Managing business process reengineering within the framework of robotic process automation
Traditional framework of business process reengineering. Robotic process automation overview, effects. Statement of the research question, research methods. Data collection, sample, data analysis strategy. Description of the results, сross-case analysis.
| Рубрика | Менеджмент и трудовые отношения |
| Вид | статья |
| Язык | английский |
| Дата добавления | 26.08.2020 |
| Размер файла | 244,8 K |
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Prior to the QCA, the primary qualitative data was transcribed and organized in a specific way to allow the software to “read” it: each question and subsequent answer were placed in separate paragraphs. Then, the cases were organized into separate folders, where each folder contained the appropriate interview transcripts and retrieved secondary data. This way, the data were easily accessible, and the possibility of cross contaminating cases' data was eliminated.
Generally, the QCA consists of 6 stages taken to achieve research objectives (Kuckartz, 2019). Firstly, careful reading through the transcripts was required to understand the general meaning and context of the responses and make sure that they are all formatted uniformly. The second stage of the QCA required forming categories of the responses based on the questions asked. It was decided to form categories related to the research objectives. Therefore, during this process, the set of 3 thematic categories have been formed:
BPR steps for RPA implementation;
Experience with RPA;
Critical success factors.
During the third stage, 3 main thematic categories were assigned to corresponding text segments, which is known as coding. This stage was performed to identify main text segments, where the themes are mentioned. Then, in the fourth stage of the QCA, the retrieval process was performed. During this process, the text passages that were assigned to specific categories, were retrieved, and combined to separate groups. Thus, 3 main groups of text segments were composed and coded again. This process provided a further development of the coding frame, as the codes were assigned to short sentences of phrases with more specific details. When all the necessary sequences of words had been coded, they were grouped in subcategories. For instance, the subcategories for the category “BPR steps for RPA implementation” included the sequence of steps such as “First step”, “Second step”, etc. Once the subcategories had been identified for all the 3 categories, the data was coded for the second time to make sure everything of importance was included and subcategories did not need to be reformulated. After that, in the fifth stage of the QCA, the category-based analysis was performed. The analysis tools chosen were creating comparison tables and thematic summaries and visualization (Kuckartz, 2019). Comparison tables helped identify the similarities and differences among the cases which assisted in the cross-case analysis, while thematic summaries summarized the cases in terms of identified categories or research objectives and facilitated in case description. Visualization tool was employed to design the revealed framework. After conducting the QCA, the final stage was to create case descriptions and document the results.
4. Description of the results
business process reengineering effect
This section contains the findings obtained from 15 semi-structured interviews conducted with executives and employees of the 3 chosen steelmaking enterprises that have performed or are currently in the process of performing RPA implementation and BPR initiatives. In this part, the results of the QCA including thematic summaries and comparison tables are presented. This section is divided into 3 parts. The first part contains the findings of the BPR framework of the RPA-centered projects. The second part contains the findings on experience with RPA implementation that uncovers industry-specific guidelines as well as presents critical success factors of such projects. Finally, the third part contains the cross-case analysis that explores similarities, differences, and patterns of the cases.
4.1 Findings on RPA-centered BPR framework
During the interviews, the respondents were questioned on whether BPR efforts are necessary for RPA implementation projects and what are the crucial steps to take. Throughout the interviews, the majority of the interviewees indicated that BPR activities are crucial to undertake when considering RPA deployment. The respondent A1 recalled that before implementing RPA, audit and BPR of the whole group of supporting processes was carried out. The respondent B2 stated that: “(…) it is absolutely necessary to redesign the processes before they are robotized to ensure process standardization and optimization”, while the respondent C2 mentioned that even better effects are achieved when BPR activities and RPA implementation are “performed simultaneously”. However, both B1 and C1 respondents mentioned that they would only reengineer processes if the audit revealed clearly redundant and non-value adding activities. Among reasons for BPR efforts prior to automation the interviews revealed a variety of responses. While most respondents from all the 3 companies mentioned that BPR “makes sure right processes are automated” and “reduces the cost of possible incorrect RPA implementation”, respondents A4 and B3 also said that BPR “harmonizes” and “standardizes the processes which facilitates frictionless RPA implementation”. Another reason mentioned by the respondents A3 and B1 is: “BPR can significantly simplify a process or eliminate it completely”, which “reduces the need to deploy RPA in the first place”.
When asked about whether the RPA-centered BPR activities required new team composition with employees possessing more technical skills, respondents from enterprises B and C responded positively. The interviewees B1, B3 and C1 mentioned that they prefer to form “multifunctional” teams with experience in reengineering, audit, automation, business analytics, information systems, etc. They indicated that multifunctionality facilitates in having “an overall perspective on business processes”, while “getting RPA aligned with the organization”. Interviews with the actual project participants such as B4, C3 and C4 confirmed that they were employed specifically for such projects due to possessing necessary skills. For instance, the interviewee C4 was to “conduct a business case and create a process roadmap”, while his colleague C3 was to “develop and test the software bot”. Contrastingly, the enterprise A took on an approach of hiring candidates with general knowledge and experience in economics and process or project management and train them to work with RPA technology. The respondent A1 stated: “(…) due to the IT-center working closely with the RPA projects teams, the latter get all the necessary technical support”. For instance, the respondent A5 recalled his experience of starting an RPA project: “Prior to working on this project I didn't have any special RPA skills, but I learned fast and it turned out software bots are not as intimidating as it might seem”. However, the respondents from all the enterprises indicated that before the RPA implementation “introductory trainings” were conducted to ensure the technology is “well understood”.
Another aspect discussed during the interviews was BPR steps taken to facilitate RPA implementation. Most of the respondents did not recall any particular BPR steps that are different from how BPR is usually conducted. Nonetheless, the respondents stated that the considerations for RPA were constantly “embedded” into their BPR activities. The respondents A3, B4 and C5 mentioned that in certain processes reengineering had to be performed “specifically for the RPA to be implemented” or “reengineering just made sure that certain processes can indeed be automated”. In all the cases BPR and RPA initiatives were assigned to specific centers, independent establishments, where all the supporting business processes were centralized. To do that, the enterprises A and C, established new centers or organizations under the vertically integrated business models, while in the enterprise B such center had already existed prior to RPA implementation. According to the respondents A1, B1 and C1 such centers “perform corporate functions”, while also “implementing innovative technologies across organizations”. The interviewee A1 recalled that “it was a major BPR initiative that helped integrate and centralize supporting processes and data, which paved the way for RPA implementation”.
Summarizing, the data collected showed that in general, steelmaking enterprises prefer to conduct BPR activities before implementing RPA, while employing multifaceted teams and centralizing the processes to be robotized. In its essence, the BPR framework stays traditional, but takes RPA into consideration in each step.
4.2 Findings on RPA experience and critical success factors
The findings regarding experience with RPA implementation and critical success factors of such projects revealed a variety of different insights. Ten respondents out of 15 named reengineering efforts crucial to smooth RPA integration into business processes. The reason why the rest five respondents did not acknowledge the benefits of BPR might be the fact that they were involved into the projects from the technological side, developing and testing software bots. For instance, the respondents B5 and C4 mentioned that they could not tell if reengineering was necessary, but they would not “underestimate its benefits”. The other respondents positively said that BPR efforts either “put RPA on another level” or “make RPA a huge part of the organization and a first step towards to its overall digitization”. According to the interviewee B1: “digitization was the strategy that we adopted to innovate our business practices. RPA is one of the stages in this strategy and it should be done right. BPR is definitely helping with that”.
Some other respondents shared their experience of RPA deployment and mentioned several aspects that they perceived as valuable outcomes and insights. Some of the respondents stated that “an organization should be ready for RPA implementation”. From the perspective of the respondent A2, organizational readiness implies that “processes and data are appropriate and ready for the RPA”, while the interviewee B2 mentioned that “not only processes, but people in the organization should be ready for such transformation”. In fact, within all 3 organizations the lack of skilled and experienced with RPA employees occurred at some point, which created the need for training or hiring. The respondent B1 recalled: “(…) we needed to create a digital atmosphere within our department, to educate employees on the matter of RPA, because at that point no one knew how to work with bots”. The respondent C2 stated: “(…) we looked at best practices of RPA all over the world because no one even had the slightest bit of experience of RPA implementation when we started out”. The need for RPA-related knowledge and experience facilitated knowledge sharing among departments of the organizations and encouraged creation of multifunctional teams. Another aspect of human-related organizational readiness was mentioned by the respondents A5 that works closely with end-users of robotized processes: “Personally, I met quite a lot of people that were looking at RPA negatively and were almost opposed to its implementation”. Thus, the human-related aspect of organizational readiness is composed by skilled and experienced employees and employees that are ready for organizational digitization.
From the technological side, some respondents stated that IT support was crucial during RPA-related projects. The interviewees A2 and A5 confirmed that in their department “a constant communication with IT services was established”, and the reason for that was to “provide constant IT support such as access to the data base, creation of the accounts, etc.”. The interviewee A1 stated: “RPA efforts has to be linked to the current IT system, otherwise its implementation might be counterproductive”. On the other hand, the respondent C3 stated that “there is the need for multifunctionality in RPA developers, they need to understand IT systems as well”. Among other technological aspects were mentioned: choosing the right processes to automate, choosing the right RPA platforms and vendors, carefully analyzing and generalizing pilot project results.
Overall, these findings indicate that for an RPA-related project to be successfully launched, an organization should be ready in terms of its processes, technology and IT systems and people.
4.3 Cross-case analysis
Whereas the previous section was dedicated to the general findings that the thematic summaries of the semi-structured interview yielded, this section is based on identifying similarities, differences and specific patterns across cases accomplished through comparison tables. One comparison table (Table 3) is based on the questions and the responses about BPR activities and framework and followed by the generalized RPA-centered BPR framework. Another comparison table (Table 3) is based on the responds regarding the RPA experience and critical success factors.
From the comparison Table 3 it can be seen, that the studied steelmaking enterprises maintain relatively similar approach towards BPR efforts for RPA deployment. The data indicates that the enterprises recognize the need for BPR activities before or simultaneously with RPA implementation, as it harmonizes, standardizes, and optimizes the processes and prepares them for further automation. Moreover, BPR initiatives facilitates in choosing appropriate for RPA processes. The data also indicates that all the studied enterprises created specific centers dedicated to RPA and other innovative technologies, except for the Company B which already owned one prior to RPA implementation. Finally, 2 out of 3 enterprises prefer to create new teams to complete RPA-related BPR projects as multifunctionality of teams promise better results. Nonetheless, all the companies conducted RPA-related training for the employees to increase digital and robot-awareness. Based on the cross-case comparative table regrading BPR frameworks and activities of the studied businesses, the reimagined BPR framework was designed (see Appendix 2).
Table 3 - Cross-case comparative table, BPR framework
|
Case A |
Case B |
Case C |
||
|
The need for BPR before RPA |
yes |
yes |
yes |
|
|
Difference between traditional BPR and RPA-centered BPR |
yes |
yes |
yes |
|
|
RPA is “embedded” into BPR framework |
||||
|
Creation of the RPA center to centralize supporting processes |
yes |
no (already existed) |
yes |
|
|
Creation of new teams |
no |
yes (multifunctionality) |
yes (multifunctionality) |
|
|
RPA-related training |
yes |
yes |
yes |
|
|
Choosing processes specifically for RPA |
yes |
yes |
yes |
|
|
Reengineering processes specifically for RPA |
yes |
yes |
yes |
|
|
The reasons for BPR before RPA |
Harmonizes, simplifies processes |
Standardizes, eliminates processes |
Optimizes processes, should be performed simultaneously with RPA |
|
|
Makes sure right processes are automated, reduces possibility of misalignment and incorrect implementation |
||||
|
Pilot projects |
yes |
yes |
yes |
The comparison table 4 shows the main aspects that contributed to the success of RPA-centered projects within the studied organizations. The data indicates that the contribution of BPR initiatives to RPA projects success is acknowledged in the enterprises. In the Company B, BPR facilitates in pursuing the strategy of digitization. The data also shows that generally an organization should be ready for implementing RPA or other automation technologies. Organizational readiness is composed of 3 factors: people, processes and technology. In all the 3 enterprises people are trained to work with a novel technology, however besides that, companies B and C practice hiring candidates with appropriate skills and experiences. The companies had to overcome resistance to change or fear of innovative technologies, create a digital atmosphere or give examples of successful RPA projects from all over the world. These activities prepared employees for further changes and increased organizational readiness. Processes had to be prepared by checking if they were appropriate for automation or redesigning them to fit for RPA implementation. Finally, the technological aspect of organizational readiness indicated that it was crucial for organizations to synchronize and harmonize RPA solutions with the IT systems, while choosing technically appropriate RPA platforms and carefully checking results of RPA pilot projects before generalizing them.
The detailed description of each case (company) is provided in Appendices section (see Appendix 3)
Table 4 - Cross-case comparative table, RPA critical success factors
|
Case A |
Case B |
Case C |
||
|
Contribution of BPR initiatives to RPA projects success |
yes |
yes (BPR as a facilitator towards digitization) |
yes |
|
|
Organizational readiness: People |
training (fighting resistance to change) |
hiring + training (creating a digital atmosphere) |
hiring + training (setting examples) |
|
|
Processes |
yes the processes should be appropriate for such automation |
|||
|
Technology |
linking IT systems to RPA solutions |
choosing appropriate RPA platforms |
pilot project results need to be analyzed and generalized carefully |
Conclusion
The results have shown how steelmaking companies perform and adjust their BPR initiatives in order to implement RPA solutions. Based on the qualitative content analysis of 15 semi-structured interviews conducted in 3 leading steelmaking enterprises, this study yielded the reimagined RPA-centered BPR framework. It consists of 6 stages and describes the activities that companies perform when deploying RPA tools. When the designed framework was compared to the traditional BPR framework (Kettinger et al. 1997; Motwani et al. 1998; Figure 1) and the framework suggested by Deloitte (2017), some similarities and differences have unsurfaced. Firstly, RPA is included or embedded into every stage and activity of the reimagined framework as was previously suggested by Deloitte (2017). Considerations for RPA solutions are taken from the very first stage (Recognize & Understand), while the rest of the stages are aimed at getting an organization ready for RPA and integrating RPA into its structure (Bevilacqua et al., 2015). Traditional BPR framework does not include any considerations for RPA, due to this technology being developed years later (Syed et al., 2020). Secondly, as opposed to the traditional BPR framework and the one proposed by Deloitte (2017), the reimagined framework accentuates composition of multifunctional BPR teams, which, as data showed, should contain employees with experience not only in reengineering and RPA, but also in audit, business analytics, information systems, etc. This evidence supports the argument of Gunasekaran and Kobu (2002) that in the beginning of any reengineering project it is important to understand the existing systems so that as many employees as possible are involved in the BPR. This finding also seems to be in accordance with Sutcliffe's (1999) statement that adequate choice of team members is crucial to BPR projects. The third and fourth stages of the reimagined BPR framework appear to be in alignment with the other frameworks as they contain steps and activities usually taken during traditional BPR process such as documenting the current processes, identifying non-value-adding activities, conducting a pilot project, etc. The fifth stage, on the other hand, emphasizes an unexpected step - creating a specialized RPA and innovation department that centralizes all the robotized supporting processes and facilitates in their integration with IT systems. This step is not included into any of the BPR frameworks mentioned and analyzed within this research, which might imply that it is either specific to steelmaking organizations or organizations with vertically integrated business models. As the data suggests, all 3 analyzed enterprises completed this step during their RPA-related BPR projects, and it assisted in further digitization of the companies.
Overall, in regards to the reimagined RPA-centered BPR framework, this study supports the argument of Deloitte (2017) that traditional BPR processes should be altered to retrieve the full potential of RPA and new BPR framework should be designed with RPA embedded into its structure. Considering the stages and steps of the framework, they are in alignment with the traditional ones (Kettinger et al. 1997; Motwani et al. 1998; Figure 1). The redesigned framework contains of 6 main stages and includes classic BPR activities, but at the same time uncovers insights obtained from the interviews such as the emphasis on the BPR teams multifunctionality, creating specialized RPA and innovation centers and integration of RPA solutions with IT systems.
Considering the critical success factors of RPA-centered BPR projects, the study showed that organizational readiness is vital to assess before starting such initiatives which supports the findings of Abdolyand et al. (2008). They proposed to look at critical success and failure factors of BPR efforts as organizational readiness, which can be estimated to predict potential outcome of BPR project. However, their argument regarding organizational readiness implied only human-related factors such as managerial commitment and encouragement and low resistance to change, whereas this study identified 3 main aspects of organizational readiness: people, processes, technology. The evidence shows that human-related factors include not only such aspect as low resistance to change (Abdolyand et al., 2008), but also the presence of digital atmosphere among employees as they learn how to work with bots and fulfill their knowledge about innovative technologies. This study revealed, that within the human-related factor, an organizational readiness can be improved or increased by appropriate training, setting examples of successful RPA projects, and hiring candidates with adequate experience which correlates with the arguments of Deloitte (2017). Regarding processes as the component of organizational readiness, the findings show that chosen processes should be appropriate for RPA, which can be assessed by the BPR team using specific criteria (Deloitte, 2017; Syed et al. 2020). From the technological perspective, on the other hand, organizational IT systems should be ready to support RPA implementation, whereas the BPR team needs to ensure that the chosen RPA platforms are adequate to organizational needs and technological capabilities. These findings appear to be in accordance with results of previous research conducted by Sutcliffe (1999), Rao, Mansingh and Osei-Bryson (2012).
Overall, this study found support for the argument of Abdolyand et al. (2008) that critical success factors of BPR projects, in this case, RPA-centered, can be viewed as organizational readiness. However, this research further developed this idea and expanded organizational readiness to 3 groups of factors such as people, processes, and technology, instead of limiting it to strictly human-related factor.
This research makes an original contribution to the existing literature on the matter of BPR and RPA by giving a comprehensive overview on how BPR and RPA function together and relate to each other in iron and steel manufacturing enterprise context. This study takes ideas of Love and Gunasekaran (1997); Bevilacqua et al. (2015); Deloitte (2017) as direction of research but explores them further in industry-specific context.
Firstly, as one of the results of this study, the reimagined RPA-centered BPR framework has been presented as followed by the proposition of Deloitte (2017). This framework describes the BPR efforts with regards to RPA implementation in steelmaking enterprises and is compared to the traditional ones designed by Kettinger et al. (1997) and Motwani et al. (1998). The reimagined framework extends the traditional framework by embedding RPA into every stage and provides steps and guidelines that are tailored to particular industry or business model, which Deloitte's (2017) reimagined framework lacked.
Secondly, this study partially fills the research gaps identified by Syed et al. (2020) as it explores RPA implementation and its critical success factors in the industry-specific context. This research provides insight from the steelmaking industry which has rarely been researched from the perspective of BPR and RPA in prior literature. Moreover, it extends the knowledge on critical success factors of RPA-centered BPR efforts proposed by Abdolyand et al. (2008).
This research also yielded some implications for managers. These implications are related to managing BPR project teams that work on implementing RPA technologies. Firstly, it is crucial for managers or leaders of such projects to consider the level of organizational readiness (Abdolyand et al., 2008) and the degree of digitization of the company and take measures to increase them. The study showed that these aspects are viewed as critical success factors in RPA projects. Therefore, managers should express support and commitment while creating a digital atmosphere among employees, since it can potentially affect outcome of the projects. Trainings, educational sessions, and benchmarking examples are needed to raise awareness about the novel technology and overcome the possible fear of robotics among employees (Zande, vd D., 2018).
Secondly, as the evidence pointed, managers should consider multifunctionality of their project teams. Gunasekaran and Kobu (2002) stated that in the beginning of any reengineering project it is important to understand the existing systems so that as many employees as possible are involved in the BPR, while Sutcliffe (1999) considered that adequate choice of team members is crucial to BPR projects. Thus, RPA-related BPR project teams shall include not only BPR and RPA specialists, but also members with the background and experience in business process audit and analytics, users of processes that are to be robotized, IT systems administrators and specialists. As this study reveals, team multifunctionality facilitates in having a broader perspective on business processes, while getting RPA aligned with the organization.
Lastly, managers should consider concentrating supporting processes in one center, which can not only perform corporate functions, but also assist in frictionless RPA adoption and easier administration of robotized processes. Such centralization approach was adopted by all 3 steelmaking organizations analyzed in this case study. As the findings suggest, it could be a helpful BPR initiative to undertake as it integrates and centralizes supporting processes and their data, which facilitates in RPA implementation and further organizational digitization, while integrating IT systems with innovative technologies.
Some limitations of the research include, firstly, lack of generalizability of the results. The research strategy chosen was qualitative multiple case study of 3 Russian steel and iron manufacturing enterprises. The pursued research strategy did not allow for more than 4 or 5 cases (Creswell, 2013), while the chosen industry limited it to just 3 cases, since only a small number of steelmaking enterprises in Russia adopted RPA solutions (Deloitte, 2017). Therefore, the results might differ for other companies within the industry as the rate of RPA adoption grows as predicted by Gartner (2018). Besides, results might differ for companies in other industries as well.
Another limitation of this study is the use of propositions or directions for research since they were useful to explore the relationship between BPR and RPA in a framework. These statements need to be transformed into hypotheses and tested by a larger data collection within the industry as well as across different industries, which can statistically determine whether the hypotheses are valid or not.
The choice of purposeful sampling strategy for the semi-structured interview participants could potentially result in sampling bias as they were chosen upon advice of the managers or executives of RPA departments as the ones who can provide useful information. Due to all the limitations mentioned above, the findings of this study need to be interpreted with caution.
Since this study considered only 3 steelmaking enterprises in Russia, further research in the industry needed as RPA implementation increases. As Syed et al. (2020) suggested, obtaining a deeper understanding and developing a framework of critical success factors of RPA in various business contexts are still needed as they may differ from industry to industry. Thus, future research is welcome to explore BPR and RPA in other business industries. Although this research extends the current literature on BPR in the context of RPA, further quantitative research is needed to find persuasive and statistical evidence for these results, test the redesigned RPA-centered BPR framework and therefore strengthen these findings.
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Appendices
Interview Guide (in Russian)
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Вводная часть |
Добрый день! Хочу поблагодарить Вас за согласие принять участие в моем исследовании и уделить время этому интервью. Меня зовут Дарья Насибова, и я хотела бы побеседовать с Вами насчет Вашего опыта в области реинжиниринга бизнес-процессов и их роботизации (технологии RPA). Конкретнее, я исследую, каким образом внедрение RPA влияет на сам процесс реинжиниринга, и как они взаимодействуют между собой в контексте конкретных предприятий. Интервью займет не более 40 минут. Интервью будет записано с помощью функции записи звонка, поскольку даже несмотря на то, что я буду делать некоторые пометки на бумаге, записать все от руки не представляется возможным. В связи с тем, что разговор будет записан, пожалуйста, проверьте, работает ли Ваш микрофон, и старайтесь отвечать четко. Это интервью носит конфиденциальный характер, то есть Ваша личная информация не будет разглашаться за пределами исследования, так же, как и в его результатах. Если у Вас есть какие-либо вопросы по ранее мной сказанному, прошу задать их сейчас. |
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Вопросы |
Часть 1: общие вопросы Какую должность Вы занимаете? Как долго Вы находитесь на этой должности? Какими полномочиями и компетенциями Вы обладаете? Часть 2: Реинжиниринг и роботизация А) Для начальников/менеджеров центров роботизации и автоматизации С чего начинался путь к роботизации бизнес-процессов на предприятии? Как давно? Какие уже есть результаты? Каким образом предприятие готовилось к роботизации? Каким образом оценивается эффективность внедрения роботизации? С какими проблемами Вы столкнулись при внедрении роботизации? Как Вы преодолевали эти проблемы? Нужен ли, на Ваш взгляд, реинжиниринг бизнес-процесса перед его роботизацией? Если да, то назовите основные причины. Каким образом происходит реинжиниринг бизнес-процесса перед его роботизацией? Выделите основные фазы. Формируется ли новые проектные команды по реинжинирингу для роботизации? Какими навыками должны обладать участники команды? Какие факторы, на Ваш взгляд, влияют на успех и эффективность таких проектов? Б) Для RPA-специалистов/ разработчиков/участников проектных команд С чего началось Ваше знакомство с RPA и каким образом Вы были вовлечены в деятельность, связанную с роботизацией? Каким образом Вы были или на данный момент задействованы в проекте роботизации бизнес-процессов? С какими проблемами Вы столкнулись при внедрении технологии роботизации? Как Вы преодолевали эти проблемы? Каким образом, в рамках Вашей деятельности, связаны реинжиниринг и роботизация бизнес-процесса? Нужен ли, на Ваш взгляд, реинжиниринг бизнес-процесса перед его роботизацией? Если да, то назовите основные причины. Формируются ли новые проектные команды по реинжинирингу для роботизации? Какими навыками должны обладать участники команды? Какие реинжиниринг-стратегии и инструменты используются в командах? Какие факторы, на Ваш взгляд, влияют на эффективность таких проектов? |
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Заключительная часть |
Хотели бы Вы добавить что-то еще? Анализ данных, предоставленных мне Вами и Вашими коллегами, будет проводиться в течение месяца. В случае, если Вы заинтересованы в результатах исследования, я могу отправить Вам копию отчета. Спасибо за беседу и уделенное мне время! |
Reimagined RPA-centered BPR Framework
Case descriptions
Case 1 (Company A)
Company A started to realize the benefits and opportunities of RPA technologies and tools in 2016. For Company A, the major potential benefit of RPA was reducing the costs. In 2017, a platform and partners for RPA implementation were determined. Company A owned a separate entity that was responsible for financial accounting and business process audit functions; this entity was also made responsible for choosing an initial basis for RPA technologies to be implemented. After completing a business process audit of the whole group, several processes were deemed as fitting for a pilot RPA deployment project. The pilot project was estimated to take from 2 to 3 months from the initial stage to first seen results. The pilot project turned out to be successful since by the end of the implementation, all the initial goals had been reached. After the pilot project was proven to be beneficial, plans were made to put RPA on a larger scale. In the end, around 50 processes were identified as appropriate for RPA in total. However, only 7 of them were chosen for further implementation, and the process for getting the organization ready started.
At the beginning of 2019, a new entity emerged. The entity was created to centralize, generalize and simplify managing of all the general business processes of Company A. This new entity was responsible not only for all the corporate functions, but also for innovative technology implementation across all entities of Company A. This entity's main purpose is increasing efficiency by reengineering and optimizing the supporting processes, implementing best practices and developing IT tools. Within this entity, a separate center for robotics and automation implementation was created. The center identifies the processes for RPA deployment, defines platforms, tools and precise solutions for implementation, administrates processes that went through RPA to make sure the transition goes smooth. RPA team works closely with the IT-center that allows access to the internal database upon request and provides necessary IT resources for complicated projects.
The RPA center mostly sources employees with competencies in economics and management rather than IT-related knowledge. Such approach is deemed the transfer of accumulated knowledge about RPA to new employees easier and more efficient. The core staff of the entity is composed by administrators of robotized processes. They provide consultancy services for end-users, administrate the bots themselves, activate and deactivate them, update the user manuals, modernize the robotized processes, coordinate all the necessary documentation with the IT-center, fix bugs and make minor corrections when needed.
To date, Company A has automated 10 business processes based on RPA. Robots process provision (allocation) daily and carry out bank statements. The effect of processing appropriations reaches 40% compared to manual processing, and the efficiency of the statement tends to be 100%. Statistical reports are also generated quarterly with the help of robots, and requests for data unloading for the Federal Tax Service and other control bodies are processed on request. In addition, the processing of personnel accounting documents is robotic: primary documents are automatically downloaded from e-mails and entered into the HCM-system of the enterprise. Robots also create document packages daily and inform counterparties about the shipment of products, register invoices in the system, form reconciliation certificates and send e-mails to suppliers. The key parameter for evaluating the effectiveness of robotics in the entity is the economic result of the implementation. In addition, the quality and time of operations are evaluated. According to the entity's experts, the robotic process should pay off in 1 year. In some cases, the payback of RPA is 2 years.
Case 2 (Company B)
Company B is a steel and steel-related mining group of companies managed under vertically integrated business model. The majority of its operations and assets are located in Russia, some of them - in other regions. It is considered one of the leading Russian steel producers and has a competitive advantage of cost leadership.
Company B owns a separate entity that performs all the corporate functions and operates supporting business processes such as audit, finance, HR, procurement, etc. Using a systematic approach and having modern technologies, the entity operates both individual processes and whole groups of processes, considering the possibilities of their transformation to achieve greater business effect. The entity started its automation project in 2013, but at first was just using macroinstructions (macros) to automate some of the routine business processes. The pilot project for automation was the process collecting and calculating KPIs, which before took approximately a week to complete, but after automation - one day without human involvement. After the pilot project was proven to be successful, automation macros solutions were applied on a larger scale. The application process was completed via specially trained agents that automated some routine business processes in their operational divisions. However, the need for centralized administration of automated processes was then realized and it was then when Company B decided to opt for RPA solutions. In the end of 2016, RPA implementation started.
The preparation of the company was divided into three stages: creation of a certain digital environment in the team to prepare employees for interaction with robots; audit and changes in the business processes that are planned to be robotized; taking measures to protect data, code and software. To create a digital environment, Company B started with an introductory training course on RPA and business process architecture, for those who would be in the future working group on process setup and robot implementation. They were taught that robots follow a certain logic and all business processes would need to be rebuilt following this logic. On the other hand, at that stage employees were to understand how to interact with robots and that they are managed by ordinary PC users. After the introductory course, Company B analyzed the business processes to be robotized. Audit and optimization or reengineering of business processes led, for example, to the complete reduction or radical change of processes. Considerations were made whether the processes were appropriate for automation and whether the costs for RPA implementation would be covered by the economic effect. Processes were organized according to the following characteristics: rule-based, repeatability and stability of the process, presence of many errors due to the human factor, structured information at the input. Next, the order of processes for RPA to be implemented was determined. The most complex, labor-intense and expensive in terms of labor costs were to be automated first. RPA solutions and vendors were then identified, with only 2 of them to be used. After that, software bots piloted in a test mode, while employees were trained how to operate them. The test mode helped estimate the costs and economic effects for other RPA-centered projects. In the calculation, all costs of FTE involved are estimated, including rent, training costs, then targeted investments in license and robot implementation and support are subtracted. Effects, both quantitative and qualitative are calculated and compared with previously estimated costs.
To date, Company B has created around 400 software bots to automate routine business processes and saved approximately 50 FTE or nearly 100000 hours of human labor per year.
Case 3 (Company C)
Under the vertical integration business model, Company C owns a separate entity that is responsible for developing, maintaining and supporting the corporate information system; maintaining continuous 24/7 functioning of all the automated production systems; developing, deploying and maintaining solutions for architecting information infrastructure. On the basis of this entity, a specialized RPA implementation center was created. The main activity of the RPA competence center is to increase the efficiency of business processes through the application of RPA tools. This automation project started in 2018 and affected finance and economics, accounting, supply, logistics, personnel and staff units. Plans have been made to expand the project to other units. Today, dozens of software robots are in full function, which help employees to keep records and pay for raw materials, process electronic sick leaves, issue orders for employment records, analyze the use of transport, keep records of productivity, collect and systematize data from various sources, coordinate various documents, generate reports and much more.
The RPA implementation process was composed of 7 main stages. Firstly, Company B conducted benchmarking analysis to explore the best RPA practices in the world, possible issues that might occur on the way. Then, the RPA competence center was formed to develop the strategy and the roadmap for RPA implementation. The third stage was to choose a platform and a provider of RPA solutions and to create and test pilot projects. The pilot projects identified areas for improvement, however showed no signs of technological restrictions. After that, the processes for potential automation were analyzed. The processes had to fit certain criteria for proper RPA deployment and BPR activities were carried out to ensure correct implementation. The fifth and sixth stages consisted of increasing the RPA center personnel and RPA training. RPA competence center employed or trained developers from IT and industrial automation areas and business analysts with experience in ERP and banking software implementation. The last stage was to implement RPA on a large-scale process.
To date, more than 50 business processes are robotized and by the end of 2020, 100 more are expected to be. Efforts are targeted to expand RPA functionality with artificial intelligence technologies, machine learning and natural language recognition techniques. A pilot project using machine learning and artificial intelligence technologies is already being implemented, which will allow to identify factors affecting the cost of certain types of raw materials.
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