The Queensland Tertiary Admissions Centre (QTAC) is a non-profit organisation and Queensland’s trusted leader in tertiary admissions for more than 40 years. QTAC provides a one-stop shop for 17 higher education providers in Queensland and northern New South Wales, across both undergraduate and postgraduate admissions.
Vaxa Analytics was engaged to assist QTAC in the development of a financial model regarding a new public-facing product. The Vaxa Analytics team worked to find and integrate a range of third-party datasets and relevant industry benchmarks to build the model under several scenarios.
The team were able to project the growth of the product under those scenarios, accounting for various revenue stream, growth profiles and sensitivities over time.
Additionally, Vaxa Analytics provided advice at a strategic level on the analytics and data-driven development of the new product, what QTAC should be looking to integrate into the product to understand which features users are using, and how that impacts long-term product retention.
“Curtis and the Vaxa Analytics team helped us determine what data we should be accessing and how best to use it to get the most accurate results and insights,” QTAC’S Head of Strategic Growth, Kristie Fankhauser, said.
“It was great having a specialist external team work on this project rather than impacting our internal team who were extremely busy during a peak time for our business.
“Vaxa Analytics delivered over and above my expectations, were professional, transparent in everything they did, and easy to work with. I have already recommended them to many people and hope to continue our working relationship on this project and future ones.”
The outcome
The financial model was reviewed by QTAC’s engaged forensic accountant who agreed with the assumptions presented in the model and the data used. The model was an important part of the work required to progress further development of the product. Work is continuing.
Process optimisation is the process of identifying and eliminating inefficiencies in business processes to improve efficiency, reduce costs, and increase profits. It is a key component of business success, as it allows organisations to operate more efficiently and effectively.
Why process optimisation matters to your organisation.
There are several reasons why process optimisation should matter to your business:
Increased efficiency: By streamlining your business processes, you can increase efficiency, which can lead to cost savings and increased profitability.
Improved customer experience: Optimising your processes can help you deliver a better customer experience, which can lead to increased customer satisfaction and loyalty.
Enhanced competitiveness: By operating more efficiently, you can gain a competitive advantage over your competitors.
When is the right time to look at process optimisation?
There is no one-size-fits-all answer to when the right time is to look at process optimisation. However, there are a few signs that your organisation might be ready to look at process optimisation:
You are experiencing bottlenecks or delays in your processes
You are seeing an increase in errors or mistakes
You are facing increasing pressure to reduce costs
You are experiencing declining customer satisfaction
How does data analytics help with process optimisation?
Data analytics can help with process optimisation in several ways:
Identifying inefficiencies: By analysing data on your business processes, you can identify bottlenecks, delays, and other inefficiencies that are impacting your efficiency and profitability.
Measuring performance: Data analytics can help you measure the performance of your business processes, allowing you to track progress and identify areas for improvement.
Prioritising improvements: Data analytics can help you prioritise improvements by identifying the areas that will have the greatest impact on your efficiency and profitability.
Identifying trends and patterns: Data analytics can help you identify trends and patterns in your business processes, allowing you to make data-driven decisions about how to optimise them.
Monitoring and measuring the impact of improvements: By analysing data before and after process optimisation efforts, you can measure the impact of your improvements and identify areas for further optimisation.
Key steps in a process optimisation project.
Here are the general steps we follow when undertaking a process optimisation project with you:
Define your goals: The first step in any process optimisation project is to define your goals. What do you hope to achieve through the process optimisation?
Assess your current processes: Next, you’ll want to assess your current processes in detail. This might involve mapping out your processes, gathering data on process performance, and identifying inefficiencies and bottlenecks.
Identify opportunities for improvement: Based on your assessment of your current processes, you can identify opportunities for improvement. This might include streamlining processes, automating manual tasks, or introducing new technology.
Develop and implement a plan: With your opportunities for improvement identified, you can develop and implement a plan for optimising your processes. This might involve updating your processes, training your team, or introducing new technology.
Monitor and review: It’s important to monitor and review your process optimisation efforts to ensure that they are delivering the desired results. This will help you identify any issues or challenges, and make adjustments as needed.
Practical examples
Here are a few examples of practical use cases for process optimisation in different industries:
Manufacturing: In the manufacturing industry, process optimisation can be used to streamline production processes, reduce waste, and improve efficiency. For example, a manufacturer might use data analytics to identify bottlenecks in their production process, and then implement automation or other improvements to eliminate those bottlenecks.
Healthcare: In the healthcare industry, process optimisation can be used to improve patient care and reduce costs. This might involve streamlining processes for scheduling appointments, ordering supplies, or managing patient records. For example, a healthcare provider might use data analytics to identify trends in patient demand, and then optimise their appointment scheduling process to better meet that demand.
Real estate: In the real estate industry, process optimisation can be used to streamline property management and improve efficiency. This might involve automating tasks such as rent collection, maintenance requests, and lease renewals. For example, a real estate company might use data analytics to identify patterns in maintenance requests, and then optimise their maintenance process to better meet those needs.
Retail: In the retail industry, process optimisation can be used to improve inventory management, reduce costs, and improve customer satisfaction. This might involve optimising the supply chain to reduce lead times and improve delivery times, or using data analytics to identify trends in customer demand and optimise inventory levels accordingly.
Services: In the services industry, process optimisation can be used to improve efficiency and reduce costs. This might involve streamlining processes such as billing, customer service, and employee onboarding. For example, a services company might use data analytics to identify bottlenecks in their billing process, and then implement automation or other improvements to eliminate those bottlenecks.
Process optimisation is a critical component of business success. By identifying and eliminating inefficiencies in your business processes, you can increase efficiency, reduce costs, and improve profitability. Data analytics can be a powerful tool for process optimisation, allowing you to identify opportunities for improvement and measure the impact of your efforts. Whether you’re in manufacturing, healthcare, real estate, retail, or another industry, process optimisation can help your organisation operate more efficiently and effectively.
So why do things the hard way?
We’ll help you do what you do best, more efficiently.
Logistics optimisation helps businesses improve the efficiency of their supply chain by identifying and eliminating inefficiencies. It can involve a wide range of activities, including analysing data on supply chain performance, identifying bottlenecks, and implementing improvements.
How does logistics optimisation work at a high level?
Our work in logistics optimisation typically sees us bringing in a team of our experts to analyse your supply chain and identify opportunities for improvement. This broadly involves gathering data on your current processes, analysing that data to identify inefficiencies, and then developing and implementing a plan to address those inefficiencies.
Why logistics optimisation should matter to your organisation.
There are several reasons why logistics optimisation should matter to your business:
Increased efficiency: By streamlining your supply chain, you can increase efficiency, which can lead to cost savings and increased profitability.
Improved customer experience: Optimising your supply chain can help you deliver a better customer experience, which can lead to increased customer satisfaction and loyalty.
Enhanced competitiveness: By operating more efficiently, you can gain a competitive advantage over your competitors.
When is the right time to optimise your logistics and supply chain?
There is no one-size-fits-all answer to when the right time is to optimise your logistics and supply chain. In most cases, it’s always a good time.
However, there are a few signs that your organisation might be needing assistance urgently:
You are experiencing bottlenecks or delays in your supply chain
You are seeing an increase in errors or mistakes
You are facing increasing pressure to reduce costs
You are experiencing declining customer satisfaction
How does data analytics help with logistics?
Data analytics can help with logistics in several ways:
Identifying inefficiencies: By analysing data on your supply chain, you can identify bottlenecks, delays, and other inefficiencies that are impacting your efficiency and profitability.
Measuring performance: Data analytics can help you measure the performance of your logistics processes, allowing you to track progress and identify areas for improvement.
Prioritising improvements: Data analytics can help you prioritise improvements by identifying the areas that will have the greatest impact on your efficiency and profitability.
Identifying trends and patterns: Data analytics can help you identify trends and patterns in your logistics processes, allowing you to make data-driven decisions about how to optimise them.
Monitoring and measuring the impact of improvements: By analysing data before and after logistics optimisation efforts, you can measure the impact of your improvements and identify areas for further optimisation.
Practical examples.
Here are a few practical examples of targeted logistics and supply chain optimisations:
Automating manual tasks: Automating manual tasks can help reduce errors and improve efficiency. For example, you might use automation to handle tasks such as scheduling deliveries, tracking inventory, or processing orders.
Optimising routes and schedules: Optimising routes and schedules can help you reduce costs and improve efficiency. For example, you might use data analytics to identify the most efficient routes for deliveries or to optimise production schedules to better meet demand.
Consolidating shipments: Consolidating shipments can help you reduce costs and improve efficiency by reducing the number of separate shipments you need to manage.
Reducing lead times: Reducing lead times can help you improve efficiency and reduce costs by reducing the amount of time you spend waiting for deliveries or processing orders.
Improving inventory management: Improving inventory management can help you reduce costs and improve efficiency by reducing excess inventory and improving stock levels. For example, you might use data analytics to optimise your inventory levels based on trends in customer demand.
Logistics optimisation is a critical component of business success. By streamlining your supply chain and eliminating inefficiencies, you can increase efficiency, reduce costs, and improve profitability. Data analytics can be a powerful tool for logistics optimisation, allowing you to identify opportunities for improvement and measure the impact of your efforts. Whether you’re in manufacturing, healthcare, retail, or another industry, logistics optimisation can help your organisation operate more efficiently and effectively.
So why use an inefficient supply chain?
We’ll help you set up your supply chain and logistics processes to enable your sustained growth into the future.
Endeavour Foundation is an independent, for purpose organisation established in 1951 with a vision to support people with an intellectual disability to live their best life. Endeavour Foundation Lotteries fundraising contributes to programs and services that deliver on making possibilities a reality for people with a disability.
Vaxa Analytics was engaged to analyse the performance of their complex lottery product, with an aim to discover and unlock growth opportunities using data-driven decision making. Endeavour Foundation Lotteries chose Vaxa Analytics to undertake this scope of work based on the proven track record in the foundation’s commercial industry arm, Endeavour Foundation Industries.
The outcome
The Vaxa Analytics team delivered critical pieces of data management infrastructure – including the introduction of state-of-the-art marketing automation and behavioural analytics platforms and providing project services on the replacement of the core lottery software system – towards an automatic and unified view of customers and the product. The team designed and operationalised new data sets via self-service reporting and enabled more advanced capabilities like machine learning and multivariate analysis. These capabilities delivered accurate answers on complex questions like customer churn, segmentation, purchase propensity and revenue forecasts. By understanding Endeavour Foundation Lotteries’ long term product strategy and building and delivering an aligned data strategy, Vaxa Analytics unlocked millions in annual revenue improvements and laid the foundations for improved customer experience and product growth moving forward.
I’ve been recently searching for a new place to live. Along with that comes all the usual considerations: house or apartment, rent or buy, number of bedrooms, kitchen size, etc. But there’s one key point that’s equally important but much more difficult to get accurate answers on: commute time.
There are some recent studies suggesting that long commutes can be detrimental to mental health. Obviously, it plays a component in our all-important work-life balance too. For those of us lucky enough to work from home during the COVID pandemic, the extra time savings were probably pretty noticeable!
Yet a household’s commutes are complicated. You might have two or more people commuting, by different modes, at different times of the day, and even different schedules during the week. And what about traffic?
A simplistic approach would be evaluating each house listing individually by using Google Maps to do a mock navigation, but it’s immediately clear that this isn’t scalable — not with the rate that rental and house listings are posted! Nor is it an approach based on true first-principles thinking.
So instead of asking “does this house minimise my commute?”, I asked myself the question: “where is my house to minimise my commute?”
Our goals
As with all our data projects, I approached this from our three pillars:
Data strategy or “what am I trying to achieve here?”: I want to know where I can live to minimise my commute for a better work-life balance
Data management or “how am I going to get the data?”: we’ll need to build or find a data source on commute times
Data analysis or “how to gain intelligence from data points”: how do we merge commute times into a fair and representative model for my household?
A simple first step
I know my ideal location lies somewhere within, say, 25k of the Brisbane CBD, so we firstly break up the city into a grid of coordinates.
Our data management pillar tells us we need to find a good source of commute data, and luckily we have this in the Google Maps Distance Matrix API! This API lets us simulate thousands of commutes in an instant.
By using the API on the grid of Brisbane-based house locations, with a destination of the Vaxa office in Paddington, we can see how travel times look across the city in the morning.
No surprises here, the quickest commutes are those that originate closest to the office! But our household does more than commute to the Vaxa office every morning.
Gaining the full picture
Our data management pillar again tells us: we need more data (in the right format) — before we do further analysis.
This missing picture here is a model of when and where we commute over the course of a week. For our household, this looks like (well sort of – randomised for our privacy):
Curtis: 2.5 visits per week at Vaxa, 1 visit to eastern suburbs, 1 visit to northern suburbs
My partner: 3 visits to Kelvin Grove, 1 visit to Brisbane CBD, 1 visit to Caboolture
Now we also have a grid of destinations in addition to our Brisbane grid!
We can input this into our model — which talks to the Google Maps API — to determine a weekly travel time for our household (filtering out excessively long commutes – so we can see what we’re dealing more clearly):
Great! We can start to see some really strong contenders appearing under this model now. But is it fair?
A fairer model
As it stands, we’ve only looked at “whole of household” travel times, but this is a bit unethical; what if it highly favours one person’s commute times at the expense of another’s? And how do we do that while fairly weighting the overall travel time?
Firstly, we can address this by updating our model to consider weekly travel time spread — the difference between the longest commutes and shortest commutes.
Secondly, we can establish weights for each of the parameters (overall travel time and spread). In this case, lets say we consider them to be equally important.
Combining these two factors gives us the below graph, where the green line shows our overall model outcome.
In real terms, the green line shows us which houses give the best overall outcome for both travel time spread and overall travel time.
I’ll say we only want to consider outcomes where the value is less than 0.5. This is mostly arbitrary as I want to narrow the suburb search quite dramatically, but one could fine-tune this to fit their needs, of course.
Applying this to our map view leaves us with an overall ranking of 47 remaining suburbs — 1 being the most desirable under this model.
Now we can see exactly where we ought to be looking! I hope this has shown you how a well thought out and executed analytics approach can bring accurate answers to complex problems.
Have a complex problem of your own? Get in touch with the Vaxa Analytics team for a chat.
In early March 2020, Vaxa Analytics and Vaxa Bureau were contracted by Aspen Medical to provide procurement and logistics advisory support to fulfil their obligations in restocking the National Medical Stockpile with vital Medical PPE.
The mission was clear – to secure supply chains and facilitate the procurement and safe passage of vital medical PPE offshore to Australia in a very short timeframe.
Upon receiving an initial briefing, including an overview of the extent of existing systems, tools and processes, the task and context of the requirement changed rapidly, requiring additional skill sets and personnel, namely data analysis and software design to support the effort.
Our compact, nimble team of data analysts, software developers, procurement, supply chain and logistics personnel worked closely with the Aspen team to develop a robust plan which included the need to build bespoke software to manage the entire process, ensuring goods were Delivered In Full and On Time.
We undertook tasks including modelling, logistics management, contract negotiations, data analysis and management reporting, supplier vetting, supplier management, facilitation of quality control mechanisms, communications to the Commonwealth and more.
Underpinning the overall success of the program were Vaxa Analytics’s data and software capabilities. Our team of specialists evaluated and strategised the software and data required to support project outcomes.
We built a bespoke ERP system to manage and report on logistics and business intelligence to enhance data capture capability which enabled the executive team to make accurate and informed decisions. The system need was analysed, developed, tested and deployed in less than two days to meet ambitious targets set by the Government, and was a vital component in managing the successful program.
The system managed the collection and stewardship of data and, via integration, fed live reporting through to the preferred reporting suite.
In doing so, the program enabled the management of critical components including manufacturing timelines, essential compliance certifications and quality programs, and freight management components to allow for efficient load mastering, air charters, contract management, and delivery of goods into the NMSP across hundreds of stock movements by ground, sea and air from dozens of suppliers. It also served a historical record, demonstrating value-for-money delivered for the National Medical Stockpile.
The outcome
Whilst other governments and commercial organisations were facing challenges to analyse and deliver their critical goods, working in conjunction with the various actors in the program, we delivered all items in full and on time, providing the Commonwealth with confidence in the systems to procure further items through this supply chain.
