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Digital transformation of enterprises, be it services or manufacturing-oriented, attracted importance and popularity in recent years. In an enormous scope, digital transformation provides tools and connectivity to enterprises to re-engineer their current processes, communication, and interactions and bring out opportunities to create better user experiences, new business models, and new revenue streams. Like other change-governing disciplines, a strategic and unified deployment approach model would boost digitalisation success, create the best value for investment, provide a bridge between the technology providers and technology users, and finally catalyse the transition to a “digital future”. However, most small and medium-sized enterprises (SMEs) have not initiated the digital transformation due to the production and internal processes’ digitalisation challenges (Imgrund et al., 2018; Saam et al., 2016). The problem of capitalisation and technology identification/selection as a question of “How to identify the technology to adapt to which process?” has been on the agenda of researchers in recent years (Denner et al., 2018; Gimpel and Röglinger 2015; Hirt and Willmott 2014; HBRAS 2015).
Due to lack of the accumulated theoretical approaches and practices as cases or applications, today manufacturing organisations mostly tend or has to follow intuitive methods where the digitalisation ideas emerge from a black box, rather than analysing the actual needs of the organisation's strategies and processes (Vanwersch et al. 2016; Zellner 2011; Vergidis et al. 2008). The manufacturing industry, especially SMEs, need tools and approaches to understand their process needs and design responses from digital transformation technologies and offerings. Current practices have rarely utilised the widely well-known and mature quality management and process improvement approaches to guide digitalisation strategies and actions. The importance of business process management for digital transformation is a growing research area (Stjepić et al., 2020).
In this context, this study aims to present a combined model that utilises quality management techniques (Voice of Customer, Process Risk Analysis) for selecting the process components that bear high operational risks and need improvement through digitalisation. In the MCDM adoption of Digital Opportunity Priority Assessment (DOPA) model, we adapted the TOPSIS method for ranking the causes of potential adversary effects. The methodology utilises the AHP and Fuzzy AHP (FAHP) techniques to validate the CTQ ranks for their importance in comparison to conventional expert scorings for CTQs. This way, we aimed to eliminate the redundancy and inconsistency of expert opinions. The findings underline the contribution of MCDM techniques to digitalisation decisions' precision, consistency, and validity. A case study from the industry also presented an application for validating the model.