Robotic Process Automation (RPA), which automates repetitive, rule-based operations, is becoming a crucial component of today’s enterprises as they compete in more dynamic business contexts. This study intends to provide implications on the Benefits Realization Key Success Factors (BRKSFs) appropriate for RPA projects, given that between 30% and 50% of RPA initiatives fail. The methodology of this study comprises three stages: identify the main contributing BRKSFs, develop a hierarchical relationship model for BRKSFs, and provide real-world examples to show the usability of BRKSFs using two case studies. The results show that having a clear, well-defined, and unchanging process is the most important BRKSF because of its strong influence over other factors. Three factors, namely, aligning the objective of the RPA initiative with the organization’s strategic objectives, choosing the right process for automation, and change management, have lower driving powers but high dependence powers than other factors. The five factors that have both high driving and high dependence powers are: prioritizing the benefits that can be obtained through the RPA initiative, performing a feasibility study, assembling a cross-functional team, having a team leader and receiving support from top management. This study sheds light on the interdependencies between BRKSFs for academics and business professionals, enabling them to determine which variables need to be considered most for RPA initiatives.

The ready-mix concrete supply chain is highly disruptive due to its product perishability and Just-in-Time (JIT) production style. A lack of technology makes the ready-mix concrete (RMC) industry suffer from frequent production failures, ultimately causing high customer dissatisfaction and loss of revenues. In this paper, we propose the first-ever digital twin (DT) system in the RMC industry that can serve as a decision support tool to manage production risk efficiently and effectively via predictive maintenance. This study focuses on the feasibility of digital twins for the RMC industry in three main areas holistically: (1) the technical feasibility of the digital twin system for ready-mix concrete plant production risk management; (2) the business value of the proposed product to the construction industry; (3) the challenges of implementation in the real-world RMC industry. The proposed digital twin system consists of three main phases: (1) an IoT system to get the real-time production cycle times; (2) a digital twin operational working model with descriptive analytics; (3) an advanced analytical dashboard with predictive analytics to make predictive maintenance decisions. Our proposed digital twin solution can provide efficient and interpretable predictive maintenance insights in real time based on anomaly detection, production bottleneck identification, process disruption forecast and cycle time analysis. Finally, this study emphasizes that state-of-the-art solutions such as digital twins can effectively manage the production risks of ready-mix concrete plants by automatically detecting and predicting the bottlenecks without waiting until a production failure happens to react.

Lockdown measures adopted to contain the Coronavirus (COVID-19) pandemic resulted in severe disruptions to mobility, both in demand and supply of passenger and goods transport and supply chain activities globally. This research was designed to understand the immediate reactions of households and society during the curfew imposed in Sri Lanka, a developing country, to curb the spread of the pandemic. This paper investigates psychometric perceptions across different socio-economic characteristics of households using statistical techniques to explore the association between the sample and population parameters. Results prominently indicate that these immediate impacts of health advisories and lockdown on personal mobility and consumption patterns were short-term in nature and unlikely to continue beyond the curfew. These results would be useful in understanding how society would deal with a similar unforeseen event in the future if it were to arise.