Digital innovations in business process management

Abstract

Organizations face concurrent pressures to digitize operations and meet sustainability targets, yet many still depend on traditional Business Process Management (BPM) practices that struggle to keep pace with rapid technological change. This study addresses that gap by examining how established improvement methodologies: Business Process Reengineering, Lean/Six Sigma, and Agile can be purposefully combined with digital innovations such as Robotic Process Automation, Artificial Intelligence, Business Intelligence/analytics, cloud services, and the Internet of Things to deliver measurable gains in efficiency, adaptability, and environmental performance. The objective is to propose and assess an integrated BPM framework that aligns operational excellence with long-term competitiveness and sustainable development. Methodologically, the research employs an integrative literature analysis and structured comparative assessment of documented cases. Process outcomes were organized around cycle time, error rate, cost, throughput, flexibility, and sustainability alignment; qualitative evidence was synthesized thematically, and quantitative indicators were collated where available to support triangulation. Results show that the integrated model consistently shortens decision and execution cycles, reduces rework and manual effort, and lowers operating costs through targeted automation and analytics-driven control. Agile routines accelerate learning and responsiveness, while Lean/Six Sigma safeguards stability and quality as digital components are deployed. The framework also strengthens sustainability by improving resource efficiency, traceability, and risk resilience across the process lifecycle. The study concludes that unifying traditional BPM discipline with digital technologies provides a coherent path to productivity, flexibility, and sustainability, offering managers a pragmatic roadmap that sequences diagnostic assessment, redesign, incremental automation, and data-driven governance. While findings are grounded in synthesis and comparative evidence rather than multi-industry field trials, the proposed framework and indicators provide a transferable basis for implementation and evaluation. Future work should validate the approach through longitudinal, cross-sector case studies and refine adoption guidelines for differing organizational capabilities and regulatory contexts.