Quality Application: SEWSS Successfully Integrates with
Steelcase Shop Floor Coordinate Measuring Machine (CMM)

StatSoft's STATISTICA Enterprise-wide SPC System (SEWSS) was recently featured as a "Quality Application" in the September 2002 issue of Quality Digest. The Case Study describes the successful SEWSS implementation in the basic division at Steelcase Inc., a company that produces interior architectural products worldwide.

The case study describes how Steelcase was able to improve process control for automatic die presses and manual press brakes and quickly identify that measurement error was a key source of variation using the Interactive QC Charts module of SEWSS. After switching from caliper measurment to a coordinate measuring machine (CMM), a team at Steelcase worked with StatSoft technical support so that the data collected by the CMM would be directly output into the SEWSS database and the latest data is always available for quick analysis with any of the SEWSS modules. Now, machine operators using the shop-floor CMM for inspection receive immediate feedback on the quality of key part features, and engineers have real-time access to the CMM inspection data for solving problems, making continuous improvements and verifying ongoing capability.

According to Mike Linde, quality engineer at Steelcase, "The enterprise-wide nature of the software makes it easy to share data for collaborative problem solving. Current data is available for review almost anywhere. Real-time charts and reports can be reviewed on the shop floor, at an engineers's desk or at a team meeting." To read the entire application story, click here.

STATISTICA Data Miner's impressive array of data mining algorithms solved a variety of classification and prediction problems for Oklahoma State University graduate IT students.

STATISTICA Data Miner was the "premier choice" for OSU's Department of Management Science and Information Systems, according to Dr. Dursun Delen, Assistant Professor. Delen states, "Based on my years of experience in industry and in academia, I can confidently say that STATISTICA Data Miner has one of the most comprehensive data mining algorithms on the market." He also calls the graphical tools and their outputs "phenomenal."

Delen lists many other advantages over other data mining tools, including a graphical, interactive, user-friendly interface, rich set of visualization tools, processing speed, and its ability to be launched as a Web application. In spite of these many strengths, Delen also found STATISTICA Data Miner to be "less expensive than other comprehensive toolkits in the market."

OSU students used STATISTICA Data Miner for the prediction of diabetic illnesses based on demographic, social and recreational parameters; target marketing models for better promotional mailing; prediction of financial indicators such as the S&P 500; and foreign exchange rates, among others.

Click here to read the entire case study.

STATISTICA Data Warehouse - the ultimate, high-performance, scalable system for intelligent management of unlimited amounts of data, distributed across locations worldwide.

STATISTICA Document Management System - a scalable solution for flexible, productivity-enhancing management of local or Web-based document repositories (FDA/ISO compliant).

STATISTICA OLAP - a powerful exploratory, analytic, and reporting add-on application that integrates OLAP services with the analytic and data mining power of STATISTICA tools.

WebSTATISTICA Knowledge Portal - the ultimate knowledge-sharing tool incorporates the latest Internet technology and includes a powerful, flexible report generation tool and a secure system for information delivery.

Click here for the complete list of STATISTICA 6 products.

Featured Textbook Topic - Association Rules

How association rules work. The usefulness of this technique to address unique data mining problems is best illustrated in a simple example. Suppose you are collecting data at the check-out cash registers at a large book store. Each customer transaction is logged in a database, and consists of the titles of the books purchased by the respective customer, perhaps additional magazine titles and other gift items that were purchased, etc. Hence, each record in the database will represent one customer (transaction), and may consist of a single book purchased by that customer, or it may consist of many (perhaps hundreds of) different items that were purchased, arranged in an arbitrary order depending on the order in which the different items (books, magazines, etc.) came down the conveyor belt at the cash register. The purpose of the analysis is to find associations between the items that were purchased, i.e., to derive association rules that identify the items and co-occurrences of different items that appear with the greatest (co-)frequencies. For example, you want to learn which books are likely to be purchased by a customer who you know already purchased (or is about to purchase) a particular book. This type of information could then quickly be used to suggest to the customer those additional titles. You may already be "familiar" with the results of these types of analyses, if you are a customer of various on-line (Web-based) retail businesses; many times when making a purchase on-line, the vendor will suggest similar items (to the ones purchased by you) at the time of "check-out", based on some rules such as "customers who buy book title A are also likely to purchase book title B," etc.

Unique data analysis requirements. Crosstabulation tables, and in particular Multiple Response tables can be used to analyze data of this kind. However, in cases when the number of different items (categories) in the data is very large (and not known ahead of time), and when the "factorial degree" of important association rules is not known ahead of time, then these tabulation facilities may be too cumbersome to use, or simply not applicable: Consider once more the simple "bookstore-example" discussed earlier. First, the number of book titles is practically unlimited. In other words, if we would make a table where each book title would represent one dimension, and the purchase of that book (yes/no) would be the classes or categories for each dimension, then the complete crosstabulation table would be huge and sparse (consisting mostly of empty cells). Alternatively, we could construct all possible two-way tables from all items available in the store; this would allow us to detect two-way associations (association rules) between items. However, the number of tables that would have to be constructed would again be huge, most of the two-way tables would be sparse, and worse, if there were any three-way association rules "hiding" in the data, we would miss them completely. The a-priori algorithm implemented in Association Rules will not only automatically detect the relationships ("cross-tabulation tables") that are important (i.e., cross-tabulation tables that are not sparse, not containing mostly zero's), but also determine the factorial degree of the tables that contain the important association rules.

To summarize, Association Rules will allow you to find rules of the kind If X then (likely) Y where X and Y can be single values, items, words, etc., or conjunctions of values, items, words, etc. (e.g., if (Car=Porsche and Gender=Male and Age<20) then (Risk=High and Insurance=High)). The program can be used to analyze simple categorical variables, dichotomous variables, and/or multiple response variables. The algorithm will determine association rules without requiring the user to specify the number of distinct categories present in the data, or any prior knowledge regarding the maximum factorial degree or complexity of the important associations. In a sense, the algorithm will construct cross-tabulation tables without the need to specify the number of dimensions for the tables, or the number of categories for each dimension. Hence, this technique is particularly well suited for data and text mining of huge databases.

Click here for more information on Association Rules.

Go to the Electronic Statistics Homepage for the complete textbook.

STATISTICA 6 Products "Take Traditional Quality Further"
According to Quality Digest Software Review (September 2002, p. 61)

Felix Grant, a well-known lecturer and reviewer in the UK, recently commented on the latest versions (version 6) of STATISTICA QC Charts, Data Miner, and Neural Networks in a software review published in the September issue of Quality Digest magazine.

In the review, Grant remarks on STATISTICA QC Chart's ability to handle large data sets, multiple databases, and real-time auto-updating saying, "There must be limits to the density and variability of data flow, but I’ve not yet discovered them -- despite some very demanding work that would make most software packages cry."

In the same review, Grant calls STATISTICA Data Miner "the best tool I’ve seen yet for actually applying what you learn at the sharp end of industry" and "the easiest to use data mining control I’ve encountered." He goes on to praise its friendly user interface and strong set of exploratory analytic routines.

Lastly, the reviewer addresses STATISTICA Neural Networks. Although admittedly tentative when it comes to using nn software, Grant finds that STATISTICA Neural Networks has "particular strengths for unfamiliar users and those with reporting or team working priorities." Grant summarizes,

"These new additions to the STATISTICA 6 product range take traditional quality further, make it more accessible, and introduce a new level of integration that yield considerable synergy payoffs. If you have unsolved analytical problems, try STATISTICA 6."

To read the complete review go to the Quality Digest Web site.

Click here to view other STATISTICA awards, comments from users, and a complete summary of STATISTICA's unmatched record of reviews.