Optimizing Resource Utilization Using Workforce Management in a Wholesale Lockbox
Client: A Wholesale lockbox company that processes more than 3 billion checks per year
Industry: Financial Services
Service: Staffing and Capacity Modeling
- The company was experiencing significant volume fluctuations
- There was not an easy way to adjust staffing to maximize efficiency and cost
- Review the existing volume, staffing, and operational procedures
- Develop an accurate volume forecasting tool
- Create productivity recommendations and staffing models
- Streamlined operations
- Savings of more than $528,000 due to improved staffing flexibility and productivity
- Reduced lead time by 48 percent, or 27 hours
This high-volume financial services processing center was operating at a dizzying pace. The workload for approximately 100 clients requires round-the-clock staffing to process 3 billion checks, 197 million customer payments, and more than 675 million customer statements each year.
However, that work flow is uneven in its composition and execution. The company had little true understanding of how different types of payment processing needed to be handled, including the steps and time involved. In addition, volume fluctuated significantly by day and by hour. Two-thirds of the company’s entire weekly volume arrives between Thursday and Sunday, typically ranging from 7.3 percent on Thursday to 17.8 percent each on Saturday and Sunday, while only 54 percent of weekly staffing resources were scheduled during that time. Hourly fluctuations were also having a negative impact. The third shift typically receives half of the week’s volume, but has only one-third of the weeks’ staffing resources scheduled.
With such significant discrepancies between the amount of work that needed to be done and the staff available to do the work, the company was experiencing delays and back-ups that cost time, productivity, and money. Overall lead time for projects was problematic: Sometimes mail could take as much as 63 hours before processing began.
Of course, the problem wasn’t just staffing. The type of work was also a significant factor. The company was making a change from working with paper-based systems to image-based systems. Work for new customers who were transitioning to image-based protocols took longer because of the work necessary to set up the framework and imaging requirements of the account. There was little understanding of the amount of time it took to process images for clients at various stages of implementation.
Using a team of Guidon consultants, company representatives, and various subject matter experts, a work flow map was created, analyzed, and distilled into a new staffing model. The model broke out tasks by day and by shift, and made recommendations about the number of employees required. The model illustrated that, using productivity rates at that time, the site was overstaffed by 25.5 employees, yet still falling behind productivity targets. Once the work product was fully migrated to the image platform, that would rise by another three employees.
The new staffing model looked very different. More employees were scheduled for the high volume Thursday through Sunday periods and on the third-shift to handle the additional work volume typical of these time periods. The slower second shift would be reduced and focus on handling high-dollar check processing to reduce errors in that area. This staffing redistribution helped the company reduce lead time by 48 percent, or 27 hours. Increased productivity and reduction in overstaffing has made a $528,000 difference to the company’s bottom line.
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