Biarri Workforce New Feature Release: July Edition

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

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

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

  1. Roster Export Additional Employee Columns  

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

2. Include shifts from non-primary roster

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


3. Customisable report titles

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



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

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

5. Powerful roster view filters

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

6. Fatigue and Qualification Compliance Reporting 

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

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

Workforce Contact Form

A night to ‘Raise the Roof’ with the AEIOU Foundation for children with autism

On the 12th of June, the team at Biarri were invited to attend and support the ‘Raising the Roof’ Gala Ball held by the AEIOU Foundation at the Emporium Hotel in Southbank, Brisbane. The night was filled with great food and drink and fun as the team stood in solidarity with AEIOU’s efforts in raising funds for Australian children and families living with autism. Through a silent auction and many generous donations, AEIOU managed to raise over $100,000 dedicated towards the construction of their new learning centre.  

The Biarri team treated to a five piece band and illusionist as they enjoyed their exquisite 3 course meal

AEIOU Foundation for children with autism was founded in 2005, with a mission to provide early intervention that enables children with autism to live their best lives. Since 2005 and the establishment of their first centre in Moorooka, the Foundation provides early intervention learning programs for children aged between two to six across Queensland and South Australia. Fast forward to the present and AEIOU continues to make a positive impact on Australian families with autistic children through their educational services.

The Biarri Team Enjoying Themselves

Biarri and AEIOU partnered in 2019 and worked in tandem to change the landscape of how AEIOU delivered their learning programs. Challenged with managing their clinical data and limited staff, AEIOU approached Biarri to create a streamline application to digitally capture children’s data for clinical assessment and create a platform to deliver their learning programs. Through Biarri’s ‘Little Steps’ educational application, parents and carers are now supported with an intuitive application that:

  • Improves the efficiency of staff content delivery; 
  • Streamlines generating reports;
  • Removes manual large paper folders and records and 
  • A centralised data hub to be analysed for treatment and progress insights 

Biarri would like to extend a warm thanks and congratulations to the team at AEIOU Foundation for their continual work in creating awareness and raising funds to assist children with autism. Biarri champions ‘Positive Impact’ as one of our core values and we take great satisfaction in being able to assist great causes. We look forward to continuing our partnership and are excited to see the positive changes the new learning centre will bring to children and families.

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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.

Results

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.

Takeaways

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.

Caveats

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.

References

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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AI Driven Business Decisions

AI Driven Business Decisions

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

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

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

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

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

The AEIOU Challenge

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

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

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

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

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

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

But how? And why?

AI Driven Business Decisions

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

So what is the best way to do this?

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

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

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

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

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

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

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

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

AI Driven Value Creation

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

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

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

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

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

What role does Biarri play in this transformation?

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

How do we do this?

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

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

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

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

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

Matthew Walsh, CFO, AEIOU

Does this apply to me?

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

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

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Worker Fatigue

Workforce Fatigue Management

It was around two years ago that Anita knew that she’d had enough. However, the ensuing two years had passed so quickly repeating the same task over and over again. 

So here she was again.

Anita is the workforce manager at a company which manages a large temporary, contracted, part time and/or casual workforce. They carry out a set of known tasks, some of which require qualifications, many of which don’t. In any given week, she will be juggling her Excel sheet and a phone to desperately contact replacements for people who call in sick that day while ensuring that any replacement doesn’t violate any of their strict fatigue requirements. 

Although fast paced, it’s not the type of fast paced that she had hoped for as most of it was trying to carry out tasks which can be easily automated. So why not automate?

The reality with Anita’s work is that it can be automated and there do exist tools which can do most of her job and actually make both her as well as her rostered employees’ lives better. Anita could spend less time manually carrying out error prone tasks and, simultaneously, her rostered employees could get the shifts they want – meaning less work for Anita having to offboard unhappy employees.

By automating the error prone and repetitive tasks, this would leave Anita more time to more accurately cost up rostering jobs, better manage her staff so the good people keep coming back and focus on more strategic aspects of rostering.

The dream state.

Biarri’s Workforce Fatigue Management Rostering Tool

Biarri’s Workforce rostering tool was built to solve problems exactly like Anita’s. It was designed for companies which have built their success off the back of flexible Excel sheets, however, have now outgrown them. The tool excels when the workforce has become too large or too variable and the rules too complex to efficiently allocate people to the required work in a way that keeps everyone happy.

This was a situation faced by one of Biarri’s partners, Field Mining Services. Their process was Excel driven like most companies in the region and required a lot of manual work and time intensive copy of data between spreadsheets. 

In addition, customer rules/regulations and reporting obligations were a major pain point, with countless hours spent working with Excel and Word to create reports manually. 

However, the real pain point arose when you had many users working in a single Excel spreadsheet causing significant errors and delays for the team who were trying to understand who made the change which broke everything.

These challenges triggered a roster to invoice overhaul which Biarri assisted to complete by configuring its workforce tool to make it optimally suited to FMS’ situation.

The team at Biarri were fantastic to work with. They really listened to the pain points of our business and delivered a fit solution.” – Field Mining Services

Workforce Fatigue Management Tool Capabilities

At its core Biarri’s Workforce tool is built to take the complexity out of rostering by allowing a powerful optimisation engine to do the high quality rostering for you. The engine automatically and dynamically creates compliant and desirable rosters. 

When creating the rosters, the engine takes into account fatigue and EBA requirements. The EBA and fatigue management capabilities are able to take into account things like enforced breaks after working certain periods depending on: 

  1. Location
  2. Type of work
  3. Time of work
  4. Length of work
  5. And more 

As well as many other similar rule types.

Biarri’s Workforce tool allows the bespoke configuration of a rich set of constraints suitable to any industry. When it comes to managing a highly flexible workforce (comprising many part timers, casuals and contractors), the engine can optimally look at options and do things like sharing employees across locations to improve flexibility. The benefits are:

  1. Smooth out demand, and the resulting work, to better distribute tasks to people who don’t have enough work to do in one location or too much in another
  2. Resolve skill mix requirements at a location where key skills are missing
  3. Reduce the amount of onboarding, hiring and redundancies of staff due to more flexible use of staff
  4. Create a better employee experience by creating the flexibility to cater to staff needs
  5. And more

The functional user interface allows the easy management of flexible and continually changing rosters for staff, for example, when employees call in sick.

As well as being capable of catering to complex work requirements, the tool employs modern web based technologies to integrate seamlessly via REST APIs into payroll and T&A tools. This means you can avoid complex integrations and replacements of existing tools. 

The Biarri Workforce Rostering Application integrates into any stack with predefined interfaces.

Typical Outcomes

So what are the benefits of using optimisation and automation to do your rostering? For customers like Field Mining Services it is:

  • Better workforce fatigue management
  • A complete understanding of the value chain from mine to port
  • Substantially increased compliance with site specific access rules including fatigue, D&As, and site specific qualifications.
  • Increased visibility of workforce fatigue across multiple job sites.
  • Significantly reduced overall administration effort
  • The ability to tackle the next phase of growth with certainty around one of the most challenging areas – staff and fatigue management
  • Transparency across all departments and a single source of truth.

In other industries we have also seen benefits with improved employee engagement and experience by being able to take into account employee preferences and desires and then automatically catering to these needs. This leads to employers becoming employers of choice and staff bringing their experience and good habits back to your organisation instead of another casual staff provider. 

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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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