How to Prevent Corporate HR Management from Bleeding Money

For the modern enterprise, HR processes are increasingly complex and fraught with potential waste and error. Cycles that should take only two days may take 10 days or more. Perhaps an offer letter has been generated, but approval is repeatedly delayed. This leads to missed opportunities, and each process reboot erodes the ability to recruit and hire talent.

Surprisingly, many companies continue to make the mistake of labelling HR management as a non-strategic cost. Ironically, this approach is quite costly since poor hiring and people management practices drain money and resources

Still, there’s never been a better time to implement proactive, technology driven strategies. In fact, some experts estimate that for a company of 500 employees over $261K per year can be saved by implementing HR technology.

How can you improve HR function and increase employee productivity? How can you find and fix bottlenecks? What about automating onboarding and bulk processes for faster outcomes?

Corporate HR management can be dramatically improved with process mining, robotic process automation (RPA), and machine learning (ML) — collectively referred to as hyperautomation. Starting with a simple CSV file, hyperautomation does the rest.

Let’s explore how these powerful tools can improve your human capital management processes.

Process Mining for HR Management

When optimizing HR processes, many firms make the error of automating too early. While automation is essential for peak performance, a flawed automated process will always be limited due to inherent inefficiency.

Process mining solves this by mapping out and analyzing HR processes from end-to-end. Then process flow visuals can be developed to detect choke points.

For instance, where does planning, budgeting, and requisitions occur? How is talent acquisition happening? Are you using QR codes, walk-ins, internal job posts, another portal, or an external agency? After talent is acquired, how is it sourced? What is the flow for screening, shortlisting resumes, interviewing, pre-offer verifications, and final offers to acceptance?

All of this can be analyzed by first sending an event log (CSV file – JSON). Next, a Process mining engine generates event conformance information and a process map to spot performance gaps.

Once inefficiencies are detected and corrected, automation will be all the more effective, even seeing up to 15x process acceleration.

Process mining can:

  • Process event logs as CSV, FTP or Database extracts in a specified format.
  • Identify and detect the cause of bottlenecks or event non-conformance.
  • Map output to industry standard KPIs or company financial data to generate business impact metrics.

Robotic Process Automation (RPA) for HR Management

Once the underlying process flow has been ironed out, it’s time to automate. Now, instead of manually loading new employee profiles or updates, preformed templates can be used. Tasks for onboarding, benefits enrollment, training, expense & time management, performance management, and separation can all be automated.

Uploading employee benefits or deploying mass changes becomes a matter of a few clicks compared to manually implementing changes one by one.

RPA can:

  • Develop web based automated flows for multiple HR activities.
  • Create templates, store and run automated flows on the cloud based on predefined schedules.
  • Quickly build, test and deploy automated flows for identified use cases.
  • Provide data integration & upload capability.

Machine Learning (ML) for HR Management

HR departments can also leverage machine learning by creating talent models. ML algorithms then provide insight about current job market conditions. This is accomplished by collecting and analyzing employment data from HR resources available on the internet

Insight generated by ML includes current salary ranges and the probability that a candidate will accept your offer. The tool automatically figures out the best outcome based on age, previous salary, qualifications, and any other metrics you choose. The final result is the ability to provide the best offer based on calculated probabilities.

Machine learning can:

  • Develop predictive analysis related to retention, reducing costs, talent acquisition, etc.
  • Accept data in CSV formats or via direct database connectivity.
  • Cleanse data and build learning models to support automation metrics.
  • Tweak models based on evolving parameters and meet accuracy requirements.
  • Integrate with PDF-OCR data capture and ensure data accuracy using pre-built models.

Are you seeking ways to accelerate and improve your HR management performance?

Connect with a NuMantra hyperautomation expert to schedule a demo and see how your organization can reduce costs and boost efficiency.

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