is the composition of the skills our workforce and how do we optimize scheduling for the next shift? How is that composition projected to evolve over the medium and long-term? Who is the ideal trainer for a particular piece of equipment or process? Who would benefit from remediation or reinforcement learning, especially when there is a teachable moment? In many cases, the answers to these questions can be found by mining the historical outcomes of OJT and proficiency evaluations. We’re seeing companies start this effort by combining existing data that comes from equipment, scheduling and HR systems with new data collected from training processes (e.g., time to train, sign-offs, evaluations, etc.). By taking a “supply-chain” view of how employees go through training processes and attain mastery in different skills, managers and leaders can benchmark a variety of performance indicators, as well as take steps to produce better outcomes.

It’s worth noting that each of these practices (e.g., competency training, peer learning, and data collection) requires an upfront investment on the part of the company in terms of time and money. There is an increasing number of data-points that suggest these investments are paying off, and our hope is we can provide tangible evidence of what is and what is not working in different training environments as we speak to more and more OJT practitioners. In future posts, we’re excited to dig into these innovative practices and others we encounter.