Operational modeling from strategy through to execution is an excellent start to understanding how your company functions and being able to react quickly to change. But there is an issue with models – many times they are representations of what is thought to be true, but unless they are very tightly tied to real data, they have a tendency to remain removed from reality. Models frequently get a bad rap as requiring a lot of effort to put together, only to become quickly outdated and untrusted. The key is to use the model as a framework that real business data attaches to. When the model becomes a contextual vehicle to deliver business intelligence, rolling information up into management-level reporting that supports executive decision making, that model will demand a new level of respect. Here are three ways you can reinforce the link between the model and how business is really working:
At the top of your operational model live business strategies and critical success factors. Key Performance Indicators are established to assess their achievement, and processes are identified that are monitored and measured against them. This model doesn’t have to be maintained independently while KPIs and Process Performance Indicators are housed in separate excel files; the process data can be collected and presented directly in the model in the context of the process landscape and related strategies. This enables viewing and understanding how processes are performing in the context of the overall business model, which helps to see what parts of the business may need to be tweaked to achieve different results. This also keeps the model “alive” and validates that it closely resembles reality.
The process models themselves can be used to visualize where performance indicator data is being generated, and can show how tasks are linked back to the indicators in the strategy model. You can even show reporting tasks in process models by using Extended BPMN notation. These tasks will explicitly identify what data is being captured at given points in the process, allowing analysts to easily identify where and when processes are affecting Process Performance Indicators and / or Key Performance Indicators.
It’s important for models to be able to exist outside of their own implementation. Not every aspect of a process is automated, not all parts of workflow are executed in a digital system. The holistic and end-to-end picture of how work flows through systems and processes and across departments is complex enough that it must not be only portrayed from an IT-centric context.
That being said, there is a huge advantage to linking models to real implementation to help preserve the accuracy of the model. Whether it be through tight blueprint-based integration with large ERP systems, by using model-driven deployment of workflows, or a combination of both, integration will help cut down on the disconnects that frequently occur when analysts model and maintain process documentation while another group is implementing and making adaptations on the fly – many of which will not get reconciled with the model. Models that are out of alignment with reality before projects even go live become throw-away, and hardly can be used as an asset that enables ongoing agility.
When using what-if simulation as a tool for process improvement, the more real your baseline the better. By bringing in real transactional process data into the model for simulation, you greatly increase your chance of a realistic simulation of the outcome of whatever changes you are planning. For many, the problems is figuring out how to get that data. This is an opportunity to think about automating certain processes in order to collect information on task durations, number of times transactions take certain decision paths, etc. Armed with this type of data, your model becomes much more leverage-able for analysis, not only to see how processes can be improved, but to understand impacts of changes that might be stemming from other decisions elsewhere in the organization. Again, having the model based on real data will give your company the analysis capabilities make good decisions and stay agile in the face of change.
For more on the Strategy to Execution Blog Series, check out the next post: Remove Technology Barriers to Achieving Change.