Optimizing Service Flow with quantitative analysis method

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Since the 90 's of the last century, with the expansion of service industry, the broadening of services and the innovation of service industry, modern service industry, represented by finance, logistics, Commerce, tourism, advertisement and exhibition, has become a new growth point of China's economy. This trend is particularly evident in coastal economic cities. Taking Shanghai As an example, historical data show that in 1990-2000, the average growth rate of service industry in Shanghai increased to 13.8%, the proportion of the total city's GDP increased from 31.9% to 50.6%, and the annual increase was nearly 2%. According to incomplete statistics, 2006 1 to November, only 184 of Shanghai's key producer Services Enterprises completed the operating income of 124.81 billion yuan, an increase of 37.7%, achieved a profit of 8.3 billion yuan, an increase of 149.9% year-on-year, profit growth significantly exceeded the same period of increase in operating income. The latest statistics are more gratifying to show that 2007 Shanghai's third industry accounted for the proportion of the city's GDP increased to 51.6%, higher than the second industry's 47.6%, reached the highest level in recent years.

At the same time, market competition is heating up. In the near-white-hot market competition, customer satisfaction and loyalty become the focus of service-oriented enterprises. Have a stable customer base is to win the benefit of enterprises in the competition in an invincible position. The quality of service provided by the enterprise and its management level play a decisive role in satisfying customer's demand and attracting more customers. How can we keep unbeaten in the fierce competition? Looking at the more mature services such as HSBC, HSBC and Marriott International, the successful experience of a multinational company renowned in different service industries, such as the United States and Japan, It is not difficult to find a common feature: Based on data analysis, the service process is strictly controlled quantitatively. In the process of data analysis, the application of statistical analysis software for professional quality management has become the common choice of most famous service enterprises. The world's best-known statistical software provider is the SAS company in the United States, its specialized in quality management, process optimization and product design and development of desktop statistical analysis software JMP is a very professional product, it has been outstanding visualization effect, strong analytical ability and excellent ease of use of the industry, The comparison of this paper is also based on the analysis of JMP software.

Some people say that the service industry's products are intangible, the objectives of the services process is dynamic, the service process is constantly evolving workflow, so the monitoring of the service process is lack of quantitative standards, it is difficult to be as adept as the manufacturing industry to apply statistical analysis methods. Is that true? from two types of typical representative to the service standard expression contrast (traditional service standard: 1. Timely delivery 2 treat the patient with enthusiasm 3. Products are easy to use and do not require special technology and training; modern service standards: 1. Deliver the goods to 2 within 36 hours of receiving the customer's order. In the patient in the doctor seat sitting in the 5 seconds head up facing the patient said hello 3. Any healthy adults with no more than 3 minutes to read the instructions can be in 10 minutes to install the product, the tools needed are only wrenches and screwdrivers, it is not difficult to feel a deep understanding of business processes under the premise of "timely delivery", " The abstract and general concepts of "service enthusiasm" and "easy to use" can be clearly displayed in the digital way, so that our quality management can be traced.

The quality of service can be evaluated and improved after further measurement of the actual data obtained from the process operation. Of course, for the continuous improvement of the industry, the specific data analysis can be done with the help of statistical analysis software jmp quickly and efficiently, do not need to invest too much energy and resources. The following is an example of a hotel's customer service management to illustrate the practical application of this concept.

Background: XX Hotel is a large-scale international hotel group, only in Shanghai, China has three five-star hotel. Through a third party consultancy study, China's management has realized that guest retention is closely related to his first impression of the hotel, and that it is one of the most important factors that affect the guest's first impression from the time the guest registers at the front desk and his baggage is delivered to the room. So what is the current performance of different business locations within the company? To this end, the company spent a week in the Shanghai area of three hotels conducted on-site sampling survey ...

When we collect the relevant data, we can use a suitable statistical graph to observe the overall situation. If the results are similar to those shown in figure I (orange represents sample data from Hotel1, blue represents sample data from Hotel2, Brown represents sample data from Hotel3), it is clear that hotel1, Hotel2 and hotel3 Three hotels in this respect, the service level is very close, the average service time is about 8.7 minutes, there are only some random fluctuations, no obvious advantages and disadvantages.

Conversely, if the results are similar to those shown in Figure II, it is clear that there are significant differences in the level of service in the three hotels. Specifically, the HOTEL3 service is the best, its average service time is only about 3.0 minutes; Hotel2 Service level second, its average service time is about 8.7 minutes, Hotel1 service level is the worst, its average service time unexpectedly is about 14.0 minutes.

However, in real life we are not so fortunate to get the above two obvious scenarios, and it is possible that the results are similar to those shown in figure three. The service level of three hotels does exist difference, but because the difference degree is between the first two situations, the data collected is only the sampling result, cannot distinguish whether the difference is mainly composed by the difference between the group of three hotels, or by the random error of sampling.

At this time, statistical analysis tools have a useful. Through Anova (ANOVA, the full name is analysis of variation), all people who have different experiences and may hold different views will get a common conclusion on an objective scientific analysis platform. The analysis process is based on the variance analysis table as shown in table two, according to the final calculation of the P value = 0.0006, less than the default threshold of 0.05, so that we have a greater grasp of the decision: three hotels in this area of service level there are obvious differences.

The people who like to ask questions do not satisfy the existing results, and they will often cross-examine a question: "The obvious difference" is that the service level of three hotels is obviously different, or the service level of a certain hotel is obviously distinct from another two similar hotels? In the second case, Which one is obviously different from each other?

Statistically speaking, this belongs to the category of "multiple comparisons (ListBox comparisons)" In advanced variance analysis, which involves mathematical deduction more complex than the general principle of variance analysis. I do not want to do this from the obscure theoretical point of view, but through a novel visual way-the comparison of the loop map, to everyone vividly introduce the practical application of multiple comparisons. As shown in Figure four, in variance analysis, the data distribution of each subgroup can be expressed in a circular representation, when the angle of intersection of two rings is greater than 90 degrees, there is no significant difference between two subgroups, and when the intersection of two rings equals 90 degrees, it indicates whether two subgroups have significant differences in marginal state, It is similar to the P value in the hypothesis test = 0.05, when the intersection of the two rings is less than 90 degrees, even when there is no intersecting part, there is a significant difference in two subgroups.

Based on this principle, we can further analyze the data in figure three and get the results as shown in Figure five. In the comparison of the ring diagram, the ring representing the Hotel1 and the other two annular detachment, and the bright red, representing the Hotel2 and hotel3 of the two rings have the equivalent of a part of the area staggered, gloomy gray, indicating that the Hotel2 and HOTEL3 service levels are not significantly different, But there are obvious differences between them and Hotel1 service level. As a result, we can extract more information from existing data. This has a more practical significance for China's management: Hotel1 's service level is significantly behind Hotel2 and hotel3, which needs attention and improvement.

The quality improvement of the service industry involves all aspects, the quantitative indicator is the basic component of the improvement work, and the control of the service time is an important part of it, because there is a proverb in the industry: late service is like rain to send an umbrella. The concept of customer-oriented and data analysis is of great significance for us to find the key factors affecting the quality of the service process. With the help of JMP such professional quality management statistical analysis software, we can open the fine management of the door, so that we can easily and confidently respond to the fierce competition in the service industry.