Business Process Optimization Explained (Plus, 4 Real-World Examples)

Every organization has business processes, but too often processes go unimproved for years because they’re “the way things have always been”—or because the prospect of optimizing them is overwhelming. When outdated processes are shrugged at and left to run untouched, it costs your business speed and quality. And it’s not just about a better current state. Business process optimization helps you grow and innovate in a rapidly changing world. 

Business process optimization is an aspect of business process management (BPM) that refers to improving a process to make it more efficient. These improvements can range from small—tweaking one task in the workflow—to large—reworking an entire process. Business process optimization can lead to more speed, higher quality, improved customer experience, lower costs, and ultimately, increased revenue. 

What does it take to achieve these results from business process optimization? Most process improvement methods use a variation of the steps in DMAIC. This is a common continuous improvement methodology in which you define the process and the problem, measure its current performance, analyze the cause of the process problem, improve the process, and evaluate the effect to control success. And it bears mentioning: done well, your optimization initiative should fit into a continuous improvement framework, rather than being a one-and-done project.

In this first step of business process improvement, you’ll identify the process to be improved and do some research to establish what the process looks like currently—looping in the process owner and any other stakeholders who should be involved in redesigning it. You’ll also want to set KPIs for your process.  

In this phase, you’ll want to mine insights from your current process to see how it’s performing. Common measurements include cycle time, lead time, and throughput. 

Review your original process and determine where potential problems could be, and analyze how these inefficiencies are affecting your KPIs. Conformance checking—comparing your as-is process to your ideal process—in a helpful way to prioritize where to focus your efforts.

Once you’ve done the hard work of analysis, you can implement solutions. Process optimizations might involve using AI and automation, for instance, to quickly extract data from email attachments, or using robotic process automation (RPA) to input data from documents into a system without an API integration. These technologies save you from having to assign the task to a support representative, freeing your employees to focus on higher value work.

You’re not done yet. Once you’ve completed the process redesign, you’ll need to assess how it’s performing and compare it to the original process performance, lest the changes you thought would perform better actually end up introducing new bottlenecks. 

Tip: In the past, many companies have done this legwork manually. But instead of taking the manual approach, which involves collecting the data, tracing the steps, and conducting interviews, look for a platform that includes process mining. It’s just what it sounds like: the technology mines event logs from your process to reconstruct what your process looks like visually. Then, you can quickly see where to optimize.

You may struggle to optimize your business processes unless you have a platform to orchestrate and automate them. The list below includes the top capabilities you’d want to look for in a business process automation platform.

• Process automation. Capabilities like AI, RPA, and unified workflows enable seamless handoffs between employees and digital workers, address regulatory issues to keep you compliant, and streamline needed-but-repetitive tasks.
• Data fabric. A data fabric enables you to easily connect and collect the data you need to investigate inefficient processes across systems. 
•  AI. Artificial intelligence improves efficiency in critical use cases like email classification, document classification, and document extraction. And by combining the power of AI and low-code, developers can build new process workflows faster than ever. 
•  Process mining. With process mining, you can visualize and measure a discovered process with all its variants—a feat that would be almost impossible to do manually.

Take a look at the examples below to see how these technologies have helped real organizations solve process problems. 

Aon built a new claims workflow application to support digital transformation, enabling employees to shift their focus to client advocacy and enhanced service delivery. Using prebuilt plug-ins and API frameworks, Aon seamlessly combines vast amounts of previously siloed data and logic, accelerating the creation of new claims workflows across the enterprise while leveraging existing algorithm, claims, and billing logic.

Every year, the Federal Transit Administration (FTA) is required to collect data from its local and state agencies to decide how to best allocate funding. The FTA’s data collection process was outdated and relied heavily on their legacy systems. All 4,000 transit agencies in the US would send data to the FTA, and analysts had to manually comb through systems and spreadsheets, trying to eliminate inconsistencies and comparing new data against previously submitted data.

FTA optimized this business process by using integration to bring data together from several formerly disjointed reporting modules and create one source of truth. They also added automation into the application to help guide the analysts, auto-generating data input, validation tasks, and enforcing timely completion. These improvements make the process more accurate and free analysts from repetitive tasks.

Addiko Bank set out to digitize internal processes to improve the customer experience. The bank looked first at how it managed loans and trade finance requests. The work involved manual tasks for finding, keying, and re-keying data. Their processes needed to be more consistent across countries, and, overall, took too long. When a customer submitted a simple loan request, the time it took for Addiko to approve the loan—the “time to yes”—was at least seven business days.

Addiko chose a process platform to speed up their loan and trade finance processes. They unified core banking and underwriting systems into a data fabric. Next, they automated contract and documentation creation and reported exception requests on-demand for Addiko employees to review. With their new process, Addiko shortened the “time to yes” for loan approvals from at least seven days to three days, cutting the average customer wait time in half. 

Poste Italiane, the Italian postal service provider, needed to reduce complexity of case processing. They wanted to optimize the process to improve back-office operations, make customer service feel more seamless, and transform operations governance. They used a process platform to build one single interface to handle fraud analysis and case management. They can now easily control and monitor these processes from end to end—including tasks they automated with robotic process automation. 

When evaluating their new process, they found that back-office staff save time and effort and deliver better customer service because they don’t have to switch between systems. The new process has also reduced Poste Italiane’s cost to serve: they’ve been able to digitize half of their documents and reduce lead times by at least 25% for main products.