TOC for hiring part 2 - Sorting through the CVs

From a TOC point of view handling the classical hiring process (advertise opening, get CVS, sort, interview, hire) is entirely an operating expense (OE), it does not create any throughput, even though the end goal of the process is, indeed, an increased throughput. Then at the end of the process what you get is "marriage after a blind date", with neither sides really sure if the right decision is being made. Taking that into consideration – perhaps there is place for paradigm shift in hiring.

But assuming we have to use the current process – how can we minimize the impact it has on out throughput? As always – it is done step by step.

So, once a company announces an opening it will receive candidate CVs and most of the times it will receive a lot of CVs, much more than needed to create a meaningful selection. We can divide these CVs into three basic groups:

• The "No-s" – these CVs present candidates that are clearly not relevant when compared to the defined characteristics for a good enough candidate (as discussed here)
• The "Yes-s" – CVs that represent candidates with good fit to the defined characteristics
• The "Maybe-s" – CVs that are not clear cut, perhaps there is great fit in some of the defined characteristics but not in others, perhaps something else is creating doubt

How to treat the "No-s" and the "Yes-s" is clear, assuming there are enough CVs that are not "No-s". So the only problem is the "Maybe-s". This should not be a problem, if we look at the situation from a satisficer point of view. First these should only be considered after the "Yes-s" have been exhausted.

Once it is clear that there is need to go into the "Maybe-s" pool, the first step would be to re-analyze them. There is more data and knowledge in the system now, so perhaps some of these are actually "Yes-s" or "No-s" and should be treated accordingly. Then process them in order from the most promising to the least promising. Stopping as soon as possible (when the position has been manned, there are enough eligible candidates to move to next phase etc.)

One point to keep in mind is that often times bias leads to wrong decisions in hiring. This does not have to be the "regular suspects" of bias such as gender, race, religion and so on. There can be many hidden assumptions about the position, the appropriate candidate and so on that will lead to bias.