Guiding Principles for Process Optimization—Part 2

If a tree falls in the forest with no one to hear it, then does it make a sound? Similarly, if you make a change to a process and you have no way to measure the change, then who can say if you made an improvement?

In the first blog of the series we discussed how to document your operations/business processes, and how having that understanding and context positions you for optimization activity. Once you are ready to start optimizing your processes, it’s imperative that you are able to measure your processes and any changes you make to those processes as well.

Measuring, collecting, and reporting on the right data is a critical capability for facilitating and enabling process optimization.

A common issue/error we see with clients is that they jump into optimization activity without being able to adequately measure their process performance. Needless to say, it’s very difficult to improve a process that you can’t measure. From an implementation and change management perspective you need performance metrics to:

• Measure the degree to which process performance improvements are actually being achieved
• Calculate the impact of process changes to develop a business case for funding optimization activity and investments
• Provide quantitative feedback on the success of your implementation, in order to report to stakeholders and sponsors

From a process engineering perspective you need performance metrics to:

• Identify where the constraints are in your process
• Provide quick feedback on how well process changes are affecting performance, so that you can refine and modify changes as needed to achieve and fine-tune improvements
• Identify and address areas with high performance variation, verifying and enforcing standardization and adherence to the new process
• Verify the process performance is controlled and meeting expectations/requirements
• Identify unintended consequences that need to be resolved; i.e., improving one process at the expense of another

More and more with the automation and digitization of processes, the availability of data for generating metrics is not the problem. The problem is identifying the relevant data, and developing meaningful metrics that intelligently inform the business. With the proper focus, we understand our processes (because we completed our process analysis and documentation per the prior blogs), we have insight into where the data is being generated throughout the process, what activities are generating data, and how the data is being captured. Therefore, the next step we recommend is understanding what is important and of value to the internal/external customers, and developing customer centric performance metrics. Based on those insights, we develop the second level of metrics needed by the operations/process performers and owners to ensure they are efficiently delivering the expected value.

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To identify the “right” metrics you need to know what is important to your customers. Understanding what is of value or expected by customers is best achieved through Voice of the Customer (VOC), Voice of the Business (VOB) and Critical to Quality (CTQ) exercises. VOC exercises ensure you connect with your external customers, understand what is of value to them, and develop metrics that align with these values (CTQs). For example, if customers express the need to get their questions answered right in one call, then some type of First-Call-Resolution CTQ metric may be appropriate to confirm you are delivering the value that they need.

Similarly, Voice of the Business (VOB) exercises require you to align with your internal customers (business stakeholders) to identify what is important (i.e., what is of value) to the business; e.g, reducing operating costs, improving end-of-line quality, etc. We recommend driving these types of business values into metrics associated with cost, quality, and speed. A lot of work can go into VOC and VOB research, which really deserves a separate discussion to do it justice.

Once all the customer metrics are accounted for, operational metrics need to be developed to facilitate and ensure customer value is delivered efficiently. Operational metrics describe and characterize how efficiently the business and operations are delivering customer (internal and external) value. For example, assume VOC research has indicated that customer expectations for home loan approval is 2 weeks or less. A CTQ metric could be overall loan cycle time, which would inform the business if they are meeting the 2-week-or-less expectation. If the CTQ metric indicates that it’s actually taking 15.1 days on average compared to 14, then obviously the process must be improved to meet customer expectations.

If you understand the loan process is composed of 6 steps, then you can develop cycle time metrics for each of these sub-process steps to identify and prioritize areas of improvement. (For example, Appraisals constitute 30% of the overall cycle time; maybe we should look to make improvements there first…). These sub-process cycle times would be a type of operational metric that provides insight into how the process is performing and what activities are driving the CTQ metric to be 15.1 days.

It’s one thing to achieve your customer-centric metrics, but if you can’t figure out how to deliver efficiently, then you will ultimately fail operationally. And, as mentioned earlier, to develop operational metrics that provide insight into your delivery performance, you have to understand your operations/business processes. This reiterates the importance of our first guiding principle — Rapidly Assess and Develop Processes. The foundation created through process analysis and documentation is used to identify and define the right metrics and the right way to collect and report on those metrics to enable the business to succeed.