Research (2019) reported that by 2026, the RPA market would be $8.8 billion. However, Grand View Research (2018) shows that in 2018, the total RPA market size was around $600 million well short of the progress that had been anticipated. Although until this point RPA has been looking more like hype than reality, we believe that this technology is worth further investigation.
Rote, Repetitive Tasks Ripe for Automation
If you are still on board to continue examining a task that you would prefer not to do yourself or have your local staff to handle, there are still a few more steps to identify whether it is the right candidate for RPA. Traditional process automation in the BPM sense where systems are configured to interact with each other requires many of the same cases of a task to be done in a short period to justify the costly investment. RPA, on the other hand, offers a cheaper and quicker implementation to target tasks that do have repetition, but a small amount of variation spread out over a longer time but still have enough scale to consider automation (van der Aalst et al. 2018). Insurance and credit card companies have utilized process automation as they had a large pool of claims and payments that were often being handled in very similar ways.
Jesuthasan and Boudreau (2018) explain that there are three characteristics to categorize the components of jobs to identify if they are suitable for automation. The first characteristic of the task is looking at whether it is repetitive or variable. Repetitive tasks are those that are done the same way every time, such as credit application, which pulls a report using the same customer data on each application, as opposed to a variable task, like human resource consultants whose work will vary greatly for each customer. The second characteristic is whether a task is independent or interactive. Independent work can be done by one person without interacting with others like generating accounting statements as opposed to collaborative work that requires communication skills like would be needed by an employee working for a call center. The last characteristic of a task ripe for automation is whether it is physical or mental work. This distinction is somewhat obvious: physical work requires dexterity and strength, whereas mental work requires cognitive ability. The tasks that are best fit for RPA and other forms of automation are those that are repetitive, independent, and mental.
Fung (2014) defines his criteria for potential RPA tasks as follows: (i) having low cognitive needs in terms of subjectivity or interpretation needs, (ii) being large in volume, (iii) needing to move between different applications, (iv) having small amounts of variability and exceptions, and (v) a task that has demonstrated human entry errors that have caused issues in the past. Since most RPA bots lack the cognitive capabilities of AI and machine learning (ML) algorithms, it won't be able to handle tasks with a large amount of variance, and due to it being software, it won't be able to complete any physical work (Jesuthsan and Boudreau 2019). An RPA bot is programmed to perform actions on the computer in the same way that a human would by navigating interfaces through clicking and typing. The bot is not smart in the sense that it knows what information it needs to pull or what button it needs to click; it simply knows where the button and information are and when the interaction should happen. Since the bot functions using location data to navigate elements of the interface, any changes to the interface or the appearance of the page will cripple the bot's functionality.
Process Considerations of Implementing RPA
While the initial capital expenditure in having the bot created by one of the 20+ RPA companies is continuing to drop, the return on investment (ROI) for that investment is still unclear for small‐ to medium‐sized companies who do not have the scale to take advantage of automating these rote and repetitive tasks. Companies need to carefully identify certain processes that have a rule‐based structure and are draining a considerable amount of resources (Lowers et al. 2016). Variability in the types of processes that logistics companies could automate and the 12–15 players included in almost every international shipment make it very difficult to map out how many of the tasks need to be accomplished. System errors, changes in forms, variability in documentation types, etc. all create the need for RPA tools to have the ability to “learn” like a human would. However, the tools are not developed to that level yet (van der Aalst et al. 2018). According to an article published by Inbound Logistics, automation not only can help processes to run more smoothly but also enables companies to monitor processes over time in hopes of that continuous improvement goal that many large logistics companies so proudly advertise. What may not be abundantly clear is that the “monitoring” of tasks in terms of an RPA bot has a cost associated with it, as you have to have trained staff or outside consultants available to ensure the continuity of the process being done correctly. Not only is there a cost to monitor bots, but also the more significant investment is also in process mining, process mapping, and training the bot to do the work.
Motivating Example: Konica Minolta Using RPA
The Japanese tech giant Konica Minolta Inc. adopted RPA in 2018 through the RPA provider Automation Anywhere and has since seen massive returns through working hours saved. In 2018, the company saved 19 000 full‐time employee (FTE) hours through 55 automation deployments with higher aims in the coming years. Achieving these kinds of returns can be tough though. An IT director at Konica Minolta explained that “of course, like every other company, we have faced many obstacles while going through the journey of RPA before becoming successful. For example, in Konica Minolta's Asia Pacific team, we [had to] create an RPA governance structure, training framework for the region, internal RPA application process, disaster recovery plan. [Afterwards, in the local team], such as Konica Minolta Hong Kong, [had] to go through a series of learning workshops.” This sheds some light on the complexity that comes with rolling out RPA in a large multinational company without even scratching the surface of the planning and preparation that must be done.
To successfully roll out RPA, Konica Minolta had to increase its RPA training program to include nearly five times the number of employees that were in training when they decided to adopt throughout 2018. Some of those trained would become RPA champions in different branches who oversaw further training, identifying processes with potential for automation and creating an RPA culture within their offices. This was a massive shift for parts of their workforce going from knowing little about RPA to being leaders of its development within their company. In addition to the careful planning and testing that comes before RPA is rolled out, upskilling and gaining buy‐in from workers represents another huge challenge. However, navigating these complexities can be seen to have large returns when done correctly. After their successful initial RPA rollout, Konica Minolta aimed to nearly double the number of hours saved in the following year, and they show no intention of slowing down. The IT director shared, “The RPA journey in Konica Minolta has provided an invaluable insight into how automation can change a company's culture and the ways its employees work. However, we will not stop here, and we have already planned RPA 2.0, which is allowing staff to interact with the RPA bot in real‐time. This is for sure an exciting time for all of us.” With returns like Konica Minolta has seen, it certainly shows that RPA can be rolled out if the investment in training is there.
RPA is best understood when compared to a Microsoft Excel macro. In an Excel macro, you can automate cell activities by using VBA coding and Excel functions. RPA adds an advantage in that it can automate tasks across many different applications with simple programming language and function calls. As shown in the Konica Minolta case, one of the first tasks needed is to identify the RPA provider. The company ran through a series of RPA software provider comparisons and finally selected the application from Automation Anywhere, a leading provider in this space.
Konica Minolta's RPA Roadmap
Konica Minolta implemented RPA using the following roadmap:
1 Train a country RPA developer.
2 Identify the highly repetitive business processes that can make use of RPA from hundreds of processes and prioritize for which to develop a solution first.
3 Form a business technology communication unit to perform change management education.
4 Convert