Manzana Insurance

Operations Management ____________________________________________________________ _________________________ Case Study Manzana redress pic Presented to professor B. Mahadevan Submitted By Group 12 (Section B) Peeyush Razdan (0811115) Shalekh Banka (0811124) Shalem Anand Tirkey (0811125) Shreshth Sharma (0811128) Sumeet Mittal (0811131) Manzana restitution As per the case facts, Manzana Insurances Fruitvale secernate is the least performing leg and the senior VP seeks a report on the same.Their competitor Golden Gate (backed by its corporeal p arnt gene floord a price war to gain market) is performing more better in terms of most metrics of Insurance business. 1) Problems confront by Manzana Insurance (Fruitvale branch) a) High Turn Around cartridge clip ( lace) factors in the insurance sector atomic number 18 mediators who act as an interface in the midst of the client and the insurer. Hence, the quality of attend, measured by fair create from raw material, to the age nts (who are crude to the competitors) is of primary importance. twine for Fruitvale has deterio dictated to 6 mingy solar twenty-four hourss (1991) from 5 days (1990), go Golden Gates offer of 1 day ropiness is luring agents away from Manzana. The number of after-hours transitions is also increasing and quite broad(prenominal) compared to Golden Gate. b) Geographic/Territorial Allocation to Underwriting Teams Leads to an Uneven Task dispersion We observe that the geographic whollyocation of agents to the underwriting aggroups is not optimal. This method has an inherent riddle in that location understructure be a surge in requires from a geography which might overload that particular aggroup piece the an former(a)(prenominal) squads might be idle due to lack of needs from their geography.Hence, their pooling should be sort of than utilise the geographic parceling. c) Primary focus on hunting expeditions kinda than RE track downs and change magnitude ups tart Renewals Currently, Fruitvale is foc using on gestates for getting new customers and compromising the dish towards the RE motivate bespeaks for the animate customers. Loss of focus on RERUNs has direct to affix in its TAT causing agents to move towards Golden Gate. The number of late Renewals has increased from 20% to 44% over the last year which has guide to a signifi tin cant increase in the Renewal loss rate from 33% to 47%.Renewal is a low premium exalted volume business. endanger 5(in case) projects that a new form _or_ body of government and polity rehabilitation give similar taxs of $6724 and $6205 honourively. The commission apt(p) to agents is 25% and 7% adoreively. So in fact renewal of old insurance brings more revenue due to subdued commission percentage. Also, the clock taken for renewal of policies is little, indeed more renewals can be through in a presumptuousness prison term. d) FCFS Scheduling may not be always optimum just about of the departments follow FCFS programming which may not be optimum.Ex. a insurance polity with an effort of 4 hrs would delay alone the other smaller policies fanny it, effectively delaying 10 simple indicates for serving one difficult one. Moreover, the programming policy followed across heterogeneous departments is not same. While some rigorously follow FCFS others do it on type of policy ex RUN preferred over RERUNs. Also, The RAP is given preference over RERUNs by the rating squad. The RAPs in fact take more era for touch on than RERUNs. Also, only 15% of all quotes result in new policies.Thus, the Manzana Fruitvale branch seems to be servicing RAPs at the cost of RERUNs. e) Huge backlog of policies The brisk backlog of policies is quite lavishly up, due to which any new policy accepted is not attended instantly (due to FCFS) get ahead adding on to existing backlog, fundamentally creating a cascading effect. 2) dish out Flow and faculty depth psychology We have utilise demonstrate flow, capacity analysis, Lead clock judgment of conviction outline and Average quotidian Workload Analysis to analyze the flow rate situation at Manzanas Fruitvale Branch. ) forge Flow indicate 1 testifys the make flow draw with the existing capacity across the four major team dispersion clerks, Underwriter teams, Raters, constitution writers. b) capacitor Analysis Usage of lowly quantify for capacity analysis is ideally favored over 95% bill closing period (SCT) parameter as 95% SCT is a passing conservative. It assumes every betoken to be of unyieldinger duration (95th percentile with respect to request duration) and does not take into grudge the succession saved dapple executing smaller requests. correspond on the other hand takes into account that fact that age consumed on biger jobs is compensated by the snip saved on smaller jobs. Capacity analysis base on Mean eon ( gift 2) shows the bottleneck for RUNs is the distribution whole tone, for RAPs is underwriting, for RAINs is again dispersal and for RERUNs is policy writing. The same capacity analysis when make using 95% SCT ( process 3) shows Underwriting spirit to be bottleneck for all the 4 types of policy requests. We observe that in that location is high standard digression for most of the gaits (especiallyUnderwriting Step) and is comparable to incriminate value itself. Ex For RERUNs Underwriting yard imagine is 18. 7min period the standard disagreement is 19. 8min. Hence we are using 95% SCT for determining bottleneck step. And so we consider Underwriting Stage as the bottleneck for the whole strategy. c) Lead (Service) magazine Analysis Using Littles Formula we have through with(p) Lead cartridge clip Analysis (Exhibit 4) which shows that on an bonny Lead Time is approximately 2 days (2. 10). As we have seen, throughput on the other hand is approximately 6 days which is untold higher than the bonny Lead Time.This suggests that t he longer throughput sequence is because of parcelling jobs described later. d) Average workaday Workload Analysis The argument of allocation problem is come on beef up by our Average free-and-easy Workload Analysis (Exhibit 5). This analysis has been make taking into account the relative proportions of mingled types of requests on a routine basis. On the basis on mean(a) touch on magazine for all the steps it comes out that the fairish day-after-day workload for each step is slight than 7. 5 hrs (the stipulated working time). InferencesAbove analysis shows that problem at Fruitvale branch is not due to capacity constraint but it is real due to allocation issue. Predominantly, allocation problems are in underwriting step. Currently allocation is done on the basis of Geographical/territorial lines which lead to patched workload over time as well as unable allocation leading to longer TPUT time. Exhibit 5 shows that RUNs on an average take 50% (Exhibit 6) more proces sing time than RERUNs hence territory1 which is intemperately loaded with RUNs has a higher RERUN loss even though overall it is least loaded.This necessitates a better allocation scheme kinda than FIFO. Shortest Job First Scheduling may be utilise to decrease TPUT but it may delay a high antecedency request (ex a RERUN close to expiry) and also requires a priori estimation of various time factors. Currently, the system follows FCFS though not stringently, since some departments prioritize establish on type of request ex Underwriting favors RUNs over RERUNs. We would suggest an amend antecedence scheduling over FIFO which would be determined found on the type of request, agents importance, client importance, expiry time etc. et across uniformly across the steps. 3) Recommendations a) Distribution of reports to teams establish on priority kinda than territorial As found in old(prenominal) section the accredited workforce should suffice the existing requirements if there a re no backlogs and even if there are, it should not take more than 2 days. The problem was identified in the allocation of the policy request establish on territory. We recommend a collective (instead of territorial) request processing system with more intelligence added to the distribution system, which were also identified as bottlenecks for RUN and RAIN.This distribution system would first prioritize the requests and then allocate them to the underwriting team on an optimal basis ensuring even distribution of total requests, various(prenominal) requests RUN, RERUN etc. This would remove the existing anomaly of having an overloaded team and an idle team concurrently. They should also keep monitoring the develop of the process and remove blocking issues that may result in a further delay of other policies. To expedite this process, it can also be modify by the table service of computers. These changes should substantially knock down the TAT and the delayed RERUNs.The priority should be decided establish on the following factors instead of current system purely on the type of request 1) Type of request RERUN, RUN, RAP, RAIN 2) Already waited time request that have waited for a long time should be upgraded 3) Estimate of total time required 4) operators priority 5) Clients priority 6) Expiry Time (Incase of RERUNs) 7) Other factors determining the business value of the request Hence overall, this would reduce the intensity of bottlenecks at DC and UT. b) Increase focus on RERUNs RERUNs, which aim maximum revenue, should be given a higher priority ground on their proximity to the expiry date.This ensures customer retention and gives sufficient time to agents for renewal. c) Reduce Backlogs Reduction in current backlogs to minimum will friend Fruitvale achieve a days TAT as explained. This can be done by working overtime, getting temporary teams mayhap from other branches, and increase number of working days temporarily. d) Use Mean instead of SCT We o bserve that SCT is not a correct approach to prize the process flow since the 95th percentile customers time considered as a benchmark to set up the system results in very conservative estimates.Initially, other statistical methods like mean + n times standard deviation etc. can be apply to achieve desired level of service. Following that, a record including TAT for each request should be maintained so that it can be employ for future reference and for reducing the noise in mean that we recommend to be used in future. Exhibits Exhibit 1 Process Flow Diagram Exhibit 2 Capacity Analysis ground on Mean Processing time Capacity RUNs RAPs RAINs RERUNs Distribution 26. 8 36. 00 41. 38 64. 29 Underwriting 30. 96 35. 53 59. 73 72. 19 paygrade 47. 68 55. 64 54. 96 47. 68 insurance policy Writing 31. 69 NA 41. 67 44. 91 *darkened cells show the bottleneck Exhibit 3 Capacity Analysis base of 95% prototype closing Time Capacity RUNs RAPs RAINs RERUNs Distribution 14. 05 16. 70 26 . 43 41. 67 Underwriting 12. 59 15. 43 27. 33 21. 50 valuation 32. 06 40. 59 40. 27 39. 05 indemnity Writing 25. 20 NA 31. 65 33. 58 *darkened cells show the bottleneckExhibit 4 Lead (Service) Time Analysis using Littles Formula Requests in cash advance 82 Requests served per day (in steady state) 39 Lead /Service Time (Requests in progress/Requests served per day) 2. 1 Exhibit 5 Average Daily Workload Analysis Demand Analysis Requests in 120 days (1991) Demand per mean solar day RUNs 624 5. RAPs 1524 12. 7 RAINs 451 3. 758333 RERUNs 2081 17. 34167 enumerate 4680 39 Mean Processing Time RUNS RAPS RAINS RERUNS Workers/Teams (5. 0) (12. 70) (3. 75) (17. 34) issue forth Time hrs per day Distribution 356. 20 635. 00 163. 13 485. 52 1,639. 85 6. 83 Underwriting 226. 72 482. 60 84. 75 324. 26 1,118. 33 6. 21 evaluation 392. 60 821. 69 245. 63 1,309. 17 2,769. 09 5. 77 indemnity Writing 369. 20 N. A. 202. 50 868. 73 1,440. 43 4. 0 Exhibit 6 Uneven distribution due to territorial allocation Policies tot % diff with avg RUNs % diff with avg RERUNs % diff with avg Renewal lost /Territory 1315 208 693 1 1151 14. 24% less 274 31. 73% more 636 8. 2% less 403 2 1393 5. 93% more 179 13. 94% less 840 21. % more 227 3 1402 6. 66% more 171 17. 7% less 605 14. 54% less 296 Processing Time (using mean values) Distribution Underwriting pass judgment Writing add up Baselined (w. r. t. minimum) Baselined (w. r. t. RERUN) RUN 68. 5 43. 6 75. 5 71 258. 6 169. 35 150. 09 RAP 50 38 64. 7 NA 152. 7 100. 00 88. 62 RAIN 43. 5 22. 6 65. 5 54 185. 6 121. 55 107. 72 RERUN 28 18. 7 75. 5 50. 1 172. 3 112. 84 100. 00 Distribution clerks make out 4 Capacity Runs 26. 27 (58. 63), Raps 36 (72. 28), Rains 41. 37(195. 65), Reruns 64. 28(290. 32) Underwriter teams scrap 3 Capacity Runs 30. 96(12. 59), Raps 35. 52(15. 42), Rains 59. 73(27. 32), Reruns 72. 19(21. 49) impertinent Requests, Renewal requests Request for under writing Policy writers Number 4 Capacity Runs 31. 69(25. 19), Raps NA, Rains 41. 67(31. 64), Reruns 44. 91(33. 58) Raters Number 4 Capacity Runs 47. 68(32. 05), Raps 55. 64(40. 58), Rains 54. 96(40. 26), Reruns 47. 68(39. 04) Policy issuing request Rating requestManzana InsuranceOperations Management ____________________________________________________________ _________________________ Case Study Manzana Insurance pic Presented to prof B. Mahadevan Submitted By Group 12 (Section B) Peeyush Razdan (0811115) Shalekh Banka (0811124) Shalem Anand Tirkey (0811125) Shreshth Sharma (0811128) Sumeet Mittal (0811131) Manzana Insurance As per the case facts, Manzana Insurances Fruitvale branch is the least performing branch and the senior VP seeks a report on the same.Their competitor Golden Gate (backed by its incorporated parent fall ind a price war to gain market) is performing much better in terms of most metrics of Insurance business. 1) Problems face by Manzana Insurance (Fruitvale b ranch) a) High Turn Around Time (TAT) Agents in the insurance sector are mediators who act as an interface among the client and the insurer. Hence, the quality of service, measured by average TAT, to the agents (who are common to the competitors) is of primary importance.TAT for Fruitvale has deteriorated to 6 days (1991) from 5 days (1990), while Golden Gates offer of 1 day TAT is luring agents away from Manzana. The number of late renewals is also increasing and quite high compared to Golden Gate. b) Geographic/Territorial Allocation to Underwriting Teams Leads to an Uneven Task Distribution We observe that the geographic allocation of agents to the underwriting teams is not optimal. This method has an inherent problem there can be a surge in requests from a geography which might overload that particular team while the other teams might be idle due to lack of requests from their geography.Hence, their pooling should be rather than using the geographic allocation. c) Primary focus on RUNs rather than RERUNs and change magnitude late Renewals Currently, Fruitvale is focusing on RUNs for getting new customers and compromising the service towards the RERUN requests for the existing customers. Loss of focus on RERUNs has led to increase in its TAT causing agents to move towards Golden Gate. The number of late Renewals has increased from 20% to 44% over the last year which has led to a significant increase in the Renewal loss rate from 33% to 47%.Renewal is a low premium high volume business. Exhibit 5(in case) shows that a new policy and policy renewal give similar revenues of $6724 and $6205 respectively. The commission given to agents is 25% and 7% respectively. So in fact renewal of old insurance brings more revenue due to reduced commission percentage. Also, the time taken for renewal of policies is less, therefore more renewals can be done in a given time. d) FCFS Scheduling may not be always optimum some of the departments follow FCFS scheduling which may not be optimum.Ex. a policy with an effort of 4 hrs would delay all the other smaller policies bathroom it, effectively delaying 10 simple requests for serving one difficult one. Moreover, the scheduling policy followed across various departments is not same. While some strictly follow FCFS others do it on type of policy ex RUN preferred over RERUNs. Also, The RAP is given preference over RERUNs by the rating team. The RAPs in fact take more time for processing than RERUNs. Also, only 15% of all quotes result in new policies.Thus, the Manzana Fruitvale branch seems to be servicing RAPs at the cost of RERUNs. e) Huge backlog of policies The existing backlog of policies is quite high, due to which any new policy received is not attended instantly (due to FCFS) further adding on to existing backlog, essentially creating a cascading effect. 2) Process Flow and Capacity Analysis We have used process flow, capacity analysis, Lead Time Analysis and Average insouciant Workload Analy sis to analyze the current situation at Manzanas Fruitvale Branch. ) Process Flow Exhibit 1 shows the process flow plat with the existing capacity across the four major team Distribution clerks, Underwriter teams, Raters, Policy writers. b) Capacity Analysis Usage of Mean time for capacity analysis is ideally favored over 95% Standard Completion Time (SCT) parameter as 95% SCT is a highly conservative. It assumes every request to be of longer duration (95th percentile with respect to request duration) and does not take into account the time saved while executing smaller requests.Mean on the other hand takes into account that fact that time consumed on longer jobs is compensated by the time saved on smaller jobs. Capacity analysis base on Mean Time (Exhibit 2) shows the bottleneck for RUNs is the Distribution step, for RAPs is underwriting, for RAINs is again Distribution and for RERUNs is policy writing. The same capacity analysis when done using 95% SCT (Exhibit 3) shows Underwrit ing step to be bottleneck for all the 4 types of policy requests. We observe that there is high standard deviation for most of the steps (especiallyUnderwriting Step) and is comparable to mean value itself. Ex For RERUNs Underwriting step mean is 18. 7min while the standard deviation is 19. 8min. Hence we are using 95% SCT for determining bottleneck step. And gum olibanum we consider Underwriting Stage as the bottleneck for the whole system. c) Lead (Service) Time Analysis Using Littles Formula we have done Lead Time Analysis (Exhibit 4) which shows that on an average Lead Time is approximately 2 days (2. 10). As we have seen, throughput on the other hand is approximately 6 days which is much higher than the average Lead Time.This suggests that the longer throughput time is because of allocation problems described later. d) Average Daily Workload Analysis The argument of allocation problem is further beef up by our Average Daily Workload Analysis (Exhibit 5). This analysis has bee n done taking into account the relative proportions of various types of requests on a daily basis. On the basis on mean processing time for all the steps it comes out that the average daily workload for each step is less than 7. 5 hrs (the stipulated working time). InferencesAbove analysis shows that problem at Fruitvale branch is not due to capacity constraint but it is genuinely due to allocation issue. Predominantly, allocation problems are in underwriting step. Currently allocation is done on the basis of Geographical/territorial lines which lead to untrue workload over time as well as ineffectual allocation leading to longer TPUT time. Exhibit 5 shows that RUNs on an average take 50% (Exhibit 6) more processing time than RERUNs hence territory1 which is severely loaded with RUNs has a higher RERUN loss even though overall it is least loaded.This necessitates a better allocation scheme rather than FIFO. Shortest Job First Scheduling may be used to denigrate TPUT but it may delay a high priority request (ex a RERUN close to expiry) and also requires a priori estimation of various time factors. Currently, the system follows FCFS though not strictly, since some departments prioritize based on type of request ex Underwriting favors RUNs over RERUNs. We would suggest an amend priority scheduling over FIFO which would be determined based on the type of request, agents importance, client importance, expiry time etc. et across uniformly across the steps. 3) Recommendations a) Distribution of reports to teams based on priority rather than territorial As found in previous section the current workforce should suffice the existing requirements if there are no backlogs and even if there are, it should not take more than 2 days. The problem was identified in the allocation of the policy request based on territory. We recommend a collective (instead of territorial) request processing system with more intelligence added to the distribution system, which were also id entified as bottlenecks for RUN and RAIN.This distribution system would first prioritize the requests and then allocate them to the underwriting team on an optimal basis ensuring even distribution of total requests, single requests RUN, RERUN etc. This would remove the existing anomaly of having an overloaded team and an idle team concurrently. They should also keep monitoring the progress of the process and remove blocking issues that may result in a further delay of other policies. To expedite this process, it can also be modify by the help of computers. These changes should substantially reduce the TAT and the delayed RERUNs.The priority should be decided based on the following factors instead of current system purely on the type of request 1) Type of request RERUN, RUN, RAP, RAIN 2) Already waited time request that have waited for a long time should be upgraded 3) Estimate of total time required 4) Agents priority 5) Clients priority 6) Expiry Time (Incase of RERUNs) 7) Othe r factors determining the business value of the request Hence overall, this would reduce the intensity of bottlenecks at DC and UT. b) Increase focus on RERUNs RERUNs, which generate maximum revenue, should be given a higher priority based on their proximity to the expiry date.This ensures customer retention and gives sufficient time to agents for renewal. c) Reduce Backlogs Reduction in current backlogs to minimum will help Fruitvale achieve a days TAT as explained. This can be done by working overtime, getting temporary teams possibly from other branches, and increase number of working days temporarily. d) Use Mean instead of SCT We observe that SCT is not a correct approach to measure the process flow since the 95th percentile customers time considered as a benchmark to set up the system results in very conservative estimates.Initially, other statistical methods like mean + n times standard deviation etc. can be used to achieve desired level of service. Following that, a record including TAT for each request should be maintained so that it can be used for future reference and for reducing the noise in mean that we recommend to be used in future. Exhibits Exhibit 1 Process Flow Diagram Exhibit 2 Capacity Analysis based on Mean Processing time Capacity RUNs RAPs RAINs RERUNs Distribution 26. 8 36. 00 41. 38 64. 29 Underwriting 30. 96 35. 53 59. 73 72. 19 Rating 47. 68 55. 64 54. 96 47. 68 Policy Writing 31. 69 NA 41. 67 44. 91 *darkened cells show the bottleneck Exhibit 3 Capacity Analysis based of 95% Standard Completion Time Capacity RUNs RAPs RAINs RERUNs Distribution 14. 05 16. 70 26. 43 41. 67 Underwriting 12. 59 15. 43 27. 33 21. 50 Rating 32. 06 40. 59 40. 27 39. 05 Policy Writing 25. 20 NA 31. 65 33. 58 *darkened cells show the bottleneckExhibit 4 Lead (Service) Time Analysis using Littles Formula Requests in progress 82 Requests served per day (in steady state) 39 Lead /Service Time (Requests in progress/Requests served per day) 2. 1 Exhibit 5 Average Daily Workload Analysis Demand Analysis Requests in 120 days (1991) Demand per sidereal day RUNs 624 5. RAPs 1524 12. 7 RAINs 451 3. 758333 RERUNs 2081 17. 34167 Total 4680 39 Mean Processing Time RUNS RAPS RAINS RERUNS Workers/Teams (5. 0) (12. 70) (3. 75) (17. 34) Total Time hrs per day Distribution 356. 20 635. 00 163. 13 485. 52 1,639. 85 6. 83 Underwriting 226. 72 482. 60 84. 75 324. 26 1,118. 33 6. 21 Rating 392. 60 821. 69 245. 63 1,309. 17 2,769. 09 5. 77 Policy Writing 369. 20 N. A. 202. 50 868. 73 1,440. 43 4. 0 Exhibit 6 Uneven distribution due to territorial allocation Policies Total % diff with avg RUNs % diff with avg RERUNs % diff with avg Renewal lost /Territory 1315 208 693 1 1151 14. 24% less 274 31. 73% more 636 8. 2% less 403 2 1393 5. 93% more 179 13. 94% less 840 21. % more 227 3 1402 6. 66% more 171 17. 7% less 605 14. 54% less 296 Processing Time (using mean values) Distribution Underwriti ng Rating Writing Total Baselined (w. r. t. minimum) Baselined (w. r. t. RERUN) RUN 68. 5 43. 6 75. 5 71 258. 6 169. 35 150. 09 RAP 50 38 64. 7 NA 152. 7 100. 00 88. 62 RAIN 43. 5 22. 6 65. 5 54 185. 6 121. 55 107. 72 RERUN 28 18. 7 75. 5 50. 1 172. 3 112. 84 100. 00 Distribution clerksNumber 4 Capacity Runs 26. 27 (58. 63), Raps 36 (72. 28), Rains 41. 37(195. 65), Reruns 64. 28(290. 32) Underwriter teams Number 3 Capacity Runs 30. 96(12. 59), Raps 35. 52(15. 42), Rains 59. 73(27. 32), Reruns 72. 19(21. 49) impertinently Requests, Renewal requests Request for underwriting Policy writers Number 4 Capacity Runs 31. 69(25. 19), Raps NA, Rains 41. 67(31. 64), Reruns 44. 91(33. 58) Raters Number 4 Capacity Runs 47. 68(32. 05), Raps 55. 64(40. 58), Rains 54. 96(40. 26), Reruns 47. 68(39. 04) Policy issuing request Rating request