Business process improvement initiatives are delivering results across industries, but how do you measure success accurately? We share four challenges you might face and how to address them.
While many companies have seen success in their individual business process improvement (BPI) projects, others struggle to move past initial challenges or aptly identify where to begin in their improvement efforts. Whether your team has a continual improvement program or this is your first BPI effort, understanding how to anticipate challenges in measuring success accurately will benefit your efforts both on paper and in practice.
How Anticipating Challenges Generates BPI Success
If you’re struggling to identify which process to improve first, anticipating obstacles could reveal the path of least resistance. If you’re met with resistance all over, it can guide your team’s efforts before you begin a BPI initiative. Don’t be surprised if challenges emerge before your project even starts. In this case, preparing for them will help your team curb setbacks and increase success rates. Often, obstacles come from inefficient or outdated processes you should address before starting your BPI initiative. Finally, if you want to evaluate your BPI project’s performance post-mortem, framing your measurement in the context of the challenges you faced may shed light on where you can improve for the next project. All of which begs the question: What challenges do businesses face most frequently? Here’s our list of what we see most commonly with our clients and how you can overcome them.Challenge 1: Unclear Performance Metrics
Before you begin a BPI project, your team must align on the definition and prioritization of how you’ll measure process performance. These measurements might come from your current process or desired process, and you might need multiple indicators to make the complete picture. You’ll also want to note any processes impacted by the results you want to improve. If you don’t agree on the measurements up front, you’ll face unclear or unhelpful performance metrics that will lead to confusion and frustration for your employees. For example, the go-to measurement of project success is often return on investment (ROI). ROI evaluates the profitability of an investment and lets you compare that profitability against other investments. It aims to measure the investment’s return relative to its cost. While ROI analysis demonstrates a level of commitment to understanding and measuring the business value generated by BPI Investments, other measurements may deal directly with process instead of output. You should gather buy-in upfront to focus more on process measurements than more familiar output measures, explaining that they will more accurately capture the changes you see and guide your process improvement efforts. Let’s look at helpful metrics for a specific process leadership could ask you to improve: the movement of products through a furnace. The process requires a specific flow of nitrogen at a specific temperature and time. The production line must move at a specific rate. If any of these measurements are off, you will get variations in the process. If you don’t have accurate data on all those parameters, it will delay your identification of the root cause of variation. These metrics should govern your process.Challenge 2: Failure to Identify a Single Source of Truth
Difficulties with data access and the information technology systems involved with your target project or process can make it difficult to know which data to trust, slowing down – or outright derailing – your performance management efforts. For example, you may store the relevant data across different platforms, complicating your single source of truth. How will you determine which points of data to focus on, and how will you guarantee relevant stakeholders can view those? The larger your company, the more theatres of data interact with each other. To make sure you have access to a single source of truth, look at your process’s current state and map which data you’re using for which metrics. If any data points are missing or incorrect, do you have a process in place to check with your single source of truth? You’ll want to align your metrics to a specific data source or determine one location to direct data, so it’s possible to see everything in one location.Challenge 3: Lack of Resources for Automated Data Interpretation
Even if your data systems are in tip-top shape, the time and access required to interpret data into reports or dashboards might prove a resource strain for your team. If you have tight processes, you should be able to minimize the amount of data interpretation needed. But what if your BPI project involves tightening up a data process? You’ll want to factor in the availability of skilled resources and labor in the current climate. You’ll also want to see if this project will strain resources due to other concurrent projects or if your team prioritizes their skills elsewhere. A common solution is to look toward automation for measurement to limit your interpretation need from the start. Automation applies to rules-based data. Where before, a person would have had to manually access multiple sites to gather all the data, machine learning only involves humans at key decision points. When incorporating automation into your process, ask which steps hold you up the most. For example, how do you set aside problem items that don’t get streamlined because they have an additional set of questions or decisions needed to move forward? Letting automation do some of the lift for you will free up human resources to tackle the harder questions and interpret with a larger scope.Challenge 4: Failure to Engage Upper Management
You may fear that changing the way you measure may change how much success you see. Once you harness a more accurate way to measure success, you’ll likely see discrepancies between how you used to perform versus what your new system shows. We often see this difference deter stakeholders from more accurate measurements if the old measurements show their process’s performance in a better light. By increasing alignment and understanding across your team, especially with higher-ups, the whole team will understand when the change is coming and what it will mean for them. Being upfront with your leadership team achieves transparency around the potential bell curve of performance in the early stages of your project. Your change management process will begin to take shape around these goals. In the long run, prioritizing accuracy versus a pat on the back based on inaccurate or incomplete performance statistics will drive real, tangible improvement.
