Robotic process automation in Maersk procurement–applicability of action principles and research opportunities

Abstract

PurposeThe authors share a real-world case study of a multiple-year adoption process of robotic process automation (RPA) in procurement to add to the limited empirical research base on RPA in supply chain management. The Maersk case offers hard evidence of the value of RPA that can be used as a benchmark for decision-making in companies and as a basis for further research in key areas such as change management and behavioral aspects as well as return on investment on RPA and the need for RPA capability development.Design/methodology/approachTogether with the co-author from Maersk, the authors cover a multiple year RPA program in procurements, to capture lessons learned and develop research questions for the future. The authors consider 39 generic action principles for RPA adoption in a specific procurement context.FindingsThe authors find RPA for procurement to be an enabler of strategic progress and of advancing the concept of triple values (enterprise, customer and employee) in a supply chain setting. The authors offer RPA adoption scoping guidance and show how, with growth and maturity of the program, conditions such as predictability of volume become less relevant as a scoping criterium. The authors also find that RPA augments work rather than replace staff. Maersk utilizes staff time freed up by automation to focus employees on more strategic priorities. The authors consider 39 generic action principles for RPA adoption in a procurement-specific context and develop additional action principles based upon the Maersk case.Originality/valueThe authors study both a unique and rich case to complement limited empirical research on RPA in procurement and supply chain management and the limited research into RPA past early stage adoptions. The authors address Hofmann et al.'s (2019) questions about the change management involved in RPA and consider generic RPA action principles from literature in this specific procurement setting. The authors extend those action principles and develop a rich set of research opportunities.

Influencing supply chain practice: the action principles approach applied to pandemic risk management

“…Lacity (2019, 2022), Lacity et al. (2021) and van Hoek et al. (2022) focused mostly on emerging technologies, and we have broadened that focus in the supply chain to include dynamics with uncertainty and emerging phenomena with little prior knowledge.…”

“…The pandemic provides a good example of the dynamics in industry that may be studied using APR, but it is not the only topical area where that is the case. Lacity (2019Lacity ( , 2022, Lacity et al (2021) andvan Hoek et al (2022) focused mostly on emerging technologies, and we have broadened that focus in the supply chain to include dynamics with uncertainty and emerging phenomena with little prior knowledge. The pandemic falls into dynamics with uncertainty to a large extent, but there are also emerging technologies that may benefit from APR (e.g.…”

“…, 2021) and blockchain (Lacity, 2020, 2022)—as a method for influencing information systems practices. Building upon the recent publication in this journal (van Hoek et al. , 2022) we aim to further apply the method to supply chain management.…”

“…With roots in evidence-based management (Glaub et al, 2014), the APR approach has recently been further developed in the information technology space (Lacity, 2019)particularly in the study of robotic process automation (Lacity et al, 2021) and blockchain (Lacity, 2020(Lacity, , 2022)-as a method for influencing information systems practices. Building upon the recent publication in this journal (van Hoek et al, 2022) we aim to further apply the method to supply chain management. The APR approach shares similar aspirations as intervention based research, namely, to directly influence supply chain practice.…”

Quality of Artificial Intelligence Driven Procurement Decision Making and Transactional Data Structure

“…One direction of digital transformations within procurement are big data and predictive analytics. Predictive business analytics is a way how to improve future predictions (Maisel and Cokins, 2014) or support development of more precise and quality automation tools like bots which need to incorporate predictive techniques and quality data to be efficient as expected by procurement managers (Omar et al, 2021;Viale and Zouari, 2020;Van Hoek, Larsen and Lacity, 2022).…”