Changing the landscape of Route Optimisation


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.


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


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.

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Common challenges facing your workforce planner

Common Challenges Facing Your Workforce Planner

Roster Managers and Workforce Planners face the difficult task of planning and formulating rosters which are efficient but also address changing shifts and team member preferences and availability.

Assigning shifts can be a time consuming task that requires a lot of thought and consideration to achieve an optimised and efficient roster. Rosterers must assign the correct shift by skill and qualification all while considering staff preferences and availability, team dynamics, training requirements and enterprise agreement rules. In addition, the roster needs to deliver high overall utilisation and cost efficiency. A poor roster can result in dissatisfied employees and have a negative impact on the organisation’s bottom line. 

Here are a few common challenges that Roster Managers and Workforce Planners face when rostering and also some ideas on how these challenges can be overcome.

Managing Staff Preferences 

As a Roster Manager it can be difficult to capture and keep track of changes to employees preferences and availability. Simply rolling forward a prior period roster will not ensure that new requests can be met and worse may mean any issues or frustrations with previous rosters are repeated.  

Team members are one of the most important aspects of any organisation. When individuals can see their preferences and availability are being considered in the rosters produced it can greatly improve staff retention, job satisfaction and performance. 

In addition, rosters which repeatedly ignore staff preferences can lead to ongoing shift swapping and roster changes as employees find ways to structure their work to better meet their needs.  

Are your team members making the same shift swaps month after month to bend the roster to accommodate their preferences?

Shift work allows for employee flexibility, given there is often a range of shifts available. Flexibly considering employee preferences when rostering can enable parents to attend their children’s upcoming events, allow for staff members’ special occasions and remove the need for complex chains of shift swaps by employees once the roster is published.

Capturing staff preferences is the first step in solving this problem, followed by smart rostering to take account of many often competing preferences. This can be very challenging to get right, especially when roster teams are often equipped with nothing more than personal experience and excel roster spreadsheets. 

Using Biarri Workforce, managers and rosterers have the ability to capture all types of staff preferences and availability and take them into account when auto-generating rosters.  

Biarri Workforce has an open and flexible rules and preference interface which allows the rosterer to define preferences and availability in many ways. 

  • The structure of our rules interface means every type of roster preference a team member might have can be captured. Some examples of this include:
  • attaching preferences to individuals, groups or across the whole roster (e.g. David prefers nights but Steve, Damian and Lilibeth prefer to work weekends, whereas everyone on the roster must comply with the legislated fatigue thresholds);
  • making a rule for a once only event in that roster period, for a defined period of time or ongoing if it is recurring in nature (e.g. can I have next Thursday afternoon off to take watch my child play sports, or I would like every Monday morning off for a regular appointment);
  • defining the rule as a hard constraint (e.g. must occur) or a soft constraint (would be good if it could occur) and enabling different weightings to be set on soft constraints;
  • creating rules to support team environments (e.g to prefer a team to be rostered together and to work together on a series of consecutive shifts); 
  • by reference to other team members (e.g. prefers to work with or not to work with other individuals – this can accommodate spouses or partners who work on the same roster to ensure they will always have someone at home with kids, or alternatively both can have does off together)

The preferences, availability and other rules defined in the rules interface are then considered when Biarri Workforce auto-generates the roster.   

Last Minute Shift Swapping 

Managing and dealing with last minute shift swaps by team members can be difficult to ensure that skills, fatigue and qualification requirements are met and to ensure the changes are recorded and updated to the master roster for payroll purposes.

Life happens, events outside of our control pop up and staff will need to miss work. The next challenge is finding a suitable replacement who is available, has the necessary skill set but also a replacement who will not breach any fatigue, EBA and workplace rules.

The lines of communication can be frantic, back and forth with text messages, emails and calls searching for a replacement. Finding someone to cover the gap is often hard enough.  But wouldn’t it be great if the process was automated to not only make finding a replacement easy, but also this is seamlessly reviewed and approved and automatically updated to the master roster.  In addition, the substitute should be equivalent or lower cost and not create issues (e.g. fatigue thresholds) later in the roster period.

Biarri Workforce has an in-built shift swap feature that assists managers and employees to best manage shift swaps. Available through the Biarri Workforce Mobile App, employees can easily Swap Shifts or Offer Shifts to other available employees and management can see this activity and approve a swap before it is finalised – keeping visibility and control over the execution of the roster.

Speak with a consultant today and remove the panic of finding a suitable replacement with the right skills and availability, with the Biarri Workforce shift swap features. Equip your Workforce Planner and Roster Manager with Biarri Workforce. Whether you are currently manually rostering or looking for a more accommodating solution, the team behind Biarri Workforce are dedicated to making Biarri Workforce work for you.

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3 ways Biarri Workforce can improve rostering and workforce planning

The team behind Biarri Workforce are dedicated to helping your business get the most out of our rostering software. Whether it is simplifying the job of your workforce planners or creating a team friendly roster that increases your employees satisfaction and retention, Biarri Workforce is the solution to improve your workforce planning and rostering. Biarri Workforce is tried and tested across many industries including maintenance teams, healthcare and aviation.

Read on to learn about the 3 ways Biarri Workforce improved rostering and workforce planning for the team at CQFMS and how Biarri Workforce can help your Workforce Planners and your Business. 

Reahan McBain – FMS Group

1. Biarri Workforce Software

We’ve all heard it before: 

“It is not the tools we use that make us good, but rather how we employ them”. 

This saying doesn’t always hold true, sometimes simply not having the correct tool for the correct job restricts our ability to perform; so why not start with the right tools for the right job? 

Like so many other businesses, CQFMS managed their workforce and planned their rosters using ever increasingly complex Excel spreadsheets. While Excel is a capable tool, rostering a growing team of people over a 24/7 period all while adhering to EBA and fatigue regulations can quickly become a nightmare of complexity. Through system driven automation and the powerful Biarri Workforce roster optimisation engine, the team at CQFMS were able to significantly reduce roster preparation time, more easily ensure compliance with EBA and fatigue requirements and manage changes to their rosters more effectively

2. Onboarding New and Existing Employees

When managing a large fleet of workers, onboarding and assigning new employees to a roster can become a time consuming task. This was the case with CQFMS, with a growing workforce. Previously, onboarding was completed using another HR software system and new employee lists were manually transferred through to Excel to begin planning the roster. This was inefficient and not scalable so CQFMS requested a more streamlined approach to onboarding. Through Biarri Workforce, Workforce Planners are now able to onboard employees much easier, as well as manage their roster in the same system. Found in the Biarri Workforce ‘Employees’ tab, you are able to onboard new employees and upload documents such as certificates and other qualifications all in one simple and easy to use interface. 

3. Creating a safe roster 

CQFMS and Biarri value safety and understand that an implied safety culture goes beyond the job site and includes appropriate roster creation. It is critical when planning a roster to have the correctly trained and skilled people for the job and to ensure that employees have current certification suitable to be rostered to work on a site. Biarri Workforce has in-built validation rules which enforce training and skills compliance to enable workforce planners to plan and roster the correct workers to be deployed on site. Another crucial aspect of roster creation that Biarri Workforce automated for CQFMS was the time consuming process of checking and managing employee fatigue. Ensuring the safety of employees meant making a roster that was compliant with EBA regulations including workplace fatigue rules.  Previously, it was difficult and time consuming for CQFMS to manually update and track overtime and employee hours for a large workforce. The Biarri Workforce rules engine and validation function will not generate a roster which breaks fatigue rules and when manual changes are made to the roster the rostering team is automatically notified if the planned roster breaches any fatigue rules. Rosters cannot be published without the Workforce Planner acknowledging and/or fixing these issues.

The right tool

CQFMS is one of many valued Biarri Workforce users. By stepping away from Excel spreadsheets and adopting Biarri Workforce, CQFMS have made noticeable improvements in how they plan and manage their rosters. Reducing time and effort to generate rosters while also automating the important task of skills and fatigue compliance.

If you would like to know more about how Biarri Workforce can improve your rostering and workforce planning or have any other further questions, feel free to leave your details below and a team member will get in touch with you. 

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Creating team friendly rosters and the benefits for everyone


Creating rosters can be a complex task.  This is especially the case for difficult large workforces with a range of  employee preferences, combinations of full time, part time and casual team members, the need to adhere with EBA rules, fatigue, skills and competence requirements , meeting or exceeding customer service levels while always operating efficiently and controlling costs.  Clearly, producing a ‘good’ roster is more than making sure there is the right number of staff for the right amount of work. 

So, on top of all of this what does a team friendly roster look like and what are the benefits to employers and employees? 

A Friendly Roster

An example of what makes a team friendly roster.

Sarah is the Workforce Planner for the local hospital Emergency Department. She is tasked with organising and preparing the Doctors rosters which she does a month in advance for a 2 week working period. The roster parameters require each Doctor to work a specific amount of ‘unsocial’ hours covering morning, afternoon and night shifts.  Sarah always  does her best to consider the Doctors preferences for specific days off some of which are always the case and some are new requests each period for example to get a mid week night away to see Hamilton in Sydney or an afternoon off for the school swimming carnival.  

Sarah completes the roster by hand creating a compliant roster with all shifts covered and accommodating as many preferences as possible in as fair a manner as she can. However, is it always a ‘good’ roster from everyone’s perspective?

While a ‘good’ roster will vary from business to business and even with respect to  each employee depending on their individual circumstance, one aspect of ‘good’ roster from an employee perspective can be defined as being fair and equitable – team friendly. For example, while Sarah may have created a functional roster that ensures the ED department meets the service level requirements, Dr Brown might have worked the past 4 Saturdays in some capacity impacting on his ability to watch his children’s sports while another team member may have worked an equal amount of hours but more night shifts than other team members.  Although on the surface the roster was compliant and functional, it is not considered a team friendly roster given the unfairness and lack of equality amongst employees. 

In addition, there may be team members who prefer to work weekends, others who prefer nights, some who only work 3 days a week and still others who might be spouses and who cannot work at the same times as their partners – so that someone is home with junior.  

Further, the roster needs to consider patterns of work – moving from morning to afternoon and onto night shifts with then a number of days break.

This gets complex quickly!

A team friendly roster will consider all of these different preferences and requirements and provide shifts which are equitable and fair in providing each team member with the same proportion of night or weekend shifts (unless of course they prefer these) and evenly meets as many of their time off requests as possible.


Adopting a team friendly roster has many  benefits to both employees and employers. These include:

Better work-life balance 

Your workforce will be made up of a range of different people with different interests and commitments outside of work. Commitments such as family, study, and sport for example. A study conducted by Gallup Reports found that 53% of employees said that an improved work-life balance was important to them. With a fair and equitable roster these other commitments and interests will be accommodated to the fullest extent possible, making work rosters more manageable and providing employees enjoy an improved work-life balance. 

Staff retention 

Improve staff retention with a balanced roster that is considerate of staff preferences and importantly fair and equitable across the team. There are many factors that contribute to employees leaving, such as career advancement, pay and benefits but also the nature of the rosters they are repeatedly asked to work. Employees have shown to be happier when they are able to balance both work and life commitments and are treated fairly  resulting in employees desire to stay in that job. This is especially important when retaining skilled staff or staff with experience that is hard to come by. By ensuring rosters are fair and equitable across all employees you will reduce conflict and ensure no employees feel stuck with the ‘unsocial’ shifts. 

Biarri Workforce provides open and flexible rules and preference capture functionality so hard or soft constraints and preferences can be considered in the roster creation and the powerful roster optimisation engine in Biarri Workforce allows the Sarah the workforce planner to set the roster objectives and generate team friendly rosters automatically in a matter of minutes.

By using Biarri Workforce, you will be able to improve work-life balance for your team members and ensure your roster is fair and equitable

Speak with a consultant today and see how Biarri Workforce can improve the way your business coordinates your human workforce. Whether you are currently rostering manually or looking for a more accommodating solution, the team behind Biarri Workforce are dedicated to making Biarri Workforce work for you. 

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Data Challenge

Australia’s Data Analytics Challenge

As companies enter 2021 with a focus on returning to some form of normality, the strategic momentum continuing from 2020 into this year is the importance of data and digital. 

However, in spite of many companies upgrading their data and digital capabilities, much opportunity has not been realised and organisations we are talking to, especially the people in the senior data and analytics roles, are deeply dissatisfied with the current state of affairs.

They talk about data and analytics being paid much lip service but little or nothing happening on that front or at best once off activities to “tick a box”. So the question is, what is holding Australian companies back?

To answer this question, Biarri and McGrathNicol went out and interviewed senior executives in data and analytics roles across a variety of organisations in Australia. Our findings and results are presented in this video.

If you’d like a copy of the deck or any further information, please reach out to Evan Shellshear as shown in the video or use the contact form below to get in touch.

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Biarri Workforce New Feature: Updated Shift Swaps

At Biarri we understand the challenges that Workforce Planners and employees face when rosters are published. We understand that circumstances outside of work may change and affect the ability for an employee to complete a shift, conversely, Workforce Planners’ and Managers’ pain point of finding a replacement to cover a shift or reconfiguring the roster to suit. 

We have listened, and Biarri is excited to introduce the new enhanced Shift Swap feature on our Biarri Workforce Mobile App.

Created out of necessity, employees can now more simply either Swap Shifts or Offer Shifts to other available employees.

When swapping a shift, we’ve removed the stress of trying to identify which employees are available. Instructed by the rules engine, the Biarri Workforce App generates a list of compatible  employees to cover a shift. These compatible employees comply with rules defined in the Admin Tab, ensuring employees are compliant with skills & qualifications, fatigue and availability rules. 

See a step-by-step walkthrough of how simple swapping shifts has become.


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Worker Fatigue

Modelling Patient Flow to Improve Service Delivery

Running a hospital is a highly complex activity. There is not one division with one need, however, hundreds of areas are highly interconnected and all rely on each other to deliver life saving outcomes. Patient flow in such a complex system is a serious challenge.

In 2019 alone, there were over 11 million separations in Australia (a separation from a healthcare facility occurs anytime a patient leaves because of death, discharge, sign-out against medical advice or transfer, it is the most commonly used measure of the utilization of hospital services). These were just the patients who were discharged, let alone those who may have unnecessarily visited a hospital and sent home immediately.

Managing these millions of patients and providing the high level of care we have come to expect is extremely difficult.

This is why a Victorian Hospital recently turned to Biarri to get some help with their patient flow challenges. Biarri turned its mathematical expertise on the problem and what came out of our work was a great result with substantial improvements and clear benefits to the most important person of all – you.

The Patient Flow Challenge

The hospital approached Biarri after realising that its emergency department was consistently overcrowded at seemingly predictable times and for seemingly predictable reasons. The staff and executive team at the hospital knew that they could better plan and manage their overcrowding problem, and we at Biarri knew that we could provide them with the tools with which to inform those decisions.

One of the key metrics that quantifies the overcrowding of an emergency department is the NEDOCS score. In our case it wasn’t that the NEDOCS score didn’t provide value, it was that the staff were not able to anticipate when the score would rise or fall. Both management and clinical staff felt it should be possible to better predict when these spikes would occur, and therefore better prepare for them.

So the business problem for Biarri became, can we produce a tool that is able to accurately forecast the bottlenecks and queue lengths within an emergency department?

It was this fascinating challenge we solved and have developed a unique tool specific to this hospital.

The Approach

Biarri’s approach to this challenge was to build a custom simulation model that would act as a digital twin for the hospital’s emergency department. This allows the staff to simulate how the patients move through the different streams of the emergency department. The simulation model is built upon three main objects: 

  1. agents, 
  2. processes and 
  3. resources. 

Let’s define these relative to the emergency department.

Patients are the agents that move through the system. They possess attributes such as scan type and scan priority that indicate how long it takes them to move through the system. Agents go through processes such as triage, scanning and reporting, which move them along the emergency department. Finally, doctors, nurses and the machines they use are the resources that are required to move our patients through the processes.

All the quantitative values, such as the frequency of arrival, or the time it takes for a CT scan, etc are found using the historical data from the emergency department. Using the past data is the best way to get a digital twin that emulates the workings of the emergency department accurately. This was combined with some smart twists on existing tools such as Discrete Event Simulation to simulate a high fidelity model able to accurately model the emergency department.

The Patient Flow Solution

At its core, Biarri’s patient flow tool provides users with the information to better understand how the future of their emergency department looks like. It does this by providing staff with feedback on the development of patient queues and bottlenecks at different stages of the emergency departments process.

Given the nature of simulation modelling, the tool provides a distribution of future queue times, with confidence intervals to encapsulate the likely ranges expected in the future. This provides insight into the changes over time of queue lengths at different stages of the emergency department. Not only does it give a general overview of the emergency department, but a more granular look at each queue.

A big plus for the patients is that from this queue length we were able to derive expected wait times so that patients are now able to know how long they expect to wait based on how busy the hospital is.

The tool also allows for scenario analysis. This gives hospital staff the agency to compare how their current conditions compare against changes in rostering when it comes to influencing patient flow.

With this solution, Biarri aims to give hospital staff the information to make sound decisions around the rostering of their resources and the structure of their emergency department.

What’s next?

Biarri’s new patient flow modelling tool was a great success and has proven its ability to accurately model patient flow and provide timely and accurate warnings of future bottlenecks.

The tool is tailored to the Australian environment and now having been demonstrated on a Victorian hospital, Biarri is looking to roll it out across Australia. If you have patient flow challenges and could benefit significantly by being able to better plan and allocate limited resources, then get in touch.

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Child learning at desk

What is a dollar of Education worth?

Every year, over 16% of the Australian population enrol in schools across the country. Some of these students choose to pay for their education, whilst others don’t. Inevitably, a discussion will arise regarding whether there is any real academic advantage in a more expensive education; the age-old private vs public debate.

Due to the swift rise of data science techniques and resources over the previous decade, an understanding of this problem through data science is now more attainable than ever. This article seeks to assess the relationship between socio-educational factors and the outcome of a students’ academic achievement and, most importantly, to determine whether paying more for schooling is justified through higher academic outcomes, in particular those measured on standardised tests. We achieve this through a deep statistical analysis on both the 2016 Census, and 2018 NAPLAN datasets.

The results reveal some fascinating facts about what seems to matter for education and what doesn’t. 

The Education Problem

The goal of this research is to understand how socio-educational factors affect student academic achievement. Taking the NAPLAN data, and purely considering a school’s average score across each of the six tests, we find the results presented in the following figures.

Figure 1: Average NAPLAN Scores in Writing and Numeracy, across all schools in Australia (2018), categorised by school type. At face value, independent and Catholic schools appear to outperform government schools in both domains, but the understanding is not that simple. The bigger idea of why these schools perform better represents the aim of this article.

From the above figure, independent and Catholic schools do perform marginally better when simply compared to government schools. We could take this at face value, finish the problem here and say, yes, paying more for schooling and going to a private school holds more promise of achieving a better result. 

Unfortunately, drawing this conclusion is not realistic. Quite simply, comparing schools based totally on their results does not paint the full picture. A multitude of other factors may ultimately affect how a student performs at school, like a student’s upbringing, a population’s demographic, or a school’s characteristics. Accounting for these, we see a very different response to the problem, that is, school sector has a relatively insignificant impact on students’ performance.

For a concrete example on understanding how other factors may affect student performance, take the ACARA defined ICSEA value, best explained here. The ICSEA value tells us a variety of interesting things about a school’s student-base and their socio-educational position. If we illustrate the distribution of ICSEA scores across each school type, we find the results shown in the following figure.

Figure 2: Distribution of ICSEA across different school types. ICSEA represents an index of a school’s socio-educational position, indicating collective student advantage irrespective of their school. Clearly, there is a vast difference in socio-educational advantage between the sectors, which this article seeks to quantify.

In the above figure, we see a much wider spread on ICSEA scores for government schools, and higher averages achieved by Independent and Catholic schools. Based on this, it is a fair assumption to say we might expect Independent and Catholic schools to, on average, enrol students from more advantaged socio-educational backgrounds.

If students from a more advantaged background on average perform better, then by extension, we can assume that independent and Catholic school results are influenced by the students they enrol. So naturally, we would see independent and Catholic schools performing better than government, based on their student demographic. This simple intuition can be extended to many other factors, and represents the need to account for all possible variables when looking to truly understand how tuition fees affect student success.

Approaching the Problem

Our approach takes the NAPLAN results from 2018, combined with the ABS 2016 Census data, and uses these to understand how different socio-economic factors affect student performance. The main problem we’re trying to understand is enormously complex, with many variables that could ultimately affect academic achievement. In particular, we’re looking to understand the following:

  • A student’s background, i.e. family encouragement, parent contributions, extra-curricular activities, etc.,
  • Contributing factors to school fees, i.e. how tuition fee depends on school size, location, and reputation in a community.

Naturally, these include a huge number of possible variables, the challenge being that some of which are particularly difficult to quantify. For example, how do we express a student’s natural academic ability as a numeric value? This question is a foundation of the main challenge the problem presents.

Figure 3: List of possible factors that might affect student academic achievement, categorised between school demographics and school factors. Note this is a subset of an inexhaustible set of possible factors that may influence student achievement.

Considering this, our final solution needs to account for high levels of complexity, whilst making do with the limited data available. We took advantage of two core approaches:

  • Making assumptions to reduce the scope of the problem. Notably, to limit the effect of confounding variables, the scope of the project was narrowed down to exclusively include Melbourne schools, within grade three and five student results. This controls for background education effects which would be more prevalent if we analysed later years (i.e. after completing primary school).
  • As we don’t have the luxury of being able to carry out randomised controlled trials, we used proven and well understood statistical techniques to establish the associations between variables. We implemented our solution through Instrumental Variable analysis, a form of linear regression that can account for unobserved variations in data. This analysis ultimately gives us a means of accurately modelling the problem using a restricted dataset. Additionally to ensure the coming results are as robust as possible we have applied a number of techniques to account for confounders, collinearities and other problems which cause such analyses to go wrong.


The beauty of using a regression model is that it provides the ability to identify the strength and direction of correlations between each of our model’s variables, and school achievement. This is characteristic of using regression models, and incredibly useful in our use case.

In simple terms, the model returns a single numeric value for each variable in our model, termed model coefficients. Given a one-unit change in each variable, the predicted student academic achievement will then increase/decrease by the model coefficient. For example, in a model that estimates a variable representing the percentage of female teachers in a school to hold a value of positive four, we can assume that a 1% increase in the percentage of female teachers at the school will equate to an average increase of four marks in the NAPLAN.

Figure 4: Illustration of model results. The box plot values indicate how each observation affects student academic achievement, while bolded values represent model coefficients. Values in the box plot are simply dataset observations multiplied by the model coefficient. Note the difference in how variables positively or negatively affect student achievement.

Using this method, we can summarise the model results using the illustration shown in the above figure, which shows for each variable how changes in these values in the dataset affect average student academic achievement. Although we used the average NAPLAN score as education output to build our model (instead of each subject area, writing, numeracy, etc), a similar result also applies to separate analysis on the different NAPLAN subjects.

Furthermore, if we take the average effect of the values shown previously, we find the result shown in the below figure. You can see tuition fee highlighted in red; clearly still positive, but providing a relatively insignificant effect compared to its counterparts.

Figure 5: Comparison of the average effect from each variable on student academic achievement. Notice the relatively insignificant contribution made by the Tuition Fee variable, as opposed to volunteer ratios, student/teacher ratios, and married proportions of the population.

What is a Dollar of Education Worth?

Most importantly, the final model coefficient for tuition fee comes to 0.001044 with this being statistically significant. Intuitively then, we know that a one dollar increase in the amount paid for schooling corresponds to a 0.001044 increase in a school’s expected NAPLAN score. In simpler terms, a $1000 increase correlates to a 1.044 point score increase.

We can investigate how the NAPLAN results truly scale to get a better understanding of what this means. The below table illustrates how to achieve different NAPLAN bands based on scores in the grade three and five Numeracy exams from 2018. Note that this information is publicly available here.

BandGrade 3Grade 5
Band 192.4 – 256.1
Band 2270.8 – 318.2
Band 3328.3 – 365.6195.5 – 368.6
Band 4374.4 – 417.9379.2 – 423.8
Band 5426.8 – 475.0431.7 – 476.9
Band 6486.0 – 682.5484.3 – 522.2
Band 7530.2 – 573.7
Band 8583.5 – 777.8
Table 1: Comparison of NAPLAN Scale Scores required to achieve different bands in the 2018 Numeracy Exam for students in grades three and five. The national minimum standard for grade three and five students is a band two and band four respectively. Note most of the bands range across approximately 55 points.

The national minimum standards for this exam are bands two and four for students in grades three and five respectively. To jump from the median band two score to the median band three score for a grade three student is approximately 55 points. A similar result holds for grade five students, and subsequent jumps between bands.

Therefore, we can assume that for a student to achieve a single band increase based entirely off paying more for tuition, they require approximately 55 additional NAPLAN Scale Score points. Based on our model results based on the discovered correlations, this is equivalent to paying an additional $52,000 purely in tuition fees.

In practice, the most students from Victoria will ever pay for schooling is around $30,000, rendering scores up to 55 points impossible. Median private schools pay around $9000 on tuition, and by extension, this would give approximately 10 additional predicted points. Obviously, these bonuses cannot solely drive student achievement into higher bands.

Figure 6: Comparison of tuition fee paid and predicted NAPLAN score increase. Assuming a 55 point jump is on average necessary to move to a new band, approximately $52, 000 is then required to do this solely by paying more for schooling.

By comparison, the median percentage of people in a population whose marital status classifies as married was 40.44%. With a coefficient of 1.104, this percentage is correlated with a score increase of 44.66. The maximum percentage of married people in a population used in this study was 51.61%, which would correspond to a score increase of 56.99. The interpretation here is that in tact families assist students with their academic achievements, which helps put the tuition fee benefits in perspective.

Similarly, the median proportion of a population who regularly undertake volunteering duties was around 15.25%. With a parameter estimate of 1.72, this means the median score increase related to the number of volunteer workers was 26.28 NALPAN score points. The maximum effect based on data used in this study was a population with 24.19% of people who volunteered, offering an increase of 41.69 points.

In comparison to tuition fees, it’s clear other variables provide significantly more influence in our final model. Ultimately, this demonstrates how in practice, school results are clearly influenced by many different factors, not just the school type or amount paid. This allows parents to make better decisions as to where to invest their money and whether living in the catchment of a school with high volunteer rates, married families who have highly educated parents could be a much better investment in their children’s education than paying significant tuition fees.


Our analysis and the results of it indicate that paying more for schooling does positively relate to academic achievement. Conversely, this effect does not provide substantial enough benefit to solely allow students to achieve outstanding results as was to be expected. Instead, other more important factors seem to encourage academic achievement, including family composition, a population’s level of education, community involvement, and a school’s structure. 

The key takeaway from this report reduces to the fact that these influences, namely family contributions and population characteristics, play a far more important role in determining student outcomes than the amount paid for tuition does. Notably, this aligns with the findings of Fryer & Levitt (2004) who undertook a similar regression style approach to understanding the racial test-score gap in early-year students. Notably, they show the following are important for academic outcomes:

  • A child has highly educated parents
  • A child’s parents have high socio-economic status
  • A child’s parents speak English at home

These results go further to enforce our findings that tuition fees specifically aren’t as important in contributing to student success. For further details on the analysis that lead to the results presented here, the reader is encouraged to download the reports and discover the limitations and depth of analysis presented there. The reports can be downloaded here and here.

What Next?

Understanding this, we need to acknowledge the complexity of this problem, and the limitations of what data made available to this project. Whilst these results go some way to understanding how paying for schooling may affect student achievement, a further study involving a more comprehensive dataset and deeper statistical analysis would be necessary to truly understand the full relationship between tuition fee and academic success. In particular, the results presented here are based on regression techniques which can establish associations but cannot guarantee causation.

We encourage future researchers to build upon the results here to establish the causes of the effects we see here.


Most importantly, the work presented here only investigates academic achievement at the school level. When deciding where to send a child to school, whilst still accounting for our results, utmost importance rests on considering a student as an individual such as:

  • What do they like to study?
  • What opportunities are available at different schools?
  • What will give the student the best compromise between doing well academically and making the most of what a school can offer socially?

Whilst mathematics might suggest one thing to be true, in practice it might not always be the case, a cautionary tale that can be extended to the world of data science and machine learning in the modern-day.


Fryer R, Levitt S. Understanding The Black-White Test Score Gap in the First Two Years of School. The Review of Economics and Statistics. 2004.

For inquiries, reach out to the authors, Nicholas Thompson, Meichen Qian, Jonathon Allport and Evan Shellshear.

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.

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Markdown optimisation example

The Optimal, Best Markdown Rules During COVID-19 and Beyond

When my friends talk to me about managing a retail store they all enjoy it apart from one thing. They tell me that seeing the latest trends, working with cool brands and meeting some of the stars promoting the articles is great. It’s just that there is a recurring event that makes long days longer and they are sure can be done better – markdowns.

My friends imagine that a set of retail analysts spend days figuring out the optimal markdown to make sure customers buy the remaining goods at the highest possible price to maximise the possible gross profit. 

However, in many cases, there is no group of analysts.

Instead there is a fixed set of well-worn rules, year after year: first a 10% reduction, then a 30% reduction, and finally a 60% reduction until the product clears.

It’s artificial ignorance not artificial intelligence. The retailers have the data, it’s whether they use it – and can make use of it – or not.

As if typical markdown cycles weren’t difficult enough, COVID-19 has turned these processes into a nightmare. Companies are now stuck in a vice trying to clear stock but doing so in a way that loses minimal money and keeps the company afloat. 

The old rules are no longer working so companies are experimenting with new permanent discounts – 50%, 70% and 90%! Just whatever it takes to get the stock out the door before it’s too late…

What is needed, now more than ever, is a data driven, machine learning approach to suit each individual business. The best markdown rules use analytics.

Biarri’s research has shown that too many goods are too heavily marked down, even in a time of crisis like this, meaning lost profit margins. In addition, a one-size-fits-all approach doesn’t work – each business needs to take its own unique conditions into account for such markdowns to be successful.

The early conclusion? The best markdown rules

In the current situation, you need an advanced analytics tool (AI, Machine Learning, Optimisation) that takes into account the unique circumstances of your business and can answer questions such as:

  1. Each store has different stock coverage for an article, so how do we balance a single store’s need with a regional markdown strategy that mandates one markdown for all stores in a region? Or even more difficult, a national markdown strategy?
  2. If we allow markdowns to vary between stores, then how do we create a markdown strategy that prevents customers shopping between your different stores searching for the biggest discount?
  3. How do we accurately model price elasticity for goods with only a few sales each month and even fewer historic price points?
  4. When marking down, how do we pick a smart price point – an “anchor” – that increases demand much more than the surrounding price possibilities? 
  5. How do we accurately predict the demand for each individual article to know what its markdown sticker should show to maximise profit?

As well as much more.

A tool that maximises your gross profit needs to get all the above right for you and your business, not for a generic retailer in a generic retail environment – these disappeared a long time ago.

The way you resolve these challenges is by having an analytics tool which can:

  1. Group stores together using geographic and sales profiles to provide simplified markdown strategies which staff are prepared to implement.
  2. Provide a set of optimal markdowns, determined from the historic sales data, which can be applied equally across all stores in a similar area.
  3. Compute accurate price elasticities. Price elasticity is often like sausages; everyone likes them, but it’s better not to see them being made. Biarri avoids elasticity problems by bootstrapping data to measure how volatile the elasticity estimates are and then weights them accordingly.
  4. Cleverly choose price points based on known rules of customer pricing behaviour instead of just basing it on the markdown percentage times the price.
  5. Predict accurately the demand for an article at a given price point by not predicting a number at all – we predict a range.

The last point is key as merely having a “point” forecast is of little value, you need ranges of values so that you can meet your business needs to ensure that you will either:

  • Sell all stock with a high certainty at a good price, or
  • Ensure the shelves are never empty and you don’t leave customers with a bad impression.
Best Markdown rules forecasts

How do I use it?

So now that we’ve identified the key criteria to create the optimal set of markdowns to maximise profit but also clear out the goods, the final step of a successful markdown strategy tailored to your business is building trust in it. This means rolling out the new AI created marks in a smart way. If you roll it out across your entire range and there are any issues, the entire project will be doomed to fail – even if it was immensely successful on some articles.

The way to make this succeed is to do some simple tests in a scientifically correct way. Create a control group where you do what you normally do and test it against a treatment group with the new optimal markdowns. This can be done either on a store by store basis (A/B tests), but if that is not possible then it should be done across similar product categories so we can draw valid conclusions from the experiment to find the best markdown rules.

What are the benefits?

If instead of using intuition and “What we always did” to markdown products, you turn to analytics to do this smarter, the gross profit improvements can be significant. This technique has been the success story of a number of giants such as Walmart and Target Corporation in their retail divisions. Not only has Biarri seen significant benefits from using optimal markdowns, studies have also concluded that it is possible to improve profits by up to 20% by using analytics instead of gut feelings.

COVID-19 is affecting everyone, irrespective of industry, however the retail industry is one of the worst hit. People are scared and not visiting stores leading to a drop in foot traffic of up to 50% and supply chains malfunctioning with over half of retailers impacted by supply chain issues having too much of some stock (sporting jerseys) and not enough of another (running gear). 

So something smart needs to be done – and right now. Analytics based markdowns are exactly what are needed to turn the retail industry around. Off the shelf tools don’t cut it for a situation like this as they are not customised to cope with so-called “out of sample” events like the current coronavirus crisis.

So reach out now to find out how you can clear out unwanted stocks effectively and maximise profit at the same time. Biarri’s tools are simple to use, customised to each customer and driven by accurate and correct mathematics to ensure the best result. They provide you with the best markdown rules every time.

Better yet, to simplify things, Biarri is offering its markdown tools as a managed service, so no IT work for you to do! We’ll do the analysis and provide you with the best possible markdowns.

Biarri’s tools are trusted by

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