Employing RPA and AI to automize order entry process with individual and small-sized structures: a SME business case study

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62156489%3A43110%2F22%3A43922421" target="_blank" >RIV/62156489:43110/22:43922421 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.25142/aak.2022.017" target="_blank" >https://doi.org/10.25142/aak.2022.017</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.25142/aak.2022.017" target="_blank" >10.25142/aak.2022.017</a>

Result language

angličtina

Original language name

Employing RPA and AI to automize order entry process with individual and small-sized structures: a SME business case study

Original language description

Digitalization is a megatrend which shows an even growing importance for industrial business development and marketing since the Corona pandemic. Its productivity-boostingand efficiency-raising effects have widely been researched in science.Especially SME have a backlog to enhance their digitalization level and take profit from digitalization measures, because they lack the necessary tacit knowledge and their process environment is characterized a small number of repetitions, but a high level of individual arrangement. Therefore, their processes are difficult to be standardized and digitalized. AI (Artificial Intelligence) and RPA (Robotic Process Automation) are 2 digital technologies that can be a tool to overcome these difficulties and automize also processes that are individual and not much repetitive, in order to gain advantage of automation also for these processes. The use of AI and RPA could therefore become a driver of organizational marketing performance. Unfortunately, the use of AI and RPA which is a relatively new tool, has not been researched in the particular context of a typical SME order entry process, which is characterized by relatively few repetition and a high degree of unstructured data. There is a huge gap in understanding the role which AI and RPA could play for this task and other similar tasks in order to improve productivity of SME&apos;s administrational processes. The objective of this work is to measure the input of a combined RPA and AI application, which has been developed basing on scientific findings, on the performance of the order entry process, hypothesizing a time economy of &gt;50% towards classical ways of working. It is a second objective to conclude which role RPA and AI could have for similar business cases and which input it could generally have for business performance of industrial SME, linked to its input on administrational processes. Therefore, this article measures and explores the input of a self-developed RPA and AI tool on a SME order entry process, based on primary data from process time registrations. In a case study of SME in Germany, data has been collected from daily order entry process. Productivity criteria have been measured and compared if using the classical order entry tool, and if using the new AI application. The result is that the new AI tool shows a significant time economy (&gt;50%), payback of less than 3 years and is therefore able to improve organizational performance. However, other doubts about the superiority of RPA-AI assisted tools over traditional ways of working, especially within SME, remain.

Czech name

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Czech description

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Type

J_ost - Miscellaneous article in a specialist periodical

CEP classification

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OECD FORD branch

50204 - Business and management

Project

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Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2022

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Acta Academica Karviniensia

ISSN

1212-415X

e-ISSN

2533-7610

Volume of the periodical

22

Issue of the periodical within the volume

2

Country of publishing house

CZ - CZECH REPUBLIC

Number of pages

19

Pages from-to

78-96

UT code for WoS article

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EID of the result in the Scopus database

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