Definitive Guide to Continuous Process Improvement
Introduction: What’s preventing business process improvement?
Why aren’t many organizations continuously improving their business processes today? Most teams lack the clarity and transparency needed to understand what is working and what is not. According to the State of Process Orchestration report, 72% of IT leaders agree that real-world, business-critical processes are complex to maintain. That’s because many of these processes consist of:
A large number of different endpoints — including systems, people, devices, and even departments
Workflows that need to be executed in parallel
Situations that require exception handling
As more tasks become automated, 69% of IT leaders surveyed say that it’s harder to visualize end-to-end processes. As a result, many teams may go into “triage mode,” focusing on fixing problems in their automated processes on a local level. They may experience issues with local automations (e.g. using technology like RPA) and resolve them, but they still cannot improve the entire end-to-end process. Many teams face a particular challenge with business processes that involve human tasks.
You can’t measure and improve what you can’t see. The impact of this lack of visibility into automated business processes can, unfortunately, have ripple effects throughout the organization. Broken or inefficient processes could have downstream impacts on customer experience, employee experience, competitive advantage, and ultimately the bottom line.
In this guide, we introduce the concept of continuous process improvement, and answer some key questions, including:
How can organizations measure and improve their automated business processes?
What role do both business and IT stakeholders play in the process, and how can process visibility for all roles be improved?
How can continuous process improvement help organizations scale their automation efforts and become more strategic in their endeavors?
What is continuous process improvement?
Continuous process improvement involves monitoring, identifying issues, and implementing improvements to your end-to-end process on an ongoing basis. Much like the agile software development lifecycle, continuous improvement for processes isn’t a one-and-done effort.
The era of waterfall software development was a time when software projects followed a strict, step-by-step sequence until they were completed. However, in the 2010s, the agile development methodology gained prominence, especially with the proliferation of cloud-based applications and the emergence of geographically dispersed teams. This shift brought about the continuous integration and continuous delivery (CI/CD) lifecycle model, which significantly enhanced the efficiency and responsiveness of software development teams. So, with all these advancements in software development, why does business process automation still seem to be stuck in the past?
Continuous process improvement involves an ongoing cycle of process improvement to enhance your automation efforts over time.
When you take a step back and assess the situation, you’ll find that very few companies can consistently refine and improve their processes using a model similar to agile software development. Some processes operate continuously in a loop, or even simultaneously. Not every business process can be neatly defined as a linear sequence of steps.
Continuous process improvement involves an ongoing cycle of process improvement to enhance your automation efforts over time. The concept of continuous process improvement stems from other popular quality improvement methodologies, including Six Sigma and Kaizen, as well. The common thread among all of these methodologies is establishing a culture of continuous improvement.
For organizations to improve their end-to-end business processes, they need visibility.
That’s where process orchestration comes in. Process orchestration enables organizations to strategically design their processes in partnership with various stakeholders across business and IT teams. Using orchestration, teams can visualize, measure, and improve end-to-end processes, based on certain KPIs. Continuous process improvement enables organizations to make meaningful decisions about expanding their automation efforts to other parts of the business, leading to a more strategic scaled adoption of automation — instead of one-off projects.
The Process Orchestration Maturity Model
According to Deloitte, 92% of advanced automation adopters are using end-to-end automation as a part of their strategy now, or are planning to within the next three years. In fact, organizations that are highly mature in their process orchestration lifecycle implementations experience strategic, scaled adoption of end-to-end automation. They’re able to more effectively align automation efforts with their specific business goals and initiatives.
Improving maturity can help teams overcome the technology and people challenges standing in the way of meeting their automation goals. The Process Orchestration Maturity Model enables organizations to benchmark their maturity based on five key drivers:
Process Orchestration Maturity Drivers
Measurement is one of the most important drivers of maturity, with highly mature teams defining and measuring large-scale KPIs that demonstrate process orchestration’s contribution to business outcomes. Once teams can identify bottlenecks in their processes, these processes can be continuously improved — generating better business outcomes from automation.
Business process improvement for end-to-end processes
Process modeling is the first step toward maximizing stakeholder engagement and alignment on business processes. Automation and process orchestration are frequently used as a means to achieve specific objectives. These objectives can range from enhancing customer experiences and internal operational efficiencies to accomplishing extensive digital transformations. Unfortunately, a number of teams introduce technical solutions without evaluating their effectiveness from the perspective of business users.
In some instances, business users may even bypass the established protocols within centralized IT organizations to implement their own solutions. This can lead to process silos that don’t work for the business — along with automated processes that are broken or incomplete.
The good news is that common business process modeling and decision modeling frameworks are great for stakeholder alignment. Both BPMN and DMN are visual, flowchart-based representations of how a process or business decision will run in the real world. These resources can help business stakeholders visualize advanced workflow patterns or complex processes and/or decision rules up front, and make modifications or improvements before the process goes into production.
From there, a process orchestration system enables teams to identify the real performance of an end-to-end business process. Using process orchestration, teams can visualize what’s working and not working within their processes, using the same BPMN and DMN diagrams noted above. It’s important to choose a solution where the model remains in visual format for the entire lifecycle of the process. This makes it easier to identify exactly where process bottlenecks or issues occur, far beyond the initial implementation. Plus, technical teams can have ongoing discussions with business stakeholders about how processes are going, and where to improve.
Continuous process improvement examples
There are many practical continuous process improvement examples. Let’s start with an insurance claims process. In this scenario, an organization’s insurance claims take four weeks to process. The organization wants to get claims down to the industry standard of two weeks, since they’ve been experiencing customer churn.
A view of this company’s claims process model might show that there are multiple human steps in the process slowing it down. The organization can evolve toward straight-through processing by removing these time-consuming tasks in the process, and replacing them with fully automated components.
Based on customer feedback, a business stakeholder may decide that customers need more visibility into the status of their claims. As a result, the team might work a text message or automated status email into the process to provide regular updates to customers on where their claims stand.
Using a continuous process improvement lifecycle, it is possible to refine and improve the process gradually over time. One effective KPI for measurement might be the time required to complete the claim, but the organization can correlate this data with a higher-level benchmark, such as customer retention, to determine the impact of their business process.
Continuous process improvement with Atlassian
Delivering fast, accurate customer experiences is difficult, especially as an international company with a large catalog of digital solutions and services. That’s why global software company Atlassian turned to process orchestration to replace an RPA-based ticket support system (nicknamed Suzie).
Out-of-the-box orchestration wasn’t possible using the previous RPA system for Suzie. Non-technical personas didn’t have the option of visualizing these processes from end to end. Additionally, RPA wasn’t suitable for the team’s long-running processes or complex processes that required human interaction.
Using DMN and BPMN, the team modeled the Suzie bot’s processes from end to end, giving both technical and business stakeholders visibility into how these processes operate. Now, Suzie operates within a process orchestration system, where both sets of stakeholders can continuously monitor processes and make improvements. Developers can easily make changes on the fly, and integrate their process orchestration system with external technologies.
The original, RPA-based Suzie delivered a 45-60 minute wait time between a quote request and an answer. Today, that’s been reduced by 93% to two minutes, even when manual approval is required.
How a process orchestration platform works to improve business processes
The right process orchestration system enables teams to troubleshoot active processes, as well as visualize issues on the fly. The goal is to open up a feedback channel through whomever is monitoring processes. Technical teams can view the process flow in the testing phase, using simulations to understand what will happen in your process before it goes into production. Once the process is in production, aggregated process data can be viewed in a heatmap, a business-friendly dashboard that aligns with BPMN and DMN diagrams.
These heatmaps show bottlenecks in areas the organization might want to focus. For example, a heatmap might reveal a process improvement with a slow-to-respond API. Or, there may be opportunities to replace a human task with an automated one. External issues, such as server performance issues, can be the first indicator of a problem — identifying and mitigating them can help to improve a process.
From there, teams can correlate business process data with other business data, using their analytics platform of choice. This works by exporting machine learning-ready datasets from the process orchestration system. These datasets provide data scientists with the ability to cross-reference process data with other business data, or apply it within their own algorithms to learn a number of things about the process.
For example, data scientists might align process KPIs with higher-level business KPIs, or correlate process data with customer information. They may discover that process bottlenecks are occurring during certain times of day, or with a specific employee who requires further training. This data can also be used to determine what parts of the hyperautomation tech stack are effective, or which technologies should be replaced (like the Atlassian RPA example above).
Ideally, trends in business process performance can be used to identify where bottlenecks occur today, or where they might occur later. In the future, processes could become self-healing. Or, a process orchestration system could provide recommendations on the best optimizations to make, by linking business process improvements back to the process model.
Incorporate continuous process improvement into your automation strategy
Automation is a critical business imperative — yet it’s astounding how little it’s measured today. That’s why organizations should embrace an approach that’s similar to the agile software development lifecycle for continuous process improvement, starting with process orchestration as the foundation.
Continuous process improvement is one of the most powerful tools in an organization’s toolkit to further understand their business processes, and effectively collaborate between business and technical stakeholders. It can optimize processes to make them run faster and more efficiently. By correlating process data with other business data, teams can understand the larger business impact of automated processes. We’re not far away from a future where self-healing processes and process improvement recommendations become possible with machine learning.
The business impact of continuous process improvement spans far beyond faster, more efficient processes. Optimizing processes can improve customer experience, employee satisfaction, organizational efficiency and cost savings, competitive edge, and more.