Mitigation by Iteration

Mitigation by Iteration – Facilitating the Optimisation Journey

For companies to remain competitive, they require smart systems that solve their unique day to day business problems. However, when applying these systems many decision makers get lost in the complexity due to limited communication and collaboration within the implementation process.

We are at the cutting edge of the latest optimisation methodologies and web technologies. However, unlike many other optimisation, and analytics companies out there, one of our main goals is to make powerful optimisation accessible in the real world to bring value to our clients.

We specialise in the development of web applications and smart optimisation engines–delivered in less time than you would probably think. It’s not unusual for us to go from an initial workshop with a client, to understanding their problem, and then having a fully functional optimiser in a production environment within three months.

On top of this the same people are often involved through the entire SDLC (software development life cycle) i.e. from the spec/design, theory, implementation and delivery/support. This reduces the overhead many organisations incur by having different people in business analytics and developer positions. The people implementing the solution actually understand and work with you to solve your problem.

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Driving efficiency in the Oil and Gas industry

Driving efficiency in the Oil and Gas industry

The oil and gas industry has been under severe pressure since late 2014 when oil prices dropped significantly. The highly volatile international market and oversupply of oil has meant companies have had to reduce costs in one way shape or form.

Bill Kroger, co-chair of law firm Baker Botts told Rigzone in an interview that, “Energy companies may need to lower their prices in response to a drop in demand …. For this reason, we may see CAPEX [capital expenditures] begin to decline until there is some stability with oil prices,”

This has been evident in Australia where many oil and gas companies have reduced capital spending significantly. However, with a lot of oil and gas projects shifting towards the operational phase, how can we make processes and decisions more efficient and effective?

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Health Analytics

Analytics, just what the doctor ordered

Can Analytics and Optimisation be ‘Just what the Doctor ordered’ for struggling Australian Hospitals and Health Services?

In Australia consumers have more access to information than ever before and are demanding increasing accountability from their doctors, nurses health plans and, better health care quality. The Hospital and Health Services (HHS) industry despite struggling under the weight of an ageing population, a shortage in doctors and nurses, increased regulation, accountability, governance and budgetary oversight, are continually required to provide more with less.

The healthcare industry requires smarter, more informed decisions to enable improved efficiency, better service delivery and enhanced patient outcomes.

Research in 2012 by IBM into the Healthcare Industry in the US confirmed mounting evidence of entrenched inefficiencies and sub-optimal clinical outcomes. The report highlighted how building an analytics focus can help these Health organisations harness “big data” to create actionable insights, set their future vision, improve outcomes and reduce time to value.

The authors note that the abundance of data that bombards healthcare professionals both facilitates and complicates the ability of healthcare providers to achieve and influence desirable outcomes. It appears clear that entrenched systemic inefficiencies in the health systems are at least in part attributed to the ineffective gathering, sharing and use of information

The glut of information makes it hard to differentiate data which can be used to generate powerful insights, from clutter. In fact, the dilemma presented by too much data and too little insight – is cited in the research as an increasingly daunting obstacle standing in the way of better service delivery and improved patient outcomes.

The daunting challenges facing the healthcare industry today make for compelling arguments to expand the role of analytics

The study confirmed that analytics can provide the mechanism to sort through this mountain of complexity and data, and help healthcare organizations deliver on efficiency improvements and better patient outcomes. In Australia the introduction of Activity Based Funding (ABF) has promoted the use of data as the essential input informing critical decisions by Managers, Administrators and Clinicians. Not surprisingly HHS are increasingly looking to move from data processing to data analysis and applying insights to financial outcomes. Australian HHS are just starting to recognise how the power of mathematics through analytics and optimisation can be utilised to consume, unlock and apply new insights from information.

Analytics can provide the mechanism to sort through this mountain of complexity and data

Despite the availability of new methods of analytics that can be used to drive clinical and operational improvements, Australian HHS continue to function with a traditional baseline of transaction monitoring using basic reporting tools, spreadsheets and application reporting. As in the US Health system Australian HHS must face-up to the challenge to move from the traditional model to one that incorporates predictive analytics and enables organizations to “see the future,” and create more personalised healthcare and predict patient behavior.

Advanced analytics and optimisation approaches can take full advantage of the ‘Data deluge’ to generate powerful insights which deliver better outcomes

Today, most HHS use some form of descriptive analytics. They are typically using reporting tools and applications descriptively to understand what has happened in the past and to classify and categorize historical data. However, as their analytics expectations mature, HHS are looking more toward predictive analytics techniques, which take an understanding of the past to predict future activities and model scenarios using simulation and forecasting. The report notes that Enterprise analytics, evidence-based medicine and clinical outcome analytics can all be supported by these more advanced capabilities. For example, analytics can enable the compilation of information about trends, patterns, deviations, anomalies and relationships and reveal key insights.Biarri Optimisation Software Banner

Some Hospital and Health Services are taking a proactive approach

Gold Coast University Hospital (GCUH) is one example of an Australian HHS organisation leading the way by embracing predictive analytics to improve demand for better service delivery and enhanced patient outcomes. Most recently Biarri Optimisation worked with GCUH to enhance their understanding of expected future demand and to develop insight into opportunities to better allocate resources. Through the application of customised predictive analytics and optimisation GCUH improved their knowledge of forecasted demands for the next Financial Year, allowing improved capacity planning requirements for physical resources and staffing resources equating to better workforce optimisation.

Biarri and GCUH demonstrated the value of quantitative analysis in forecasting patient admissions and QWAUs and used this to provide more efficient capacity and resource planning.

For most organisations today, data visualisation, historic trend analysis and forecasting, and standardized reporting are the analytics elements that provide the most value. However, that is likely to change. The research showed that while data visualisation will always be a critical element, increased emphasis will be placed on simulations and scenario development and analytics that are applied within various business processes.

Biarri Commercial Mathematics

To learn more about how Biarri can help your HHS organisation benefit from advanced analytics and optimisation go to www.biarri.com or contact

Sam Rowse: Email: sam.rowse@biarri.com, Mobile: +61 458 004 220

Coal Train Crew Scheduling

An optimised approach to Coal Train Crew Scheduling

Rail is frequently used for moving coal between mines and ports, and interactions between train and crewing requirements can create highly complex problems.

Recently Matt Herbert, an optimisation consultant at Biarri Commercial Mathematics was invited to present an approach to Coal Train Crew Scheduling at the Queensland University of Technology, hosted by ASOR.

Matt provided insights into the problems many mining and rail companies face when scheduling their crews. His formulation considered many aspects of the real world problem, including restricting the number of crew changes on each service, and variable start times for crews.

This approach is able to produce weekly crew assignments with high utilisation in run times of around an hour, down from existing manual methods requiring a day or more.

Have a look at Matt’s Presentation

On The Blog

Is your decision making based on Analytics or Gut feeling?

Despite the proliferation of data and the constant drive to adopt innovation to achieve competitive differentiation;many businesses continue to make critical business decisions that are a product of intuition and haste rather than fact and rigor.

40% of major decisions are still based on your Manager’s gut feeling

The question as to why companies continue to rely on ‘best-guesses’, despite the availability of advanced business analytics, quantitative models and optimisation methods is confounding. Using data and quantitative analysis to support decision making, removes ambiguity and improves speed and accuracy. Decision making is more likely to be correct and the process has more rigor due to the application of the scientific method.

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Our philosophy is to make the power of mathematics accessible. Why? Because we think it isn’t currently very accessible, this limits the number of people who can use it to get value and reduces the value derived by those who do use it, and that’s a crying shame in a world that desperately needs efficiency. We have all seen it multiple times in multiple organisations. It’s the hard to use (probably ugly), not really fit for purpose (lots of workarounds), complicated IT (n tier, client/server, VM, Citrix, Oracle thing) approach to providing optimisation software.

COTS vs Bespoke

COTS (commercial off the shelf) puts the bars around accessible Mathematics: leads to crying babies

Our philosophy is to make the power of mathematics accessible. Why? Because we think it isn’t currently very accessible, this limits the number of people who can use it to get value and reduces the value derived by those who do use it, and that’s a crying shame in a world that desperately needs efficiency.

We have all seen it multiple times in multiple organisations. It’s the hard to use (probably ugly), not really fit for purpose (lots of workarounds), complicated IT (n tier, client/server, VM, Citrix, Oracle thing) approach to providing optimisation software.

So how did it come into being? Here’s how I see it:

“I’m unique;give me your shrink wrapped product!” – and other amusing procurement stories

Let’s assume requirements are done, I’ll save organisational scope bloat for another time. The next question is build or buy? How will we best get something that is a close match to need/requirements?

So a market search ensues only to discover that the requirements are pretty unique. So a custom/bespoke solution is required! That makes sense but most organisations quickly discover that bespoke = expensive (time and money), just like buying a tailor made suit is more expensive than buying off the rack.

It’s for this reason that hard core mathematics/optimisation solutions have mainly been consumed by capital intensive industries where spending a few million to save tens or hundreds of millions made the business case stack up.

Therefore organisations often seek a COTS (Commercial Off The Shelf) solution (often after an expensive run in with a bespoke approach), with the expectation that if they specify what they need and buy something “off the shelf”that fits then it should be low risk (time and money). It appears to be quite an entrenched view with Australian CIOs, and in some cases is justified, particularly in back office functions that don’t offer opportunity for differentiation. A point Wesfarmers Insurance CIO David Hackshall and DoD CIO Peter Lawrence make in an article by Brian Corrigan on itnews.com.au titled “How COTS became Australia’s default software setting”.

In the world of mathematics, optimisation and advanced planning and scheduling it would be a very rare occasion with a simple set of generic requirements where COTS really worked. Take one of the classical problems where mathematics are applied, vehicle routing. This is a well picked over area and sounds simple enough. Nonetheless, vendors fill niches within this niche in order to provide a match to requirements. As the Vehicle Routing survey in February 2014 issue OR/MS Today says “VR customers are different, and so are their routing needs and problems, which require flexible, innovative answers”.

Vendors react to this COTS centric procurement environment in a predictable way, and of course say they sell COTS because otherwise when they get evaluated on the inevitable RFX criteria they would fail miserably. The solution? They will (and I’ve been there) include “configuration”or “installation services”as ways to mask software development. The result? You get something that wasn’t a great fit with lots of add on development to meet your requirements. It’s hard to use, slow and doesn’t really provide the solutions you were hoping for. In many cases you end up with the worst of both worlds, the cost of bespoke but the poor fit of COTS.

As the aforementioned itnews.com.au article says “The middle ground between buying readymade software and building bespoke solutions is to customise a COTS package. Yet as many CIOs have discovered at great cost to their budgets and mental health, this can be a painful experience.”

This COTS/bespoke paradox is the problem we saw and it is what we aim to address. So what does Biarri do differently? We take the benefits of bespoke and make it cheaply and quickly. You could say we aim to provide the best of both worlds.

Do the math

How do we do it? First of all, we do the maths first! Prove you can solve the underlying problem and that’s it is worth solving before investing in the delivery mechanism. Once you know there is value in the maths, make sure people can digest it via a well-designed solution. The Biarri Workbench is our SaaS platform that allows us to very quickly develop easy to use, custom applications with unique workflows with an iterative/agile and light deployment.

Who says B2C owns good UX?

Easy to use means designed with the user in mind. In the consumer world (B2C) this is the natural order of things (thanks Apple). In the business world (B2B) this has taken a back seat, and that’s where our industrial designers come in. Working with users to really understand how they do their job and will interact with the system. Producing mock-ups/concepts and getting early feedback before a line of code is written.

So now we’ve proven the maths will provide value and designed a solution that users will love to use.

Mock up example

Our philosophy is to make the power of mathematics accessible. Why? Because we think it isn’t currently very accessible, this limits the number of people who can use it to get value and reduces the value derived by those who do use it, and that’s a crying shame in a world that desperately needs efficiency. We have all seen it multiple times in multiple organisations. It’s the hard to use (probably ugly), not really fit for purpose (lots of workarounds), complicated IT (n tier, client/server, VM, Citrix, Oracle thing) approach to providing optimisation software.

Rinse and Repeat

What comes next is turning this into reality quickly, cheaply and iteratively. Quickly and cheaply are thanks to the Biarri Workbench providing security, common database, existing UI components, libraries and widgets that enable a custom built application to be constructed very quickly. And “iteratively” is thanks to being web delivered which means we can provide early access to users to start providing feedback. Agile development takes on real meaning as users see the mock-ups they helped design come alive in their web browser mere weeks (or even just days) after designing them. Engagement and user buy-in are huge as feedback is provided, incorporated and delivered instantly. Australia Posts CIO Andrew Walduck understands this approach, “The number of times I’ve seen operating models where you start with requirements on one side, you dump it into operations on the other, and it fundamentally misses the point”.

Tool UI Example

Example of tool UI

It takes different strokes to move the world… yes it does

Do you remember the late 70’s early 80’s TV series “Different Strokes”? I use to love the theme song.

Everybody’s got a special kind of story
Everybody finds a way to shine,
It don’t matter that you got not alot
So what,
They’ll have theirs, and you’ll have yours, and I’ll have mine.
And together we’ll be fine….

When you start looking for your next optimisation, analytics or advanced planning and scheduling solution and your CIO/CFO says “budgets are tight and you can’t buy bespoke, you have to go COTS”, remember “it don’t matter that you got not a lot… you’ll have yours” because Biarri has a special kind of story.

How much tax is paid on your landfill?

What do you know about Landfill Gas Emissions? I’m betting not much if you’re not directly working in the industry. As you’ve stared forlornly at the departing garbage truck at 5am after being awoken by its imminent arrival only to recall that you didn’t put the bins out last night, you probably haven’t given too much thought to where it’s going and what happens to the garbage.
Well let me ease your mind and put you off your breakfast. After leaving you in its dust (and your pyjamas), that truck was heading to one of about 450 landfills all around Australia. When it dumps its load of garbage, bacteria are going to start a feast on your leftover spaghetti bolognese and while doing so will emit gas. That gas they emit while eating your garbage is about half methane (CH4) and half carbon dioxide (C02), both of which are greenhouse gasses.
Heard of the carbon tax? Well in basic terms the Clean Energy Regulator, part of the Department of Climate Change and Energy Efficiency, maintains a database of entities expected to pay a carbon price. Landfills will have to pay a carbon price on new waste deposits from 1 July 2012 to encourage them to capture their methane emissions, which can then be used to produce heat or electricity. If a Landfill produces more than 25,000 tonnes of C02-equivalents a year they must report their emissions under the National Greenhouse and Energy Reporting (NGER) Scheme by 31 October 2013.
So if you operate a landfill how on earth do you calculate your emissions? Well it’s pretty tricky business and the National Greenhouse and Energy Reporting (Measurement) Determination 2008 provides methods and criteria for calculating greenhouse gas emissions and energy data under the National Greenhouse and Energy Reporting Act 2007 (NGER Act). Check out some of it here if you have trouble sleeping.
The government doesn’t expect you to know all that detail however and handily provides a calculator that you can use, which you can download on this page. It does the job but is pretty basic and doesn’t do things like show you the waste generated by each deposit in isolation of the totals. It also doesn’t give you anyway to visualise your data or change the parameters used in the calculation, which I’d be pretty interested in given the impending election in September.
The breakdown by deposit and visualisation is why Boral reached out to us for a better calculator last year to calculate emissions on the Boral Western Landfill, located west of Melbourne. So we made one for them. You can read about that here.
We only recently started to ask ourselves how other landfill operators were handling the same problem and whether they also wanted the greater capability of the Biarri Landfill Gas Emissions Calculator. So we did some research and reached out to some people we thought would know better than us about the industry. We were surprised to hear that the government tool was the only option.
So we looked into their tool further and found that they give users a handy guide, the “Solid Waste Disposal On Land User Guide V2.2”. Just don’t use it for calculating your emissions this year! As they say on page 6 “this estimate is not based on the NGER (Measurement) Determination for 2012/13. For 2012/13 reporting you will need to download an updated version of the Calculator when it is available”. And I’m sure it will be made available at some point but it isn’t available yet!
So we decided to make our tool more broadly available (yes, it is compliant with NGER (Measurement) Amendment Determination 2012/13 for the upcoming reporting period).
We’ve set up a site at http://landfill-emissions.biarri.com/ where you can download a trial of the tool and of course also buy it.
So now you know what happens to your rubbish and, if you’re a landfill operator, how to calculate your emissions this year. And by the way, it’s bin night!

Biarri Landfill Gas Emissions Calculator Screen Shots

Non-Legacy Quantity Generated

Annual Generation

Annual Non-Legacy Generation

Annual Total Generation

Forecast Landfill Qty (input)

 

The BAM (Biarri Applied Mathematics) 2012

The second Biarri Applied Mathematics conference, or BAM, was hosted by the University of Melbourne over two days, November 12 to 13. The conference was a big success, with around 80 people attending, including industry representatives, academics and students.

All the talks (given by Operations Research practioners from both Biarri and from industry, as well as academics) were informative and interesting and generated discussion and questions.

Biarri once again thanks those who attended as well as those behind the scenes who helped make the event a success.

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Biarri provides Network Design Optimisation to NBN Co.

NBN Co today announced that they awarded an 8 year contract to Biarri to supply network design optimisation to support development of efficient, lower cost network construction plans.

Biarri is to provide optimisation technology to NBN Co for fibre network design optimisation. The optimisation engine quickly generates low-cost fibre network designs based on the requirements of the reference architecture. It can determine optimal fibre area boundaries, the position of fibre hubs, and the layout and route of distribution and local fibre.

See the NBN Co site for more detail.
Logic of the Fibre Optic Network Design Tool

Optimisation: Striking the Right Balance

One of the guiding principles we use in commercial mathematics is to “Model Conservatively, but Optimise Aggressively”. This means that the problem domain should be modeled with sufficient “fat”in the data to ensure that the results are both legal and robust;but given this, we should then seek to apply the best (fastest and highest quality) solution approach that we can get our hands on.

Optimising aggressively can sometimes have it’s downfalls, though, if taken too literally. I’ve been doing a few experiments with numerical weightings of the objective function in a Vehicle Routing problem, where this issue is readily apparent. (Actually it is a Vehicle Routing problem with time windows, heterogeneous fleet, travel times with peak hours, both volume and weight capacities, and various other side constraints).

Our Vehicle Routing uses travel times (based on shortest paths through the street network) that are characterised by distance and duration. Durations can vary due to different road speeds on different types of streets (highways vs suburban roads for example). This leads to the question of how (on what basis) to optimise the vehicle routes –given that the optimisation has already to some extent minimised the number of vehicles and created well-clustered routes –what is the most desirable outcome for KPIs in terms of duration and distance?

In one experiment I’ve tried three different weightings for the duration (cost per hour) while keeping the cost per distance constant. I’ve run three values for this cost per hour –low, medium, and high weightings –on real-life delivery problems across two different Australian metropolitan regions.

Region 1
Total DurationDriving DurationDistance
Cost/hour
Low74:4724:38708
Medium72:4523:55712
High72:5823:42768
Region 2
Total DurationDriving DurationDistance
Cost/hour
Low113:5446:441465
Medium107:5141:361479
High108:5143:491518

From these results, there is a (more-or-less) general correspondence between distance and the driver cost per hour as you would expect. However, if you push one weighting too far (ie. optimise too aggressively or naively), it will sometimes be to the detriment of all the KPIs as the optimisation will be pushing too strongly in one direction (perhaps it is outside the parameter space for which it was originally tuned, or perhaps it pushes the metaheuristic into search-space regions which are more difficult to escape from). This is most acutely seen in Region 2 when using the high cost per hour value. Conversely if you drop the cost per hour to a low value, the (very modest) reduction you get in distance is very badly paid for in terms of much longer durations. What is most likely happening in this case is that the routes are including much more waiting time (waiting at delivery points for the time windows to “open”), in order to avoid even a short trip (incurring distance) to a nearby delivery point that could be done instead of waiting.

The problem of striking the right balance is most acute with metaheuristics which can only really be tuned and investigated by being run many times across multiple data sets, in order to get a feel for how the solution “cost curve”looks in response to different input weightings. In our example, an in-between value for cost per hour seems to strike the best balance to produce the overall most desirable KPI outcome.