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Business Process Mining & Robotic Process Automation

Business Process Mining as a game-changer for RPA Implementation

Robotic process automation (RPA) is a proactive "Process Automation" approach that uses software robots to replicate human tasks. After recording a process workflow, a virtual bot mimics the actions performed by humans in the application’s graphical user interface and automates execution. Multiple robots form a virtual work-force that enables automation of many knowledge-related tasks and back-office work that were previously executed by humans. The virtual bots are integrated seamlessly in existing software and repeat tasks, often across multiple systems. This configuration is driven by rules and business logic. Process steps can be complied independent of time and are scalable for efficiencies, as robots can easily handle the increasing volume. With this transformation, productivity and ROI can be achieved optimally. RPA guarantees accuracy and standardization of activities, as well as ensures perfection in tasks performed. Potential use cases for RPA are, among others, data transfers and processing of high volume of data.

Process mining refers to a technique of data-driven process analysis that visually reconstructs the actual flow of business processes based on transaction logs from large IT systems. It allows to analyze what really happens within business processes including inefficient process patterns, bottlenecks and compliance issues. Through data visualization components, users of process mining software can drill down the data, spot process retard points from the ideal process and detect root causes of inefficiencies. Process mining essentially enables digital transformation by identifying improvement potential with respect to key success factors like efficiency, speed, agility and compliance. Beyond that, process mining assists in detecting solution strategies (e.g. process transformation, effective change management or technology disruption) and selecting suitable measures for strategy implementation (e.g. automation, user training or system migration). Also results of the implementation can be monitored and sustained with process mining.

Aptworks presents an approach to use the "power of process mining" to enable effective RPA activities during an ERP implementation or prior to building a case for ERP implementation as part of automation initiatives within business process transformation for building an Intelligent enterprise. For a successful synergy, Aptworks suggest the following three-step approach for process automation via RPA:

Fig. below shows Steps for a successful combination of RPA and process mining

Step1: Assessing RPA potential

The first decision within RPA refers to the discovery of potential for process automation. Typically, within a company there are dozens of process types and process steps with different levels of automation. For a successful RPA implementation, processes should be scalable, repetitive and standardized. If an organization runs complex and non-standardized processes, automation must be implemented with extra precaution. Replicating a complex process with many variants using RPA is typically tedious and requires significant investment. Moreover, the cost of maintaining and servicing the robots could outweigh their acquired savings. It is therefore crucial to understand the maturity of business processes and decide which processes are standardized enough to benefit from RPA and which processes would benefit from harmonization and standardization prior to starting an RPA initiative.

Once processes are standardized, the highest potential for automation within an organization should be detected. Most systems have some form of automation already in place due to legacy practices and compliance, so looking at current automation state within processes is critical to define RPA targets. Further, there might be specific cases, by geography, vendor or material, where manual work is widespread. By comparing automation rates, users can explore, where current automation solutions might be improved and where additional automation could create benefits like reduction of throughput times or improvement of other process-related performance measures.

Step2: Developing RPA application 

The next step, post the RPA software deployment, entails the training of the RPA application. As a best practice, users should start to train robots with the existing workflow. The trained robots start their work within a pilot project and their activities are tracked by the underlying IT systems. After a large number of executions, the generated process  instances  can  be  evaluated  by  using  the  process  mining  application.  The performance of different robots and the non-robotic supported processes should be bench-marked to identify the most effective RPA implementation.

Step3: Sustenance and Safeguarding RPA benefits 

After selection and implementation of the most effective RPA application, continuous monitoring ensures tracking the impact of the RPA initiative and especially its return on investment. Process mining enables the user to see how processes mature over time due to RPA. It also immediately detects when a process evolves and how robots need to adopt to an alternating business environment.

Five Best practices (BP) for a successful synthesis of RPA and process mining

Based on the application of the RPA use case, Aptworks proposes the following aspects to be key drivers of a successful symbiosis of RPA and process mining:

BP#1 - Selection of an appropriate use case 

RPA leverages its maximum potential on rule-based processes with high volume of manual repetitive tasks and handling time; processes with fixed procedures, that are standardized and mature; and processes where a technical integration via the back-end is too costly or impossible. In these manual processes, it helps to eradicate human processing errors. Process mining can give guidance to RPA initiatives and points at the most eligible processes for automation. Common use cases to achieve fast benefits from RPA are, among many others: transferring data from one system to another, payroll processing, customer registrations, customer profile up- dates, generation of standard reports, or data cleansing.

The figure below shows a list of processes-based use cases where RPA is recommended:

BP#2 - Standardization before automation

To be successful, variation in business processes should be optimized before starting RPA. This means that process variants must be standardized to generate high volumes of transactions per variant. Through such a standardization, users can avoid expensive loops during RPA realization, speed up implementation time, increase success rates and achieve maximum return on investment.

BP#3 - Prioritization of activities

Usually, the number of potential use cases exceeds the resources available to do all projects at once. Thus, it is important to use available resources to achieve maximum results in a short period of time. Prioritizing is a key factor of successful RPA initiatives, as it allows to realize immediate low-hanging fruits and to generate quick wins. Experience shows that starting RPA with processes exhibiting low automation rates can generate faster benefits than increasing automation of process that are already highly automatized. Process mining supports the structured prioritization of RPA projects.

BP#4 - Establishment of a central, coordinating unit in the organization

Establishing a central unit or task force within an organization helps to work out an overall RPA roadmap, to evaluate a high volume of ideas coming up, to prioritize initiatives in the best way, to accumulate knowledge and to define successful projects as benchmarks for future RPA activities.

BP#5 - Continuous monitoring of results

A RPA initiative is not a one-time project but requires to continuously track results and to use findings for ongoing improvement. This is where process mining provides fast and powerful insights into RPA’s impact on process performance KPIs such as throughput times. This aspect also includes to constantly benchmark projects with successful prior initiatives to gain maximum value from automation.

Robotic Process Automation

RPA as a technology has truly come of age and is expected to become mainstream in 2019. As per our (Zinnov research and analysis) estimates, enterprises spent more than $2.3 Bn on RPA in FY19, and we expect this to grow exponentially by 35-40%, to touch upwards of $11 Bn over the next five years. The worldwide addressable market for RPA is much higher, and stands at $50 Bn. There are currently more than 50 RPA platform providers, including 3 unicorns. Collectively, these RPA platforms have raised more than $2 Bn in venture funding till date.

According to Institute of Robotic Process Automation & Artificial Intelligence, robotic process automation (RPA) is the application of technology that allows employees in the company to configure computer software or robot to capture and interpret existing application for processing a transaction, manipulating data, triggering responses and communicating with other digital systems.

The “robots” involved in robotic process automation aren’t robots in the physical sense. Rather, they are software that can be configured on any network location and interacts directly with business applications at the graphical user interface (GUI) level. Based on the work flow design and rules configured in the system, software robots automate routine business processes, such as gathering customer data, new applications processing, adjusting insurance claims and processing backend operations.

Figure 1 shows how this looks.

Figure 2: Typical RPA components

Development tools: The developer tools such as Process Studio are used to define detailed workflow of a particular task i.e. step-by-step instructions that a robot should follows to perform a particular business process. The instructions, which need to be very detailed, may include all required business rules, conditional logic, such as if/then decisions. In general these tools often feature drag-and-drop functionality so that any business users can develop the task even though they are novice in coding. Some tools include a “process recorder” that speeds up the definition of a process by capturing a sequence of user actions. Others feature interactive diagrams that make visualizing complex processes easier. Developer tools are used only in modelling the processes and making changes to them.

Robot Controller/Orchestrator: The robot controller plays three essential roles. By serving as a master repository for defined jobs, the robot controller facilitates version control. It safely stores credentials for business applications and provides them to robots only when required, ideally in encrypted form. The robot controller also assigns appropriate roles and permissions to users, and provides controls and workflows to govern the processes of creating, updating, testing, reviewing, approving, and deploying jobs to the robot workforce. Finally, it assigns jobs to single or grouped robots, and monitors and reports on their activities.

Software Robots: Software robots (also known as “clients” or “agents”) carry out instructions and interact directly with business applications to process transactions. The list of actions a robot is capable of performing can be custom-coded. Some robots keep detailed logs of their actions and decisions for compliance and audit purposes, as well as to help companies identify additional process improvement opportunities.

RPA Implementation Strategy:
There are typically five steps to developing an RPA implementation strategy described in figure 3—Business case generation, Process mapping, Process development, User Acceptance testing and RPA deployment.

RPA Implementation Strategy:
There are typically five steps to developing an RPA implementation strategy described in figure 3—Business case generation, Process mapping, Process development, User Acceptance testing and RPA deployment.

Figure below is Deloitte's proven methodology for RPA

 

Figure 3: Standard RPA Implementation Methodology

  1. Business case generation: This step starts with the business process review. Subject matter Expert (SME) assesses process landscape to identify processes which are suitable for automation. Processes which require manual interaction with a computer interface, are largely rules-based, consume a significant amount of time, and are performed at frequent intervals are selected for automation. Once process are identified user generates business cases with all required objective.
  2. Process mapping: In this stage development team creates RPA process specification mapping document for all the business cases and baseline document after getting business sign off. 
  3. Process development: In this stage development team develops the workflow based on process specification. Performed unit testing, system testing and provide signoff for UAT.
  4. User Acceptance testing: In this stage business users perform end to end system testing based on the designed business scenario. Users also verify all the possible exceptional handling scenarios as manually people can do in business environment. Users also create process documentation and provide testing sign off for deployment.
  5. RPA Deployment: Once UAT provide signoff team will deploy the code to live environment to work on the process for which the robots designed. Monitor the efficiency of the robots creates process improvement plan.

RPA Benefits:
Entire end-to-end processes can be performed by software robots with very little human interaction, typically to manage exceptions. RPA software robots are not necessarily relevant to only a particular business function or industry: any methodical, standardized, repetitive process that follows consistent rules and is wholly executed through a human-machine interaction is likely to be a good candidate. With a license for a software robot likely to cost less than an onshore staff member or an offshore staff member, the commercial attractiveness of this approach is self-evident. There are nonfinancial benefits too, as robot-based process performance is designed to be more predictable, consistent, and less prone to errors as compared to a human process. Moreover, a robot workforce can typically be deployed in a matter of weeks. Once in place, new processes can often be assigned to them in days if not hours. Thus, RPA solutions generally have lower implementation cost, require shorter implementation time, and carry lower risk than large IT transformations. However, it is important to find the right processes and apply RPA judiciously. 

Below diagram describes the potential benefits of RPA implementation beyond cost reduction.

Figure 4: Benefits of RPA.

  1. Greater accuracy: RPA can perform the same task, the same way, every time, without error or slowdown in the processing time, and can easily be up-scaled. However, because the robot only does exactly what you tell it, it won’t apply intuition to problems and anomalies that a human would.
  2. Flexibility and scalability: Once a process has been defined as a series of instructions that a software robot can execute, it can be scheduled for a particular time, and as many robots as required can be quickly deployed to perform it. Additionally, robots can be easily reassigned when more important processes arise, since each robot is typically capable of performing many types of processes.
  3. Time and cost savings: RPA can process large volumes of work, quickly and efficiently. The scalability of the Software also makes it ideal where there is a sudden peak in demand. Software robots are designed to perform tasks faster than a person and do not require sleep, making 24/7 operations possible.
  4. Employee satisfaction: RPA can have a positive impact on the lives of staff members as it takes away mundane processing and frees up their time to work on more interesting tasks. Employees relieved of these activities can be refocused on higher-value activities that are often more rewarding.
  5. Speed of deployment: RPA can be implemented relatively quickly. For example, an RPA solution can go from concept to launch in as little as 30 days, depending on the complexity of the process. Such a short time frame can help to keep costs down and prevent undue disruption. One reason for these efficient timescales is that introducing RPA typically doesn’t require making changes to existing tech architecture. The mix of existing and new IT systems can be a major barrier to introducing transformation programs but RPA bypasses this barrier: it sits on top of any newly implemented or indeed old legacy systems and works across them.
  6. Data traceability: The tasks performed by a software robot can be monitored and recorded at every step, producing valuable data and an audit trail that can support further process improvement efforts and also help with regulatory compliance.