Mining Your Warranty Data Finding Anomalies (Part 1)

Transcription

1 Mining Your Warranty Data Finding Anomalies (Part 1) Rob Evans Support Warranty Analyst, IBM 3 December 2010 The problem One of my jobs each month is to review all of the warranty data (claims, part usage, and costs) for IBM System x worldwide. The data consist of hundreds of thousands of transactions from labor and parts recording systems in each country, which record how many hours were expended and how many parts used. All the labor and part transactions are transmitted to IBM Financial Warehouse for consolidation. Figure 1-1 Transactions are stored in IBM s financial warehouse (IW) My task is to review the monthly charges looking for trends and anomalies. While I am not concerned with fraud, I am interested in identifying accounting and system mistakes. For example, a new accountant might enter the wrong sign and accidentally convert a credit to a cost or a costing system could malfunction and provide the wrong unit cost. In addition, I am interested in trends like increasing claims, which I report to the quality team. With hundreds of thousands of financial transactions per month, I needed a faster way to detect anomalies and trends. I was using Hyperion and Excel to extract and manipulate data in different ways (e.g. sort high to low) looking for insights. The result was a manually intensive, customized process that was difficult to replicate each month. In addition, new anomalies develop all the time, which are missed using the repetitive elementary analysis each month. IBM SPSS Modeler makes the job easier by providing: A visual analysis environment The ability to connect to numerous data sources Powerful built-in functions, called nodes in SPSS Modeler, that are ready to use with minimal training and customization required by the user Copyright IBM Corporation All 1 rights reserved.

2 2 The solution I use IBM SPSS Modeler 14 to process my data each month to find anomalies in a fraction of the time it used to take. Below is a diagram of the stream for my analysis. A stream, in SPSS Modeler, is a series of nodes linked together to define a given process. My goal in this article is to show the ease and simplicity of setting up some initial steps via nodes. I first describe how to set up the source node, which defines what data I want to use in the analysis, and how to create a simple matrix table for visualizing my data. In future articles, I will go into more detail on the rest of the analysis process and will explain the modeling nodes I use. While I analyze part, labor, and financial transactions worldwide, in this article I am only going to concentrate on part usage in the Asia Pacific (AP)region. Figure 1-2 Stream for the complete warranty analysis program on the IBM SPSS Modeler canvas Accessing the data source IBM SPSS Modeler supplies a number of nodes to access a variety of data sources. Selecting the Sources tab at the bottom of the canvas displays a number of different input sources that can be used. Figure 1-3 IBM SPSS Modeler source node options on the Sources tab Figure 1-4 below shows a variety of ways to extract data. While I use many methods to extract data, traditionally I used Hyperion to a remote mainframe DB 2 database to graphically create queries and produce reports and graphs. So, which source node is best to use?

3 3 You can: Move data from the remote DB to your local DB system, which is great for speed and working offline. Use SPSS Modeler s database source node to the remote DB system, which is simple and direct. Query the data using Hyperion, export as an Excel file, and use SPSS Modeler s Excel node, which is convenient for those without a local DB installed. Export a CSV file from the remote DB or Hyperion and use SPSS Modeler s Var. File node which can be used when the number of transactions exceed Excel s 65k limit. In this article I used the second choice (databasesourcenodetoaremote DB), whichisthe easiest and most direct method. I have used all of the above methods, each having advantages and disadvantages which I hope to illustrate in future articles. Figure 1-4 Schematic diagram of the different methods to access source data

4 4 Setting up the source node Drag the Database node from the Sources tab to the canvas as shown in Figure 1-5. Right-click on the Database node, open the menu, and select Edit to access the configuration options. Figure 1-5 Right-click menu of the Database node dragged from the Sources tab The database configuration options are rather simple for this example. On the Data tab, select either Table to select the table you want to query or select SQL Query to enter a query directly. My SQL query is rather complicated, so I chose SQL Query andcut andpastedmyquery into the input box.

5 5 Figure 1-6 Data tab of the Database node with SQL Query selected and showing the pasted SQL In the Hyperion program, as is common in many query programs, there is an option to see the SQL query that is generated. For Hyperion, that option is under View > Query Log, which shows the generated query and allows you to cut and paste the query into the IBM SPSS Modeler Database node as shown in Figure 1-6 above. At this point, I have selected 67 fields. As I build the model, I will eliminate many of the fields which have little value for analysis. Now that the SQL query has been pasted into the Database node, select the Types tab (on the Database node) and click Read Values to load the table fields. This populates the node with the field names, assumed measurement level, and value ranges, as shown in Figure 1-7.

6 6 Figure 1-7 Types tab of the Database node after reading the database SPSS Modeler may automatically mark the measurement level of some fields as Typeless,which indicates that they are assumed to have no analysis value. At this point, you may want to eliminate some additional fields which have little or no informational value. For example, CNTRY_CODE is the country number IBM assigns to a country, which is redundant with the country name (DESC), so it can be omitted. In addition, I selected the PART_QTY field, which is the number of parts used in a repair, and specified Target for its Role. Marking a field as a target propagates the field through the model with each subsequent node picking up the selection. At this point, I suggest right-clicking on the Database node and enabling the cache feature by selecting Cache > Enable, as shown in Figure 1-8.

7 7 Figure 1-8 Right-click menu of Database node with the enable cache item highlighted The cache feature stores the query results locally for faster access and reloads the node later when working offline. In my case, my remote database is not always available, so by saving the results locally I can work when the database is not available. An additional advantage is that the model runs very fast with local data, which facilitates rapid prototyping. If I was working with a bigger data set and needed more processing power, I could install IBM SPSS Modeler Server on the DB server itself. That would minimize the movement of data between the server and the client and I would be using the server s processing power for my analysis. For my current work, performance is adequate using just the client. Displaying the results Now that we have set up the Database node, set the fields we want to keep, identified the target field, and enabled local caching, we are ready to build a simple model (shown in Figure 1-9) by adding a Type node and a Matrix node (DESC x NAME_FAMILY in the figure).

8 8 Figure 1-9 Stream for simple model showing a Database source node, Type node and Matrix node The Type node shown in Figure 1-10 below is a powerful node that specifies field metadata and properties. It allows you to specify the measurement level (continuous, nominal, ordinal or flag), how to handle missing values, and the role of each field Input for a predictor field, or Target for a predicted field. Figure 1-10 Types tab of the Type node Figure 1-11 below shows the options for the Matrix node, accessed by right-clicking the node and selecting Edit. Under the Settings tab, select the rows and columns for the matrix. I chose to compare the country where the claim occurred (DESC) in the rows against the type of part replaced (NAME_FAMILY)inthe columns. Iaminterestedinhow thenumberofharddrivesused in China compared to other countries and how usage in each country compares to all the Asian countries to spot local quality problems, or where more local training is needed. After we take action, I run the model again to measure our effectiveness.

9 9 Figure 1-11 Settings tab of the Matrix node By selecting the Appearance tab on the matrix node, I can choose how I want the data to be displayed. I typically look initially at the top five anomalies, counts, and expected values to start my analysis. Select Highlight top and click the up arrow until the value 5 is selected; then select the Counts and Expected values check boxes. Figure 1-12 Appearance tab of the Matrix node

10 10 Running the model generates the Matrix node output displayed in Figure 1-13 below. The top anomalies are highlighted in red to make them easier to spot. The Matrix output shows we expected 758 system planars to be used in Japan but 555 were used. Was that due to the recent service training? In China, 1062 planars were used while we would expect 905. Is more service training needed or is a quality problem developing? Finally, 430 Memory Dimms were used in China while 381 were expected. Are counterfeiters becoming more active? Using this information on what occurred and what would be expected, I can now further investigate these commodities in these countries for a budding quality problem or the need for additional service training. And, after the plan has been implemented, I can measure the results by running the model again on recent data. Figure 1-13 Matrix node output showing items to investigate About the author Rob Evans, a Bachelor of Mechanical Engineering (Georgia Institute of Technology) and a Master of Engineering (Dartmouth College), is a Warranty Analyst for IBM where he has worked for 28 years. Rob has experience in IBM s development, sales, support, and now warranty areas. He has been awarded three patents and published numerous papers on topics ranging from material science to artificial intelligence. You can reach Rob at vrevans@us.ibm.com. Other Papers on Data Mining Warranty Data: Mining Your Warranty Data Using IBM DB2 Intelligent Miner for Data - Association Method (

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