1Academic Level / 17.07.2019
THREE VICTIMS: MALE, CAUCASIAN BETWEEN THE AGE OF 50 AND 62 FOUND DEAD IN A 50 MILE RADIUS.
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Author: Exams
1Academic Level / 17.07.2019
WHAT ARE THE PRIMARY CHALLENGES IN APPLYING SPC TO A SERVICE INDUSTRY COMPARED WITH MANUFACTURING?1. Why is DAV using Statistical Process Control (SPC)? What are the primary challenges in applying SPC to a service industry compared with manufacturing? Statistical Process Control involves using techniques to measure and analyze the variation in processes. It is most often used to monitor the consistency of manufacturing processes. The goal of SPC is to get and keep processes under control. In this case, SPC is a better method for DAV that it previous simple average method to improve its customer service operation, because it provides consistency in quality control and benchmarks for managers to verify the process.(Ritzman, Krajewski, Klassen, and Malhotra,2016). The challenges are i) Difficulties of deciding and selecting what to measure ii) too much uncertainty because of human behavior. 0. If you were to apply (in about thirty minutes) the concept of a p-chart to, say, a group of bank tellers who have little background in SPC, how would you it? To introduce the SPC to DAV, We will show the SPC’s definition its fitness and advantage into DAVcamparing to the previous method, together with a sample to illustrate how it works and controls. SPC involves using statistical techniques to measure and analyze the variation in processes. It is most often used to monitor the consistency of manufacturing processes. In this case, SPC is considerably better method for DAV than its previous simple average method to improve its customer service operation, because it provides consistency in quality control and benchmarks for managers to verify the process. Primary challenges There are basically two challenges that implementing the SPC to a service company: 1) Difficulties of deciding and selecting what to measure and 2) Too much uncertainty because of the human behavior SPC can facilitate the process measurement and improvement, but some challenges still exist in applying SPC to a service industry compared with manufacturing. One of the primary challenges is that it is difficult to determine what variation is used to measure. Different groups in DAV may have varied kinds of standards to appraise their performance, and each group may include several variations to judge its own performance as well. Another primary challenge is that the benchmark for good or bad judgment is vague to decide. There are no definite standards like those in manufacturing industries. Despite the challenges, SPC is still a very effective way for DAV to control and improve the operation process. Improvements: how SPC can fit into DAV As the SPC is initially developed for manufacturing process control, before introduced to DAV, some important issues and improvements should be marked out. From the feedback of the two-month’ experiment, improvements can be summarized as following: It seems to be contradictory that the better-performance teams have to do more sampling. However, as long as the managers and employees understand that the objective of the sampling is to draw a P-chart to detect errors, there should be less confused on this issue. The managers should be reminded that ultimate goal is not to compare the accuracy, but to find errors and mistakes to further improvements. 0. How large should each sample size be for the experiment described by Schoss and Kluck To ensure an adequate sample size, we recommend a minimum sample size of 40, with a maximum of 300 individuals per experiment (1% error limit). If plotting the data displays insufficient results, the sample size should be increased until trends are easy to observe. If outliers are apparent, researchers will need to assess to see if there are further errors in the experimentation process. 4) The first 12 weeks of the data on exhibit 4 represent the diagnostic period for the policy extension group. What are the 3-sigma control limits for the process? In which of the subsequent weeks is the process out-of-control (if any)? For the first twelve weeks, there was a total of 188 errors and the total sample size is 3600. Therefore p= Total Error/Total Sample Size=188/3600=0.0522. Variance: p(1-p)/sample size= 0.0522(1-0.0522)/300= .00016492 Standard Deviation:√.00016492= .0128= .013 Upper Control Limit: 0.0522+3(0.0128)= 0.0906 Lower Control Limit: 0.0522-3(0.0128)= 0.0138 Therefore the subsequent range for the control limits is (0.0138 to 0.0906). In the following weeks, processes tend to go out of control in the 23 and 24 weeks, where there is an abundance in errors. 5) As Annette Kluck, what are your specific implementation plans? How do you intend to begin improving the performance of the operation? Deutsche Allgemeinversicherung (DAV) is one of the largest and recognized insurance Companies in the world. Annette Kluck was the head of all Operations in DAV Company and she wanted to improve customer service quality and statistical data entry. The specific implementations that can be done include: 1) Setting up a policy plan for project measurements by ensuring that there are statistical experts before starting any operations. The experts would ensure that proper and accurate sampling takes place. 2) Identifying more clearly the critical processes in DAV’s operations. Implementation on what people should consider as wrong or right would help DAV have better data collection and employees understand the processes better. 3) We could also have the right professional in each department such as lawyers for handling legal issues and statistical analysts for data entry. Performance can be improved by setting continual improvement team including the professionals across all levels for development of new policy and making more reliable decisions. 4) Selecting proper sample size which could reduce the chance of making errors could be done by constructing the systematic criteria to select sample size. References: Ritzman, L. P., Krajewski, L. J., Klassen, R. D., & Malhotra, M. K. (2016). Foundations of operations management. Toronto: Pearson.
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1Academic Level / 17.07.2019
THE STEPPINGSTONE FOUNDATION
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