Executive Summary Deutsche Allgemeinvericherung (DAV) was founded in 1966 and has become the second largest insurance company in Germany and one of the largest in the world. DAV operated about 51 percent of its business in Germany of which 60 percent comprised of retail insurance including, for example, health and property insurance. In addition, the offerings included life insurance and disability income protection. As the second largest firm in Germany, DAV was aware of its prominent position and begun to think carefully about how to maintain that position as smaller insurance companies armed them to attack its primary markets. Managers in other firms attributed DAV’s success to two key factors: sound, traditional insurance management and outstanding customer service. Customer service was becoming an important tool for differentiating one firm from another. Quality in customer service had progressively become a critical element in DAV’s strategy. Therefore, to improve on its quality DAV adopted a new quality improvement initiative called Process Measurement and Improvement (PMV) headed by Annette Kluck. This system was adopted from the manufacturing style of statistical process control (SPC) measurements and Annette’s goal was to use this statistical tool to improve current operations to be more efficient and help to differentiate DAV in the industry and hopefully maintain its prominent position.
Immediate Issues
SWOT Analysis
Strenghts:
• Company has a diversified business portfolio that included health, property and life insurance
• Current technology at DAV is top in the market
• Company is set up in specialized manner to insure accuracy and efficiency
• Excellent customer service
• Outstanding management team Weaknesses:
• Data entry process that was not at 100% accuracy rate.
• Setting up new customers and policies was manual process
• Multidivisions across the country make it difficult to measure improvements consistently

Opportunities:
• Adopt newer technologies to service customers better
• Expand current business and penetrate the market in other regions in Germany
• PMV project brings opportunities in operations Threats:
• Competition
• Increases customer demands
• Increasing labour costs
• Measuring services in a quantified manner

Insurance has become a commodity in this business. The insurance business has become very competitive and thus companies have to constantly come up with ways to improve business and retain clientele. Customer service has also become an important factor that all organizations must focus on as this service is the crucial factor that clients consider when deciding what firm they want to give their business to. Data entry efficiency is the major factor as this is where the errors occur and results in dissatisfied customers. The company has resorted to using temporary staff in efforts to save money in times of high volumes. The use of temporary staff means that they are not familiar with the expectations of the firm any may not pay attention to detail as the full time employees.
The use of same day processing may make employees anxious to complete the workload and if there are a large number of files to be completed in any given day they might be putting in long hours and thus be tired which could cause them to make further processing errors.

Environmental and Root Cause Analysis Customer service was the key factor that DAV recognized that would differentiate DAV from its competitors. In order to achieve to be the leader in customer service DAV recognized that it needed to reduce variability in its process. DAV launches a manufacturing –style improvement technique called Process Measurement and Improvement and uses SPC to analyze the data in the new policy set up pilot project. P-charts are used to measure performance of each person on five components each hour. Since there are different departments involved and Annette is not familiar with all their day to day processes it is up to each department to come up with what factors to measure and find out a better way to doing it. This project relies on employee commitment and input as its success depends on whether they take this serious or not. The project is divided into two phases. In the first phase all date is collected and gathered using the SPC chart. In the second phase the SPC charts would be studied by management to determine what processes need attention and improving. In order to ensure that the project is successful the accuracy of the data is very critical and the employees who are responsible for collecting it need to be aware of this. SPC is a tool that is used in the manufacturing industry and has proven to be an effective tool in measuring process and control. However, this process is difficult to apply in the service industry since service is harder to quantify and measure but if DAV can figure this out it could be an effective tool in improving productivity and customer service. However, DAV faced some challenges in the process development.
1. Determining sample sizes as there were some departments that had a high accuracy rate and so the data sampling size has to be larger in order to catch errors. This large sampling size was becoming cumbersome on employees.
2. Manual charting of samples was also proving to be very time consuming for employees that have a large sample to map.
3. The legal department had its own issues as they could not figure out what is “good” or “bad” work in the group.
4. Some employees were afraid that management may react negatively to some of the selected performance indicators so they would pick variables that would produce the most positive results.
The PMV project used P-Chart to track the errors during a selected period of time of 30 weeks, see exhibit 1 below. From the P-Chart analysis it shows stability in the process, however, there are some anomalies in week 4 and week 24.
In week 4 there shows that six mistakes made but the data point lowered down to LCL, so this is something that needed more attention from management into what might have gone wrong. One issue could be that data might have been made up so therefore not true.
In week 24 there were 46 mistakes made making the data point higher than UCL. This might suggest that there may have been work overload for employees or there might be people on vacation or sick leave.
Alternatives and/or Options
1. For each department set up criteria that can be measured and easily tracked without being cumbersome for employees.
Pros: will help to breakdown the employees’ workload into a manageable checklist and therefore make it easier to record results.
Cons: some employees may feel that if they produce the “incorrect” results they may lose their jobs and therefore may purposefully alter data.
2. Set up management teams that are well educated in the new policy set-up in order to ensure smooth process transition.
Pros: having expert management assures employees that the new policies are being done correctly.
Cons: employees may feel pressured to perform and be on board even if they don’t want to.
3. Have automatic charting.
4. The legal department may send out surveys to customers to measure their performances in order to get unbiased data.
Recommendations
As Annette I would use all of the above recommendations. However, it is important for management to ensure that all employees are on board with this project and that they feel comfortable and secure to collect data in order to make this project a success. The working hours for employees should be closely monitored so that they are being overworked especially with the same day processing rules for new accounts. DAV should also think about applying some sort of reward system for employees in order to reach the goals as this will increase employee motivation and morale. This should be available to both full time and part time employees so that there is consistency. Address employee concerns so that everyone has a clear understanding of what the expectations are.

Implementation
1. Set up a process flow chart that highlights critical components that are required in the new policy set-up department.
2. Clearly define process measures
3. Identify the sample size
4. Determine measurement period
5. Provide training sessions to ensure accuracy of recorded data and expectations
6. Create a monitoring system and use P-chart to control the process system. Here would be the ideal time to use automated charting.
7. Monitor the process daily and make adjustments and improvement as needed.
8. Once this is successfully implemented in the new policy set-up then apply these concepts to other departments.

Monitor and Control
Timeline Task
Daily Management meeting to address concerns
Daily Employee meetings to address concerns
Daily Monitor data collection
Daily Gather data and plot on main chart

Conclusion
The P-chart is used in this experiment so that there could be controls set up to catch the defective samples. From Exhibit 1 we can calculate the upper and lower control limits to be UCL=29.28, LCL=5.12, mean=17.2. From the sampling over the 30 week period we can see that weeks 4, 23 and 24 should be considered because in these weeks the errors were either in the LCL or exceeded the UCL. This means that in week 4 there was a sudden change and in weeks 23 and 24 the errors were out of control. Management needs to address these weeks to determine what the factors for these anomalies were. For example, was it overtired employees or something else. In the rest of the weeks the errors fall between the LCL and UCL, leading to the conclusion that the input process is working successfully.

Exhibit 1
Sample Size of 300
Week Errors LCL UCL
1 18 5.120066 29.27993
2 15 5.120066 29.27993
3 18 5.120066 29.27993
4 6 5.120066 29.27993 UCL 29.27993
5 20 5.120066 29.27993 LCL 5.120066
6 16 5.120066 29.27993 Mean 17.2
7 16 5.120066 29.27993
8 19 5.120066 29.27993
9 20 5.120066 29.27993
10 16 5.120066 29.27993
11 10 5.120066 29.27993
12 14 5.120066 29.27993
13 21 5.120066 29.27993
14 13 5.120066 29.27993
15 13 5.120066 29.27993
16 13 5.120066 29.27993
17 17 5.120066 29.27993
18 17 5.120066 29.27993
19 21 5.120066 29.27993
20 18 5.120066 29.27993
21 16 5.120066 29.27993
22 14 5.120066 29.27993
23 33 5.120066 29.27993
24 46 5.120066 29.27993
25 10 5.120066 29.27993
26 12 5.120066 29.27993
27 13 5.120066 29.27993
28 18 5.120066 29.27993
29 19 5.120066 29.27993
30 14 5.120066 29.27993
Total error: 516