Do you optimize first or dive right into automation?
To most the answer is obvious. It isnât. Recently I chaired a Process Excellence conference for the OPEX Exchange in San Diego where leading companies from Financial Services, Pharma, Healthcare and other industries as well as BPM providers and top consulting firms participated in a workshop on that very question. We had all heard the expression, âautomating a bad process just produces bad outcomes faster,” so a quick poll of the group produced a clear consensus: optimize first, then automate.
For decades, applying technology to improve the way business processes worked was no small undertaking, whether done through internal development or external purchase of an application or platform. From ideation to implementation took from months to years depending on the scope and costs could run into the millions of dollars. Designing without the customer in mind or failing to simplify the process or not addressing underlying quality issues would inevitably result in time lines slipping to the right, cost overruns, and user dissatisfaction. If you were looking for quick wins, for “rapid cycle improvement,” the preferred path was a Lean or Six Sigma project. Automation came later after the process was standardized and optimized.
These were the experiences we brought to that discussion supporting the âprocess firstâ approach. But that was then, this is now:
Â· Robotic Process Automation projects, start to finish, can be done in a few weeks
Â· Actual design and coding of the bot can be done in a few hours
Â· Cost to implement can be less than buying a new PC
Let that sink in â an entire RPA implementation can be completed in less than the time it used to take us to set up a steering committee for automation programs of the past.
How can that be? Two reasons:
- RPA target processes that are clearly defined, repeatable and rules based. The bots execute transactions based on these simple business rules.
- The robots – or “bots” – are implemented in the presentation layer, the IT layer where information is captured from and presented to the user. This means that RPA implementations do not require changes to core processing and platforms.
This targeting enables fast capture of business requirements, rapid translation into bot design, ease in implementation and lower risk – all translating into lower cost.
Here are few examples of processes well suited for RPA:
- Marketing to prospects via email or social media
- Creating a client profile or customer account
- Registering a patient
- Onboarding an employee
- Processing a customer application, order or claim
- Updating customer account information
- Order updates and shipping notifications
- Response to customer inquiries or complaints
- Client, customer or patient billing
- Reconciling accounts
- Exception processing
By focusing on business areas which require significant manual work and potential sources of delay, implementations like these save hundreds of hours of cycle time and hundreds of thousands of dollars, eliminating labor cost to execute manually.
Further, RPAâs short project cycle times and small team resource requirement allows for parallel deployment and multiple waves enlarging the scope of what can be automated within a specified block of time. RPA deployment is more like Agile than waterfall, moving quickly from one opportunity to the next.
Now back to our discussion at the conference.
As we mulled over this new information, sharing recent stories of how much digital transformation could get done how quickly, of how much impact could come from only automating a step vs. an entire process, which came first optimize or automate became less clear. A new paradigm began to emerge:
- Automate first when scope is focused on a process step or two and elimination of manual work not only reduces cost but also maintains or improves quality output.
- When scope is broader, more end to end, optimization first is preferred, ensuring redesign takes full advantage of opportunities to simplify flow and enhance value.
To be clear, process design and improvement tools must play a key role at the front end of automation projects if they are to succeed – documenting the way work is done, noting variation, identifying key inputs and requirements for outputs, applying lean principles in the design. Some version of these steps can be found in all RPA methodologies. But the focus here is automation, not optimization.
So back to our question – which comes first, optimize or automate?
In the age of Digital Transformation, the answer is â it depends.
To learn more about Process Excellence in the Digital Age, contact us to schedule a free briefing.
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