The digital era continues to reshape industries, compelling organizations to integrate new technologies to maintain competitive advantage. To assist companies embarking on digital transformation (DT) projects, researchers have developed innovative frameworks for assessing potential risks associated with these initiatives. A new study presents a hybridized decision support system (DSS) employing picture fuzzy sets (PFS) to manage the uncertainties inherent to DT project risks.
The research introduces the PF-DM-RANCOM-ARAS methodology, which combines distance measures and additive ratio assessment methods to provide comprehensive risk evaluation. This integrated approach incorporates both objective and subjective weights derived from decision experts, effectively addressing the complex nature of risks related to DT.
Digital transformation encapsulates the integration of digital technologies across all areas of business, fundamentally altering operations, management styles, and customer interactions. While the benefits are substantial, companies often fail to anticipate the myriad of risks accompanying DT projects. The absence of clear insights on these risks not only stymies project success but also hinders organizational growth.
To design the PF-DM-RANCOM-ARAS model, researchers conducted extensive literature reviews, alongside dialogues with experts from the digital economy sector. They identified multiple risks and associated criteria to create a structured methodology delineated over several stages: risk measurement, decision-making model development, and application to specific project scenarios.
Key to the model's strength is its use of picture fuzzy sets, which allow it to handle incomplete and ambiguous data commonly encountered during risk assessments. The PFS framework includes members' positive, neutral, and negative degrees of belonging, adding nuance to how risks are evaluated.
The study also delves deep within the parameters of its model, exploring case studies from various sectors, including automotive and renewable energy, which highlight the practical applicability of this structured risk assessment approach. The researchers applied the model to assess various DT projects, including Oracle fusion cloud Enterprise Resource Planning software, Augmented Reality-based warehouse management, and IoT-based predictive maintenance.
The findings underscored {the} importance of certain risks, with 'inflexible system architecture' identified as the most significant risk factor when evaluating DT projects. Findings indicated this risk could considerably derail project progress if not adequately addressed, drawing attention to the importance of adaptable infrastructures capable of integrating with new technologies.
Subsequent analysis revealed the priority among identified risks, enabling organizations to focus their strategic efforts on the most pressing challenges associated with digital transformation. The case study components provided practical evidence of how the PF-DM-RANCOM-ARAS method can be operationalized, supporting its validity and efficiency within real-world contexts.
The researchers highlighted the significance of including both objective and subjective assessments, arguing this dual approach enriches risk evaluations, aligning closely with the multifaceted nature of decision-making processes within firms. The empirical tests affirmed the model's robustness, presenting compelling advantages over existing fuzzy-set models.
Despite showing promising results, the study acknowledges its limitations, including the need for future research to incorporate interrelationships among various risks and to expand the dataset of decision experts. Future investigations are expected to apply the developed framework to broader sectors, thereby increasing its applicability across various risk landscapes.
Overall, this innovative hybrid framework offers organizations valuable insights for successful navigation of digital transformation projects, enhancing their ability to identify, assess, and mitigate risks effectively.