Manzana Insurance: Fruitvale Branch
Autor: shiva narravula • October 5, 2015 • Case Study • 1,070 Words (5 Pages) • 937 Views
Manzana Insurance: Fruitvale Branch
Using data on processing time averages from Exhibit 4, calculate how much time it would take to process a request, on average, under ideal circumstances. Compare this number to the average turnaround time given on page 1 of the case. In your interpretation, what causes these two numbers to be different?
Exhibit 4 by itself cannot be used to calculate an accurate lead time as it does not have the necessary information such as backlog (inventory). Assuming that all products have the same quantity, below table estimates the average time per request.
Resource | RUNs | RAPs | RAINs | RERUNs |
Mean time per Distribution clerk (Mins) | 68.5 | 50.0 | 43.5 | 28.0 |
Mean time per Underwriter (Mins) | 43.6 | 38.0 | 22.6 | 18.7 |
Mean time per Rating (Mins) | 75.5 | 64.7 | 65.5 | 75.5 |
Mean time per Policy Writing (Mins) | 71.0 | 0.0 | 54.0 | 50.1 |
Average timer per product (Hours) | 4.3 | 2.5 | 3.1 | 2.9 |
Average time per request (Hours) | 3.2 |
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The turnaround time on Page 1 is an actual lead time calculated through historic data. The actual turnaround time is greater than the calculated values due to couple of reasons. The Manzana team does not implement and follow the FIFO (First in-First out) practice. According to the case study, they seem to prioritize RAPs/ RUNs and tend to neglect RERUNS. This practice elongates the lead time for RERUNS and thereby has an effect on the total lead time. Manzana team also processes RERUNS the day before the due date (This is done for accuracy purposes), this practice further skews the lead time.
Page 2 provides a simple capacity analysis of the process. Can you provide a more detailed picture that (a) differentiates between the three different territories for underwriting teams outlined in Exhibit 7.
From Exhibit 7 we were able to calculate the capacity for the underwriter’s team for each territory. As denoted by the table below, the work flow for each territory is uneven. This is one of the main problems with the territory based service model. Territory 1 is extremely busy while 3 is under used.
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