Discovering Automation Opportunities in a Federated Model
Process Discovery – Crowd Sourced vs. Operational Diagnostic
Before going into too much detail, it is important to understand the difference between of the two main types of process discovery for your automation program – crowd-sourced and operational diagnostic.
The “crowd-sourced” technique involves simply asking your business counterparts to nominate their best ideas for where to apply automation. You can improve the quality of ideas by educating staff on what makes a good automation candidate, but ultimately you are relying on them to provide you with valuable and automatable process candidates to assess.
The main advantages of this approach are that it is cheap, easy, and fast – many of the things that you want when you are in the initial stages of your automation journey. The drawback is that you are essentially outsourcing a core function of the automation program to your (internal) customers. This can lead to great candidates being missed and poor candidates being presented for automation.
In contrast, the “operational diagnostic” approach refers to a structured and methodical technique that carefully looks at every process a team, department, or business unit performs and undertakes a rule-based assessment to find the best improvement opportunities that align with your program’s strategic objectives.
The benefit of this approach is that you assess an entire cohort and leave no stone unturned, and can be sure you are not leaving any value on the table. You also can be assured that candidate processes will provide the necessary value to your organization and that they are high-quality opportunities. The downside with this approach is that it can be time-consuming (i.e., expensive) to undertake when compared to crowd-sourcing – as such it lends itself to more established programs with the required resources.
Process and Task Mining technologies are quickly reaching maturity and gaining traction as tools to support in rapidly identifying and qualifying opportunities for improvement and automation. We will not address these in detail in this blog, but you can find more details on these here.
What new features/functionality are introduced as part of a federated model and how should they be rolled out?
There are many ways in which a federated model can exist in the context of an automation program. The main premise is that responsibility for one or more of the key components of an automation program (govern, discover, deliver, manage) are ceded to different parts of or functions within the business. When deciding how a federated program manages the identification of automation candidates, an organization must ultimately determine how much central control of the program they want to retain.
The responsibility for finding process candidates can be democratized to each business unit combined with a light-touch oversight role maintained centrally. This approach does not require considerable resource overhead and shifts the responsibility to the business unit. However, you can lose an element of control which can introduce risk. There are ways to mitigate against this risk (including commercial or contractual options), but ultimately you are still ‘outsourcing’ the identification of value for your program.
Alternatively, the process identification function can be kept centralized and provided as a service to the federated business unit (i.e., to the ‘spokes’ in a hub and spoke model). This eliminates the risk of outsourcing control and allows you to ensure only candidates who align with the program’s strategic objectives are considered for automation. The downside of this arrangement is that this has to be resourced centrally with skilled personnel.
What needs to be considered to be successful in finding and qualifying processes?
There has been much written about what makes a good automation candidate (think: repetitive, mundane, rules-based, etc.), so there is no need to go into detail, but the most important thing to consider when prioritizing processes for delivery is value.
“What value does automating this process bring to the organization? How does that value align with the program’s strategic objectives?”
These are key questions that should always be asked. Value might not necessarily be just financial – or even tangible — it can be many other things depending on the organization, industry, maturity of organization, etc. It is important that these value drivers are defined for your program and that the value lens is applied to candidate search and assessment. Our experience has shown that collaboratively developing an Enterprise Heat Map with program sponsors and executives, is a useful means to guide process discovery resources as they move from team to team. By collaborating with stakeholders at this level, you ensure there is complete alignment on the sources of value and the direction of the program.
How to ensure teams are finding good opportunities to automate
The simple answer is through central and transparent reporting and dashboards. Regardless of whether the process discovery pillar of your program has been federated to business units or is centrally maintained, it is imperative that your program has a robust pipeline reporting infrastructure.
Reporting should capture all data that is collected as part of the discovery process (e.g., process name, business unit, case handle time, volume, etc.) and roll that up to multiple views. These views should cover everything from the results of the discovery sprint for the week to the executive-level view of pipeline value. There are many tools on the market that can facilitate this reporting including UiPath’s Automation Hub and our own Reveal RoboManager®, but it is most important to ensure the tool is enterprise-grade. Using a series of Excel files to manage your pipeline in a federated model is a recipe for confusion, duplication of effort, and ultimate disaster.
In summary, when establishing the process discovery function of your automation program, it is important to consider a combination of crowd-sourced and a more holistic approach like Reveal Group’s Operational Diagnostic methodology. With the right governance and reporting, process discovery can be successfully applied at scale in either a central or federated model. The main consideration should be that process candidates are prioritized based on their alignment with the organization’s strategic objectives and that the pipeline is transparently reported on.
We’re here to help you introduce a robust methodology to scale your process discovery across the enterprise. Simply Contact Us to learn more.