Learn how analytics-driven process improvement and automation can drive results for your organization.
Part seven of a series.
Rarely has there been a more potent union of technologies than process automation and analytics. Each one provides value to your company, but in combination, they create unparalleled benefits: competitive advantage, maximum efficiency, and profit optimization.
Analytics consumes that operational data, often through a data warehouse. Then, it provides insight on how to improve operational processes, create new processes and develop long-term business strategies.
See the virtuous cycle? In a related blog, learn how process-driven analytics yields much higher value from the IT investment than focusing on the data alone. It is somewhat baffling, then, that so many organizations take a pass on combining these advantages.
Driving Results with Analytics-Driven Process Improvement and Automation
Where do process improvements really start? Many argue (and I tend to agree) that understanding the current state of a process and associated pain points provide a solid foundation for identifying and implementing process improvements. That’s why it’s important to look at process and data together as part of an improvement effort.
Regardless of where your company is on the process maturity curve, it is necessary to gather the necessary data to appropriately analyze your processes. In less mature organizations, you might be able to obtain this data through sampling efforts. More mature organizations might be able to use system data.
By basing your analysis on a solid data foundation, your business can feel more confident in the changes they will implement and less on their “gut feeling.” Advanced analytical tools offered by methodologies such as Lean and Six Sigma can be leveraged in order to determine the root cause of current process issues and more accurately predict the outcome of any proposed changes.
After you improve your process, what’s next? Some organizations may then decide to take it a step further with automation efforts. You might ask:
“Why not start with process automation?” Here’s what I would say to that: “Automating a bad process leads to bad results faster.”
It just further ingrains bad habits within an organization. For example, a process that is currently completed in five steps, but could really be done in two, will always be done in five steps if it’s built into the technology solution.
Automating an already optimized process, however, produces even more data that can be utilized for further analytic and improvement efforts. While the first iteration may rely more heavily on business data (order details, margins, etc.), additional iterations can apply process data from BPM or workflow solutions (volumes, task durations, etc.) to identify additional improvement opportunities.
This cycle can be repeated multiple times to continually enhance process performance. The formula: Data equals process improvements, which results in automation opportunities, which drives higher quality data.
Organizations that recognize the importance of this cycle and invest in key data analytics and process automation tools can more easily anticipate and react to industry demands and changes.
While you don’t want to necessarily reinvent the wheel multiple times, the data analytics and process automation tools that you leverage to drive these improvement efforts can often mature along with your data and process capabilities. Companies often start with desktop solutions and evolve into enterprise solutions.
An Example of Process Automation
So what does this mean to you? Let’s look at a general example of sales order processing to highlight how this data and process cycle can improve an overall process.
If a company is manually processing sales orders (exchanging emails between sales and operations), using a technology solution to automate the information exchange will provide general process visibility and produce process data necessary to identify the main process issues. These can be addressed using appropriate business process improvement methodologies. Then, technology automation can be extended to the entire process at a more detailed level.
While initial efforts may lead to pointed results (modify staffing at certain processing points, eliminate redundant data entry, etc.), analytics-driven improvements can be much more impactful (organizational redesign, eliminating entire process steps, etc.).
Securing Leadership Support for Process Automation and Analytics
Even the most mature process automation and analytics often requires a hefty budget and leadership buy-in. Building grassroots support can help cement organizational demand.
At Centric, we help clients go through the following exercise to prove the concept and build support:
- Find an Opportunity: Identify an area of the business ripe for process improvement and develop a thesis to improve the process.
- Establish a Monitoring Scorecard: Create a small scorecard that automatically aggregates the relevant operational data. This can easily be done in desktop software such as QlikView, Tableau and others.
- Improve and Monitor: Make periodic refinements and regularly monitor the scorecard as process improvements are made.
- Market and Promote: Gather testimonies and market the results to other business stakeholders. Build a case to expand this Lean approach through a dedicated initiative.
By following these steps, you can show your COO/CFO that process automation and analytics is worth the investment.
After you’ve made your case, the next step is to evaluate existing processes and tools to develop an improvement roadmap. Developing process-driven analytics creates a foundation to help your organization realize great benefits.
Writing and contributions by both: Jeff Kanel and Tom Ujvagi.
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