In running a business or running a project, a leader’s hunch is a contributing factor to success. However, the most important factors to ensure the project’s success are measurement and quantification. At this time, the leader will need both qualitative and quantitative indicators.
When an enterprise launches an automation project – using RPA technology, its IT leaders also should measure the project’s success through indicators to be able to continuously improve it, optimize the effectiveness of the project and provide credible proof to prove the necessity of the project.
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1. Some specific measure RPA project success cases
According to Deloitte’s (2020) survey based on responses of 523 executives from 26 countries, 8% of firms / organizations have started using automation extensively, which is double the rate from the year before and has most likely increased substantially since then. This demonstrates that the automation application is gaining traction in the corporate world. In which, RPA technology is the preferred automation solution for many firms and organizations.
When evaluating the effectiveness of an RPA project, or any project, we frequently look at ROI (Return on Investment). However, the fact is that there are several indicators that can be used to assess project success. Let’s take a look at some cases of analyzing the efficacy and success of RPA projects to see what metrics were used in each specific situation.
1.1. Antony Edwards – COO of Eggplant Case
According to Antony, the ultimate aim of implementing an RPA project is to replace repetitive, manual, time-consuming processes. In turn, he believes it is possible to look at the comparison between before and after the implementation of the project (or comparison between the new process and the old process) to assess the success of the project.
- Old = how much effort was it taking before multiplied by the salary of the individuals performing the task
- New = cost of RPA solution + cost of managing the RPA
This method allows for the use of reasonable estimates in calculation. When monitoring over a long period of time, if the new cost is lower than the old cost at each similar timeframe, the RPA project is demonstrating financially worthwhile or in other words, the project has aided enterprises in saving a significant amount of money.
The fact is that today’s IT faces the great challenge of the “do more with less” banner. Because CEOs, BODs like to spend as little money as possible on a project, they prefer to minimize investment expenses. As a result, cost reduction appears to be a significant benefit of an RPA project.
1.2. Aaron Bultman – Director of Product at Nintex
Despite the fact that comparisons between old and new are frequently used, cost reductions are not the sole way to measure success. Nintex’s Product Manager – Aaron Bultman, takes a different approach. To measure the project’s success, he employs a variety of metrics, including both quantitative and qualitative indicators.
His assessment uses the following 6 indicators:
- Productivity: According to Aaron, bots operate 24 hours a day, 7 days a week at a very high rate of speed. Therefore when implementing a RPA project, IT leaders should evaluate whether processes are running faster and/or more frequently than before.
- Accuracy: Because bots can complete their tasks perfectly with zero errors, the success of a RPA project may be measured by looking at the accuracy index (error reduction / outcomes accuracy improvement).
- Consistency: Bots do work in an identical process without variation. Thus, the consistency of the process from which IT managers/leaders may generate very accurate predictions can be used to measure the success of an RPA project
- Reliability: Bots, unlike humans, work nonstop and are constantly ready to take on new tasks. As a result, according to Aaron, the ability to decrease downtime or increase output is also a factor in determining RPA project’s success.
- Compliance: Bots will follow the process step by step, fully complying with the regulation. Because rules compliance helps establish effective processes, IT leadership may use this to measure success of RPA projects.
- Employee satisfaction: This is a qualitative factor, and determining the level of employee satisfaction is difficult. Although there are quantitative measures such as turnover, it is difficult to correctly evaluate total employee satisfaction. This is an excellent example of how qualitative data may be just as significant as numerical data.
1.3. According to IBA Group
IBA Group also provides metrics to evaluate RPA project performance and success. The metrics are as follows:
- Operational metrics:
- Errors: The first step in analyzing RPA effectiveness is to keep track of errors. We can find patterns by recording errors. Once we’ve identified them, it’ll be much easier to take the required steps to correct them.
- Robot Utilization: Evaluating and measuring robot utilization, i.e. understanding if robots are being used, where and how they are being used, can help optimize project efficiency.
- Duration: Automation problems might be identified during duration evaluation. In order to enhance the status and optimize the implementation success, automation errors must be recognized early and fixed immediately. As a result, it is entirely possible to use duration evaluation as a success indication.
- Performance-accuracy rate: The performance-to-accuracy rate enables IT leaders to predict how RPA automation will perform when the project is implemented. These indicators can demonstrate how RPA is doing and if it is working appropriately and effectively.
- Overall Business metrics:
- Process Outcomes: It is essential to quantify and verify the outcomes of an RPA project. Because knowing what your goals are can help you determine what’s next.
- Return on Investment (ROI) – Rate of Return: Measuring and assessing return on technology investment is an essential measure for all enterprises conducting RPA projects. As a result, if you want to assess the project’s success, this indication cannot be overlooked.
1.4 According to Cigen
Cigen believes that tracking the outcomes of RPA project implementation is important. When evaluating the success of RPA projects, there are two major factors to consider:
- Operational insights: which refer to information related to RPA projects execution but do not detail benefits such as cost savings or time savings (.e how many hours a software robot has worked in a certain amount of time).
- Business insights: this information is related to the business issue of enterprises so it will clearly demonstrate the advantages that the RPA project delivers to the enterprise (i.e the total number of invoices processed by bots).
Cigen provides quantitative and qualitative metrics that measure the success of RPA projects based on these two factors:
- Implementation cost has a major impact on the ROI of an RPA project. The financial benefits of the RPA project may be shown through looking at the implementation expenses. Therefore, it must be used as one of the success indicators.
- Cycle Time: Since Bots may work at higher speeds and indefinitely without error, a performance estimate based on the total time required to complete a process can be used to gauge success.
- Throughput: Increased throughput can create bottlenecks in the workflow since no more processing can be done, and throughput is a metric because it is a measure of output at a specific point in time so this is a highly dependable metric of measuring success.
- Accuracy: One of the most significant benefits of RPA projects is increased accuracy so accuracy criteria must be included in the project success index system.
- Compliance: This metric may be quantified in two ways: the number of compliance failures (if any) and the cost of resolving them. The RPA project will improve the process’s compliance issues, so this indicator should be used to measure RPA project’s success.
- Qualitative indicators – for example, customer and employee satisfaction: This indicator cannot be directly assessed, but it may be quantified indirectly through customer satisfaction or employee morale. This indicator is necessary for assessing project success because its absence will result in a loss of the qualitative viewpoint of a technological project.
- Process outcomes: This metric is generated by comparing process results before and after the RPA project’s implementation. The results demonstrate what the RPA project was able to create, indicating how the enterprise’s business goals are supported. This is an especially important metric to assess project outcomes.
2. Metrics (Qualitative – Quantitative Indicators) to measure RPA’s project success
Indeed, every enterprise has distinct demands, benefits, and various processes with different pieces of relevant indicators. Moreover, measuring success of an RPA project involves several levels of analysis, ranging from apparent financial advantages to more complicated, qualitative factors such as employee – customer satisfaction.
Several metrics show similarities based on the specific cases mentioned above. In summary, a set of metrics may be developed to evaluate the success of RPA projects:
2.1. Financial Impact Indicators Group
- Average annual cost savings
- Five-year cost savings
- Payback period
- Five-year return on investment
2.2. Business Operation value Indicators Group
- Process efficiencies: processing time
- Process efficiencies: daily throughput
- Improvement in data analytics capability
- Improvement to compliance/accuracy
2.3 Workforce Impact Group
- Number of employees reallocated
- Annual labor hours saved
2.4. Strategic Alignment
In comparison with the outcomes of the process before and after the implementation of the RPA project, the indicator is also calculated as usual. This shows how RPA activates progressively, driving other corporate goals to a larger extent. As can be seen, process outcomes will be in this group of indicators.
It is obvious that it is necessary to use both quantitative and qualitative indicators to gain the broadest picture when evaluating the efficiency and success of a project in general and of an RPA project in particular. Through specific cases, a system of index groups may be created incorporating a wide variety of distinct qualitative and quantitative indicators for enterprises to measure their RPA project success.
The tip for IT leaders in RPA-projects is that a system of efficiency and success indicators is difficult to develop for general application due to the diversity between enterprises. They should thus consider selecting the right indicators for their enterprises’ conditions and situation.
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