Finally got walk on music for a presentation.
That's fantastic.
So I'm coming from the water industry,
so I'm hoping some of the things I'll be talking to you about
will resonate with some of your industries,
whether it's production or service organizations, what have you.
But I'm going to make you all water and wastewater experts
in the next 15 minutes.
And then I'm going to walk you through some examples
of how we've been able to leverage Foundry as an accelerator
for things we were already working on.
I'll start out with a little bit of background about water and wastewater treatment.
We all use it every day, but you probably didn't know what goes behind that.
I'm going to talk about how the in-house subject matter experts
and in-house data analytics isn't enough to get you to execution.
At least that's what we found.
We've been working on this for several years
and it's really been the flywheel of having Foundry
that's gotten us over the edge and moving forward.
And I want to talk about some of the examples of
how we've been able to do this in real world.
So a little bit of basics.
Water and wastewater is really a Victorian age invention in a lot of ways,
at least in the modern sense.
Sure, the Romans were building aqueducts.
But the way we think about modern water and wastewater treatment today
really didn't kick in until ballpark Civil War.
Unfortunately, we hadn't figured out disinfection yet,
so we lost 23 million people in the United States to cholera
between the Civil War and World War One
before the innovation of modern water and wastewater treatment.
50 years ago, the EPA came along and codified what we really need to do
for water and wastewater treatment in the Clean Water Act.
This is really what my industry works under,
is a regulatory framework that this is important because it will be important later.
It makes a criminal liability or yeah, it's criminal liability
if you are negligent in water and wastewater treatment.
So you go to jail as an operator of a water and wastewater plant.
If you don't fall within the requirements of the Clean Water Act.
So that's a big business driver for us, which I'll talk about some more.
So let me give you a quick tour of --
there's no such thing as a typical water or wastewater plant.
They're all bespoke.
They're all different.
But I'll give you a quick tour of how they work.
This is one of the plants that we actually built and designed.
We've been running this particular facility for nine years,
and it really works the same as what Mother Nature does
when you take your dog to the park and they do their business and you didn't clean up.
It's not there next week, right?
Nobody picked it up, though.
It's nature doing its thing.
All we're doing is accelerating what nature does in the treatment plant.
We're taking something that takes days or weeks down to hours.
Several processes remove the grit.
But the real heart of it is these big tanks here with a lot of bacterial treatment.
Again, natural stuff you feed aeration into it, bubble oxygen into it,
into these different chambers.
And that reduces the pollutants all naturally.
But it's a very expensive process.
The water coming out of those tanks goes to these clarifiers.
So it's already very clean the water coming out of here.
It's often this is as far as treatment goes in most plants in the United States for wastewater.
This particular plant goes through filters.
It's a higher level of treatment called tertiary treatment,
and then it goes into essentially a swimming pool.
We add chlorination or other disinfectants to remove any pathogens,
and then the finished water in this case can be used for re-use like irrigation at a golf course.
Oftentimes it goes to a river or a lake or other surface water,
which becomes part of the drinking water supply ultimately.
Right.
So it's very important -- back to the EPA -- that we do this right.
So that's a driver for us.
It's not easy.
There's a lot of variability -- to what Shyam was talking about.
There's tons of variability.
So you may have a plan on how you're going to treat your water,
but does it survive the first hour of operation?
The first curve on the left is called the diurnal curve.
Over the course of one day,
you get a very wide fluctuation in flows and loads to a water or wastewater plant.
Very hard to design and operate in a continuously changing regime like that.
Weather has a big impact and even time of year, temperature, what have you.
So all of these are drivers for our business that make it very difficult,
in a normal sense to manage operations efficiently.
Real quick about my company, Jacobs: big Fortune 500 engineering company.
We're very good at the subject matter expertise.
We are, based on some industry publications,
we're a number one or number two as a design firm,
as a water wastewater subject matter expert.
We also happen to be one of the top operations firms.
So as a private operator for municipal water and wastewater plants,
we're one of the bigger ones on that.
We've also built a lot of data science capability because we saw the need
when there's a lot of data we generate, what are we going to do to optimize how we do things?
So we've built a lot of data science expertise.
Despite all of that, we have so much variability.
With 300 or so treatment plants we're trying to manage
and every one of them is different that it wasn't enough to have all the subject matter expertise,
to have the data science, to have the digital twins.
Executing, getting this into the field was very difficult for us.
A lot of reasons for that.
There's very little consistency from one plant to the next.
As a contract operator for us.
We inherited whatever the client had built five, ten, 20, 50 years ago.
So everything we walk into is different.
So you can start to see when we're starting to scale up, how challenging this could become.
We have a lot of databases, but again, to a comment earlier,
not a lot of insight coming from that.
We build a bunch of dashboards, that's great.
Problem with the dashboards:
(in my business at least) is the end users who need those insights.
They're in the lab or they're in the field.
They're running the plant.
That's what they're liable to do.
That's what the regulatory framework is driving them to do.
Optimizing around the best plan isn't their priority.
Doing something that's in compliance is their priority.
All right.
This was not sustainable for us.
Our business imperative was the complexity of treatment is going up.
Regulations are getting more stringent.
The labor force we have to work with, that's qualified to do this kind of work, is going down.
The grey tsunami, lots of terms for this.
So what we had to do was come up with a digitizing plan.
So we broke it into various parts.
There's a bunch of tools we wanted to implement
for deployment in the field to support our field staff.
A lot of different things we worked on on that.
This is a sampling.
There's many more.
A lot of things around data management we had to figure out.
How clean is our data, because we knew where this is going.
So let's clean up our data, spend a lot of money and effort on this effort.
We are we identified early on that we were going to need a platform partner.
Give everybody a guess who we landed on.
Okay.
And then finally our end goal are those last four on this particular table on the processes
we're trying to get to with these complex processes
and to offset our labor issues and our cost issues.
We're trying to get to more machine learning driven operations to help us bridge that gap.
That's what our end goal is.
It's existential for us.
We have to do this.
But there's a lot of things we could focus on.
Probably too much right now.
So from an ROI standpoint and to generate the momentum within our organization,
we wanted to pick the ones that could have the biggest bang for the buck, so to speak.
Let's pick the ones are going to really make an impact very quickly,
hit those hard and bring those proofs internally.
And then ultimately for us, we're looking to bring these proofs to market.
So we do operations for ourselves,
but we also consult for all the tens of thousands of plants around the world.
So we want to bring these solutions as a market solution as well.
So two drivers here.
These are the ones we landed on: power and chemicals.
There's a lot of advantages to that.
Has ESG, environmental, social governance, sustainability, things, which is a big driver for us.
Back to that, you know, believer thing that Dr. Karp was talking about.
Maintenance management.
These plants are big, expensive, complicated,
hundreds of millions of dollars for some of these plants.
So managing that cost is a business process.
Less data science driven, more business process driven.
So I'm going to show you both use cases.
For us, we start with our data.
One of the biggest sources of data at these plants I was showing you
is industrial control systems (SCADA), specifically a historian system that captures all that data.
This is very rich material for us that was collecting digital dust.
We weren't using it.
So that's something that our data scientists were building.
Were these models and digital twins to try to get us to that next stage,
trying to deal with all this fluctuation in flows and loads and conditions
to try to come up with actionable results.
This is pretty much where we stopped, though.
We were kind of stuck.
How do we deploy this to our field operators?
And this is really where the partnership with Foundry has been so valuable for us,
not only just ingesting data from all those different databases I was showing you
and connecting to those SCADA systems which can be challenging and have cybersecurity risks,
but deploying and pushing end results to our field staff.
So what the Foundry platform has is some really powerful tools to give us that user interface.
We needed to meet our staff, our O&M staff,
most of which really have high school level education to a large extent.
We need to meet them where they're working.
So with smartphones and tablets at all of our sites.
That's how we're pushing out our recommendations, and they're very stripped down.
You don't have to be a super user standing at a screen, at a computer screen, to figure this out.
We're giving them the answer, the actionable activities they need to take,
which has been great.
And as you can imagine, I could have done a whole talk just on the social change aspect of this,
but the adoptability of this is a big deal.
So getting them involved in helping us build this UI really helped us along.
The results have been fantastic.
It's a little complicated of a graph.
Those of us within this particular program are really excited by this graph, though.
The horizontal (more or less horizontal) green and red lines,
are two years of historical power usage for the plants.
Ballpark 2000 kilowatt hours per million gallons of of wastewater treated at this particular site.
This is our first demo site this year.
The blue line is what we've been doing this year.
And once we implement it, it's a vertical green line.
Once we implemented pushing recommendations to the field staff.
We dropped 20% at this particular site.
It's a small plant, 20%, still $300,000 in our budget.
That's direct money for us.
So this is tremendous.
Generated a lot of interest in the company, as you can imagine, because we have 300 sites.
You can do the math.
Another example different than using data science.
This is more of a business process one.
So again, we have these plants that are tens, hundreds of millions of dollars.
That's a lot of assets to maintain.
And we built up best practices in our industry.
But how to maintain all of this?
How to extend the asset life, asset management principles, ISO 55,000, what have you?
Pretty challenging to have my mechanics who want to get out there with a wrench
to figure out how to plan and schedule their work and document their work orders effectively.
So we codified all this and it's, I don't know, 150, 200 pages.
Sure.
They're going to follow that every day.
Right? No, it doesn't happen.
So we gave this process to the Foundry team and they turned this into an algorithm for us
to be able to take all of that best practice and turn it into a planner and scheduler module
that takes into account what assets need to get worked on.
What's their risk factor?
Who's available today?
Who's out on COVID?
Do we have the spare parts?
What's the budget atright now?
So many decision gateways that our planners and schedulers and mechanics
could not do effectively, and this was able to do that for them.
This one is a new deployment for us.
We're just starting on this, but it's already really getting a lot of positive feedback from our staff.
Because, again, it's solving a problem for them.
They know they need to do a better job, but it gives them a tool to do their job better.
That's been a big thing for us, called "WIFM":
What's in it for me?
It's really helped them adopt the new technology.
One thing we found on all of this was the better we do our planning and scheduling,
the better our metrics.
So we're projecting basically a doubling in our key KPIs.
So the red the red horizontal line here is sort of historically
what we've been doing at a particular site.
And the blue line is really what this model will get us from a KPI standpoint.
We already know if we improve the KPIs, we're going to reduce cost.
So our bigger steps, our next steps, we're at about three or four sites right now.
Just in the past six months, we've gone from really rough demo to scaling.
We're pushing out for about a dozen sites by the end of this calendar year,
and it'll be 30 to 35 sites by this time next year.
So again, what Foundry's giving us is the accelerator to be able to do that, to deploy this quickly.
When we were doing this internally with our subject matter experts in our data science people,
we were stuck.
We were stuck.
Suddenly we're deploying this effectively at dozens of sites.
And when then we're going to take this to market.
We're already talking to outside clients about this.
Couple takeaways on this whole going from reactive operations to proactive:
you have to have a problem that people care about to get that buy in.
I've said this one a few times: subject matter expertise and data science alone is not enough.
You need an accelerator platform.
Measure and share results.
We have a saying you can't improve what you don't measure.
That's a big deal.
Of course, make that obvious.
And then we've really had success rolling out in phases,
finding a couple of demo sites, getting a lot of positive buzz about it,
and then using that as momentum to go to the next step as part of digital transformation
that's been very effective for us.
So that's all I've got today.
Really appreciate it.
Looking forward to talking to more of you this afternoon.
I'm really enjoying the sharing of ideas and insights.
Thank you very much.