Analytical & Process-Oriented Skills:
Process Mapping and Analysis: The ability to observe a manual task and deconstruct it into its fundamental steps is crucial. This involves using techniques like Value Stream Mapping (VSM), Business Process Model and Notation (BPMN), and creating detailed flowcharts to visualize the current state.
Root Cause Analysis: Engineers must go beyond documenting what is done to understand why it's done. They use methods like the 5 Whys or Fishbone Diagrams to identify the underlying reasons for each step, which helps in distinguishing value-added activities from waste that shouldn't be automated.
Requirements Engineering: This is the core skill of translating observations into clear, measurable, and unambiguous requirements for the automation team. It involves writing detailed user stories, defining acceptance criteria, and specifying functional and non-functional requirements.
Data-Driven Decision Making: They need to be proficient in collecting and analyzing process data (e.g., cycle times, error rates, volumes) to build a business case for automation and to establish a baseline for measuring success.
Technical Skills:
Understanding of Automation Technologies: While they don't need to be expert coders, they must have a solid grasp of what's possible with automation technologies. This includes knowing the capabilities and limitations of Robotic Process Automation (RPA), APIs, scripts, and other workflow automation tools
Systems Thinking: The ability to see how the manual process interacts with upstream and downstream systems is vital. They need to understand the data inputs, outputs, and how a new automated solution will fit into the existing IT ecosystem.
Basic Database and API Knowledge: A foundational understanding of how data is stored (SQL) and how systems communicate (APIs) is extremely helpful for defining data-related requirements accurately.
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