Simplifying (And Improving) Job Evaluation
Since mankind began exchanging money for goods and services, businesses and their staff have been negotiating the price of work. For the purposes of fair pay, organizations would ‘evaluate’ the jobs performed within them to determine their value to the organization. JobsPikr takes a look at how job evaluation has improved and simplified over time.
By considering a job’s cognitive and physical requirements, as well as working conditions and responsibilities, businesses were historically able to assess the worth of a specific role.
It became clear in the early 20th century that a system of job evaluation could bring far more benefits than just fair pay. It could boost efficiency, improve recruitment, and provide a strong foundation for operations.
But What Really is Job Evaluation?
The purpose of job evaluation is to have a satisfactory wage differential. The jobs are evaluated on the basis of its content and the complexity involved in its operations and thus, positioned according to its importance.
According to a report by the European Commission, the relative worth of a job is assessed irrespective of the qualities of the specific job holder.
So, What About Job Evaluation Automation?
All of it sounds good in theory. But we are talking about dealing with an employee’s worth in an organization. Well, at least crudely. Job evaluation eventually does determine their pay slab. According to an SHRM Foundation study, Implementing Total Rewards Strategies, compensation is a highly emotional topic that may initiate resistance to change and increase emotional turmoil.
In order to deploy this layered conflict, it only makes sense to automate this entire process. Job evaluation has evolved to serve this purpose and to make it as objective as required.
What Really are the Methods Of Job Evaluation? Can They all be Automated With Ease and made better?
Of all job evaluation methods, the point-factor method probably the best known for how it has evolved— from a purely manual function to an entirely automated system. This steps for this approach are as follows:
- Listed Jobs
- Evaluating defined factors
- Scoring degrees on these determining factors
- Per job, points allocated for each factor
- A defined wage structure
- Adjustment of the existing wage structure
The result is a spread of points and a salary range per job, similar to the image below. Any outliers calculated and need to be dealt with on an individual basis.
It is fairly evident that this rote work can easily be done by a machine. Humans are a little too cool for this.
Many job evaluation methods are subjective. Evaluators’ decisions about which jobs are worth more can be personal and emotional. If the team members know the job incumbents, they may consider employees’ personal qualities as job factors. You know where we are going with this, right? Automate the entire process and concentrate on more pressing issues. Like where should the next office retreat be?
The factor comparison method represents a combination of the ranking and point methods. The first step is to identify benchmark jobs (i.e., jobs performed by several individuals with similar duties within the organization, such as administrative assistance, stock clerk, security guard, accountant, sales representative, supervisor, and manager). In addition, the algorithm must select compensable factors and rank all benchmark jobs after completing factor analysis.
Next, the algorithm must compare jobs to market rates for benchmarking, which results in the assignment of monetary values for each compensable factor.
In simpler words, it is 2020, we shouldn’t have to worry about mechanical things like these. Make job evaluation simple. And better.
Job ranking places jobs in a hierarchy of their value to the company. It is the simplest method but is not appropriate for every organization. This method is best suited to smaller organizations that can reduce the number of positions to review no more than 100 specific jobs. Larger organizations should select another system.
Job ranking generates an estimate of the correct job hierarchy, not the exact hierarchy found in the point-factor system. Job ranking should be facilitated by a bot who can address favoritism by managers and evaluate other subjective input.
Remove all biases from this system by automating it and you’d realize that this method of job matching is not too shabby.
In the job classification method, the algorithm writes descriptions of each class of jobs and then puts them into the grade that best matches the class description. Because this process is subjective, with a wide variety of jobs and general job descriptions, you need an evidence-based approach to job evaluation that delivers transparent grading results from an independent system that is easy to use and doesn’t need the “secret knowledge” of expensive consultants.
It is almost unbelievable how far job evaluation and its methods have come. It is even more unbelievable that we believe that we are free from biases.
The good news? Our subjectivity is what makes us.
The better news? For everything else, there’s automation software.
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