Deutsche Allgemeinversicherung (DAV) is involved in the insurance business. Majority of the business is present in Germany. The company had attained a successful position within the industry and was now looking forward to leverage on two of its success factors related to sound traditional insurance management; and outstanding customer service. This case is primarily concerned with the company obtaining a new feature of Statistical Process Control to improve its operations. This paper will highlight some of the advantages that are enjoyed by companies when they utilize Statistical Process Control within their operations. It will also shed a light on some of the problems that are being faced by the company.
Statistical Process control is a method that manufacturing organizations use to reduce the variance in their operations. The process helps in preparing charts the can identify what are the areas needed for improvement. These charts can also explain whether the operations are affected by any specific cause and if the cause is harmful to the operations of a company then it must be removed (Fazeli & Sharifi, 2011).
The company was facing issues related to the handling of new customer policies as in several instances incorrect data was input by the employees at DAV while filling the form. This led to most of the customer complaints. The Statistical Process Control tools were primarily initiated to tackle this problem. However, there are other advantages to using this technique within the company.
The most important advantage of using SPC in the company was the one through which the management could find out errors in the New Policy Set-up department and use the information to take corrective action. In addition, the use of this procedure would help employees maintain quality of service and also measure their success themselves. This can then help in increasing employee productivity especially if he or she finds out that others are obtaining better or more accurate results than that employee might become motivated to perform better next time after comparing notes with each other. In addition, customers who were previously facing service issues and had to file complaints due to errors in data recording could now enjoy better service from DAV. This will then benefit the company as customers will stay loyal to them if they continue to receive the level of service needed by an insurance firm.
Although there are substantial benefits that the company will enjoy due to the SPC procedure there are certain inherent issues in the tool itself. This is because the tool was primarily developed for use by manufacturing firms where tangible data of product features (length, weight, color, etc,) is available. However, DAV is a service firm, so they are likely to face certain limitations with the tools of SPC. In the traditional concept of Statistical Process Control, the assumption is that a manufacturing process is considered good or acceptable if the values fall within a set target. If, however, the values and the mean move away from the target value then chances of the process becoming corrupted increase due to presence of variance (Škulj, Butalaa, Sluga, & Vrabic, 2013).
The main challenge that will be faced by the company in the application of this process is related to the intangible nature of service industry. In general cases, quality management for a manufacturing firm can easily be broken down into things like ideal size of the product, the weight required for a component in an assembly chain, and other physical attributes that are easy to identify and measure (Lim, Antony, & Albliwi, 2014). These things are not present in the service industry, and it becomes difficult for them to then identify measurable areas. The way to address this challenge is to base the entire procedure on outcomes of a process or a service. For instance, there has been evidence in literature related to the concept of using Statistical Process Control to judge outcomes of cardiac surgery, intensive care mortality and others (Smith, Garlick, Gardner, Brighouse, Foster, & Rivers, 2013). This same approach might be applied to DAV where individual departments and processes can judge themselves on the outcomes achieved.
There are six main problems that have been highlighted in the case related to measurement challenges. The most important problem is the one that has risen due to the confusion regarding what to choose as a mistake and what to ignore. Another problem that is also of significant severity is the possibility of departments choosing to measure only those items that showcase the improvement in performance. The two problems that seem to be least severe are the ones related to measurement of legal department’s performance and the charting issue. Both are not that severe in nature. However, the problem of better teams needing to do more sampling is an issue that is problematic, but only to the extent of additional time that it requires for sampling. Otherwise, it is not a very severe problem.
The charting problem is something that has just risen because the problem resolution group does not want to put manually in the effort that can provide them the data for generating charts. Until the IT department does not create a system for putting the process-control charts on-line automatically, the department can simply check their own records and chart the timing between problem log-in and problem resolution themselves. Email correspondence data can help them in generating these timings easily.
To measure the effectiveness of the legal group, outcome based approach is the root to solution. This problem has occurred because the legal aid is a service, and it does not have tangible good or bad criteria defined. So the department can just record the number of times a customer’s legal issue was resolved, and customer was satisfied with the solution.
The issue of associates choosing only performance measures that put them in a positive light has risen because the research data was not kept confidential. At this phase it should be encouraged that department managers don’t share the data with each other because employees might get discouraged at the pilot study point and not pursue the process completely.
To handle the issue of sampling when departments are producing good results the company must setup a benchmark scale that depicts particularly sample sizes according to the accuracy range. This will remove the ambiguity. The problem of deciding what a mistake is seems to have risen due to poor training. Each department should first identify clearly what are the mistakes and what areas might be ignored. This will then help them in maintaining consistency within the department.
Once the SPC exercise has been completed the real process of improvement should begin. The management should be made aware of the results obtained and where the weaknesses lie. Considering that this was the first time such an experiment was conducted, managers should be told not to reprimand their subordinates in case of poor performance but to encourage them and help them use the data to correct their past mistakes. In addition, the input from staff should also be taken because they can provide valuable suggestions about how to produce improvements and utilize the data obtained from the SPC procedure in a more effective manner.
Fazeli, A. R., & Sharifi, E. (2011). Statistical Control and Investigation of Capability of Process and Machine in Wire Cut EDM Process of Gas Turbine Blade Airfoil Tip. Scientific Research , 3
Lim, S. A., Antony, J., & Albliwi, S. (2014). Statistical Process Control (SPC) in the food industry – A systematic review and future research agenda. Trends in Food Science & Technology , 37
Škulj, G., Butalaa, P., Sluga, A., & Vrabic, R. (2013). Statistical Process Control as a Service: An Industrial Case Study. Procedia CIRP , 7
Smith, I. R., Garlick, B., Gardner, M. A., Brighouse, R. D., Foster, K. A., & Rivers, J. T. (2013). Use of Graphical Statistical Process Control Tools to Monitor and Improve Outcomes in Cardiac Surgery. Heart, Lung and Circulation , 22