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Eight steps to transformational operational excellence with process mining

Software
(Image credit: Image source: Shutterstock/TechnoVectors)

Organizations today are under significant financial and competitive pressure as they manage disruption at a scale not previously imagined. Customer expectations have reached all-time highs, and shifts to remote and hybrid-working models have placed unprecedented demands on employees and companies alike. 

Operational excellence is key to business resilience and growth, and yet today process owners often struggle to keep up with the rapid rate of change and lack visibility into processes intelligence and modeling tools needed for effective decision making. Further complicating matters is the natural progression of change. As business processes evolve over time, they become cluttered with waste, unnecessary steps, extraneous controls, duplicated resources, and manual workarounds – much of which is hidden from sight. 

Process mining enhances traditional approaches to operational excellence by automating the critical step of process discovery. By definition, process mining is a family of data-driven techniques to analyze business processes using event data extracted from information systems. It allows business users to identify bottlenecks, deviations, and sources of waste or rework in their processes. With the discovered models as a foundation, the pathway to improvement then becomes far easier, more accurate, carries less risk, and is significantly faster. 

Here’s how business leaders can lay the groundwork for automating process discovery and continuous improvement through an eight-step pathway to operational excellence.

Eight steps to transformational operational excellence  

Step 1: Stop Doing Non-Value-Added Work

The challenge: Much work is often still conducted from habit. For example: members of one team may follow a process that no longer aligns with the needs of another team. Eliminating this effort is fast and easy to implement and can provide immediate benefits.  

How process mining helps 

The discovered process model not only makes it easier to identify bottlenecks, rework, and areas where processes are stalled, but also makes it easier to quantify the benefits. By analyzing the difference between best practices and existing (as-is) models in a delta analysis, business decision makers can explore opportunities to eliminate waste, reduce risk and improve operational efficiency and customer experience.

Step 2: Consolidation  

Over time in large, mature enterprises, operating models change, existing products are modified, and new products introduced. The result is often fragmented and distributed processes that range across multiple teams. A critical step towards operational excellence is to consolidate similar processes, activities, and teams. Yet, consolidation is not without risk. 

How process mining helps 

Process mining’s variant analysis capabilities let users compare different paths or workflows of the same process. With this perspective, business, operations, and risk professionals can focus on the differences and their impact. This highlights opportunities for consolidation, and the benefits and risks that may be involved. This level of transparency also provides data to support the transition such as resource usage, demand profiles, bottlenecks, and more, significantly de-risking consolidation efforts. 

Step 3: Standardization  

Delivering a consistent customer experience at a lower operating cost is a hallmark of digital giants and startups. Comparing how two different teams execute a process can provide valuable insight into how work impacts positive or negative, and planned or unplanned outcomes. While people may share tips and checklists within their own team, this is increasingly difficult amid remote and hybrid working models. As a result, processes quickly drift from the norm, and quality and compliance become unpredictable. 

How process mining helps 

Comparing process variants via process mining to understand pathways, gain knowledge of activity execution times and wait times, spot bottlenecks, exceptions, resource usage and other processes quickly highlights where the opportunities to standardize lie. 

Step 4: Simplification   

Business processes inevitably evolve over time, including unnecessary steps, extraneous controls, duplicated resources, and manual workarounds. When things go wrong, the instinctive response is often to add another control or introduce a range of unsustainable, error-prone manual processes. 

How process mining helps 

The visibility, transparency and availability of process models discovered by process mining with accurate representation of the flow and real process metrics allow analysts to quickly identify the waste. Transaction types that slowing processes, duplicate controls, non-value-adding steps, broken or slow handoffs, rework loops and exceptions can readily be identified. Removing this waste directly reduces cost and improves quality, delivering a better outcome to customers faster.

Step 5: Business Process Reengineering  

By the time a process is consolidated, standardized, and simplified, identifying opportunities to reengineer it is usually the next step. To do this successfully requires not only an understanding of each “As-Is” process for the relevant channels including the detailed process metrics behind them, but also a detailed understanding of all the possible exceptions. 

How process mining helps  

Understand hidden tasks and identify poor handovers, rework, underutilized or overutilized resources, to make data-informed process improvement decisions. Identify highly repetitive, manual, and error-prone routines for automation. Process mining can discover the root causes of common delays in supply chains or workflows that impact delivery times and quality, and subsequently lead to revenue loss. 

Step 6: Tactical Automation 

Automating processes is a focal point for many companies. While a full transformation is underway, tactical automation is a common approach to deriving some of the benefits in the meantime. This may include modifying business rules, implementing Robotic Process Automation, or Machine Learning models. Because of the underlying complexity, even a reengineered process that is built on the same technology stack is hard to automate tactically end-to-end. 

How process mining helps 

Identifying the precise steps in the process where automation can make the biggest impact requires a thorough knowledge of task time, resource utilization and resource costs. Process modeling using process mining can help prioritize tactical automation efforts. It also ensures that time is not wasted on automating parts of the process that may not deliver a return on investment. Process mining enhances the traditional approach: automation opportunities, at the task or process level, can be identified via process mining, and the impact of their automation can be assessed quantitatively, based on hard data. 

Step 7: Outsourcing/Offshoring/Rightshoring  

It may seem easy to estimate how many people are required by just counting the number of people involved in the sending team. However, this may not reflect unpaid overtime or reflect the differences between what is expected and what is done. It is unlikely to allow for accumulated years of experience and knowledge, and the productivity gains that go with it. 

Moving key business processes from one location to another or from one business to another is risky and often relies on workers’ abilities to ask questions or relay how work is conducted. 

How process mining helps 

Running an automated process discovery exercise prior to transition ensures both sending and receiving teams are on the same page, and that there is a full understanding of all exceptions and the resourcing calculations that sit behind them. 

Step 8: Transformation  

Digitization can streamline business operations and processes and allow organizations to transform operating models and increase business outcomes. However, digitization is not a matter of simply replacing an existing service with a digital form or implementing more technology. To gain the full benefit, processes must be understood and ideally optimized prior to automation and digitization, a considerable challenge for organizations that have tens of thousands of processes and just as many exceptions to those processes.  

How process mining helps 

Process mining brings a much-needed transparency to how an organization works and therefore allows transformation leaders to understand where to focus, what to prioritize, and how to transform key processes for sustained operational excellence.  It’s imperative to understand “As-Is” process flows, understand where bottlenecks occur and which activities consume the highest amount of effort, and test and predict “To-Be” process models and changes before after the transformation. It is crucial to establish a clear vision of the changes required, their possible outcomes, and the customers’ reaction to them. 

Process mining underpins process maturity 

Many companies still have far to go to achieve process maturity. A key challenge is a lack of transparency and process data to help teams develop the detailed understanding of how their current processes operate. The visibility and insight that process mining provides is a realistic benchmark of progress and can stimulate considerable learning of how organizational processes work. Using existing event logs as the basis for simulation enables teams to accelerate insights and draw faster conclusions. 

Process mining enhances traditional approaches to operational excellence by automating the critical step of process discovery. The maps and models that are generated reflect business reality and are supported with operational data that is so often lacking. With the discovered models as a foundation, the pathway to improvement then becomes far easier, more accurate, carries less risk, and is significantly faster.

Marcello La Rosa, co-founder and CEO, Apromore

Marcello La Rosa, co-founder and CEO, Apromore.