JifJaff Nine Pillars of Automation – Pillar Seven: RPA Best Practice Implementation Approach
Over the coming series we will discuss how the “JifJaff Nine Pillars of Automation” facilitates successful Internal Robotic Process Automation (RPA) delivery through an internal Centre of Excellence (COE). This article is focused on Pillar Seven – RPA Best Practise Implementation Approach.
Implementing robotic process automation can follow a smooth path towards successful deployment and tangible benefits if best practise principles & milestones are followed.
An RPA Centre of Excellence (COE) provides the governance forethought which takes clients through their Automation journey beyond the first steps of implementing RPA. The RPA COE is a multidisciplinary team which helps coordinate change management and accountability. The RPA COE centralises delivery activities and thereby provides not just short-term process automation but also a coherent longer-term plan, maintaining the automation standards across the implementation stage, and further towards the business scaling internally.
RPA Best Practise Implementation Approach Key stages:
Process Identification & Assessment
Supporting the identification of the right processes for RPA, organisations must lay down a set of clear objectives with clear business outcomes. It is imperative, to select appropriate processes based on selection guidelines.
RPA technology is not suited for all processes, candidates must be evaluated against attributes that showcase RPA’s key strengths. A quick and simple way to create a valid process pipeline is to utilise a scoring mechanism that objectively rates how well each process candidate fits the attribute. Once processes have been assessed and ranked by total score, those scoring the highest are put at the top of the list for Automation delivery, resulting in a backlog of process candidates for the RPA COE development team.
The scoring should consider key metrics in assessing process suitability i.e. Rules driven, repetitive, data intensity, high number of errors, complexity, frequency, business priority to name a few.
Best practise RPA Documentation “Framework” provides structure, standardisation, governance, and accountability to ensure all aspects of the process are covered and thereby stakeholder expectations aligned on project scope and deliverables. This will ensure your internal RPA COE team adhere to Automation Best Practise principles right across the JifJaff Nine Pillars.
Comprehensive documentation protects all stakeholders to ensure Automation build is based on an agreed scope with clear communication channels and accountability. A proven structured approach results in reduced defects and avoidance of process re-design which can often delay and more importantly jeopardise Go-live.
RPA development solutions are built by following best practices which require Solution Architecture. It identifies and defines areas like performance, security, and quality of solution which in turn drives an optimised solution.
RPA solution architecture is the solution “Gateway” which identifies applications, modules, components, and processes required for development. It is a document that sets the standards that make integration, implementation, communication, and delivery easier. It also helps in drawing conclusions where it identifies problems, issues, or hurdles within process development and other requirements outside the development process like reliability, speed, throughput, availability, technology stack, security, and scalability.
Irrespective of the RPA software, automation standards and best practice principles can be grouped under the following five categories:
Readability: Ease of understanding the code by incorporating standard naming convention standards, compliance, componentisation, and simplified logic.
Configurability: Ease of managing changes and BOT calibration by inclusion of configurable parameters i.e. field parameterisation, URLs, Files & folder path(s) and email ids.
Reliability: Minimise exception rates through robust exception handling, usage of best possible navigation technique, memory leakage avoidance, and appropriately designed auto-recovery mechanisms.
Security: Clear and robust solution design regarding authorisation, authentication, credential management and business data storage.
Performance: Minimal average handling time by incorporating efficient delay management, parallel execution, usage of optimal interaction technique and efficient Bot memory management.
For an RPA automated process to successfully transition into Production, optimal and concise testing is extremely important. This can be categorised into the following four areas:
Requirement Understanding: One of the widely accepted pain points is change in requirements before and during the development of automation solution for the process in hand. The testing team should completely understand the as-is and to-be automation solution before commencing any testing.
Test Data: Test data is the most important dependency for the successful execution of any test cycle. Invalid or incomplete test data will most certainly generate incorrect test results, which can lead to invalid defects being reported, derailing the testing timeline and ultimately impact the efficacy of the RPA Implementation.
Test Cases and Scripts: Good practise is to list all test scenarios, inclusive of scenario-specific input & output data required to execute the testing with an actual results and status column to record if the test was a success or a failure. It is very important to have clear and concise test script. The more detailed the script, the less likelihood of missing any test scenarios. It is a best practice to have the test script reviewed and approved by the design team and SME (Subject Matter Expert).
Defect Management: Defects are a general expectation with any testing cycle. Defects should be documented by the testing team and must be notified to the RPA development team. Corrections can be made quicker if the testing team can provide as much information as possible. Details should include description of the test-case, capture any image of the error, recording where the process failed.
RPA Support Hypercare is fundamental to the long-term sustainability & scalability of RPA. “Run and Maintain” of delivering business operational processes is now shifted to the RPA COE Support team, as these business processes are now executed by Bots. The Hypercare RPA Support model i.e. log & receipt of issues, issue resolution turnaround times and overall supportability will a play critical role in business confidence and overall uptake of RPA.
Once processes are live it is important to have the correct governance and support SLA protocols within the COE Team (and associated internal RPA Support Levels). Post deployment, the RPA support team must have the appropriate knowledge and/or referenceability back to the respective RPA solution design documents (Keystrokes, FDD, TDD & PDI documents) to facilitate robust fixes for post go-live issues. It is important the support function is ready to ensure the business maximize adoption of the new automated processes.
Key RPA Post Go-live Checklist Items:
- RPA Process documentation (Keystroke, FDD, TDD, PDI).
- Pre & Post RPA Process walkthrough and SOP (Standard Operating Procedures).
- RPA Support Prioritisation & SLA i.e. Operational Process SLA’s and Business criticality (in case of fines, loss of service etc.).
- Key Business Process Stakeholders & SME i.e. Escalation paths for Exception handling
- Process Rollback plans & Process Contingencies.
- Bot Process frequencies, folders, file paths, reference tables, process triggers, parameterisation, credentials & logs.
- RPA Configuration file locations.
RPA Best Practise Implementation Approach is facilitated through the “JifJaff Nine Pillars of Automation” to provide the correct foundation, scalability, training, approach, structure & governance to allow the RPA COE team to deliver through a robust framework. This approach provides a clear line of sight crucial to achieving business benefits, ROI and thereby tackling common RPA scalability challenges.