In this installment of this series, we discuss the second component of the framework: prioritizing the issues to resolve. As organizations work on improving their performance, they are certain to find multiple issues that, if resolved, can help achieve the improvement goals. However, the dearth of the resources needed to tackle issues necessitates prioritization of efforts to ensure the biggest return on the improvement effort.
Prioritization can be achieved by leveraging the experience of the people who do the actual work, and/or by the utilization of analytical prioritization tools.
Talk to the People Doing the Work
When the abovementioned financial products company realized that morale was the real cause of their productivity problem, they began holding interviews with employees individually to try to understand the issues dragging down their morale. Most employees pointed to a new policy requiring them to account for their daily activity as the main grievance. The policy itself was put in place to deal with two employees who were spending an inordinate time day-trading while at work. It was clear that the “Time Accounting” was a patch-up policy that backfired by demoralizing otherwise good employees. The policy was rescinded and employee morale, and productivity, slowly climbed back to original levels. There were other grievances mentioned, but in this case, it was clear from talking to the employees what the largest problem was. It was given received the highest priority and immediate attention.
Look at the Data
In some cases talking to the employees doesn’t immediately yield answers. For example, the managers of the call center discussed above did talk to the operators about what’s driving the high average call times. But when the operator responses where categorized and tallied, no clear picture emerged; no one or two main culprits were identified. A different approach was needed.
One approach is to try to identify correlations that can point to main issues. The Quality Assurance Manager for the call center analyzed the records of thousands of calls and found one interesting correlation: the call times of one group of operators were consistently higher those of the rest of the operators. The interesting part was that these weren’t the newest operators. However, they were all hired around the same time, which suggested a problem with training. It turned out that this group had been hired when the call center was in dire need for operators and their training was cut short to get them on the phones. Retraining that group of employees reduced the average call time by 9%.
Study and Pareto
One way to prioritize the areas to tackle is to study the indicators identified to understand their effects and to tackle the ones that have the biggest effect. These indicators can be defect types, machine downtime codes, time elapsed doing different activities…etc. A Pareto chart then easily identifies the highest impact indicators to focus on.
The abovementioned call center did just that. The QC Manager and two experienced operators listened to customer calls for about a week. This allowed them categorize the time spent during a call into multiple categories. Then for two weeks they listened to customer calls and divided its calls into the categories they identified. Once the data was collected, it was arranged into a Pareto Chart that showed the biggest two time wasters during a call were confusing data on the company’s website, and a fragmented CRM system that took operators a long time to pull up all of the customer’s pertinent data. Dealing with these two problems helped the company target its resources in a way that produced the highest returns.
Prioritizing issues that drag down performance is one of the most important steps in the improvement framework. It protects against wasting time and resources on inconsequential problems and guarantees the best return on invested resources. In the next installment of this series we will discuss ways to analyze high priority issues to get to the root cause.