Businesses are oftentimes looking to new technologies to help them stay ahead, even though their processes and systems are often outdated. To address this gap and ensure high efficiency in automation, business process reengineering focuses on fixing the processes before automating the tasks. Human efficiency that’s supported by bot technology is at the crux of process reengineering.
Reengineering is one of the core components of HGS’s automation process. It includes reviewing the current process and designing a new, improved process with the goal of enhancing the automation output.
At HGS, our expertise in process automation extends across hundreds of clients, domains, and processes. This experience, combined with experts around the globe, gives us a competitive edge.
While reengineering, It is imperative to identify any dependencies on interconnected processes to understand the repercussions of automation and how to deal with them.
Most process reengineering initiatives have the following stages:
Process reengineering is imperative before automating, as it ensures efficiency and helps build stable bots. It allows us to iron out any inefficiencies in the traditional way of running the process and to address, at least at a preliminary level, people’s concerns.
Reengineering enhances customer experiences by improving the turnaround time of the processes. It also helps in fine tuning other connected processes that can serve as an input or output to the process being automated.
Process reengineering has a big impact on data intelligence and the way organizations look to consume data. The output generated by bots can be used to study the data, which can be further used for decision making.
Process reengineering can vary from business-as-usual, where organizations are simply looking for leaner processes, to strategic improvements involving a complete overhaul of the processes. Depending on the objective and scope of automation, there are multiple steps in process reengineering.
We provide end-to-end automation services.
Initially, we used RPA solutions only for clients but quickly institutionalized automation within our business. Today, we use machine learning and natural language processing (NLP) as a virtual workforce that is capable of executing tasks and learning from data sets, including decisions based on emotion detection.
Identifying the scope of transformation and type of automation leading to clear business value
Automating rule-based tasks in processes that involves a high-level interaction between software and humans
Machine-led digitization techniques for data extraction from structured, semi-structured, and unstructured sources
Automated, self-learning interactions and systemic responses towards enhancing customer experiences
Cutting costs and creating efficiencies is a top priority for today’s organizations as they look to drive digital transformation through lean operations. The ROI of lean operations promises greater employee engagement and a bigger bottom line.
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