Deutsche_Allgemeinversicherung

DEUTSCHE ALLGEMEINVERSICHERUNG OPERATIONS MANAGEMENT MGT 653 1) Why is DAV using Statistical Process Control (SPC)' DAV is using SPC because they know that the industry’s quoted accuracy numbers for their firm was incorrect and to maintain their status as industry leaders they wanted to know their true accuracy so that they could make improvements and maintain quality throughout their operation. The Head of Operations also noted that SPC was being used in his opinion because it was important to use a tool that measured the process rather than the people and one that protected employees from the wrath of senior managers. Furthermore, it was noted that within the Insurance industry ‘exceeding customer expectations for the quality of service was an important way to maintain current customers and attract new ones’. Quality in customer service was becoming an important differentiation tool and a critical element in DAV’s strategy. To this end, DAV became aware that they needed to develop capabilities that were not only valued by customers but distributed throughout the company. Therefore, in an aim to achieve their strategy of delivering quality service and maintaining their status as industry leaders they sought the use of the SPC tool to measure and hopefully improve their processes and achieve more accuracy in their data. 2) What are the challenges in applying SPC to a service industry compared with manufacturing' SPC is designed to measure and analyze variations in a consistent process and is typically used in the manufacturing industry. In this case, it was applied to measure inconsistencies in customer service activities. Some of the challenges however, in applying SPC to the service industry, compared to the manufacturing industry, lies in the intangibility and inseparability characteristics of providing a service, and in the variability of how, where, when, and by whom these services are provided. These factors make it more difficult to determine what data is important and what measurement criteria should be set in defining where problems lie in providing a particular service. The Human element inherent in service industry also creates many challenges, as individuals can be motivated to manipulate the data for more favorable outcomes thus causing the measurements to be ineffective versus an unbiased approach to measurement in the manufacturing industry. Whereas, in the manufacturing industry quality measurements are more quantitative and therefore more easily measured. Another challenge lies in customizing SPC tools to the relevance as well as the environment of a particular service industry. Challenging factors may include the reorganization of a service firm, intense training along with communication organization wide of possible changes as a result of implementing SPC. When compared to a manufacturing firm whose industry typically uses SPC, and is more familiar with the processes involved in using such tools. 3) How large should each sample size be for the experiment Schoss and Kluck describe on page 7 of the case' The sample size should be 300 4) The first 12 weeks of the data in Exhibit 4 represent the diagnostic period for the Policy Extension Group. What are the 3-sigma control limits for the process' Average of sample proportion: p = Total number of defectives p = 188 = 0.0522 Total number of observations 3600 Standard deviation of sample proportion Sp = p(1-p) .0522(1-.0522) n 300 Control Limits UCL = p + z sp .0522 + 3(.0128) = 0.0906 LCL = p – z sp .0522 - 3(.0128) = 0.0136 Upper Control Limit 0.0906 Lower Control Limit 0.0136 In which of the subsequent weeks is the process out of control (if any)' |Week |Sample Size |Errors |p | |1 |300 |18 |  | |2 |300 |15 |  | |3 |300 |18 |  | |4 |300 |6 |  | |5 |300 |20 |  | |6 |300 |16 |  | |7 |300 |16 |  | |8 |300 |19 |  | |9 |300 |20 |  | |10 |300 |16 |  | |11 |300 |10 |  | |12 |300 |14 |  | | |3600 |188 | | |Week |Sample Size |Errors |p | |13 |300 |21 |0.07 | |14 |300 |13 |0.04 | |15 |300 |13 |0.04 | |16 |300 |13 |0.04 | |17 |300 |17 |0.06 | |18 |300 |17 |0.06 | |19 |300 |21 |0.07 | |20 |300 |18 |0.06 | |21 |300 |16 |0.05 | |22 |300 |14 |0.05 | |23 |300 |33 |0.11 | |24 |300 |46 |0.15 | |25 |300 |10 |0.03 | |26 |300 |12 |0.04 | |27 |300 |13 |0.04 | |28 |300 |18 |0.06 | |29 |300 |19 |0.06 | |30 |300 |14 |0.05 | The process is out of control during weeks 23 and 24. The control limit results show that there is some evidence that the process is out of control, as samples taken during week 23 and 24 are outside the range of UCL of 0.0907 and LCL of 0.0138. See table above. 5) Develop specific implementation plans for solving the problems facing Annette Kluck that are described on page 9 of the case. a. Better teams do more sampling DAV had determined that the benchmark of 99% accuracy was correct therefore 1% defects and wanted to find at least 3 (or 2-3) errors in each sample using a sample size of 300. In assuming a higher accuracy level the sample size would have to be high to achieve the 2-3 errors: the smaller you make the chance of making the error, the larger your sample size will have to be. However assuming a lower accuracy level (more realistic) with the same errors requires less samples and therefore less work. An inaccurate sampling selection was used therefore management needs to better define the sample set. They can begin by reducing the sampling size from 300 to 150 for everyone and increase the percentage defects from 1% to 2 %. A more accurate sample selection should reduce the workload, complaints from staff and offer a more structured sample process. Everyone should use the same sample size. b. When is a mistake not a mistake' When it’s not important Collection of data was not well-defined, just good vs bad. By more clearly identifying critical processes DAV would have better data collection and employees making decisions on what is right or wrong would be made easier. In clearly identifying process, management must ensure employees in the everyday process are involved because their inclusion from the initial stage would identify such queries early on and avoid interruptions once process begins. c. Measuring lawyers The objective of law cases is to win a case, however law cases can be lengthy and therefore the process difficult to measure. Lawyers should not be included in this process as the objective for the SPC was to check accuracy of information given on application and may not be an appropriate measure for the legal department. Therefore, this is clearly a wrong measurement for lawyers, hence their frustration. It emphasizes management’s lack of education and training on SPC and its potential benefits throughout the organization. Management needs to focus on just the New policy Setup at this point. That is, communicating the purpose, importance and benefits of this tool to the policy process being measured and its impact on the organization. d. Automatic charting Management should make automatic charting available to only those involved in the New Policy process at this time and other departments can manually chart their process in an aim to help develop their process to SPC. DAV should ensure success from the New Policy set up process before expanding to all areas. e. On the prowl. Managers were zeroing in and viewing the results as department/employee failures rather than process results. Therefore, to resolve this type of behavior management need to have ongoing training and education for the managers on SPC as a measurement tool so as to have avoided misinterpretation and negative reactions to the results.