____ Create Efficiencies with Automation

“Our organisation has been under pressure for some time to produce and deliver “more with less”. With costs rising our bottom line is under extreme pressure and we can’t just rely on adding more people with more manual tasks to deliver what we need to.  The problem is, I’m not sure we have the processes and capability to automate this right now. What can we automate easily and how important is digitising these processes to ensure we meet our customer and stakeholder expectations?”

___  What is Business Automation

Most organisations believe automation is simply translating manual tasks to create automated digital ones. Our view of this is that simply translating just doesn’t work.  Creating automation of key processes is contingent on a number of key factors that specifically relate to your enterprise architecture. Getting this right is a foundational prerequisite that requires focus and strategic direction.


___  Our Methodology

Our business automation framework ensures business processes aren’t just translated to digital channels verbatim, but rather the opportunity to question, improve and optimise business processes to utilise digital features and capabilities is incorporated in the design of digital workflows and business processes.

IMS’s Automation Framework combines AI and data science with master data management, analytics, predictive models and automation, to enable organisations to achieve 24/7 customer service with improved outcomes, and higher productivity.

Automated Assessment of Applications and Risk

Our approach also incorporates machine learning for risk and application assessment, enabled by consistent master data management. The approach enables organizations to realise automatic approval of most requests/applications, thus significantly reducing the number of internal referrals required to assess these requests. This significantly reduces service wait times, and effort required by assessment staff.

IMS has successfully employed machine learning techniques to develop risk or application assessment models, which are in use by various government authorities today. Techniques utilized include:

Linear Regression

(one/multi variable) including “gradient descent method”

Linear Algebra

to develop models with multiple variables

Logistic Regression

for classifying data into discrete outcomes (e.g. approved, not-approved, refer)

Regularisation

to prevent overfitting of training data

Neural networks

to think like humans

A recent example of utilizing machine learning techniques was developing a Contractor accreditation model for a water utility, whereby a model predicts the likely construction quality based on past performance.

In the government sector specifically, moving towards automated approvals reduces the effort expended on relatively menial approvals and frees staff to focus on reducing the impact of outliers that have the greatest potential to adversely impact performance.

Along with digital automation, the framework also covers the development of leading and lagging indicators required to measure organisational performance, including data pertaining to customers, citizens, collaborators; and relevant macro factors such as weather, interest rates etc.

IMS has extensive experience building automation solutions for clients, including with the Microsoft Power Automate.

Questions You Might Have

  • Which are the easiest processes to automate?
  • How important is it to review the current and future states when it comes to planning automation?
  • Can I start on process automation before getting other data management capabilities in place?
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Potential realised.