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Knowledge Management for Faster Problem Solving & Reduced Time-to-Market in Engineering and Science (2021-EU-30MP-755)

Level: Intermediate


Wayne Levin, President, Predictum
Farhan Mansoor, Software Engineer, Predictum



Innovation in industry requires the contributions of analytical knowledge – more specifically formal and informal experimental data and predictive models – to product and process design. However, analytical knowledge is often stored and unmanaged in isolated sources for individual use only by its creators. By our estimation, analytical knowledge is typically regenerated on average about 40 percent of the time, simply because prior, relevant knowledge was not made accessible to the people who could make effective use of it.

Regenerating analytical knowledge carries higher risks, incurs unnecessary costs and delays achieving business goals. The future success of technical problem solving and process innovation requires a modern knowledge management strategy. Companies that adopt a modern knowledge management strategy will dramatically reduce the amount of time and effort in the daily work of engineers and scientists, thereby not only preventing the needless duplication of experiments, but also extending the use of past experiments and improvement initiatives.

Wayne and Farhan will present a use case to demonstrate the power of managing analytical knowledge through CoBase, an enterprise-level knowledge management system that enables engineers and scientists to share access to and collaborate mutually on past analyses and relevant supporting data.



Auto-generated transcript...




Wayne Levin Well, thanks, very much for joining us. My name is Wayne Levin and joining me in our presentation about knowledge management for faster problem solving and reduced time to market in engineering and science
is my friend and colleague, Farhan Mansoor. So Farhan is going to help me with the demonstration part of this and we're going to start out with...why don't we start with a
agenda here. I'm just going to give just a quick introduction to us...we're Predictum, as a company, just so you know a little bit about us. And the real focus is going to be how to improve productivity in science and engineering. That's what we're...
that's what we're here to talk about today. So a little bit about us. Our goal is to accelerate problem solving, improvement, and research and development.
And we do that through analytical training and consulting and we build integrated analytical systems. And we're going to look at an example of that
today, with CoBase. Just so you know, we're also a JMP partner, have been for a long time now, been associated with JMP for close to 25 years. Predictum as a company started in March 1992, so almost 29 years. So that's a little bit about us, now let's get to the matter at hand.
I want to talk about how managing knowledge, not just data, as an asset, so managing knowledge as an asset will dramatically improve productivity
among your researchers, engineers, and business analysts. And so I'm going to start by asking you a question. I'm
how often does someone regenerate what was already known in your company? If you have to put that on a scale between zero and 100,
what would you say? I'd like just like you to plant a number in your your head, okay? There's no need to confess.
Alright, so it's some problem that was solved, but it was solved before. Others have dealt with that problem or some
insight, some relationship or association between variables. Chances are others have probably made that discovery already. That's what I mean by regenerating knowledge. Do you have a number in mind?
Well we've been asking companies, dozens of companies now since
the fall of last year, and this is what we've seen. It's on average about 40% and a pretty big range as far as that goes. So
nobody has really a solid number for that, and I think that actually speaks to the problem, that we don't have a solid answer.
But you know when thinking about it, you know, we have some people who just roll their eyes and they go, all the time.
We had one company, a few weeks ago, three people were on the call. One said 80%, one said 60%, one said, oh, at least 50.
So, and we do get some who are...the lowest I've ever heard is like 20%. Why do we tolerate that? And what other...what other aspect of business would we tolerate something like that?
So you know the...regenerating what was already known imposes higher risks, it costs money, it delays objectives and it's really a lost opportunity to accelerate
problem solving, improvement and R&D. So this is the problem...I want to make sure we're clear, this is a problem we're trying to tackle here.
Now you probably know that expression. I'm not going to fill in the blank. I'll leave it to you. Blank happens. So
you probably experienced these situations, the phone rings, an email comes in, there's some customer issue or there's some production problem,
or some obstacle has surfaced and it's causing delays in a new product introduction or or process development and characterization. And you're frantic, right? You've got to take care of this, and you're thinking, okay, who knows how to solve this?
Who's got that knowledge? And you're...basically you're trying to identify the right people and that can be difficult, sometimes it's hard to to do that.
Or you can identify some good candidates and they're not available or they've retired or they're away, they're on vacation or they've been reassigned and you're not allowed to talk to them anymore. They're simply gone. They're just no longer with the organization. How do you handle that?
This is...this is a problem we want to avoid, and this is why we say companies typically manage their materials and spare parts better than they do their knowledge.
I hope that doesn't sound too harsh, but I'd like you to think about that for a moment. I was asking you earlier about how much knowledge is regenerated.
And you probably don't have a hard number for that, but I'll bet you, you probably have a pretty good number on what your work in progress inventory is, or spare parts inventory, raw materials inventory. You can probably
find somebody and ???
So why is it that we manage those assets very well, but we don't manage knowledge very well.
And that's because knowledge is...
well, this is, this is where it's at, right? The brain, we like to say, is a great knowledge creator but it's a lousy knowledge container.
We can't access it. We can't index it. know, it walks out the door at the end of the day.
So knowledge cannot accumulate if the brain is the primary storage device for knowledge.
And we believe that companies should always accumulate knowledge, so if you have...and I'll just look at just what's involved in knowledge creation, if you will. I'll do that.
And first we're going to just talk about what is knowledge? What do we mean by knowledge?
And so, knowledge is what allows us to predict, at its very core, we're talking about a prediction formula, because we use a prediction formula to predict.
And so we, you know, we can associate height to weight or height, age, and sex or whatever to to weight, and all of us who use JMP are familiar with these things. So that's the...
that's the third component, if you will, predictive model, a prediction formula, if you will. But
when we have that, we would like to know something about the analytical method or the process that was used to generate
that prediction formula and, of course, those of us, you know, with JMP, we have those over here, right? We save them as scripts, if you will, so that we can
regenerate that formula. Of course, the formula would be saved as a column to the data table and then
that's one of the things we do with the analysis. So that's what's happening over here with number two and, of course, the primary thing, the first point is is just data.
But as I said at the beginning, data is not knowledge. You know, data is data. It's the raw material that we get from instrumentation, helps us understand what's going on with products or with processes, so we really need these three components.
So knowledge is what allows us to predict, but we...we would like to go back a couple of steps as well, to really have the full context of that knowledge. So let's think about that knowledge creation here.
You know, we have engineers, scientists, analysts. I'm going to think of it from a scientist's point of view.
They are, you know, using their instruments, they're doing their work, they're collecting their data. Now we find all too often it's kept on spreadsheets, but of course we're...
we're talking JMP data tables as well, and that's that's a terrific thing. They collect it, they do their analyses, and this is why we call it personal computing.
And that picture of the computer there, it's just meant to remind us, personal computing has been with us a long time and it's relevant. It's important. It's still the case. Analysis does happen in a brain. It's a personal endeavor, if you will, that really can't be split up
so just at its core. So we've got a bunch of people who are doing this and as a result, this work is typically siloed.
All right, where do these files go? Some people will save them on Sharepoint or shared network drives. That's great, but primarily they end up on
a laptop, and so it is inherently siloed, just because that's the nature of it. And researchers, you know, we can't easily access the experience
of others. I mean, if we've got a problem we want to solve, we're thinking about it, you know, as an experiment, we might call together a group of, let's say, five people in a room for,
you know, for an hour, or 10 people in a room for an hour and brainstorm, you know, what factors, what levels to go to, and all that. It's a good and worthy activity but wouldn't it be better not to start from zero and not to occupy those people? That's a good amount of time that's that's being used.
So what we'd like to do is be able to take the experience, if you will, saved in these JMP data tables and related files, and Farhan's going to show us this, make it easy to put it in a database.
Right, this is what we call CoBase and so that way, new initiatives don't start from zero.
Anybody can go in and look up what, you know, who else has looked at a particular problem or the particular area. And this way,
they'll never unknowingly pay for the same insights more than once.
This is an important point as well. I find too often in experimentation or work, the work that folks do, because it's siloed,
they don't know that what they may be seeing is inconsistent with what others have seen in the past.
So this way they can look and see maybe they're dealing with Type one or Type two errors and it gives them, you know another,
just another dimension, if you will, that really should be considered, that sort of historical dimension that's just often not brought forward because they can't typically. So when we talk about capturing and preserving and reusing knowledge,
obviously, there's data, like I said. And many companies will have databases, of course, so they'll have
LIMS systems, and this is terrific. This is good for preserving data. Some will have a formal document management system and and, if not, they'll have some way of organizing and filing reports.
You know powerpoints, standard operating procedures, if you will, the results of the analyses that engineers, scientists and other analysts are doing.
But that's where we want to focus, we want to talk about preserving that knowledge creation work and making it identifiable, if you will, making it so others can find it. So just that, if we want to manage knowledge like an asset,
it requires that you bundle it, first of all with data and reports (that would be a good idea) as much of that as relevant as possible and package it, okay.
Because when it's packaged, it's identifiable. I keep something on my desk here.
You know, I bought a USB cable from Amazon. It came in this box, I hope this shows up all right.
And you know it's got a barcode there, it's an asset, it was to Amazon, it is to me now and it's identifiable, right, so that,
you know, we can search for it. And if you can search for it, then you can retrieve it, and if you can retrieve it, then you could reuse it.
Alright, so you can reference it, you can challenge that knowledge. We think all knowledge is open to be challenged, of course, and we can improve on that knowledge. And finally, managing it as an asset means that we keep it secure, that the knowledge is is is under your control.
Okay, so we have a couple of products in this area. We're going to switch over to the demonstration part of this talk.
First, is SashLab. We're not going to demonstrate that, but if you want to read more about it, you can download these slides and read up about it, and of course you can contact us.
What it is, it's a like a virtual lab or a digital twin. It allows you to experiment virtually or check things out virtually, if you will,
before doing...making changes or experimenting physically. CoBase...what CoBase is designed to do is capture everyday research and experimentation and analyses, improvement initiatives, whether they're formal or informal.
It puts it into a database and it...and it tags it. And so Farhan is going to show us about tagging and and it also tags...remember, I said it needs to be identifiable, so tagging is one way.
Indexing it by factors or by responses and by domains, if you will. We'll talk a bit about that and and this way anybody can go and look...
look up the knowledge that was generated by others. And
you know, both of these applications, it says at the bottom here, it's like they capture explicit knowledge as an asset. You want to keep it explicit,
right. We we don't want to just have opinions or or notions about what's happening when we asked someone we'd like, hey show me the data, the method that it was modeled and the resulting model. It's it's hard that way. It's explicit.
So we got the model, we got the development method, we have the underlying data and we make them available for reuse by others. And this way, it avoids delays
and costs and I'm going to say anxiety associated with searching for and regenerating knowledge. So if we could, Farhan, why don't you take over the screen and what we'll do is
we'll begin a demonstration here.
Just while Farhan's bringing that up, let me just add about one of the
things we hear about from people when we talk to them is that, you know, it's hard to search for things, just to begin with. It's hard to search for things, but it's really hard to search for things that you don't know exist, right, because that kind of search can go on forever. And
so this is kind of what I mean by the sort of the anxiety. It's tiring have to deal with that, and when we are dealing with re search, we would like that research not to have to involve
searching for past knowledge. We want to make that easy. So what I'm going to do here, Farhan, you've got the CoBase
interface, the primary GUI open?
Farhan Mansoor Yes.
So, so the homepage yep.
Wayne Levin So Farhan, let's say I'm...let's let's go and do a search.
And because I've got a problem here, and just before we do this, so let me just describe a little bit at what's going on.
We're not going to go into all the detail here. We just don't have the time for it. We're taking the...
the example we're using is like a manufacturing process. So down in the bottom left, we have the various steps in semiconductor manufacturing and it's just a way of grouping factors, okay. So what I'm interested in is, you know, I'm interested in
you know, let's say, the deposition here. And I'm curious to know if we look at deposition...let's look at like the deposition rate,
all right, which is a factor here. And I just want to know, has anybody looked at this in the past deposition rate, let's say between 700 and 3,000 angstroms.
It could be 100 reasons why I want to know this and I just want to know, well, what did they look at? What were they...
what were the other factors they were looking at? What were the responses? You know, what was the know, I just want to see what's there, because I want to understand this factor. So go ahead, Farhan, you you you fill this in and...
great. You clicked on search and
there we are. We've got a bunch of files so.
You know, we got about what seven files there and they...just looking at them, they go goes back as far back as 2016. So this is work that people have done previously. They've updated it to CoBase.
Now, it can be hard to look at all those files, you know, all at once. We can download them. We will do that momentarily but
on the right, we're kind of looking at it at a glance so...Farhan, do you want to just...let's look at the parameter distributions first of all. So we see that
three of the these JMP data tables also included argon flow that were all looked at in the same levels and
backside flow were involved in a couple. There's deposition rate so there's seven across there and we see the different levels there, but they're all between 700 and 3,000 and there are some others as well.
We can also look at the just some statistical things. We're going to be adding some more stuff here, but the idea is just to be able to look at
some things at a glance, so we get an idea what's going on. And so on the left, we have R squares. On the right, we have root means square errors. Each of the vertical arrangements of dots relates to a JMP file, so I'm just looking at
Exp 18-11-01.
There you are, Farhan, yeah. So those are three models that have been produced, three prediction formulas that have been saved and we see the R squares vary quite a bit there. Farhan, why don't we go download that file
and and let's let's just have a look at
at what's there. So.
There we go.
Awesome. So by the way, the modeling type just also happens to be shown bivariate or fit least squares if they're blue. So we get an idea what what it is. So there we go. Why don't we just go run those three scripts there.
Because the predicting...prediction formula is there it was generated is there. Forhan's just rerunning it so.
And, of course, the data is there, so we really have the full context of just what was being done. We can see there what the work was. Thanks, Farhan, you're arranging on the screen, I think you ran...well one one twice by the looks of it, they look identical.
Farhan Mansoor Oh yes.
Wayne Levin Yeah but that's all right. What what we can see if we even if we just look at these two, this is fine. Notice that the one that significant, if you will, it involves temperature.
The one that isn't, over on the left of it, temperature's not involved and that may explain why it's not a significant model so.
We could go any number of ways, with this, but I hope you get the idea that
we want to be able to, you know, search for something, quickly find out what's available, get at a glance what was going on,
and then, you know, look at it as much as we want. We may want to now take this data, maybe it solves a problem, maybe the problem I'm dealing with is solved right here.
So boom, I don't need to do anything further. I've got my problem solved. Or maybe I want to augment this design,
you know, and add some other runs or add some other factors. Or maybe I'm just, hey I see that I need to involve temperature when I go forward and I may not vary it, but I know I'm going to keep it up at a particular level, as I go forward
to improve the power of subsequent studies that I may do. So you see what I'm saying, like I'm drawing knowledge from the past, so I'm not beginning from zero. So that's the idea. Why don't we do another
quick look up, Farhan. Show another way of looking things up. There's various other ways, but I think a common way would be by tags. The tags are completely customizable so
we've got...what do we have there...analysts, we have project, you know. So you've probably had this. I know I've had this. Somebody new comes in and we assign some work to them and we say hey,
why don't you look up, Farhan, let's do it by project, and let's just say, hey, why don't you look up the CVD improvement project and there's another one that's kind of like it...if you...
YieldPlus. Yeah why don't you go and look at the data, the analyses that were done with this, and then go ahead and click search, and then bang.
The files that were associated with it. I know what you're seeing here, by the way, they're all JMP files, but we're going to show you that on the upload, you can include non JMP files as well. So you may want to include,
you know, some pictures, pictures of defects, pictures of equipment, instructions, other, you know, documents, anything you want, and they would be listed there as well. I'm sorry, I'm pointing to my screen so.
They'd be they'd be listed there as well, so there's other ways to do searches and more comprehensive searches, if you will, but I think you get the idea. And I'd like to ask Farhan, would you mind taking us through the upload?
Yep. Yeah why don't you talk us through that, okay?
Farhan Mansoor Let's go to the upload interface.
Just the upload interface. So what I'm going to do is I'm going to add some sample files to demo the upload process.
So you can upload both JMP file and also any other kind of non JMP file. So I'll show you the difference in the
process for both. So what I did, is I am picking some PDF, docx, spreadsheets and one JMP file. Now if your file format is JMP then what CoBase will do, it will
parse some information out of the file, for example, it will list the column names, as well as, you know, the units, models, you know, data set,
those kinds of information. It will also try to guess a standard name, so this I will show, in the end, how to set up but admin users can set up standard names for various parameters and users can choose the standard parameters from here.
And if they already exist in the system, CoBase will try to guess. So, for example, in this case temperature has been associated with
the temperature parameter that exists in CoBase right now. Argon flow, that doesn't have a assigned parameter right now, because it's new to the system so user can go and pick a standard name.
So if I know that argon F L W is the same as our flow in the deposition of step, I can
select that one as my standard parameter. Now this is optional. Users don't have to do it,
but if they standardized their parameters, their columns then it just makes makes it easier to search for various things.
But, or they could come back and do it later, but yeah, but right now, it's an optional feature, optional process.
Wayne Levin One of of the keys around this just just to make clear, the column names that are on the left, they're from the JMP data table that Farhan identified.
And in order to search for something, we have to have a standard. We have to agree to a standard. So basically what CoBase is allowing us to do, which is what the CO stands for, is we're collaborating asynchronously with you know, colleagues. There's a lot of co names here. We're cooperating.
We have to agree on the names and so what we're trying to do is make it really easy when you go to upload
to assign the proper names to it. So Farhan just said that it's optional. It's true, because we want to make sure that people
upload. We know if we make it difficult, they're not going to do it. They'll say, oh, I'll do it later today, and then they won't.
Right, then, I'll do it tomorrow. I'll get to it tomorrow, and then they don't. You know, that type of thing. The other thing that we can do is identify,
you know, tables that have been uploaded that don't have standard names in them. Like that would be an admin function, if you will, and so they can be corrected later, alright. So so
we've got now the nomenclature...are essential to facilitate, you know, system optimization. If we're going to cooperate, we have to agree to the...we have to agree to a standard so that's a big part of it. And something we just added recently, Farhan, you just mentioned the units of measurement.
So that's that's part of this as well, so if you're uploading and you'll be reminded of what the unit of measurement is or what it should be and
we facilitate changing that if, indeed, you know, somebody measured in millimeters but the standard is centimeters or nanometers whatever. So anyway, we want to facilitate that standardization.
Yeah, what else should we mentioned here? There's the tagging you can do, comments, go ahead, Farhan.
Farhan Mansoor Yeah so you can add a comment at the file level or you can add a comment at the batch level, have a general notes about the entire upload.
The tags, you can also add here, so these are the preexisting tags. So you can add, like if I want to add a tag, I can add series one technology tag, things like that.
Wayne Levin Okay, why don't we upload it.
Farhan Mansoor Yep, let's upload this.
Wayne Levin So just so you know what this consists of, is on the back end, it's a SQL server database and on the front end, it's it's a JMP add-in, so we're running this in JMP obviously and it's installed, like any other add-in. This configuration that needs to happen.
CoBase can be installed and up and running in literally in minutes. It really just depends on you. You have a script to install the database and double click, install the add-in, your configuration, and boom you're up and running. So it's pretty easy to do that and oh, we're up there and.
Why don't we do a quick search, yeah.
Farhan Mansoor So once it finishes uploading, it will kind will give you a batch ID for reference, so you can also look it up by that.
So if I do a search for that.
Wayne Levin Of course you can look it up, based on the factor names and so on, as well, but
yeah, we're just gonna put the batch ID in there, just so we're
focused just on this.
Farhan Mansoor So that this the files I just uploaded. And you can see the similar plots, parameter distribution plots, if there are any models, it will show up here, since it has only one JMP
file. There's so much, and we can download the files as well, so if I download a JMP file, it will open within JMP.
But if it's a non JMP file, for example, if it's a doc file, it will open with your default doc viewer, so in my case, it would be Microsoft Word.
Wayne Levin Right so flows back in the original search. If we search, hey, who's looked at argon flow, or what have you,
we not only get the JMP file here, like what we see here, but we'd see these other files associated with that as well, so so they would come back at you as well.
So that's a little bit about uploading. Again we're trying to
facilitate the standardization and we're, again, trying to make it easy, really easy to do.
Now of course you'd have a bunch of CoBase users out there, and you'd also have a few people who would have the administrative privileges. Why don't we just have a quick look at that, Farhan?
Because this is where...we don't get too deep into this. If you want to see more about this or talk more about this, we can talk during the questions or you can contact us
after, you know, at any point.
So, are you.
There we go.
Farhan Mansoor On it. Yeah.
Wayne Levin I want to say just briefly about how the, you know, setting up the parameters are done here.
Farhan Mansoor Yeah so on the left, you see all the steps or well, we call them domains. This could be your production steps or product components or subcomponents.
And if I click on one of the steps here, it will show you all the parameters that are currently existing...currently existing this this particular domain and also the subdomains. And admin users can come and add new parameters
or edit existing ones, so this will create those standard names that users can then select during upload.
The admin users can also assign a standard unit, so on the right side, you see all the standard units associated with the standard parameters.
Wayne Levin Right and then the tags there.
was mentioned .... there. I'm just gonna go back to the parameters for a moment. You can change the names of these parameters. I'm sorry, we missed a little something there. We could show you that, remember we uploaded a table and we changed argon flow, we changed the name.
Well, when we download that table, it will have the correct name and if we ever decide that, you know, for whatever reason, we want to change some of these standard names over time, you may decide that
something's a little more descriptive or, you know, you may just want to change it, so you can do that. You can change them here in the admin panel and that will
make, let's say, changes within the system so that now, you can search based on those new names and the history will still be brought forward.
So we've added that flexibility. It was one of the most difficult things, maybe the most difficult thing, in terms of building CoBase just to begin with. I'm sorry, Forhan, I was taking you away but let's look at the tags, just so they get a sense of that.
Farhan Mansoor You have a set of tags that admin users can create. Here you can add new tag types or new tags inside tags, tag types, right now, we can see few examples here. For example, technology tags, study type tags, things like that.
Wayne Levin Yeah we have for technology, we have one company, who said look, you know we have different eras, if you will, different technologies and we don't want to throw away stuff that, you know, was done from prior versions, if you will, for a prior technology.
So they wanted to be able to name that and so indeed they are are able to do that.
You know it's obvious, probably want to tag by analysts, you know, so you can go by somebody's name or whatever, or some project ID. You know, those are pretty obvious tags, but you can create any tags that you want, and you can add tags anytime you want,
you know, to this as as they occur to you.
So that's the the demo side of this that we wanted to show you. I hope that gives you a flavor for it and, again, you know we welcome any questions that you may have or
comments. I'm just going to ...I'm going to switch it back over to my screen. Thank you, Farhan.
And see if I can get this.
Okay, so we're happy to entertain any questions or thoughts that you may have. Oh goodness, I'm sorry we're gonna have to edit this out, this is the wrong slide.
So I'm going to back up.
If you if you have any questions or comments, in the slide, we'll have our contact information, when you go to when you download it.
Feel free to reach out. We'd be happy to do a more extensive demonstration or talk about some challenges you may have or problems you may have, and how we might be able to solve them with CoBase. And I really appreciate your interest. Thank you.