Interview with Evan Shellshear

An interview with Head of Analytics Dr Evan Shellshear on why a growing number of businesses turn to mathematical software to improve operations

An increasing number of businesses across a wide range of industries are embracing advanced mathematical software to help solve complex planning, scheduling, and operational challenges.  

Head of Analytics for Australian tech company Biarri Optimisation, Dr Evan Shellshear said as commercial mathematics technology grew more sophisticated over time, more businesses were realising it’s potential to deliver efficiency.

“We work with businesses to develop and utilise mathematical software that considers vast amounts of data and uses artificial intelligence (AI) to enable better decision making,” Dr Shellshear said.

“Essentially the software ‘does the maths’ to produce data-driven solutions to specific challenges a business may be experiencing; from planning multi-million dollar capital projects to the optimisation of small transport fleets.

“In the past year alone, we have worked with more than two dozen businesses to develop mathematical engines with modern, fit-for-purpose cloud-based web applications.

“Some of the businesses we’ve worked with include Rio Tinto, Origin, Asahi, Swissport, Aurizon, NBN Co and Qld Cotton.”

“Across all these businesses, in the past year we’ve developed more than two dozen applications or specific projects to improve operations.

Dr Shellshear said the technology took a holistic view of the data to get the best outcomes, rather than just solve singular issues.

“The software programs are customised based on the needs of each business or project and use AI to offer solutions to minimise cost, better utilise resources and maximise efficiency,” Dr Shellshear said.

“The data collected and analysed can include supply chain data, staff scheduling and rostering, project budgeting or operational scheduling – everything is considered.”

“By rapidly running scenarios to analyse and quantify planning opportunities it is helping businesses make data-driven decisions to cut down on costs, better utilise resources and drive efficiency.”

Reach out below to find out how mathematical software can assist your organisation.

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Join us as we end the year with a BAM! 

BAM is back!

After 3 long years, The Biarri Applied Mathematics Conference is back, and this year we are excited to be co-hosting with The University of Sydney. Since its inception in 2012 at the University of Melbourne, The BAM Conference has grown in popularity as we continue our mission to provide you with insights into how companies apply optimisation and other mathematical techniques to solve problems in the real world.

Why come?

The Biarri Applied Mathematics (BAM) is a conference that bridges the gap between mathematics in academia and industry. We bring together guest speakers from a broad range of backgrounds and interests – from University Research to Commercial Applications. 

BAM2022 will be jam-packed. You’ll watch expert discussions, see pioneering mathematics in action, rub shoulders with thought leaders and interact with your peers. The incredible lineup of speakers includes industry leaders from some of Australia and New Zealand’s leading organisations, as well as academics and researchers from the University of Sydney (and other universities). 

What is the theme for BAM2022?

The limits of predictability 

In the current age of AI, data and digitisation, lofty promises are made with respect to the capability of data-driven and analytical digital systems. Unfortunately, the world is a complex place, and as powerful as current modelling capabilities are, there are unarguable limits to our analytical capabilities.

This conference will explore those limits and help participants understand how mathematical techniques are defining, expanding and helping us understand our analytical limits better than ever before. Come and hear examples of projects and case studies of both successful and failed attempts to realise mathematical modelling in a variety of scenarios.

The event will help participants understand where analytical techniques can be successful and where the complexity, randomness or non-linearity of the system is too great, even for the most powerful tools we have today.

How can I register? 

In-person tickets are limited, so get in quick if you want to join us face-to-face. Visit our website ( to register. The conference will also be streamed live – if you can’t make it to the event in person, register as an online attendee!

Get your free tickets today!

setting the right objective in supply chain

Setting the right objective

“Modern supply chains are complex” is a truism. Operators are all too aware of the global forces and local details that drive weird and wonderful supply chain complexity. This complexity isn’t going anywhere, but industry innovators are pioneering approaches to manage it more effectively.

Download ‘Supply chain planning under uncertainty’ slide deck

Planning processes are mature

In a complex environment, diligent planning is required to ensure that supply chains are cost-competitive. The drive to manage cost has spawned an ecosystem of multi-step processes (some more effective than others) and supporting enterprise software. The processes and systems are human driven, and often key knowledge sits with individuals despite the presence of large systems. Which leads to an obvious question…

Can we automate planning?

Fortunately, the answer is often yes. Like other processes that take many datasets and priorities into account, supply chain planning can often be automated or semi-automated. Increasingly, operators are turning to algorithms and artificial intelligence to drive lower costs across multiple segments of the supply chain. 

Although many different algorithms can be applied to support decision making, managers can apply a general framework to frame these problems before applying algorithms.

Firstly, define all relevant Decisions. Decisions like:

  • “When should I import raw material, and how much should I import?”
  • “How much inventory should be stored at each point in my supply chain?”
  • “When should I book different transports?”

Operators and planners will easily identify the big decisions they make day to day or week to week, but when applying algorithms we need to consider the little decisions as well.

But decisions aren’t made in isolation – they’re subject to the physical, contractual and practical rules that apply to a business. These can be referred to as Constraints. They might look like capacities associated with road or rail transport legs, restrictions on site storage or throughput capacity or throughput capacity, or even specific timing rules for quarantine and or fumigation for primary products.

Decisions are made in a constrained environment, but this framework relies on an additional element to frame algorithmic approaches. This is the Objective: what result do we want from the algorithmic plan? In a world where we make decisions subject to constraints, we need to know what makes a good decision. Typical objectives often focus on planning to minimise cost, maximise profit or maximise throughput.

We can frame planning problems, then, by defining decisions, constraints and objectives.

Uncertainty is unavoidable

But how do these algorithms behave in a highly uncertain environment? How should they be applied to balance cost reduction with overall supply chain resilience? Uncertainty is unavoidable – this is true for our supply chains, regardless of scale. Uncertainty drives unexpected events, and these events can appear in many different ways. Maybe a key piece of plant breaks down for four hours, or maybe a major customer doubles their order for the next four weeks.

Most importantly, there is a big difference between a cost-optimised plan, and a plan optimised for cost-of-execution. A cost-optimised plan assumes certainty, and perfect, accurate information. Perhaps counterintuitively, highly tuned cost-optimised plans can perform poorly when reacting to change – typically these plans have sacrificed resilience to achieve cost-optimality. Algorithmic plans like these are blind to the costs associated with responding to unexpected events.

What would perfect look like?

In a perfect world, firms would have high-quality, comprehensive data, capturing probabilistic possible outcomes. Mathematical models would scale effectively when solving the largest stochastic problems. In this alternate reality, we could minimise the operating cost subject to hundreds of thousands of possible outcomes. We could use these models to make sure our worst-case outcomes rarely occurred.

This reality isn’t out of the question in decades to come, but for large and complex operations this typically won’t be possible. Even when a planning problem is small enough to approach in this way, often there is poor data (or no data) available to describe probabilities of future events. This data simply isn’t prioritised right now.

This paints a fairly bleak picture of planning to manage uncertainty. Thankfully, there are a number of highly effective algorithmic approaches to manage supply chain uncertainty when planning.

What is the best approach, given the circumstances?

More and more, algorithms using “Resilience Metrics” are proving to be an effective way to handle uncertainty while avoiding the challenges described above.

By identifying or constructing metrics that indicate a plan’s resilience to change, firms can optimise a non-stochastic model while also creating a plan that provides a greater ability to respond well to surprises.

While this approach relies on approximations, it also removes the barriers associated with genuine stochastic optimisation – it doesn’t require huge probabilistic datasets, and doesn’t become too complex to solve quickly and meaningfully.

Resilience metrics can be simple, and may even be intuitively understood and used by planning experts. Some examples of resilience metrics include:

  • “Safety stock plus” style measures for products with high demand variability.
  • Delivery vehicle route plans with characteristics that allow a second delivery attempt.
  • Slack holding capacity or processing capacity across multiple planning horizons.

Poker, not chess

Shipping is a great example of an industry with high levels of uncertainty. Vessel breakdowns and shifting demand for cargos can rapidly shift the profit-optimal plan. Previous approaches to tonnage allocation in the shipping industry have leveraged similar algorithms to those used in vehicle routing, and attempted to create “highly-optimised” plans. These planning algorithms have predictably seen low adoption and fostered a broader cynicism in the industry towards optimisation that ignores uncertainty. To paraphrase one executive at a major shipping line: “We’ve been trying to play chess. We need to play poker”.

What’s next?

As multiple industries that operate large supply chains search for improved resilience, more nuanced algorithmic planning should be leveraged to achieve genuinely cost-optimal outcomes. At Biarri, we hope to continue to play our part in moving this discussion forward.

Planning Under Uncertainty Webinar Deck

Biarri Optimisation General Enquiries

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Scaling your supply chain

Sustainably Scaling Your Supply Chain

For any business, growth is a simple metric used to measure success. For large scale manufacturers, growth often results in scaling the physical aspects of the business, resulting in larger holding facilities, new warehouse locations, more staff and greater decisions

Now, with growth in sales volumes and an increase in demand, the biggest obstacle to your organisation’s success could be your supply chain network and the ability to expand and scale. Understanding your current supply chain capabilities and its current state is important in deciding whether expanding is necessary without placing too much pressure on your supply chain and creating bottlenecks.

This new placed pressure is not exclusive to supply chains, with pressure also falling upon the shoulders of key stakeholders to make the best possible decision. Decisions around expanding, dealing with new and current infrastructure, as well as logistics are crucial to get right, usually triggering a multitude of questions, each with their own complications. 

Scaling your supply chain 

In a perfect world, it would be great to simply add a new port here and place a distribution centre in the middle of where there is rapid growth in demand and continue to run as usual. But unfortunately – we don’t live in a perfect world and the basis of our decision making is not as intuitive, or that simple. The complexities around decisions revolve around considering supply dynamics, transportation methods and arrangements, production flows, associated costs and inventory, to list a few.  

Finding the optimal combination of factories and distribution centres in the supply chain, whilst trying to satisfy supply and demand at the lowest possible cost, remains the objective when identifying when to scale and expand. 

Let’s look at a concrete example of how this can be done.

Australian Manufacturer – A Case Study

In early 2021, Biarri was consulted by an Australian manufacturing company to model the importation, storage and bulk distribution, for one of their new products across Australia. They forecasted the potential to substantially grow their sales volume over the next couple of years with their new product strategy and other business enhancements. The project was to determine if there were opportunities to scale their supply chain network and expand their current port storages, explore the potential of utilising inland storage solutions and whether they should look at new transportation methods. 

In order to satisfy their new demand, they required cost effective decisions and solutions that explained how, when and where to expand existing infrastructure; whether to introduce new storage facilities; understand where to focus increasing demand for their product and how best to drive the growth of the business over both short and long term. 

Our Approach 

Utilising our Network Optimisation software, we had to first create a baseline model that ensured that the current approach to modelling the network was sound and the data transformations were accurate. This meant understanding the inputs and outputs of their current network, using the current supply dynamic with supplies being co-shipped from overseas into their four main port storage locations scattered across Australia; right down to using historical data of deliveries to each sales district to understand the current state of demand. 

Once the baseline model was built, it was important that it was validated, confirming the model  was a fair representation of the current state with major assumptions, data transformations outlined and agreed upon. This step was critical and set a basis for comparisons of future state models and other scenarios which represent alterations to the current state of the network. 

Future State Models

Now that the baseline was established, it was important to apply projected demand profiles to the current network to uncover the difficulty of servicing future demands with the current network constraints. 

Biarri modelled 3 alternative future state models, each with incremental increased levels of demand, predicted by their growth strategy. The results were conclusive with each resulting in potentially unsustainable levels of both import frequency and delivery freight rates. Due to their limited storage capacity in the network, and high demand forcing a large number of imports with high risk time intervals, Biarri reasonably showed that without reconfiguration to the network this would introduce high levels of risks to the client. 

The Results

Biarri successfully built a representative model of the clients distribution network for their new product. With the current model, Biarri were able to uncover the strategic capability. This includes investigating options to increase storage, adding ports, various freight rate tiers and co shipping partners, and varying capacities and costs of different storage, along with many other potential constraints and features. 

Would you like to know more? 

Speak to an expert today and discover more about network optimisation and  how to sustainably scale your supply chain network below.

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optimising your supply chain

Optimising your supply chain: 5 best usages of optimisation in supply chain

Dare I say the dreaded the ‘C’ word? We have all lived through one of, if not the biggest disruption to businesses across the globe. Businesses and supply chains were crippled by the pandemic, placing an unprecedented level of pressure on supply chains to keep the world going. 

But upon reflection, supply chain leaders continue to hold onto manual processes such as Excel spreadsheets, supported by 73% of supply chain leaders stating that this is their most used method of supply chain planning. But how would your supply chain fair if you were equipped with the correct optimisation tools? What role can a supply chain optimisation tool play in preparing your supply chain? 

We explore why and give you the 5 best usages of optimisation in supply chain.

Dealing with disruptions

With a large portion of the industry heavily reliant upon Excel spreadsheets, dealing with major disruptions will continue to be difficult to manage. Planning teams spend a great deal of time dealing with the here-and-now, with little time to plan for the future and the ‘what-ifs’. What if next week our production levels fall by 10%? What if next month the absenteeism rate amongst our staff increases to 25% because of widespread illness? What would we do in that situation?

If the planning process for one day takes almost one day, then the planning team doesn’t have any time to consider future scenarios. It’s a deal-with-it-as-it-happens situation, where the team is constantly reacting to the situation on the ground, and making decisions with little or no thought of how it will affect them in the future. “Just get it done now!”, not realising that the resource they use to get it done will then not be available the next day, causing an even bigger problem. 

Forward Planning 

Excel spreadsheets remain the preferred tool amongst the Supply Chain industry, Using an optimisation tool, allows the planning team to operate more efficiently, allowing them to consider the whole week, not just the present day. This can result in better decision making, and better visibility of how the business is trending in the medium to long term. 

Without an optimisation tool, business as usual may be fine. Planners have their routine that keeps the business running smoothly, and the existing processes are working. But when things change quickly, maybe due to a major disruption, or because the business is growing rapidly, are those processes scalable? Do they allow the planners to adapt to the changing conditions?


Efficiency – a ‘buzz’ word not privy to the supply chain industry, but undeniably a pain point most if not all supply chain leaders try to address. As supply chains continue to grow and diversify, cross functional collaboration becomes more important, with efficiency requirements varying from each function. From managing inventory, to reducing manufacturing costs to optimising distribution; not having an optimisation tool across your supply chain can result in large inconsistencies and sub optimal outputs. Understanding your supply chain capability and having the correct strategy around how and where to improve can result in a faster response time, consistent processing times and better utilisation of human resources to name a few. 

Doing more with less

We often think of improving efficiency as a way of lowering costs, or doing the same thing for less. While this is one benefit of an optimisation tool, the other side is being able to do more with the same resources. With the lack of visibility an Excel spreadsheet provides, it can leave key decision makers in the dark of understanding the full potential and capability of their supply chain. 

Decisions around whether to include another distribution centre, or hiring extra staff can all be well thought through with an optimisation tool. Biarri worked with one of the largest liquid and petroleum gas distributors in New Zealand, to optimise their delivery of LPG gas bottles across the country. With the use of Biarri’s Run and Route optimisation tool, they were able to increase their capacity and increase their productivity whilst not having to increase their staff and delivery fleet. How, you ask? By having the correct optimisation tool that considers more effective options. Sometimes, finding a way to meet all your requirements with the resources you have is just too difficult to do “by hand”, with more powerful methods required. 

Improved Bottom Line

We have seen through optimising your supply chain with the correct tool can lead to improved planning, better use of resources, and being more efficient, but the culmination of these benefits lead to lower overhead costs. By maximising resources and improving planning, your business can have greater control over your expenses whilst ensuring the quality of your products and services don’t suffer. Optimising your supply chain can lead to removing unnecessary expenses throughout your operation like production and logistics. 

Make your supply chain your competitive advantage by implementing the correct optimisation tool. Reach out and speak to an expert today.

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Digital Transformation of Emergency Medicine: How commercial mathematics is helping hospitals adapt to COVID challenges

As travel restrictions began to ease across Australia at the start of 2022, hospitals experienced an increased strain on resources, brought about by a peak in COVID cases.

Planning to expand their emergency department (ED) to compensate for lost space from accommodating COVID query patients, one of SEQ’s largest public hospitals engaged the services of Biarri to ensure they were achieving optimal utilisation of their facilities.

Though the new building was originally designed for the treatment of minor injuries, the impact of COVID, as well as historically high levels of demand on the ED prompted a reconsideration of the design and intended usage for the expansion. 

The hospital needed to understand how they should change the existing ED whilst utilising the beds and resources of this new physical area, to best improve the flow of patients, reduce their waiting times, and treat them as efficiently as possible while continuing to provide excellent patient outcomes.

Over the 4 months in 2021, the average time a patient spent waiting for a ward bed exceeding the expected time of 75 minutes by 25%. 

Within a restricted time frame, Biarri was able to deliver insights based on a custom-built simulation model, that showed the department how best to optimise throughput, maximise their use of space, and minimise the overall time patients spent in the ED. 

Simulation Modelling

You have a problem. You’ve come up with some possible solutions that work in principle, but you want to be certain of their effectiveness prior to implementation. Often, it’s impractical to test them in reality, as they’re either too expensive, too time consuming, or there would simply be too much interference with the normal operation of your business. You may need a simulation

Simulation is a time and cost effective way of testing ideas and theories across a huge number of disciplines, from wind tunnel testing on scale models in the aerospace industry, to predicting animal behaviour in large groups, or recreating the formation of our Milky Way galaxy. 

Because they are easily configured to take advantage of randomness, running a simulation multiple times can produce a wide spectrum of possible outcomes, which sometimes makes them a better choice to model reality than more traditional techniques. 

Using this method of exploring the problem space, a business can develop their operational plans based on a typical or likely day (or month), while also gaining visibility of, and preparing for the worst-case scenarios. 

By representing the allocation and flow of resources as a series of discrete events, simulations can serve as a digital testing ground for a business, and provide the opportunity for fine tuning and optimisation of business processes. 

Hospital Emergency Department – A Case Study

A simulation algorithm was developed for the ED that allowed the identification of bottlenecks in the department, and could be used to explore the impact of reducing wait times, reallocating beds to different types of patients, and expanding the areas reserved for COVID patients. 

The simulation treated patients as individual agents who were tracked from arrival, through to triage, into a particular area in the ED, where they could then either leave after being examined, or be admitted to the ward. 

The algorithm made use of 4 months of historic data regarding the expected frequency of patient arrivals, expected time in triage for each patient type, and processing treatment times by ED area. Expected flows between the areas of the ED, for example the percentage of acute patients admitted to ward were also estimated. 

The outcomes of the simulations were measured in terms of a NEAT score. 

“The National Emergency Access Target (NEAT) stipulates that a predetermined proportion of patients should be admitted, discharged or transferred from Australian emergency departments (EDs) within 4 hours of presentation”

For example, it was found that reducing the time that patients wait for a ward bed could significantly improve flow in the ED. If patients only waited 45-60 minutes for a ward bed, compared to the current average, the NEAT for Resuscitation patients could increase around 25%.

By tweaking the parameters of the simulation, different scenarios could be investigated. In one such scenario, it was found that if the COVID area was not expanded, even 40 additional patients per day would lead to significant queuing, as the current allocation of 10 beds would be insufficient to handle the demand. 

COVID queue length (10 beds)

Length of queue for beds over a two day period for COVID patients in the situation where the COVID area has not been expanded. The green curve represents the “average” (or 50th percentile), the yellow a worst case scenario (95th percentile) and the blue a best case scenario (5th percentile).  

COVID bed utilisation (10 beds)

The utilisation of beds in the situation where the COVID area has not been expanded. Within a few hours there is no longer any capacity for additional COVID patients. 

However, expanding the COVID area into the acute and clinical decision units (adding 12 beds) meant the ED could handle an increase of roughly 70 COVID query patients per day.

COVID bed utilisation (22 beds)

The utilisation of beds once an additional 12 beds have been added, in the case of 70 additional COVID query patients. If the COVID pod was expanded into the Clinical Decision Unit area, this amount of space should be enough to meet the anticipated capacity.

Simulation in Mining and Construction

Biarri has used simulation modelling in a variety of industries, such as modelling the movement of roof supports in longwall mining applications to significantly reduce the time taken for their recovery, transport, and installation. Another application for simulation is traffic and the movement of goods through a network. In the animation below, trucks with a random arrival time move through an underground parking structure to deliver pallets to a goods lift, which might have a randomised waiting time. The bottleneck in the structure acts like a traffic light system to control the flow of trucks through the structure. 

Would you like to know more? 

Speak to an expert today and discover how your business can begin leveraging the power of commercial mathematics and simulation today. 

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Basket Analysis

How to sell below cost price to make a bigger profit

Look into your shopping basket next time you shop and you may discover something. Almost half of your entire basket will most likely be items you never planned on purchasing when you came to the store. No, a store clerk didn’t secretly put them in there to increase your spend – you did it yourself.

According to recent research, depending on the age group, anywhere up to fifty percent of all purchases are based on impulse. These items were only bought because you entered the store today to buy your milk but happened to have picked up three or four other items along the way.

If you think about this for a moment, as long as a retailer can encourage you to choose their store to purchase that milk, then the cross selling can be worth a lot more to them than simply that liter of milk.

In fact, due to your other purchases, maybe the best strategy is for them to give that milk away for free?

Quantifying the power of cross selling

The team at Biarri has deep experience in retail to help make better pricing decisions such as markdowns. So it was natural that we turned our attention to this problem.

Biarri recently completed a year long project with UQ Master of Data Science students to pioneer a way, via Shapley Value Market Basket Analysis, to finally quantify the value of a product based on its power to cross sell in a basket.

Traditionally retailers have used Market Basket Analysis as one of the most common data mining approaches to analyse customer purchasing patterns in retail stores. These techniques provide association rules for the items that frequently appear together. However, they fail to recognise the significance of item quantity and price only helping retailers understand which products are purchased together and which products may lead to the purchase of other products.

How is a retailer to use such information to improve their operations?

Place items that are purchased together closer together? But why? Will this really lead to more cross selling? Who knows.

The only way this information can become valuable is if we actually quantify the cross sell value back to the item causing the cross selling. Then we can tweak the price of the original item to attract more customers and hence more cross selling!

It is only when we begin to quantify this information that we can make useful decisions to improve the operations of retailers.

The Approach – Shapley Value Market Basket Analysis

To achieve this Biarri leaned on an academic field enjoying a resurgence in recent years due to AI. This field provides a core part of the technology used to create some of the shocking deep fakes videos and pictures in the area of Generative Adversarial Networks. The key trick to creating these deep fakes is based on core concepts from this research area of (Non-cooperative) Game Theory such as the Nash Equilibrium.

Similarly, Biarri has leveraged the concepts from Cooperative Game Theory to develop a new way to quantify the true value of items in a shopper’s basket. The new approach utilises the Shapley Value (another concept playing a pivotal role in deep learning) to allocate the value to a set of items in a basket based on the value each contributed to the basket.

What this means in practice is that for all customers of a store, we examine each basket of items purchased. We label each item purchased as a different player (A, B, C, etc in the diagram below) and then look at the total value of the basket. Averaged across all baskets we can find out which items contribute to the purchase of other items and attribute that additional value to the cross selling good and understand what their true revenue contribution is – not just what their sticker price contribution is.

The Problem and Solution

Using the Shapley Value is a novel idea, however, just naively using it won’t work. The computation of it is extremely slow (combinatorially slow) so it cannot be used on anything but toy datasets. So the Master of Data Science students at UQ came up with a clever way of speeding up the calculation so that it scales linearly with the number of items and not exponentially.

This algorithmic breakthrough then made it possible to test the new method on a dataset with 500,000 transactions to come up with a unique set of items which drive additional cross selling revenue for the retailer. When combined with price elasticity calculations, these results can be used to optimally price the goods to maximise gross profits.

The results demonstrated that many items were only worth as much as their sticker price but there were a few valuable gems in the product catalogue which were driving a large chunk of the online retailer’s profits. The below diagram shows the spread of “additional” attributed value (sometimes it is negative when an item is actually worth less than what it is selling for!).

Where to from here?

Using the Shapley Value Market Basket Analysis, Biarri has finally shown how we can answer specific questions on the value of individual purchases. Biarri’s results have been accepted for publication in the peer-reviewed International Game Theory Review journal and will be published soon. We’ll update this post once they are available to ensure everyone has access to these breakthrough results.

Announcement: AWS Data and Analytics Competency Partner

Making the world more efficient with a world class cloud platform

Biarri continues to strive for excellence to deliver on our promise of making the world more efficient, as we are proud to announce that we have successfully been listed as an AWS Data and Analytics Competency Partner.

AWS Data and Analytics ISV Competency

“The AWS Competency Program validates and promotes AWS Partners with demonstrated AWS technical expertise and proven customer success.” – AWS Competency Program

Since partnering with AWS and joining the AWS Partner Network (APN), Biarri has been recognised for helping organisations solve complex problems. Biarri successfully demonstrated through our proven track record that we have a ‘deep expertise’ in solving complex data and analytics challenges for large scale organisations, as seen with Alliance and Schweppes

“Being accredited with the AWS Data and Analytics ISV Competency gives our customers confidence. Biarri are leaders in providing data analytics and predictive modelling, and this certification supports that. 

Evan Shellshear – Head of Analytics, Biarri. 

AWS Partner Network

Biarri is proud to be a part of the AWS Partner Network. At Biarri, we offer a suite of cloud based software to help improve businesses and organisations through the power of optimisation. With Biarri’s continual commitment to data privacy and security, partnering with AWS allows Biarri to leverage their market leading technology and cloud services.

Through leveraging AWS services, we have greater confidence in developing tools that align with our mathematical and analytical Biarri approach. AWS provides a platform that is not only quick but secure, giving our clients around the world assurance.

Our tools

Our tools are built from the ground up leveraging AWS services toolkit as part of the solution such as AWS EC2 to run the optimisation workloads, AWS S3 to store application information,  AWS PostgreSQL RDS to manage the workload data tables, AWS EKS to ensure a highly available and scalable workload, and AWS Elasticache to queue and manage tasks. Leveraging the AWS toolkit we were able to create a world class platform to assist our clients. 

If you would like to find out more about Biarri and our AWS competency reach out in the form below.

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technology investment boost

The Technology Investment Boost and what it means for your business

Did you hear?

In this year’s Federal Budget, the government will introduce a technology investment boost that will apply to eligible expenditures. The program is designed to motivate SMEs to invest in digital or subscribe to cloud-based services. 

Here’s what you need to know about this program and how it can benefit your business.

What does it mean?

The Digital and Skills Tax Boost will lower the barriers to going digital by encouraging businesses with less than $50m annual revenue to invest. The technology investment boost enables a ‘bonus’ 20 per cent tax deduction on expenses, including subscriptions to cloud-based services. This means a $120 tax deduction for every $100 spent on digital tools and training. 

A $120 tax deduction for every $100 spent on digital tools and training.

Are there any conditions attached?

A new initiative from the Government, of course, there are conditions! 

Although discussions and measures are still ongoing, the initiative hasn’t yet been passed into law. Even so, given the hype, the time to begin your budget process is now. Kick-off a new project or use this initiative to support an existing business case for digital transformation. 

The important conditions to note are:

  • Applies to eligible expenditure incurred from 7:30 pm 29 March 2022 – 30 June 2023
  • It is not yet passed into law
  • Keep in mind that the additional deduction on any costs incurred in FY22 cannot be claimed until FY23.

 Also, remember that if you want to apply the TFE, the asset needs to be installed and ready for use by 30 June 2023, so watch out for those lead times on capital assets, and plan ahead!

The time to start building your business case is now. 

Who can get it?

Small businesses (those with an annual turnover of less than $50 million) will be entitled to deduct an additional 20% of the cost incurred on business expenses and depreciating assets that support digital adoption, such as subscriptions to cloud-based services. There is an annual cap of up to $100K, meaning that a maximum spend of $100K will entitle a small business to a $120K deduction. 

What other benefits are available?

This increased digital adoption will ensure SMEs have the tools to remain competitive while securing billions for the Australian economy. It also allows SMEs to invest in technology to better their businesses and remain relevant in industry.  And it will significantly increase funding to assist small businesses to improve their capability and capacity to digitally transform.

If you’ve been holding revamping your outdated legacy rostering or scheduling systems, now could be the perfect time to begin the conversation. Let’s see how we can help streamline your workforce capability and take advantage of this incentive. 

If you would like to find out more about the above or more about how Biarri can digitise your business operations, reach out via the form below and a consultant will be in touch!

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reactive to proactive in supply chain with ai

From Reactive to Proactive in Supply Chains with AI

As companies move to using more data to help them make better decisions, they are often left with an uncomfortable feeling. This feeling has nothing to do with going outside comfort zones or changing the way of working, it is based on the fact that even after using the data many people feel like their questions still aren’t answered properly. 

And it is really common.

Research published in the Journal of Organizational Effectiveness People and Performance showed how more measurements, data and visualisation of HR processes lacked the ability to diagnose problems with business performance and lead to relevant and actionable insights. Our experience shows that this is not just limited to HR, it is across the whole business.

So why does this happen?

During COVID-19 (and even before it), supply chain companies around the world underwent significant digital transformations that led to the collection of more and varied data on all aspects of their businesses. To manage and understand this data, organisations invested heavily in BI tools such as Qlik, Tableau, PowerBI and more. Even though these tools are able to present good dashboards of the historic business operations, they haven’t produced revolutionary ways of doing business and, most importantly, they aren’t helping organisations deal with their most important challenge – the future.

From reactive to proactive

When businesses look at their dashboards full of data, they see patterns but are never 100% sure if they are real or not. In their historic data lies the answers to their future questions but how do we reveal them with confidence and precision? 

Where BI provides you with the review view mirror, AI (artificial intelligence) provides you with a well calibrated telescope of the future. By using advanced analytics, companies are able to go from reactive to proactive by statistically validating intuitions about the future hidden in their data to give decision makers the confidence to place bold bets and target ambitious goals – a step change in the way of doing business. Often what the analytics discovers are things you “knew” but now you have quantified them and can properly compute the effects of your decisions on your bottom line.

Those sectors that stand to benefit most from using AI to go from reactive to proactive are those where there exists high variability in inputs and processes or demand for outputs. For example, industries which use inputs from nature such as mining, resources and agribusiness need to be able to deal with natural variation in their inputs. Any company selling into markets with volatile demand also needs ways to deal with this challenge. 

And then there are a number of industries like logistics and agribusiness which face, and solve, volatility on both inputs and outputs.

How supply chains solve their predictive challenges

Biarri has worked with many logistics companies and agribusinesses to help them move from reactive to proactive. Instead of organisations harvesting crops or moving goods around trying their best to manage their volatility via inventory overcapacity, staff overtime and high wastage, they are now moving to scalable analytics with the business preemptively selling and optimising the expected yield from their crops, animals and delivery trucks. 

A good example of this is Alliance Group in New Zealand who use Biarri’s supply and demand management tool Wolf, which has allowed them to better allocate supply to demand and smooth out their production cycles while simultaneously increasing their profit margins by selling more high margin products in niche markets in a scalable and low effort way. During COVID, this tool allowed them to quickly respond to changes in volatile global and local markets managing both high volatility on the input side (lamb sizes and grades) to the high volatility on the outputs side (COVID lockdowns drastically affecting demand). 

In addition, moving to proactive can have other surprising benefits. Work Biarri has done with Australia’s largest grain exporters shows how a proactive view can reduce storage requirements, lower labour costs as well as save energy – let alone better serve their customers more confidently. Although not always the goal of predictive tools, the ancillary benefits can sometimes outweigh the initial business goals.

Why we all must do this

In moving to proactive thinking via advanced analytics companies are not only improving their bottom line and making better decisions, they are improving local and global markets. By smoothing out supply and demand, market volatility reduces and creates a better business environment for all. The benefits of this accrue to society via a better management of our resources leading to lower prices and higher living standards for all. 

Moving from reactive to proactive via AI tools allows businesses to disrupt their current markets in a scalable way. Not only can you look around corners to know what is coming next, you can do it in a scalable way. Reach out to Biarri now to find out how.

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