Insurance

Manzana Insurance

Problem:

Loss of business due to late renewals, long lead times on new policies and quotes Competitive pressure (long lead times have created opportunity for competitor to take market share) Overemphasis on new policies causing loss of profitability (insurance losses)

Using Little’s Law:

Lead time = WIP/TP WIP = 82 policies Throughput = 39 policies/day => Leadtime approximately 2 days

Turnaround Time

Worst case scenario quoted for each operation (95% SCT) E.g. distribution of RUNs avg 41, 95% SCT 128.1 minutes Final “turnaround time” 2-3 times actual lead time => loss of business

Priorities

Should be first come, first served Changed by underwriters to RUN, RAP, RAIN, RERUN => RERUNs done at end of day (rather like producing a batch size = 1 day’s demand) RERUNs released one day before due – why?

Queuing Inputs

Processing time to a station: p Time between arrivals to a station: a Utilization: p/a Squared coefficient of variation: CVp, CVa Process flow diagram Number of workers at each station

1

Note that there are actually 5 products:

RUNs RAPs that are accepted (15%) – RAP/RUN RAPs not accepted RAINs RERUNs

Processing Rates (Exhibit 4)

RUN DC UW R PW Labor input per policy Hours 68.5 43.6 75.5 71 258.6 4.3 RAP/RUN RAP RAIN RERUN Avg. 50 50 43.5 28 41 38 38 22.6 18.7 28.4 64.7 64.7 65.5 75.5 70.4 71 0 54 50.1 54.8 223.7 152.7 185.6 172.3 194.6 3.7 2.5 3.1 2.9 3.2

Calculating Utilization for Distribution

Arrival rate = 39 policies per day Distribution service rate per day:

– – –

Utilization for all operations

DC UW R PW 0.89 0.82 0.76 0.64

41 min/policy on average 7.5 hours * 60 minutes * 4 DC clerks = 1800 min/day available => Distribution capacity 1800 min/day / 41 min/policy = 44 policies/day

Utilization = arrival rate/service rate = 89%

But policy creation is not all that the distribution clerks do!!

“Distribution was also responsible for analyzing and disseminating data published each month by...