Relationship Between Rpa and Continuous Improvement

In this final instalment of our RPA Implementation lifecycle blog series, we shift our focus to the Optimise phase. We will look at maximising the impact of RPA, automating the business processes in their 'as-is' state, by analysing execution data, identifying bottlenecks and making Improvements to the existing RPA bots. More importantly, we will explore how to reach the next level in Business Process Optimisation initiatives moving towards truer to-beDigital Business Operations.

RPA Optimisation levels

When considering RPA optimisation, one needs to not only improve the ongoing RPA efforts to automate the existing business processes 'as-is' on top of the existing technology landscape in a non-intrusive way. But also, examine how to make the critical shift from this Optimised Legacy Operations state to a state of renewal where the existing business and technology landscape is digitally transformed into a to-be state of Integrated Intelligent Operations.

There essentially exists two levels of optimisation –

1. Level 1 Optimisation – Optimised Legacy Operations state – the aim here is to continuously improve the existing RPA and associated automation initiatives, to include all the different forms of RPA i.e. attended, unattended and moving towards hybrid, and further optimise the 'as-is' business and technology landscape from a cost and efficiency standpoint
2. Level 2 Optimisation – Integrated Intelligent Digital Operations state – the aim here is to digitally transform into the 'to-be' business and technology paradigms resulting in a customer-centric Intelligent Business Operations.

The below table summarises the key characteristics of the 'as-is' and 'to-be' states from both business and technology perspective which we will discuss further in the article-

Table 1 – From Optimisation to Transformation

Level 1 Optimisation- Analyse and Improve

To continuously analyse and improve the existing RPA initiatives, below are the key areas to consider-

• Key Performance Indicators (KPIs) – Important KPIs such as-
  • % automation achieved with different RPA types i.e. attended, unattended
  • Return on Investment (ROI) generated based on the number of successful executions being achieved indicating the savings in operational hours to justify automation development and maintenance cost
  • % utilisation of Robots to justify the investment in RPA tooling & infrastructure
• Operational Insights – Key insights generated from the execution data-
  • % Success or failure for different automated processes- to highlight processes with most ROI, most failures, or exceptions
  • Performance Metrics– to indicate the average transaction times for different automated processes and highlight any performance issues
  • Bottlenecks – to be able to zoom into the transaction time and execution logs for an automated process broken up at the individual components/process steps levels and identify any Peaks where there are performance issues or exceptions
  • Log Analytics –execution analytics in addition to dive into detailed execution logs such as Start Time, End Time, Number of Errors/ Warning/ User Exceptions, etc.
• Real-Time Data Monitoring- generated by the RPA platform such as-
  • Current Execution Activities – indicating the number of robots currently in use, zoom into recent statistics for last hour or day i.e. number of automations executed in a queue, failures, pending automations, etc.

Level 2 Optimisation- Transform into Intelligent Digital Operations

To move from Level 1 to Level 2 i.e. from optimisation to transformation, one needs to consider key aspects both from Business and Technology standpoint as presented in Table 1 above.

From a business perspective, it is about transforming from the 'as-is'cost-centric models (such as outsourcing, shared services, and other related forms of centralised and cost optimisation initiatives) to customer-centric business models(that are customer-focused providing a personalised experience through digital channels, models that can be conceptualised, developed and delivered in an iterative and rapid fashion by applying low code techniques and collaborative ways of working between business and IT). It is about breaking up the operational silos between front and back offices to create joined-up business processes that are focused on customer outcomes instead of individual team structures. Lastly, it is about integrating the different pieces (people, process, technology) that are needed to accomplish a customer-focused end-to-end journey to allow real-time straight-through intelligent-processing of the customer needs (i.e. business logic, rules, events, data, bots, humans, machine intelligence, etc.) to realise Integrated Intelligent Operations that are self-improving and autonomous and are not just optimised legacy operations.

From a technology perspective, the transformation is about putting aside older technology legacy systems & data(that are expensive to maintain and harder to integrate with) to adopt newer digital applications, channels and data formats for creating a multi-device cross-channel multi-modal digital experience for customers. It is to move away from on-premise infrastructures (that restrict the operational capacity and require significant hardware and software maintenance cost) to cloud-based infrastructures that are scalable, cost-effective, and flexible. Finally, it is about retiring monolithic architectures (that are slow to develop, pose significant deployment & change complexities, and reliability challenges) to implement distributed architectures (adopting microservices, APIs, serverless computing and containerisation) for continuous deployment of business software.

On one hand, the 'as-is' business landscape in under increasing pressure to integrate the current siloed processes and operational structures into customer-driven joined-up 'to-be' Intelligent operations. On the other hand, the 'as-is' technology landscape has a different set of forces that are pushing it to disintegrate from the current legacy applications & data environment, on-premise infrastructures and monolithic architectures to explode into newer Digital (apps, channels, devices) and as a service economy, distributed architectures, agile & iterative ways of working, and continuous deployment modes.

These two contrasting trends can be nicely presented by adapting and applying the original Double helix framework that MIT Professor Charles Fine presented in the book "Clockspeed: Winning Industry Control In The Age Of Temporary Advantage" where Charles Fine drew a parallel from the two-stranded molecular structure of DNA, that holds the "the secret of life", with how industry structures evolve between vertically integrated industries to horizontally disintegrated industries as a double loop cycle.

Follow the link to read the other articles from this RPA Implementation Lifecycle series:

Robotic Process Automation Index

Contact usat SQA Consulting, to see how we may assist you in developing the necessary skills needed for implementing RPA projects.

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Source: https://sqa-consulting.com/rpa-optimise-phase-analyse-improve-transform/