Loan Processing at Capital One It was in late July $2004$ and Rick Weis, operations manager of the loan processing center at Capital One, was looking over the marketing forecast for the upcoming quarter. Following several months during which Capital One had funded significantly fewer loans than targeted, Capital One$$s marketing team was now planning for a significant direct marketing effort. This marketing effort, which was planned to take the form of a major mail drop, was designed to increase the volume of funded loans in about six weeks when potential customers start returning these applications. It was clear to everyone at Capital One that the operations of loan processing would play a major role in determining if the upcoming mail drop would be a success. $$With $14$ funded loans processed per associate every month and a total of $25$ associates on the team, the department does not have the capacity to handle the application volume leading to our target of $700$ funded loans per month that we set following our increased marketing effort$,$ observed one of the managers working for Rick, $$What we need is a significant increase in staff. We also need to heavily invest in information technology to further increase the productivity of the existing staff$.$ While it was clear that the forecasted increase in loan applications would provide a serious challenge for the underwriters, there was no consensus on what actions should be taken. As was observed by one of the executives in charge of consumer loans: $$When I benchmark the productivity of our underwriting team with other companies in the industry, $14$ funded loans per associate per month is not a number we can be proud of$.$ It takes about $3$ hours of actual work to fund a loan, and that includes everything from the initial interview to underwriting, quality inspection, and closing. We have $25$ associates, that each works about $150$ hours per month. So each associate should be able to process $50$ applications per month, which gives us $1250$ applications per month for the team. Even if we fund only every other loan that we underwrite, we would just need a little bit of over time to get $700$ units funded.$$ Several others at Capital One agreed. As it was put by one of the associates in charge of direct marketing: $$Frankly$,$ if you asked me$,$ there seems to be a lot of potential for improving productivity in our processes. I am optimistic that our upcoming mail$-$drop will lift productivity and utilization scores in the underwriting process since there will be a lot more work coming in$.$ As the person in charge for operations management, Rick had mixed feelings about these comments. On the one hand, it was true that his department$$s productivity metrics had not been stellar in the past. But his associates worked very hard and were very capable. This case was developed solely as the basis for class discussion. It is not intended to serve as an endorsement, source of primary data or illustration of effective or ineffective management. All data in the case has been disguised. Rick was relatively new to this role, though he was a highly accomplished operations manager with a history of taking on tough challenges and producing strong results by redirecting his teams towards better prioritization, teamwork and focus on strategically important activities. As he looked over the marketing forecast and the target of $700$ funded loans for the next month, Rick wondered what the upcoming mail drop would do to his department? And, more importantly, what could he do to help Capital One grow its consumer loan business in the most optimal way? Capital One: Background Information After graduating first in class from the Stanford business school in $1981,$ Richard Fairbank joined Strategic Planning Associates $($SPA$),$ a strategy$-$consulting firm. In $1986,$ Fairbank met Nigel Morris, a young associate at SPA. While analyzing the operations of a major money center bank, the two reviewed the firm$$s credit card operations. Both of them were struck by the enormous profitability relative to the rest of the bank. The young consultants concluded, $$Credit cards are not banking $$ they are all about collecting information on $200$ Million people that you$$d never meet, and, on the basis of that information, making a series of decisions about lending money to them and then hoping they would pay you back.$$ Fairbank and Morris recognized the potential of customizing credit card products based on characteristics and behavior of their customers and taking advantage of the technological advances in computers that offered companies the ability to record, organize and analyze large amounts of customer data. They realized that few products in the credit card industry were being direct marketed and that even fewer firms were fully exploiting the power of statistical analysis. Fairbank and Morris were able to convince the bank to run a test using this strategy. The test work