Award Mining Services

Award Mining Services - a holistic approach

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Historical Adjustments

Identification of historical errors and suggested adjustments.

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Award Interpretation

Codification of Awards for future payments.

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Real-Time Adjustments

Automation of Roster changes into Payroll and Rostering Systems, award verification in real-time.

Why are we still making payroll mistakes?

Complexity

Australia’s award rate pay system is incredibly complex and no single payroll system can interpret all awards and standards. This leads to multiple systems, expensive system customisations and a small knowledge base.

Expertise

Historically lack of payroll expertise inhouse, prioritisation centred around processing the volume then understanding the numbers.

Governance

Governments give very little guidance on application of awards and standards – everyone interprets differently. Along with audit’s lack on knowledge and sample size mentalities minimise governance.

Remain payroll compliant with Award Mining Services

Automation of Award rules and historical transactions

Codification of awards gives the ability to interpret various timesheets in real-time, which existing systems often cannot complete, due to access requirements to historical work patterns and counts that the system would have taken a significant amount of modifications to calculate. This solution also creates the ability to forecast the impacts of work condition changes down to cost level. With immediate access to this data, forecast potential for future breaches becomes possible along with enabling and automating bottom-up budgeting.

Future

Establish Award rules and digitise

Algorithm analyses timesheets to validate business rules (Fatigue etc)

Algorithm applies award rules

Interpreted file is exported

Human in loop to manage error handling

Historical

Establish Award rules and digitise

Algorithm analyses historical timesheets or data

Algorithm applies award rules

Notification that employee has triggered overtime

Human in loop to manage error handling

Award Mining use cases already in production

Award mining services implemented by a retail wholesaler and large supermarket chain include:

Codifying GRIA (General Retail Industry award) to handle issues that Etivity and Kronos could not do.
8 weeks
Creating a Roster Management Platform MARS to diagnose if certain roster changes would break rules.
10 weeks
Creating a Roster Management Platform MARS to diagnose if certain roster changes would break rules.
10 weeks

Benefits

  • 100% historical transaction audit and analysis
  • Automated Testing Solution for award changes
  • Allows organisation to no simulate changes to understand impacts
  • Organisations can decouple timesheet and payroll systems
  • Reduce Payroll non-compliance risk, by doing monthly automated audits

Example

Through codifying the awards, such as General Retail Industrial Award, Aged Care Award, Children Services Award, and Banking, Finance and Insurance Award, managers now have the ability to manage and maintain roster updates and check whether a Mutual Variation Agreement is required when discussing working hour changes with an employee through a single portal, instead of lodging a query with HR.

Establish Award rules and digitise

Create platform for desired roster entry

Employee submits roster

Platform applies award rules

If compliance manager approves roster

Further Workforce Insights

Workforce efficiency – by bringing in timesheet and rostering data into a central hub, we can use Machine Learning to bring real-time insights into your organisation’s Power BI platform.

Overtime predictions

  • Forecasts overtime – ML will consider internal factors such as tracking employees regularly taking overtime along with external factors like weather, sporting events or concerts, public and school holidays, or other seasonal variations.
  • Roster optimisation - with ML and analytics, understand why overtime is required in certain shifts and locations, allowing rostering to reduce overtime to a minimum.

Leave analytics

  • Forecasts leave and absence – similar to overtime forecasts, ML will consider internal factors and external factors.
  • Data insights – algorithms can assist in identifying patterns and relationships that may have not been considered in the past to identify issues, such as aversion to a certain shift, certain employee trends with sporting events.