Biarri Workforce New Feature Release: July Edition

With the end of the Financial year coming to close, and as Businesses continue to adjust to our new normal, the team behind Biarri Workforce continue our commitment to improve and simplify workforce rostering and planning. With our users in mind, our development team has updated and created additional functionality to equip you the user with the necessary tools to improve the way you:

  • View KPI’s and create reporting for exporting;
  • Manage your employees work schedules across multiple rosters 
  • Improve visibility of rosters when planning with simplified user interface functionality and
  • The way employers manage employees fatigue and qualification compliance 

Read on to see how Biarri Workforce continues to simplify and prepare your workforce scheduler to create optimised workforce rosters and work schedules. 

  1. Roster Export Additional Employee Columns  

Reduce confusion and assure your employees with the option to increase your employees visibility with additional ‘Employee Fields’. When downloading or exporting your roster, you have the choice to include or exclude the following employee details from your roster. Better manage your employees’ details with the new employee columns. 

2. Include shifts from non-primary roster

Better manage individual employee rosters by being able to include shifts from non-primary rosters. When downloading rosters, include or exclude shifts from non-primary rosters with Biarri Workforce’s new toggle option. Perfect for employees who work across multiple departments and perform multiple functions, keep track and improve the way you plan and organise their roster.


3. Customisable report titles

Create clearer reports and configure ‘titles’ and ‘subtitles’ before downloading your roster. With the ‘Suggested Titles’ selector – correctly title and label your rosters based on intelligent suggestions.



4. Roster KPI’s – Paid hours/ total days sub totals 

Make more informed decisions with Biarri Workforce’s improved ‘RosterKPI’s’. With the enhanced KPI functionality, users will have the option to view the sub-total paid working hours and total days. Helpful and useful when dealing and managing fatigue, increase your workforce planners and users visibility over working hours, ensuring employee hours do not exceed CBA regulations.

5. Powerful roster view filters

Improve your user experience with the new Biarri Workforce filter options. By clicking on the following ‘filter’ icon dropdown in the Rostering’ tab, your user will now be able to organise and switch views by selecting the necessary filters, creating a more clearer and purposeful roster.

6. Fatigue and Qualification Compliance Reporting 

Found in the ‘Admin’ tab, users will now be able to export fatigue and qualification reports under the ‘Roster Validation’ sub header.
Make compliance and safety a priority by ensuring you have the correct employee with the correct qualification, to perform the necessary tasks. With Roster Validation’ reporting, your Workforce Planners and users will be able to stay up to date with employees who do not satisfy the requirements to perform specific tasks and roles. From alcohol tests to site compliance, roster validation will highlight when employees are in breach of rule specifications outlined.

If you want to know more about any of the features mentioned above, we invite you to leave your contact details in the contact form below and one of our team members will get in touch with you. Or if you want to know more about Biarri Workforce follow this link.

Workforce Contact Form

Changing the landscape of Route Optimisation

Introduction

Getting from point A to point B is a simple enough task to be completed on most devices, through various different apps and software. But what happens when you have to get from point A to point B and now point C with consideration of other factors like availability windows and route preference? Scopta have developed Run and Route a route optimisation software that deals with the complexities of vehicle and delivery routing.

Read on to discover Run and Route and how it is changing route optimisation and vehicle routing.

Scenario

Barry is the Operations Manager at a warehouse depot for a biscuit company and is in charge of the planning and organising of the distribution of goods sold across Sydney. Barry is tasked with delivering 400 orders to be delivered between 20 trucks, exactly 20 orders per truck. For the last 10 years Barry has used a combination of Excel and Google Maps to figure out their delivery routes. He plans his delivery schedule a week in advance, and spends a large portion of the week carefully mapping out delivery routes. Barry is restricted by both time and cost, trying to figure out the fastest and most efficient route.

Planning out a delivery schedule by hand is notoriously difficult and time consuming, not to mention subject to human error. Manually working between Excel and Google Maps to find the best delivery order is inefficient and also raises challenges such as the Travelling Salesman Problem or TSP, which is simply finding the best order in which to visit a set of locations. Through traditional methods of Excel and Google Maps it won’t tell you the best way to order those stops to give you the overall shortest or fastest route but instead show you the quickest route from point A to point B. Now say you throw in point C and point D? An extra level of complexity is added with additional locations. How is Barry to know which location to begin with and the order to complete the rest of his deliveries?

With Run and Route, Barry will have a centralised solution that will allow him to input information about his locations and trucks, and automatically configure the fastest and most cost effective delivery routing schedule. Traditionally, Barry might have begun his route at point A, followed by point B, C and D in that order. By inputting this data in Run and Route, Run and Route will determine the optimal delivery schedule that would show that this particular truck should begin his delivery route at point C, then point A, then point D and finishing at point B. 

Another challenge Barry and other Operations Managers face is creating a schedule that considers delivery time windows and customer availability. Factoring customer availability and time windows is a crucial and important aspect of determining the optimal routes for a fleet of delivery vehicles. For example one shop in Bondi has a strict 2 hour delivery window between 5:00am and 7:00 am, while another customer in Redfern is a bit more flexible and is open for deliveries from 6:00 am to 12:00 am. Manually working through these intricacies one truck at a time not only requires an incredible amount of thought and time, the level of complexity dramatically increases with scale

run and route

Solution

Run and Route is Scopta’s Vehicle Routing offering, designed to simplify the planning process for last-mile delivery. It is useful for businesses with multiple vehicles that each perform multiple deliveries per day. Run and Route can help your business cut variable costs and improve your customer service. Remain efficient and competitive with a quality Vehicle Routing Solver that simplifies the role of your Operations Manager and the way you plan and schedule your deliveries. Be confident and assured with the quality and accuracy of your schedules with Run and Routes powerful engine.

Want to know more? Speak to a team member today and find out how Scopta Run and Route can automate your vehicle routing.

Run and Route Contact Form

Sales forecasting tool

Sales Forecasting Tool to Plan Accurately With Some Simple AI

Throughout my career I’ve worked with salespeople, as a salesman, and in roles supporting sales activities. Sales is one of the most important functions of a business as without sales, you have no business, no matter how great your product or service is. 

Sales is the fuel for any business to survive and thrive and this makes planning and forecasting sales one of the most important activities a business does.

That’s why running a business without effective and accurate sales forecasting is a bit like flying a plane without a fuel gauge. Of course, an accurate fuel gauge is not necessary or sufficient for generating or maintaining lift – the Wright brothers got away without one. But there’s a reason why modern planes have them – it gives pilots access to data to make the flight decisions to get from A to B. 

So how are you flying your venture?

Probably the same as most other businesses. 

You gather your sales team and ask them, ”how many sales will we have this year?” 

In the best case scenario, they review last year’s sales and make a guess based on gut feeling and intuition (which is not always wrong). 

Commonly enough though, a misalignment of incentives and company sales culture can manifest as a mismatch between targets (optimised for remuneration incentives) and forecasts (optimised for accuracy).

We can do better. 

And to do this we need to use data. But why is data so important?

According to the Professor of Digital Practice at QUT, Mal Thatcher, the 21st century will be the century where,

“By the middle of the century the only tangible asset on an organisation’s balance sheet will be data”

and this is true for your sales too.

To give you and your business a competitive advantage, we at Biarri have developed a simple, easy-to-use Excel sales forecasting tool for you. So it is time to become data driven now and with Biarri’s new tool this is extremely easy. 

Biarri has taken some basic AI techniques and put them into a spreadsheet that requires no macros, no plugins and nothing to install. The AI techniques in this Excel tool will help guide your sales team to make more accurate predictions for the coming year. 

You don’t need to be an expert in AI to leverage the tool. It does all of the hard work for you and provides you with data driven monthly predictions for the coming year based on quarterly sales patterns. You don’t need to know cutting edge AI to use the tool, just how to copy and base a small amount of data.

You can download the tool below for free. There is no need to leave your email address or anything. Biarri’s mission is to make the world more efficient via better decisions powered with mathematics and we believe this tool has the potential to make a difference for your organisation.

Your New Sales Forecasting Tool

Before you download the tool, it is worthwhile telling you what it is, and how to use it.

It uses historic data to establish a pattern and then extrapolates this pattern to be able to predict the coming year’s sales. 

Not only does the tool provide monthly predictions, it also takes into account quarterly sales cycles. Forecasting quarter-by-quarter aligns it with typical quarterly reporting and also captures the variance in quarterly sales. This quarter-by-quarter approach is designed for industries like retail which have some quarters with greater sales (e.g. Xmas). 

There is also a “bad month flag”. This allows users to indicate if something bad has happened in the past during months (e.g. COVID) and if similar events are predicted to occur in the future (in the PREDICTIONS tab). 

This spreadsheet comes prefilled with data to show you what it should look like. To use it for yourself, remove the data from the Monthly Sales column in the Data tab and replace it with your own data. The calculations and updates will be carried out automatically. All other cells are locked for your safety. 

How do I use the sales forecasting tool?

The steps to using the sales forecasting tool are as follows:

1. Collect exactly 36 months of contiguous sales data leading up to the month you would like to predict from. E.g. if you want to predict the yearly sales from January 2022 until December 2022, then collect the 36 months of sales data from January 2019 until December 2021. The model is set up for exactly 36 months of data, not more or less.

2. Copy this sales data into the Monthly Sales column in the Data tab (in green). The top most entry should be the oldest (e.g. in the example in 1., January 2019) and the bottom most entry should be the newest (e.g. December 2021 in the example in 1.).

Sales forecasting tool monthly sales column

3. In the Data tab now enter the first month for the monthly sales data in the month tab by choosing from the drop down menu (this cell is green). Also choose the year in the year column from the drop down menu (this cell is also in green).

Sales forecasting tool month selection

4. For each month, choose whether a bad event occurred (or not) by selecting Yes or No in the Bad Event column. If normal trading and fluctuations were occuring, then put No. Otherwise, if something truly unusual (e.g. COVID) occurred that significantly impacted your sales volumes, select Yes on the months which were affected by this (this column is coloured green).

Sales forecasting tool bad event input column

5. Once this data has been entered, go to the Model Analysis tab to see the outputs of the model. In the Predictions tab, if you predict that there will be any bad months in the future, select Yes in the corresponding months in the Bad Event column (which is in green). For this to have effect, similar Bad Events need to have occurred in the past otherwise this will have no effect.

Bad event output column

6. Finally, your predictions are shown in a graph in the Dashboard tab, with a table showing the cumulative results for each quarter.

Monthly sales prediction graph

Download the sales forecasting tool by clicking on the button below.

What does Biarri do?

Most companies begin with Excel sheets like the one provided here to start making once off decisions on key parts of their business. It is like the first flight of a plane with an often inaccurate fuel gauge caused by data issues. At some point, organisations need to lift up from Excel to correct, secure, easy to use and more powerful tools and this is where Biarri helps.

Biarri’s main value proposition is to help clients realise operational excellence in the way they run their business via AI. The core of this is excellent, data driven decision making. Biarri catalyses AI driven business decisions by using its cloud based set of mathematical tools, the Workbench.

To discuss how you can leverage your data and turn it into value to reach new operational heights, reach out with the form below now.

Get in touch

  • This field is for validation purposes and should be left unchanged.

AI Driven Business Decisions

AI Driven Business Decisions

As COVID descended upon the assisted care industry, many were unsure how they would survive. Providing education services for children with disabilities such as autism is not something that can be easily carried out remotely. As the healthcare crisis mushroomed into a potential years long drama, people were questioning whether the service providers could even survive.

This feeling of despair was facing most industries as they tried to desperately pivot their businesses to stay afloat. Organisations in sectors like e-commerce were well positioned to grow, others like the assisted care providers less so. 

However, there have been some pioneers who have thrived. We can learn from their successes.

All businesses which have pivoted to digital have discovered one thing – a deluge of data. This deluge of data means that the next phase of growth out of COVID will be defined by two paradigms:

  • Businesses harnessing their digitally generated data to enter a new phase of growth defined by:
    • Cheaper and personalised marketing, 
    • Extended flexibility in delivery of services and products
    • Better operating margins
  • Businesses who are unable to leverage their digital assets and will continue to struggle throughout the crisis praying for it to end unable to meet the challenge.

The AEIOU Challenge

AEIOU is a provider of educational services to children with autism. Their mission is to provide early intervention that enables children with autism to live their best lives. 

In early 2020, it became clear to their dedicated team that the year was going to be different. As the global economy grinded to a halt and social distancing became the norm, the staff began worrying that possible COVID outbreaks in their centres could shut them down.

However, they had a trick up their sleeve – the Little Steps educational platform.

Over the previous nine months, the AEIOU team had been working indefatigably with Biarri to bring a disruptive new technology to the industry which would:

  • Remove the need for large paper folders transported around in trolleys;
  • Improve the efficiency of staff content delivery;
  • Make home based delivery possible and
  • Create a treasure trove of digitally collected, consistent and high quality data to be analysed for deep treatment and progress insights

And it would be this final point which has the potential to not only revolutionise the disability care sector but all sectors.

But how? And why?

AI Driven Business Decisions

Like AEIOU, across the globe many businesses are in the final stages of a planned digital transformation or one brought on by COVID. Those businesses which have already completed this process are now looking for ways to leverage the data being collected by the new digital processes and turn it into value.

So what is the best way to do this?

By combining your data with intelligent analytical tools to help make better decisions.

Having good quality data in a consistent format, collected by digital channels, allows companies to apply powerful analytical tools to this information and use it to help understand:

  • What will the future look like? I.e. make reliable predictions
  • What is the best decision to make? I.e. optimise choices to maximise returns and organisational growth
The Biarri Workbench

The problem is that data driven, decision-making normally begins with the in-house development of low-tech tools to manually solve key business problems. As businesses grow, they must move their Excel sheets to the automation of core business processes via mathematical tools to enable better decision making. Why?

Replacing error prone, slow, manual and insecure processes with robust, fast, automated and secure AI tools enable new phases of growth.

By providing digital tools to make optimal decisions, front line staff can move from manual, repetitive and error prone tasks to high value scenario analysis and answer key questions for senior management around future states and optimal strategies. What does this achieve?

It increases the value output per employee via automation and outmaneuvers competitors with better decisions

The above activities are the core of an AI Driven Business Decisions and form part of an AI Driven Digital Transformation which you can read more about here.

AI Driven Value Creation

By using AI to create value, companies can begin the AI driven digital transformation journey as shown in the below image.

But how can a company climb this curve? The details of this transformation are represented in the following diagram.

With the early learning platform, AEIOU have built the foundation of their digital strategy and the logical next step in their transformation is ground-breaking. By digitally capturing the data on learning outcome improvements for children with autism, they can discover new methods that can transform the journey for some of society’s disadvantaged.

Even greater for AEIOU was that their digital platform, Little Steps, was ready as Australia went into lock down. They were able to leverage it to continue the challenging remote learning regime required to not interrupt the learning process for their children.

This wasn’t the first app Biarri had built to enable companies to thrive during the COVID challenge. Biarri has built over a hundred apps to help companies all along the journey of turning data into value.

What role does Biarri play in this transformation?

Biarri’s main value proposition is to help clients realise operational excellence in the way they run their business. The core of this is excellent, data driven decision making.

How do we do this?

Biarri catalyses AI driven business decisions by employing its cutting edge Workbench platform. The Workbench platform empowers Biarri’s customer base in the form of value-creating production tools. 

In the words of businesses we work with, the benefits of a data driven approach leveraging mathematics are that it:

  • Helps make better decisions
  • Improves efficiencies & saves time
  • Reduces cost
  • Improves a business’ core product / service delivery
  • Is easier to use than alternatives (e.g. better than Excel)
  • Allows real time and scenario planning abilities

In AEIOU’s case we built a digital platform with plug and play analytical capabilities. This could tap into automated and optimised rostering tools and lead a true AI driven digital transformation. In the words of their CFO:

The development process with Biarri has been a great success. The team went above and beyond to deliver on our requirements and were engaged, helpful and responsive in understanding the complex needs of our business. The challenge Biarri solved was complex, however, the entire process from development to operation was collaborative and professional and we look forward to continuing our partnership with them.

Matthew Walsh, CFO, AEIOU

Does this apply to me?

Biarri delivers solutions to a wide range of industries. The mathematics which powers our AI knows no boundaries and one powerful model can underpin the efficiency gains in profoundly different industries, from aviation through to the healthcare.

To discuss how you can leverage your data and turn it into value, with AI Driven Business Decisions, reach out with the form below.

Get in touch

  • This field is for validation purposes and should be left unchanged.
Agribusiness Optimisation Solutions

Maths and Machine Learning for Agribusinesses

Mathematics powered by computers is changing the world we live in. At Biarri we see this everywhere, across every industry, and I’m sure you do too. Recently we have delivered a number of Machine Learning and Mathematical Optimisation solutions for Agriculture businesses in Australia and were fortunate enough to be invited to speak at the recent Case IH agri-business conference in Mackay.

Ash Nelson, Biarri’s co-founder, presented on Maths and Predictive Analytics for better business decisions. He described how our everyday lives are being changed by corporations leveraging large data sets, advanced statistical analysis and powerful computing resources. Ash then outlined how these same set of technologies can be utilised to improve business decisions in agriculture. This includes optimising agricultural supply chains and port operations, reducing unplanned equipment failures by using intelligent predictive maintenance algorithms or to improve health and safety outcomes for farm workers by better identifying areas of best practice to inform injury prevention initiatives.

Are you interested in leveraging your data using advanced maths to make better business decisions? Don’t hesitate to get in touch with our friendly team.

Supply Chain planning

Making the most of storage facilities in large construction projects

Large construction projects have a huge logistics component often requiring equipment from all over the globe to be brought together through a complex supply chain. In an ideal world, our suppliers would produce items on time and they would be transported to our construction contractors just as they needed them.

Unfortunately, we don’t work in an ideal world – suppliers can fail to produce on time, problems can occur in transit, and external forces outside our control can interrupt the construction schedule. For this reason, we want a contingency plan – usually in the form of a storage facility where we can hold spare or excess items if our supply and demand schedules don’t match up.

These facilities are used heavily over a short period of time, meaning that any inefficiencies quickly add up to large, unnecessary costs. However, given the size of these projects, identifying and fixing these issues is far from easy or intuitive. It is through analytics that you can drive quantitative answers and support effective decisions in your logistics and facility planning.

Read more

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.

Read more

Biarri FIFO Management

Grounding the complexity to Fly in Fly Out management

Being able to close the labour and skill gap is a critical factor in sustaining growth and maximising profitability for remote operations. It is imperative that companies have the tools and skills available to unravel the complexity to FIFO management.

FIFO workforces are commonly used by large infrastructure and resource projects in remote regions including rural and offshore. These regions often don’t have adequate infrastructure or an available local workforce with the right skillset which leads to companies requiring the use of workers from interstate and sometimes overseas.

The FIFO problem is complex for many companies. It involves determining efficient ways to move people via aircraft, taking into consideration: multiple projects at various phases over multiple locations, with a dynamic workforce utilising different skillsets on a variety of roster patterns, as well as using a fleet consisting of different types and numbers of aircraft.

Often the goal with FIFO management is to determine the number, and type, of aircraft needed in order to minimise cost whilst working with the opposing objectives of ensuring: the staff arrive before the start of their shift (but not too early), depart after the end of their shift (but not too late) and keeping travel durations to acceptable lengths (to ensure low fatigue).

Balancing FIFO Complexity

Analytics to break through the complexity

With this level of complexity, a traditional excel approach lacks the rigour and power to find the most efficient and effective results. As a result we’ve developed a number of different FIFO optimisers at Biarri to help ensure the best outcome for clients.

The reality is that there are often many more factors that need to be considered which complicates the problem further. Each FIFO optimisation problem often turns out to be quite different once the detail of the problem is better understood.

High Level FIFO Requirements

Some companies just want us to help them “define their fleet, or travel requirements” so they can then go out to tender (it also helps to keep the vendors honest), others actually want an operational tool. Others may be looking to see if there is a business case for upgrading an airport (e.g. if the airport is upgraded, then larger aircraft can be used which can reduce the need for bus in bus out (BIBO) which will alter their risk profile due to road km and can dramatically alter travel durations).

Specific FIFO requirements

Our clients often want different levels of detail in the solution. Some are happy with a solution that ensures adequate movements at the week level (e.g. 15 flights of aircraft type A between locations B and C per week), others want very detailed minute by minute schedules which take into account: turnaround time, time between takeoff and landing, number of aircraft gates with solutions showing exactly who is travelling on which flight and aircraft and when.

Across Multiple Projects

Our clients have also had multiple projects which are often on the go at the same time and sometimes different priorities are given to different projects. These priorities can be used to ensure that if all the people movement demands can’t be met, then the lower priority movements are less likely to be satisfied.

Optimising the time horizon

The optimisation time horizon can also vary significantly with some clients optimising over a 24 hour period (or even less if they want to re-optimise in the middle of the day due to unpredictable events such as delays due to weather) through to clients wanting higher level schedules over several years to help them make strategic decisions and determine how their fleet needs to change over time.

Understanding the constraints

Constraints such as: the maximum distance an aircraft can travel before needing to refuel, maintenance schedules and the refuelling locations themselves often also need to be considered. We’ve dealt with both fixed and rotary wing (helicopters) aircraft. Helicopters have the additional complication of sometimes having to take more fuel (and thus weight) to travel further, which results in the reduction of passengers because of the helicopter’s limited total payload capacity.

Finding the right FIFO parameters

We have outlined some of the parameters that our FIFO optimisers have considered. It is by no means comprehensive and we can always include new parameters if a different problem requires them but it gives a good understanding into the different variables that can, and should be considered.

Some of the typical inputs include:

  • Location
  • Hours of operation
  • Refuelling capability
  • Refuelling duration
  • Availability (i.e. you can specify a start and end date for which the airport is available)

  • Serial number
  • Category (e.g. fixed wing or rotary wing)
  • Type (e.g. DASH 8-200)
  • Average speed
  • Passenger seats
  • Maximum payload
  • Fuel density
  • Fuel tank capacity
  • Re-fuelling time
  • Fuel burn rate
  • Base location
  • Availability (i.e. you can specify a start and end date for which the aircraft is available)
  • Costs

  • From location
  • To Location
  • Distance
  • Aircraft types able to fly this leg

  • Origin
  • Destination
  • Project
  • Number of passengers
  • From Date
  • To Date
  • Arrive Before (i.e. must arrive on their first working day of the roster by this time)
  • Depart After (i.e. must depart after this time on the last working day of the roster)
  • Roster Pattern (e.g. 14:14 = 14 days on, 14 days off)
  • Day of week (i.e. which day of the week can this person travel)
  • Group (demands can be grouped together to allow the user to specify which demands can be grouped on the same aircraft)

Some of the typical outputs include:

  • Total flights
  • Total distance flown
  • Total fuel burned
  • Total number of aircraft required
  • Utilisation Percentage
  • Total unused pax capacity
  • Total passenger demand
  • Total passenger demand satisfied

  • Serial number
  • Date
  • Total pax
  • Total hours flown
  • Total distance flown
  • Total fuel burned
  • Total flights
  • Total legs
  • Cost

  • Flight ID
  • Resource ID
  • Pax capacity
  • Available pax capacity (this is < pax capacity if the fuel weight is a limiting factor)
  • Total used pax
  • Utilisation Percentage
  • Departure location
  • Departure date and time
  • Arrival location
  • Arrival date and time
  • Day of week
  • Total distance
  • Total hours flown
  • Total fuel burned
  • Fuel weight at start of leg
  • Refuel at destination (true or false)
  • Turn around time
  • Cost

  • Flight ID
  • Origin
  • Departure date and time
  • Destination
  • Arrival date and time
  • Project
  • Pax

  • Project name
  • Total demand
  • Total satisfied demand
  • Total unsatisfied demand (e.g. this will be non zero if there is not enough capacity to transport demand)
  • Total impossible to satisfy demand (e.g. this will be non zero if a flight path has not been specified in the inputs that results in some demand being impossible to satisfy regardless of aircraft resources available)

  • Flight ID
  • Number of instances (i.e. how many times is this flight route flown at the same time – but on different dates)
  • Resource
  • Date of first flight
  • Date of last flight
  • Day of week
  • Departure time
  • Arrival time
  • Total people
  • Total distance
  • Total hours flown
  • Total fuel burned

Unravel the complexity to FIFO Management

The work we have done for companies such as Arrow, Origin, QGC, BMA, IBS, and Santos has shown us that despite having FIFO problems, they all required different approaches in order to achieve the right result.

This has demonstrated to us that when approaching a FIFO problem, where so many different variables have to be considered depending on the client, a standard approach (Commercial off the shelf product) and excel models will generally struggle with the complexity.

Having a tool built around specific variables demonstrates the benefits to bespoke solutions for FIFO problems.

Find out more about Biarri in Mining >>
Find out more about Biarri in Oil & Gas >>
Find out more about Biarri and FIFO Scheduling >>

Or, Get in contact so we can discuss your requirements.

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?

Read more

Biarri Optimisation - 21

One tech to rule them all

Back in high school when you were battling polynomials with derivatives, you learnt one of your first all-purpose optimisation techniques. Given a function, take its derivative, set it to zero and solve for all optima. Voila! After you had coerced the equation to give up all its maxima secrets, you could then be safe in the knowledge that you had found the optimal values.

If you enjoyed computing the optimal speed to jump your calculus hurdles, then you probably went on to university to learn more about other mathematical tools. Once there you most likely met your second all-purpose optimisation technique, Linear Programming. Given a simple linear function with linear constraints, you learnt that you could use any of a number of tools such as the Simplex Method to easily and quickly solve such problem.

Ant Colonay Optimisation in Action

Figure 1 Ant Colony Optimisation in Action

From there, if you had more of an applied orientation, you probably went on to learn about the exotic optimisation creatures. For example, Genetic Algorithms, Simulated Annealing as well as a host of other nature-inspired algorithms such as Ant Colony Optimisations, Swarm Optimisation, Artificial Bee Colony Optimisation techniques, etc. In the 1990s and 2000s, for almost any type of problem, it seemed that one could find a new nature-inspired optimisation technique to solve it better than previous more mainstream tools.

However, in spite of the swarm of new optimisation tools, most problems for which these new techniques were developed could be solved with existing tools. Although probably not as efficiently. Hence the question was which method was best? The answer to this question came in 1995 when two researchers, David Wolpert and William MacReady at the renowned Sante Fe Institute, proved a number of theorems collectively referred to as the “No free lunch” theorems. These results could be seen as implying that there is no one optimisation method that is best for all problems. In addition, when we average over all problems, we expect all methods to be equal.

This result has important and deep implications. It means that if you swear by a single generic optimisation method and try and use it to solve all your problems, then don’t expect it to perform better than your three year old son who guesses random solutions to all your different stochastic multi-objective constrained optimisation techniques.

Given this it would seem strange then that I am about to suggest the idea of solving numerous problems with a single optimisation technique. Besides the fact that a number of these problems don’t look like optimisation problems at all! The reason for doing this is to see the power of an interesting optimisation technique, as well as its flexibility and generality. In addition, one needs to bear in mind that the “No free lunch” theorem is a result about every imaginable optimisation problem, which is just a tad more than the few I will touch on here.

The ST5 antenna’s final optimised design

Figure 2 The ST5 antenna’s final optimised design

The class of optimisation technique that I want to discuss here is generally referred to as Genetic Algorithms. They have been successfully used on thousands of research and industrial problems and continue to amaze researchers with their potential to solve problems far beyond their original scope. For example, one of the most famous applications of Genetic Algorithms was by NASA in 2006 to develop a new antenna design for their ST5 antenna. It can be seen that the optimal design was anything but intuitive and most likely would not have been found by a “standard” optimisation technique based on initial human guesses.

So what type of problems would be considered inappropriate for Genetic Algorithms? First of all, you couldn’t do much worse than write some code to give to your son or daughter so that in their next algebra exam they can solve x + 5 = 6 with your tailored made Genetic Algorithm. They could just let your code chug away and sit there patiently while it aces the exam for them. Although not probably the most effective use of Genetic Algorithms, it is entirely possible.

So let’s take that thought one step further. What about solving the humble quadratic equation with a genetic algorithm? It has been done (and done againand again). But the quadratic equation belongs to pure mathematics right? In addition, it’s an equation you can solve directly isn’t it? Yes and yes but interestingly enough Genetic Algorithms have started to make their way into some of the purest area of mathematics to help solve problems that are stumping pure mathematicians, This is truly one area where you would not expect the tools of applied mathematicians to come to the rescue of pure mathematicians.

We have only scratched the surface of unexpected applications of Genetic Algorithms. In fact, they have made an appearance in almost every endeavour of research from physics to economics, from architecture to chemistry and even all the way back to their nature-inspired beginnings with numerous applications in biology. So in spite of our knowledge that there is no one method to solve all problems, Genetic Algorithms present us with a versatile and powerful tool that seems to have a lot more in store for our future problem solvers.

Read the second part here.