|
 |
|
|
|
|
 |
|
|
|
SIGMASTAT RESOURCES |
|
|
|
|
|
|
|
SYSTAT : SigmaPLOT : SIGMASCAN : TableCurve2D : TableCurve3D : PeakFIT : AutoSignal |
SIGMASTAT & STATSOFT
AVOID WIND AND WELDS FOR SUCCESSFUL MANUFACTURING
Statistical analysis revealed that potters should watch which way the wind blows when preparing their clay. Felix Grant reviews the successes of SigmaStat and Statistica
I've been intermittently tied up with two different challenges over recent months. Both were concerned with industrial quality issues but, despite that superficial semblance of a link, they couldn't have been more different. One was in a large plant, part of a regional economic development plan with government start-up funding; the other concerned a small village co-operative. One could offer me live access to its own data stores. The other had no process records or quality assurance expertise of any kind, until I started to design some; no telecommunications either, and the staff would have to do most of the work themselves under my intermittent direction. Both were concerned with manufacture of containers: milled steel in one case; fired clay in the other. Losses in the first, though so eye-wateringly large that my contingency fee was a pinprick, were being covered by deficit funding in the interest of public relations; those in the other were tiny, but would quickly cripple the co-operative, if not stemmed.
The two cases called for different approaches to a similar question: what production factors led to delayed fracture faults in a significant minority of the finished units?
I had encountered a detective-style case similar in some ways to the milled steel problem a couple of years ago, putting a review copy of Statistica Data miner release 6 to good use. I learnt a lot on that job, and applied the knowledge to the current concerns. It also happened, by a happy coincidence, that Statistica release 7 had just come to hand as this current pair of challenges hove into sight - so I earmarked it immediately for the larger-scale plant. The small village enterprise, though, called for a very different approach: something that could be applied by inexperienced and untrained users, so that every stage didn't have to wait on communications back to me. I had a copy of SigmaStat 3.0 lying around, which had impressed me by the ease with which it was accessed by novice users during a trial run in another industrial context; Systat International advised that a new 3.1 update was available, and sent a copy of for use in the co-op.
 |
| Click to View Larger Image |
I'm a great believer in horses for courses, and SigmaStat, a very different breed from Statistica, was the appropriate choice for the ceramics group. Even quality-assurance professionals are not always entirely happy with statistical analysis; people with no background in quality control beyond a quick visual assessment of the finished product, even less so. Friendly is an over-used description for software, but if any analytic product deserves it, then SigmaStat does. Try, for example, doing a t-test on unsuitable data that fails the normality test. SigmaStat neither pushes on regardless to a meaningless answer, nor primly displays a minimal error message. Instead, it calmly mentions the problem and asks if you would like to run a Mann-Whitney rank sum test instead? All you have to do is say 'yes'... you get a Mann-Whitney result, some helpful background information, and a plain English explanation of what it means. Everything is presented in a neat, clear, rich-text report, ready for printing out on half a sheet of paper. Better still is another equally helpful half sheet on why that t-test wasn't a good idea, so you can learn for next time. Nor would my potters have reached that attempt on a t-test unaided. The 'Statistics' menu, instead of the usual maze of statspeak, is organised along task-oriented lines. The first decision is not between tests named after long-dead researchers they last heard of heard of in college (if at all), but things they might want to do. Pick from headings like 'describe data', 'compare two groups', or 'before and after'. I don't want to suggest that the user never has to do anything. This isn't nursery school; they have to come up against the nitty-gritty eventually, but they will at least arrive at the task though sensible guidance, with helpful advice at their side, and an appropriately selected toolkit to hand. All of this helped to maximise their autonomy, increasing their ability to manage phases between my necessary interventions.
 |
| Click to View Larger Image |
Usability, albeit for a different market, is also a feature of Statistica - and has gained new dimensions in release 7: the new 'by group' facility, for instance; project organisation; extension of spreadsheet behaviour, and metadata provision. Always very flexibly configurable, the working environment becomes even more so.
'By group' enables easy, automated generation of graphical or analytic results for categorised subsets. Selecting the variables 'supervisor' and 'lathe' for by-group use, for example, causes the desired analysis to be repeated for data every combination of lathe and supervisor: data for lathe 1 under supervision by A; for lathe 1 under supervision by B; lathe 2 under supervision by A; and so on. Not a startling new idea, but effective streamlining of an existing one: systematic compilation of an overview is much quicker and much less tedious, encouraging thoroughness. Extended brushing facilities and enhanced operational access to text operations (including formula and case handling) offer related gains; so do further enhanced sheet management options. A close and methodical control of process is also encouraged by the project organisation tools, most of which will be familiar in essence but are no less welcome for that. Automatic updating of linked visualisations to reflect changes in the data set, for instance, is part of a move to make the data sheet more like generic spreadsheets. Last but by no means least, comes metadata on analyses, cases, variables and visualisations - property information stored alongside the particular object and available to a range of selection and formatting operations. In my particular case, the primary use of metadata was to flag, track, and utilise through subsequent explorations those subsets identified as possible but unconfirmed error zones; another modality added to the data sleuth's psychoperceptual toolkit.
SigmaStat's data worksheet also invites comment. It is one of the most intuitive around, for the majority whose operational norms have been shaped by MS Office. Sticking with organisational issues, there is also a welcome new SigmaStat 'Notebook Manager' with the now familiar explorer tree giving windowed access to open notebooks, graph, worksheets, and reports. There are spreadsheet improvements, too, most of them contributing to a more Excel-like environment and greater, more intuitive flexibility. Transforms are fast, effective and easy to use - from milling and failure dates to a survival analysis report took me just eight mouse clicks, mostly on column headings. If you prefer to stick with the real thing, as my co-op users did, an Excel sheet (blank or full of existing data), can be opened inside SigmaStat, but the methodological advantages of using SigmaStat's own sheet are considerable. It is well designed, statistically aware and, at 32 million rows by 32 thousand columns, offers greater capacity.
 |
| Click to View Larger Image |
SigmaStat has put function expansion aside in this incremental release, but the previous full digit upgrade to 3.0 brought a survival analysis kit which is very capable given the intended audience. Since the co-op replaced every fractured pot, we were able to put this module to good use in quantifying the level of the problem. By introducing a serial numbering system, linking each pot to data about its entire history back to the digging of its clay, we were also able to make use of this returns and replacement policy to make a very accurate and complete population census of both fractured and non-fractured units.
A big difference between the two products, of course, is automation. SigmaStat is designed to be flown manually; it has a transform function language (extended in this release), but not a macro or control system. This was entirely in line with what the co-op needed: automation would have been neither feasible nor useful in an environment where all other activity was fluid. Statistica, on the other hand, has the impressive Visual Basic superset dialect, which replaced two previous proprietary languages a while back - and this too has been enhanced.
And the causes of the fractures? On the milled steel line, Statistica identified the indicators as a particularly obscure combination of milling machine sequence, output delivery rate, bar replacement time, time of day, and temperature. From there on, the detective work had to be extended to factors beyond the production line itself. The culprit turned out to be a poor spot-weld hidden beneath a conveyor surface, where no spot weld should be. Apparently, it was an ad hoc repair, never reported, to a smooth sheet that should have been replaced. Only in the particular set of circumstances identified, were components passing between production stages 'wobbled' by this in a way that interfered fractionally but significantly with the alignment of the immediately subsequent drilling phase.
Meanwhile, in the pottery, SigmaStat located a single critical variable: wind direction and strength outside the building, when damp clay is being prepared for use. We haven't yet figured out how this works, but experimental trials confirm the accuracy of the diagnosis. For the time being, production clay prep is abandoned for other tasks when the particular conditions prevail - and SigmaStat continues to analyse such things as airborne grit content, humidity, internal ventilation, and suchlike.
 |
 |