In the past on here, I’ve been addressing people who often have an interest in training or higher leadership professions, and therefore expect to work in an office atmosphere with a specific large set of personality types and tropes. I feel that like many of my colleagues, I’ve given some insight and some good advice in doing this, but I notice that I’m leaving some demographics out. Not all businesses are big, traditional or entirely corporate in environment. So, I think it’s high time I talked about how to train workers in a wider range of professional environments. This includes unique environments such as food service, industrial workplaces and retail environments, where walk-in customers are part of the equation. This is far from saying that these environments are less sophisticated or flexible than traditional corporate office environments, but they are definitely not the same thing. And just as with any other environment, these types of places need a good strategy for how to train workers, too. Most of the models we talk about claim to work in any environment, and truth be told, they do. However, they’re optimized for the corporate environment primarily, so they’ll be less productive or efficient in more unique applications. So, I’ll talk about three techniques here that can be applied to these models to improve their effectiveness in these unique work environments. During your team training sessions, you’ve undoubtedly taken on organizational learning systems, a non-traditional grading system, and the gamification models that’ve become so hot in business today. So, I won’t pretend to outline training from scratch. Let’s just take this basic model, and tweak it to work more natively in your environment. The first thing to do is to incorporate a roleplaying technique, especially for environments with walk-in customers. In these role playing sessions, which are shuffled frequently so everyone tries every role, some should be customers, others various positions in the hypothetical environment. Various contingencies and scenarios should be tried and re-tried, to give realistic, tangible learning of how to deal with a customer environment in many different scenarios. Offices don’t need this so much, so most models skip it or only give it a passing nod. Second, there should be “field learning” put more into play, especially in industrial or retail environments. This is controlled “on the job” training with some redundancy of people onhand, so that trainees needn’t worry about accidents or mistakes. This will help to reinforce safety, proper work protocol, and confidence. Most models actually focus on this a little, but in your environment, it’s best to give this far more use. Finally, incentivizing needs to be reinforced much more in this environment. Daily work routine is tedious for retail, industry and food service, and people physically work hard, as well as mentally, to make things work. They need something real for their effort, and this is only right. Ergo, reinforcing learning with actual tangible rewards such as benefits and the like, is far more important here. If you combine all of these techniques in a higher level of implementation with most standard models, you find you have a derivative of the model which now works far better in your unique environments than it once did. So, for you, how to train workers is merely shifting some focus, but these big models will still work for you.