After 3 long years, The Biarri Applied Mathematics Conference is back, and this year we are excited to be co-hosting with The University of Sydney. Since its inception in 2012 at the University of Melbourne, The BAM Conference has grown in popularity as we continue our mission to provide you with insights into how companies apply optimisation and other mathematical techniques to solve problems in the real world.
The Biarri Applied Mathematics (BAM) is a conference that bridges the gap between mathematics in academia and industry. We bring together guest speakers from a broad range of backgrounds and interests – from University Research to Commercial Applications.
BAM2022 will be jam-packed. You’ll watch expert discussions, see pioneering mathematics in action, rub shoulders with thought leaders and interact with your peers. The incredible lineup of speakers includes industry leaders from some of Australia and New Zealand’s leading organisations, as well as academics and researchers from the University of Sydney (and other universities).
What is the theme for BAM2022?
The limits of predictability
In the current age of AI, data and digitisation, lofty promises are made with respect to the capability of data-driven and analytical digital systems. Unfortunately, the world is a complex place, and as powerful as current modelling capabilities are, there are unarguable limits to our analytical capabilities.
This conference will explore those limits and help participants understand how mathematical techniques are defining, expanding and helping us understand our analytical limits better than ever before. Come and hear examples of projects and case studies of both successful and failed attempts to realise mathematical modelling in a variety of scenarios.
The event will help participants understand where analytical techniques can be successful and where the complexity, randomness or non-linearity of the system is too great, even for the most powerful tools we have today.
How can I register?
In-person tickets are limited, so get in quick if you want to join us face-to-face. Visit our website (www.bamconf.com) to register. The conference will also be streamed live – if you can’t make it to the event in person, register as an online attendee!
https://biarri.com/wp-content/uploads/2022/09/BAM-Eventbrite.png10802160Marketing Teamhttps://biarri.com/wp-content/uploads/2022/05/Biarri-White-Logo-Tag-Line-700-×-700-px-300x300.pngMarketing Team2022-09-01 11:38:592022-09-20 08:03:35Join us as we end the year with a BAM!
“Modern supply chains are complex” is a truism. Operators are all too aware of the global forces and local details that drive weird and wonderful supply chain complexity. This complexity isn’t going anywhere, but industry innovators are pioneering approaches to manage it more effectively.
In a complex environment, diligent planning is required to ensure that supply chains are cost-competitive. The drive to manage cost has spawned an ecosystem of multi-step processes (some more effective than others) and supporting enterprise software. The processes and systems are human driven, and often key knowledge sits with individuals despite the presence of large systems. Which leads to an obvious question…
Can we automate planning?
Fortunately, the answer is often yes. Like other processes that take many datasets and priorities into account, supply chain planning can often be automated or semi-automated. Increasingly, operators are turning to algorithms and artificial intelligence to drive lower costs across multiple segments of the supply chain.
Although many different algorithms can be applied to support decision making, managers can apply a general framework to frame these problems before applying algorithms.
Firstly, define all relevant Decisions. Decisions like:
“When should I import raw material, and how much should I import?”
“How much inventory should be stored at each point in my supply chain?”
“When should I book different transports?”
Operators and planners will easily identify the big decisions they make day to day or week to week, but when applying algorithms we need to consider the little decisions as well.
But decisions aren’t made in isolation – they’re subject to the physical, contractual and practical rules that apply to a business. These can be referred to as Constraints. They might look like capacities associated with road or rail transport legs, restrictions on site storage or throughput capacity or throughput capacity, or even specific timing rules for quarantine and or fumigation for primary products.
Decisions are made in a constrained environment, but this framework relies on an additional element to frame algorithmic approaches. This is the Objective: what result do we want from the algorithmic plan? In a world where we make decisions subject to constraints, we need to know what makes a good decision. Typical objectives often focus on planning to minimise cost, maximise profit or maximise throughput.
We can frame planning problems, then, by defining decisions, constraints and objectives.
Uncertainty is unavoidable
But how do these algorithms behave in a highly uncertain environment? How should they be applied to balance cost reduction with overall supply chain resilience? Uncertainty is unavoidable – this is true for our supply chains, regardless of scale. Uncertainty drives unexpected events, and these events can appear in many different ways. Maybe a key piece of plant breaks down for four hours, or maybe a major customer doubles their order for the next four weeks.
Most importantly, there is a big difference between a cost-optimised plan, and a plan optimised for cost-of-execution. A cost-optimised plan assumes certainty, and perfect, accurate information. Perhaps counterintuitively, highly tuned cost-optimised plans can perform poorly when reacting to change – typically these plans have sacrificed resilience to achieve cost-optimality. Algorithmic plans like these are blind to the costs associated with responding to unexpected events.
What would perfect look like?
In a perfect world, firms would have high-quality, comprehensive data, capturing probabilistic possible outcomes. Mathematical models would scale effectively when solving the largest stochastic problems. In this alternate reality, we could minimise the operating cost subject to hundreds of thousands of possible outcomes. We could use these models to make sure our worst-case outcomes rarely occurred.
This reality isn’t out of the question in decades to come, but for large and complex operations this typically won’t be possible. Even when a planning problem is small enough to approach in this way, often there is poor data (or no data) available to describe probabilities of future events. This data simply isn’t prioritised right now.
This paints a fairly bleak picture of planning to manage uncertainty. Thankfully, there are a number of highly effective algorithmic approaches to manage supply chain uncertainty when planning.
What is the best approach, given the circumstances?
More and more, algorithms using “Resilience Metrics” are proving to be an effective way to handle uncertainty while avoiding the challenges described above.
By identifying or constructing metrics that indicate a plan’s resilience to change, firms can optimise a non-stochastic model while also creating a plan that provides a greater ability to respond well to surprises.
While this approach relies on approximations, it also removes the barriers associated with genuine stochastic optimisation – it doesn’t require huge probabilistic datasets, and doesn’t become too complex to solve quickly and meaningfully.
Resilience metrics can be simple, and may even be intuitively understood and used by planning experts. Some examples of resilience metrics include:
“Safety stock plus” style measures for products with high demand variability.
Delivery vehicle route plans with characteristics that allow a second delivery attempt.
Slack holding capacity or processing capacity across multiple planning horizons.
Poker, not chess
Shipping is a great example of an industry with high levels of uncertainty. Vessel breakdowns and shifting demand for cargos can rapidly shift the profit-optimal plan. Previous approaches to tonnage allocation in the shipping industry have leveraged similar algorithms to those used in vehicle routing, and attempted to create “highly-optimised” plans. These planning algorithms have predictably seen low adoption and fostered a broader cynicism in the industry towards optimisation that ignores uncertainty. To paraphrase one executive at a major shipping line: “We’ve been trying to play chess. We need to play poker”.
As multiple industries that operate large supply chains search for improved resilience, more nuanced algorithmic planning should be leveraged to achieve genuinely cost-optimal outcomes. At Biarri, we hope to continue to play our part in moving this discussion forward.
https://biarri.com/wp-content/uploads/2022/07/Copy-of-Sustainably-scaling-your-supply-chain-1200-×-675-px-1.png6751200Dave Lynchhttps://biarri.com/wp-content/uploads/2022/05/Biarri-White-Logo-Tag-Line-700-×-700-px-300x300.pngDave Lynch2022-07-20 11:57:162022-08-03 14:02:45Setting the right objective
For any business, growth is a simple metric used to measure success. For large scale manufacturers, growth often results in scaling the physical aspects of the business, resulting in larger holding facilities, new warehouse locations, more staff and greater decisions.
Now, with growth in sales volumes and an increase in demand, the biggest obstacle to your organisation’s success could be your supply chain network and the ability to expand and scale. Understanding your current supply chain capabilities and itscurrentstate is important in deciding whether expanding is necessary without placing too much pressure on your supply chain and creating bottlenecks.
This new placed pressure is not exclusive to supply chains, with pressure also falling upon the shoulders of key stakeholders to make the best possible decision. Decisions around expanding, dealing with new and current infrastructure, as well as logistics are crucial to get right, usually triggering a multitude of questions, each with their own complications.
Scaling your supply chain
In a perfect world, it would be great to simply add a new port here and place a distribution centre in the middle of where there is rapid growth in demand and continue to run as usual. But unfortunately – we don’t live in a perfect world and the basis of our decision making is not as intuitive, or that simple. The complexities around decisions revolve around considering supply dynamics, transportation methods and arrangements, production flows, associated costs and inventory, to list a few.
Finding the optimal combination of factories and distribution centres in the supply chain, whilst trying to satisfy supply and demand at the lowest possible cost, remains the objective when identifying when to scale and expand.
Let’s look at a concrete example of how this can be done.
Australian Manufacturer – A Case Study
In early 2021, Biarri was consulted by an Australian manufacturing company to model the importation, storage and bulk distribution, for one of their new products across Australia. They forecasted the potential to substantially grow their sales volume over the next couple of years with their new product strategy and other business enhancements. The project was to determine if there were opportunities to scale their supply chain network and expand their current port storages, explore the potential of utilising inland storage solutions and whether they should look at new transportation methods.
In order to satisfy their new demand, they required cost effective decisions and solutions that explained how, when and where to expand existing infrastructure; whether to introduce new storage facilities; understand where to focus increasing demand for their product and how best to drive the growth of the business over both short and long term.
Utilising our Network Optimisation software, we had to first create a baseline model that ensured that the current approach to modelling the network was sound and the data transformations were accurate. This meant understanding the inputs and outputs of their current network, using the current supply dynamic with supplies being co-shipped from overseas into their four main port storage locations scattered across Australia; right down to using historical data of deliveries to each sales district to understand the current state of demand.
Once the baseline model was built, it was important that it was validated, confirming the model was a fair representation of the current state with major assumptions, data transformations outlined and agreed upon. This step was critical and set a basis for comparisons of future state models and other scenarios which represent alterations to the current state of the network.
Future State Models
Now that the baseline was established, it was important to apply projected demand profiles to the current network to uncover the difficulty of servicing future demands with the current network constraints.
Biarri modelled 3 alternative future state models, each with incremental increased levels of demand, predicted by their growth strategy. The results were conclusive with each resulting in potentially unsustainable levels of both import frequency and delivery freight rates. Due to their limited storage capacity in the network, and high demand forcing a large number of imports with high risk time intervals, Biarri reasonably showed that without reconfiguration to the network this would introduce high levels of risks to the client.
Biarri successfully built a representative model of the clients distribution network for their new product. With the current model, Biarri were able to uncover the strategic capability. This includes investigating options to increase storage, adding ports, various freight rate tiers and co shipping partners, and varying capacities and costs of different storage, along with many other potential constraints and features.
Would you like to know more?
Speak to an expert today and discover more about network optimisation and how to sustainably scale your supply chain network below.
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https://biarri.com/wp-content/uploads/2022/07/2.png788940Marketing Teamhttps://biarri.com/wp-content/uploads/2022/05/Biarri-White-Logo-Tag-Line-700-×-700-px-300x300.pngMarketing Team2022-07-07 09:56:372022-07-07 10:39:46Sustainably Scaling Your Supply Chain
Dare I say the dreaded the ‘C’ word? We have all lived through one of, if not the biggest disruption to businesses across the globe. Businesses and supply chains were crippled by the pandemic, placing an unprecedented level of pressure on supply chains to keep the world going.
We explore why and give you the 5 best usages of optimisation in supply chain.
Dealing with disruptions
With a large portion of the industry heavily reliant upon Excel spreadsheets, dealing with major disruptions will continue to be difficult to manage. Planning teams spend a great deal of time dealing with the here-and-now, with little time to plan for the future and the ‘what-ifs’. What if next week our production levels fall by 10%? What if next month the absenteeism rate amongst our staff increases to 25% because of widespread illness? What would we do in that situation?
If the planning process for one day takes almost one day, then the planning team doesn’t have any time to consider future scenarios. It’s a deal-with-it-as-it-happens situation, where the team is constantly reacting to the situation on the ground, and making decisions with little or no thought of how it will affect them in the future. “Just get it done now!”, not realising that the resource they use to get it done will then not be available the next day, causing an even bigger problem.
Excel spreadsheets remain the preferred tool amongst the Supply Chain industry, Using an optimisation tool, allows the planning team to operate more efficiently, allowing them to consider the whole week, not just the present day. This can result in better decision making, and better visibility of how the business is trending in the medium to long term.
Without an optimisation tool, business as usual may be fine. Planners have their routine that keeps the business running smoothly, and the existing processes are working. But when things change quickly, maybe due to a major disruption, or because the business is growing rapidly, are those processes scalable? Do they allow the planners to adapt to the changing conditions?
Efficiency – a ‘buzz’ word not privy to the supply chain industry, but undeniably a pain point most if not all supply chain leaders try to address. As supply chains continue to grow and diversify, cross functional collaboration becomes more important, with efficiency requirements varying from each function. From managing inventory, to reducing manufacturing costs to optimising distribution; not having an optimisation tool across your supply chain can result in large inconsistencies and sub optimal outputs. Understanding your supply chain capability and having the correct strategy around how and where to improve can result in a faster response time, consistent processing times and better utilisation of human resources to name a few.
Doing more with less
We often think of improving efficiency as a way of lowering costs, or doing the same thing for less. While this is one benefit of an optimisation tool, the other side is being able to do more with the same resources. With the lack of visibility an Excel spreadsheet provides, it can leave key decision makers in the dark of understanding the full potential and capability of their supply chain.
Decisions around whether to include another distribution centre, or hiring extra staff can all be well thought through with an optimisation tool. Biarri worked with one of the largest liquid and petroleum gas distributors in New Zealand, to optimise their delivery of LPG gas bottles across the country. With the use of Biarri’s Run and Route optimisation tool, they were able to increase their capacity and increase their productivity whilst not having to increase their staff and delivery fleet. How, you ask? By having the correct optimisation tool that considers more effective options. Sometimes, finding a way to meet all your requirements with the resources you have is just too difficult to do “by hand”, with more powerful methods required.
Improved Bottom Line
We have seen through optimising your supply chain with the correct tool can lead to improved planning, better use of resources, and being more efficient, but the culmination of these benefits lead to lower overhead costs. By maximising resources and improving planning, your business can have greater control over your expenses whilst ensuring the quality of your products and services don’t suffer. Optimising your supply chain can lead to removing unnecessary expenses throughout your operation like production and logistics.
Make your supply chain your competitive advantage by implementing the correct optimisation tool. Reach out and speak to an expert today.
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https://biarri.com/wp-content/uploads/2022/06/Optimising-your-supply-chain-3.png788940Paulohttps://biarri.com/wp-content/uploads/2022/05/Biarri-White-Logo-Tag-Line-700-×-700-px-300x300.pngPaulo2022-06-06 21:58:052022-06-07 08:39:49Optimising your supply chain: 5 best usages of optimisation in supply chain
As travel restrictions began to ease across Australia at the start of 2022, hospitals experienced an increased strain on resources, brought about by a peak in COVID cases.
Planning to expand their emergency department (ED) to compensate for lost space from accommodating COVID query patients, one of SEQ’s largest public hospitals engaged the services of Biarri to ensure they were achieving optimal utilisation of their facilities.
Though the new building was originally designed for the treatment of minor injuries, the impact of COVID, as well as historically high levels of demand on the ED prompted a reconsideration of the design and intended usage for the expansion.
The hospital needed to understand how they should change the existing ED whilst utilising the beds and resources of this new physical area, to best improve the flow of patients, reduce their waiting times, and treat them as efficiently as possible while continuing to provide excellent patient outcomes.
Over the 4 months in 2021, the average time a patient spent waiting for a ward bed exceeding the expected time of 75 minutes by 25%.
Within a restricted time frame, Biarri was able to deliver insights based on a custom-built simulation model, that showed the department how best to optimise throughput, maximise their use of space, and minimise the overall time patients spent in the ED.
You have a problem. You’ve come up with some possible solutions that work in principle, but you want to be certain of their effectiveness prior to implementation. Often, it’s impractical to test them in reality, as they’re either too expensive, too time consuming, or there would simply be too much interference with the normal operation of your business. You may need a simulation.
Simulation is a time and cost effective way of testing ideas and theories across a huge number of disciplines, from wind tunnel testing on scale models in the aerospace industry, to predicting animal behaviour in large groups, or recreating the formation of our Milky Way galaxy.
Because they are easily configured to take advantage of randomness, running a simulation multiple times can produce a wide spectrum of possible outcomes, which sometimes makes them a better choice to model reality than more traditional techniques.
Using this method of exploring the problem space, a business can develop their operational plans based on a typical or likely day (or month), while also gaining visibility of, and preparing for the worst-case scenarios.
By representing the allocation and flow of resources as a series of discrete events, simulations can serve as a digital testing ground for a business, and provide the opportunity for fine tuning and optimisation of business processes.
Hospital Emergency Department – A Case Study
A simulation algorithm was developed for the ED that allowed the identification of bottlenecks in the department, and could be used to explore the impact of reducing wait times, reallocating beds to different types of patients, and expanding the areas reserved for COVID patients.
The simulation treated patients as individual agents who were tracked from arrival, through to triage, into a particular area in the ED, where they could then either leave after being examined, or be admitted to the ward.
The algorithm made use of 4 months of historic data regarding the expected frequency of patient arrivals, expected time in triage for each patient type, and processing treatment times by ED area. Expected flows between the areas of the ED, for example the percentage of acute patients admitted to ward were also estimated.
The outcomes of the simulations were measured in terms of a NEAT score.
“The National Emergency Access Target (NEAT) stipulates that a predetermined proportion of patients should be admitted, discharged or transferred from Australian emergency departments (EDs) within 4 hours of presentation”
For example, it was found that reducing the time that patients wait for a ward bed could significantly improve flow in the ED. If patients only waited 45-60 minutes for a ward bed, compared to the current average, the NEAT for Resuscitation patients could increase around 25%.
By tweaking the parameters of the simulation, different scenarios could be investigated. In one such scenario, it was found that if the COVID area was not expanded, even 40 additional patients per day would lead to significant queuing, as the current allocation of 10 beds would be insufficient to handle the demand.
COVID queue length (10 beds)
Length of queue for beds over a two day period for COVID patients in the situation where the COVID area has not been expanded. The green curve represents the “average” (or 50th percentile), the yellow a worst case scenario (95th percentile) and the blue a best case scenario (5th percentile).
COVID bed utilisation (10 beds)
The utilisation of beds in the situation where the COVID area has not been expanded. Within a few hours there is no longer any capacity for additional COVID patients.
However, expanding the COVID area into the acute and clinical decision units (adding 12 beds) meant the ED could handle an increase of roughly 70 COVID query patients per day.
COVID bed utilisation (22 beds)
The utilisation of beds once an additional 12 beds have been added, in the case of 70 additional COVID query patients. If the COVID pod was expanded into the Clinical Decision Unit area, this amount of space should be enough to meet the anticipated capacity.
Simulationin Mining and Construction
Biarri has used simulation modelling in a variety of industries, such as modelling the movement of roof supports in longwall mining applications to significantly reduce the time taken for their recovery, transport, and installation. Another application for simulation is traffic and the movement of goods through a network. In the animation below, trucks with a random arrival time move through an underground parking structure to deliver pallets to a goods lift, which might have a randomised waiting time. The bottleneck in the structure acts like a traffic light system to control the flow of trucks through the structure.
Would you like to know more?
Speak to an expert today and discover how your business can begin leveraging the power of commercial mathematics and simulation today.
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https://biarri.com/wp-content/uploads/2022/05/How-commercial-mathematics-is-helping-hospitals-adapt-to-COVID-challenges-1.png788940Marketing Teamhttps://biarri.com/wp-content/uploads/2022/05/Biarri-White-Logo-Tag-Line-700-×-700-px-300x300.pngMarketing Team2022-05-24 09:51:042022-07-05 16:00:12Digital Transformation of Emergency Medicine: How commercial mathematics is helping hospitals adapt to COVID challenges
In this year’s Federal Budget, the government will introduce a technology investment boost that will apply to eligible expenditures. The program is designed to motivate SMEs to invest in digital or subscribe to cloud-based services.
Here’s what you need to know about this program and how it can benefit your business.
What does it mean?
The Digital and Skills Tax Boost will lower the barriers to going digital by encouraging businesses with less than $50m annual revenue to invest. The technology investment boost enables a ‘bonus’ 20 per cent tax deduction on expenses, including subscriptions to cloud-based services. This means a $120 tax deduction for every $100 spent on digital tools and training.
A $120 tax deduction for every $100 spent on digital tools and training.
Are there any conditions attached?
A new initiative from the Government, of course, there are conditions!
Although discussions and measures are still ongoing, the initiative hasn’t yet been passed into law. Even so, given the hype, the time to begin your budget process is now. Kick-off a new project or use this initiative to support an existing business case for digital transformation.
The important conditions to note are:
Applies to eligible expenditure incurred from 7:30 pm 29 March 2022 – 30 June 2023
It is not yet passed into law
Keep in mind that the additional deduction on any costs incurred in FY22 cannot be claimed until FY23.
Also, remember that if you want to apply the TFE, the asset needs to be installed and ready for use by 30 June 2023, so watch out for those lead times on capital assets, and plan ahead!
The time to start building your business case is now.
Who can get it?
Small businesses (those with an annual turnover of less than $50 million) will be entitled to deduct an additional 20% of the cost incurred on business expenses and depreciating assets that support digital adoption, such as subscriptions to cloud-based services. There is an annual cap of up to $100K, meaning that a maximum spend of $100K will entitle a small business to a $120K deduction.
What other benefits are available?
This increased digital adoption will ensure SMEs have the tools to remain competitive while securing billions for the Australian economy. It also allows SMEs to invest in technology to better their businesses and remain relevant in industry. And it will significantly increase funding to assist small businesses to improve their capability and capacity to digitally transform.
If you’ve been holding revamping your outdated legacy rostering or scheduling systems, now could be the perfect time to begin the conversation. Let’s see how we can help streamline your workforce capability and take advantage of this incentive.
If you would like to find out more about the above or more about how Biarri can digitise your business operations, reach out via the form below and a consultant will be in touch!
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https://biarri.com/wp-content/uploads/2022/04/Copy-of-Budget-Business-1200-×-628-px.png6281200Marketing Teamhttps://biarri.com/wp-content/uploads/2022/05/Biarri-White-Logo-Tag-Line-700-×-700-px-300x300.pngMarketing Team2022-04-27 09:39:372022-04-27 09:39:39The Technology Investment Boost and what it means for your business
As companies move to using more data to help them make better decisions, they are often left with an uncomfortable feeling. This feeling has nothing to do with going outside comfort zones or changing the way of working, it is based on the fact that even after using the data many people feel like their questions still aren’t answered properly.
And it is really common.
Research published in the Journal of Organizational Effectiveness People and Performance showed how more measurements, data and visualisation of HR processes lacked the ability to diagnose problems with business performance and lead to relevant and actionable insights. Our experience shows that this is not just limited to HR, it is across the whole business.
So why does this happen?
During COVID-19 (and even before it), supply chain companies around the world underwent significant digital transformations that led to the collection of more and varied data on all aspects of their businesses. To manage and understand this data, organisations invested heavily in BI tools such as Qlik, Tableau, PowerBI and more. Even though these tools are able to present good dashboards of the historic business operations, they haven’t produced revolutionary ways of doing business and, most importantly, they aren’t helping organisations deal with their most important challenge – the future.
From reactive to proactive
When businesses look at their dashboards full of data, they see patterns but are never 100% sure if they are real or not. In their historic data lies the answers to their future questions but how do we reveal them with confidence and precision?
Where BI provides you with the review view mirror, AI (artificial intelligence) provides you with a well calibrated telescope of the future. By using advanced analytics, companies are able to go from reactive to proactive by statistically validating intuitions about the future hidden in their data to give decision makers the confidence to place bold bets and target ambitious goals – a step change in the way of doing business. Often what the analytics discovers are things you “knew” but now you have quantified them and can properly compute the effects of your decisions on your bottom line.
Those sectors that stand to benefit most from using AI to go from reactive to proactive are those where there exists high variability in inputs and processes or demand for outputs. For example, industries which use inputs from nature such as mining, resources and agribusiness need to be able to deal with natural variation in their inputs. Any company selling into markets with volatile demand also needs ways to deal with this challenge.
And then there are a number of industries like logistics and agribusiness which face, and solve, volatility on both inputs and outputs.
How supply chains solve their predictive challenges
Biarri has worked with many logistics companies and agribusinesses to help them move from reactive to proactive. Instead of organisations harvesting crops or moving goods around trying their best to manage their volatility via inventory overcapacity, staff overtime and high wastage, they are now moving to scalable analytics with the business preemptively selling and optimising the expected yield from their crops, animals and delivery trucks.
A good example of this is Alliance Group in New Zealand who use Biarri’s supply and demand management tool Wolf, which has allowed them to better allocate supply to demand and smooth out their production cycles while simultaneously increasing their profit margins by selling more high margin products in niche markets in a scalable and low effort way. During COVID, this tool allowed them to quickly respond to changes in volatile global and local markets managing both high volatility on the input side (lamb sizes and grades) to the high volatility on the outputs side (COVID lockdowns drastically affecting demand).
In addition, moving to proactive can have other surprising benefits. Work Biarri has done with Australia’s largest grain exporters shows how a proactive view can reduce storage requirements, lower labour costs as well as save energy – let alone better serve their customers more confidently. Although not always the goal of predictive tools, the ancillary benefits can sometimes outweigh the initial business goals.
Why we all must do this
In moving to proactive thinking via advanced analytics companies are not only improving their bottom line and making better decisions, they are improving local and global markets. By smoothing out supply and demand, market volatility reduces and creates a better business environment for all. The benefits of this accrue to society via a better management of our resources leading to lower prices and higher living standards for all.
Moving from reactive to proactive via AI tools allows businesses to disrupt their current markets in a scalable way. Not only can you look around corners to know what is coming next, you can do it in a scalable way. Reach out to Biarri now to find out how.
Speak to an expert
https://biarri.com/wp-content/uploads/2022/04/Reactive-to-proactive-Post-1.png6281200Marketing Teamhttps://biarri.com/wp-content/uploads/2022/05/Biarri-White-Logo-Tag-Line-700-×-700-px-300x300.pngMarketing Team2022-04-05 08:23:412022-07-13 12:05:43From Reactive to Proactive in Supply Chains with AI
Biarri EMI has rebranded to Cru Software, refining their rostering and scheduling offering to meet the specific needs that make them one of the leading software companies for the resources industry.
Cru Software is dedicated to improving the process of workforce planning and allocation, and are constantly striving to improve their Saas solution in order to provide organisations with cutting edge technology that help to streamline their workforce processes, save time and money, increase productivity and profit.
Our mission is to simplify, organise, and anticipate the demands of the world’s most complex workforces. We are excited to take on a new direction, tackling the complex issues of workforce rostering and scheduling. As a result of what we offer, resource teams have the right tools to automate time-consuming processes, provide insights on business demand and capacity, and leverage the powerful optimisation tools from the Biarri group to ensure the right people are doing the right jobs on the right day – Jason Cameron, CEO, Cru Software
Intelligent Workforce Rostering Software
Cru Software’s innovative and agile rostering software is designed to simplify complicated and manual processes. Cru Rostering removes the complexity so many organisations face, giving your team more control to create efficient and effective rosters, while prioritising the health and safety of your employees with improved fatigue management.
Having helped over 90,000 workers worldwide, Cru Rostering provides a powerful Cloud based Saas platform to simplify the complex workforce. The other unique aspect of Cru Rostering is the unprecedented level of visibility. Forward planning has never been easier, by being able to visualise months of data and having the necessary information on hand.
Smarter Scheduling Software
By consuming key data, Cru Scheduling enables your team to produce an optimised schedule, ensuring consistent and repeatable results every time. Previously executed through disparate manual systems, Cru Scheduling streamlines and consolidates multiple sources of data into one platform simplifying how your team approaches scheduling. Having the correct information allows schedulers to make intelligent and informed decisions around availability, work priorities to minimise planning effort and operational downtime. Combined with the smarts of Cru Rostering, build better schedules for your FTE and contractor workforce.
Read how Cru Software improved Origin Energy’s operations after adopting an automated rostering software and optimised scheduling.
Cru Rostering and Scheduling has a proven track record with some of Australia’s largest Energy and Mining companies, helping businesses maximise value from resources. To book a demo or for a more comprehensive breakdown of Cru Rostering and Scheduling, visit Cru Software, and discover how you can simplify complex rostering and scheduling with the right tool.
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From the paddock out west to the head office in Brisbane, to the dining room table in Beijing. The journey of farmed products isn’t always as seamless as they seem with the supply of harvest and livestock products. Producers face challenges in managing crops and livestock, battling tough environmental factors, and competing in volatile markets. Identifying the best markets with the greatest margins remains one of the greatest challenges that producers in agribusiness face, until now. This blog will explore the shortfalls of ‘textbook’ S&OP and the limitations it can have for agribusiness, and will also give you an insight into how you can mitigate these challenges with the correct solution and tool.
For agribusinesses, the endeavour to maximise returns within a dynamic marketplace can largely fall on the shoulders of one or a few, generally executing a standard S&OP process through legacy systems or a single spreadsheet on Excel. Their experience is vital as they understand not only the business’ production and processing capability, but also they understand their customers’ demands and the parameters they need to be delivered.
Sales and Operations Planning (S&OP) is a business management process that aligns supply chain functions to enable executives to make the best financial and business decision
As markets continue to diversify internationally and demand for quality farmed products increases, agribusinesses need to make decisions based on natural resource variability and market volatility to receive the greatest return on their product. But in the current climate, can those producers rely on textbook S&OP to make the most informed decisions? How in this modern global market can agribusinesses remain sustainably profitable?
Let’s take a closer look.
With significant (and growing) complexity in agribusiness supply chains, a standard S&OP process is often propped up by the experience and expertise of a ‘seasoned agri veteran’. For years, this individual may have filled gaps in a standardised process with deep knowledge of business idiosyncrasies and strong intuition in addressing supply or demand changes. The decision-making process and the framework these decisions are made upon go beyond the capability of a standardised sales and operations plan.
Relying on one or two individuals limits sustainable success and reduces time spent on valuable activities like contingency planning. Future-proofing your businesses success is reliant on contingency planning by implementing systems and processes that automate decision-making, removing the burden and pressure placed on key individuals.
The unpredictable nature of ‘harvest’ and ‘livestock’ farming is a prime example of why agribusiness requires a more flexible approach to S&OP. Take for example a meat producer; supply dictates the number of different SKUs available, which is an administrative nightmare to reconfigure and coordinate when market demands shift.
The unpredictable nature of supply has ramifications on the sales front with overselling and not being able to fulfil agreements. The flow-on effect can be critical to a businesses reputation and jeopardise relationships.
So, how can businesses change their approach and move away from the conventional way of ‘commodity selling’ and have more control over their supply and sales and increase the value-add of their products at the same time?
Simple – move towards ‘Forward selling’. Forward selling creates synergy across sales, supply and production by knowing the exact number of products there is available to sell at a point in time and negotiating contracts accordingly. I.e. move from reactive to proactive.
Are we on the same page?
While it is important to consider the correct approach to S&OP, it is just as crucial to have a system and tool to support it. Too often businesses are stuck with a static excel spreadsheet or a dated enterprise business application that requires a huge amount of effort to maintain and use effectively.
We’ve experienced a real appetite for producers to better leverage their data to make more informed forward looking decisions. There is a shift in the industry from reactive selling to proactively ensuring Australia’s high value agricultural products get the premium they desire, not languishing in the supermarket budget section
Jonathon Allport – Agribusiness Analyst
A spreadsheet or enterprise application can sometimes be effective in keeping a business running, but the slow and static process brings other challenges and issues. One of the shortfalls of these systems is the inability to conduct what-if scenario modelling or to adjust to new and later opportunities. Comparing options and finding the best outcome becomes difficult, and business decisions have to be made from limited information and modelling.
Having the correct data but not being able to compare options can have large financial ramifications and can result in a loss of opportunities due to poor decision making.
Let’s look at a concrete example of how this can be done with a modern tool.
A new way with WOLF
Wolf is Biarri’s modern web based tool that bridges agribusiness sales and production planning, making decisions quicker and easier with a quantitative model. This approach and tool was applied with one of the world’s largest producers of lamb and mutton and can also be applied to all types of agribusiness.
Alliance Case Study
In such a fast moving and volatile market place, making the right decision consistently, is difficult with the instability in demand, production and logistics. Alliance faced this common agribusiness challenge, and they required a system and approach that improved their day to day sales and production process to cope with their high production volumes.
Alliance Group is the world’s largest processor and exporter of mutton and lamb, representing over 15% of the world’s cross-border trade. Its facilities process over 87 Million lamb and sheep per annum, exporting to 65 countries, and they produce over 1,200 products.
“Being able to plan and maximize returns within our dynamic market place has always been challenging, so when we talked with Biarri about developing a new optimization model with more scenario modelling capability we could see the potential of planning with confidence that every variable had been optimized to minimize cost and maximize revenue within our market plan.
The team at Biarri were quick to understand the variables within our planning process and have developed a platform that enables us to react to the moving market conditions with confidence.”
Craig Spence, Marketing Accounting & Administration Manager, Alliance Group
Before coming to Biarri, Alliance solved its S&OP problems with some advanced mathematical modelling in Excel. After using this tool for a number of years, it was realised that it simply wasn’t enterprise grade lacking proper governance, user control, historical data audits, data lineage and much more. It was at this point that they came to Biarri and Biarriintroduced them to Wolf.
With the integration of Wolf and a new optimization model, Alliance were able to achieve:
A reduction in planning effort, with trivial decisions being automated giving time and effort back to high priority tasks;
Greater control through visibility. Alliance had an unprecedented levels of visibility, arming key stakeholders with greater control over their meat sales and production planning activities. The increased level of transparency allowed for faster and smarter sales and production decisions through scenario modelling;
Increased revenue through identifying optimal product mix. Production and sales team could now plan and select the right option with confidence, knowing that Wolf had optimised every possible variable to minimise cost and maximise revenue.
Want to know more?
Speak to an expert today and discover how you can better manage natural variation and market volatility below.
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Few places on earth can break people like the Australian outback. Toughness is not enough to survive out there, let alone thrive. You must be smart, resourceful, and innovative to stand a chance.
In this part of the world, 2019 saw the culmination of a devastating nine-year El Nino. Things die during a regular El Nino. During this one, it was common to see livestock strewn along the road where they had perished for want of food and water. Then in the second half of the year, one of the worst bushfire seasons Australia has ever seen began and it made a difficult situation impossible.
Mike is a crop farmer, one of those tough-as-nails, wily, and innovative Australians who make their living in this environment. Reeling from this latest catastrophe and to keep ahead of the next disaster, he began exploring smart farming techniques enabled by artificial intelligence (AI). The technology he was pitched overlaid AI onto Big Data, and would have been integrated into his irrigation, pest control, and soil management systems to allow “precision farming.”
In theory, by refining the focus of his practices from the paddock level to the level of the individual plant, the technology could reduce wastage by up to 80 percent. That is a lot of extra (desperately needed) cash when you are trying to compete in a global market with little assistance from your government, against foreign farmers with lots of assistance from theirs. Add to that an environment where little will grow.
The salesperson’s pitch to Mike was compelling and showed a familiarity with the existential challenges faced by Australian farmers – supported by a clever application of AI generating potentially significant returns. But there was a problem: the business case the salesperson outlined raised doubt in Mike’s mind. The initial investment would be $500,000 with additional costs of $80,000 per annum for data storage and processing alone before maintenance and repair.
To put this in context, in 2020-21, average Australian farm cash income in US dollars, out of which the farmers pay their families’ income, was about $137,500 . Profit was $79,000, which translates to a 1.6 percent rate of return on the farm assets (and the financial year 2020-21 was a good year for Australian farmers). Within that, average expenditure on crop and pasture chemicals (herbicides, etc.) and fertiliser was $63,000. Including water rights takes that expenditure to $100,000.
Assuming that the technology performed at its absolute maximum (80 percent cost reduction), and that it did not add to any other costs then, for the average Australian farm, this technology would, at the very best, be profit neutral going forward and would put it significantly into deficit for the first year. In short: if Mike’s farm were roughly average, adopting AI may have bankrupted his family.
The cart before the horse: technology before business
We live in a new era of obsession with AI. The technology is at once enchanting and increasingly pervasive, and everyone has something to say about it, from Elon Musk to your Uber driver. Our opinion is that almost nobody is looking at the main problem that it presents for businesses. Almost all are caught up in the engineering of what AI can do, while many more ought to be caught up the economics of what AI is worth.
To illuminate the problem, let us go back to the foundational question: what is AI? Fundamentally, artificial intelligence is the pursuit of the motivating dream that lays at the dawn of computer science in the 1950s. John von Neumann and Alan Turing, two of the “fathers” of the discipline, both explicitly imagined building machines that could mimic the operations of an intelligent mind . Computers would become an AI insofar as the programs which “read”, operated on, and “wrote” data, represented in mechanical states and dynamics, could mimic the perception and processing of an intelligent mind.
Thus, for the next half-century and beyond, AI developed as a subdiscipline of computer science mostly dedicated to the original motivating dream of the discipline. Over time, further additions have extended this core: the growing integration between AI and robotics, the potential for linking advanced analytical systems using the internet (e.g., the Internet of Things), the use of AI to operationalise Big Data, the application to artificial intelligence to save lives, etc. However, the underlying drive and advance of the technology has been in the same direction: the development of algorithms (programs) that can mimic the operations of an intelligent mind, or better still, a super intelligent mind. Hence, the hype surrounding AI.
Humans may yet follow in the footsteps of gods and create a new form of intelligence to rival our own. At the least, we now have at our disposal technologies that offer the possibility of automating a broad range of human activities. We are not just talking about the automation of manual labour on the production line, for many of the major advances in AI particularly in the past decade have been in automating data processing and analysis . One of the most thrilling (and terrifying) advances has been the advent of the GPT-3 algorithm from Open AI, the closest we have come yet to general AI, which can write entire, cogent essays from a single question on virtually any topic.
The world has lived through eras of obsession with AI before with two of them ending in so-called “AI winters” – per Michael Woolridge’s history of the field, The Road to Conscious Machines . The first major winter began in the mid-1970s when the limitations of symbolic logic systems (basically, AI systems whose syntax was like the symbolic logic of mathematical proofs) were revealed, and funding for academic research quickly dried up. Essentially, it took far too much work to produce AI systems that could produce far too specific tasks, and the effort exploded exponentially the more you wanted to broaden them. The second major winter began in the late 1980s, when these troubles in academia caught up with industry, and corporations stopped investing in expensive AI decision aids based on expert systems, switching to cheap personal computers.
In the case of both winters, these advanced systems fell short of hype in what they could do, and what they could do was of limited economic value. It was not that AI was not useful, far from it. It was that the intuitive concept of AI, and the wild flights of imagination it encouraged, led to expectations front running the realised value propositions of the technology. The backlash over-corrected and set the scientific and economic advance of the technology significantly backward.
The current hype associated with AI is familiar to those with knowledge of the aforementioned history of the technology, and a warning sign that business, government, and public expectations about the technology may be running ahead of the reality. Scientific advances in neural networks and machine learning, vast improvements in computing power, and the advent of distributed computing have brought about a qualitative change in the capabilities of the technology. But modern AI is still not limitless, and it is still expensive, especially when machines must be trained to learn specific and novel task sets. If (when) disappointment with the attained value of modern AI is realised, we invite yet another AI winter.
The problem with AI is that the focus is presently on what it can do, not even with what it does do. At present the engineers are in the driver’s seat, and engineers’ strength is not always business outcomes because of their focus on possibility. Economics realises many things are possible, but also that resources are constrained and must be directed to the best possibilities. Paraphrasing the famous words of Lionel Robbins : economics is the study of life as a relationship between ends and scarce resources which have alternative uses. Economics needs to be put in the driver’s seat when it comes to the question of AI adoption for any organisation, and engineering put under the hood. The question must not be the engineering of what an AI can do but the economics of what an AI is worth.
The solution: put economics back in the driver’s seat
The problem with AI is that the technology is presently steering the conversation, and that instead we need to lead first with economics and not engineering when it comes to AI adoption in organisations. Let us unpack why, so that we can better pose a solution to the challenge of restoring economics as the primary decision-making framework for AI adoption .
The first thing is to firmly establish what, exactly, and as simply as possible, the economic criterion for AI adoption is. This is straightforward enough: an AI system ought to be adopted by an organisation if and only if the profit the organisation can obtain after adoption is greater than the opportunity cost of that profit. Now, the single easiest error to make in economic reasoning is to forget opportunity cost, focus only on whether profit after adoption is positive, and commit to a suboptimal, possibly even bankrupting decision . That is why it is so important to always remember opportunity cost: the value of the next best alternative. Typically, this is the profit achieved under the status quo, but it can also be the profit achieved by an alternative strategy (e.g., expanding payroll).
Consider the logic of Figure 1. Economists will recognise this formula is a particular case of a consumer (buyer) surplus maximising decision, where a rational consumer looks at their set of decisions and chooses the one which maximises their consumer surplus. The surplus concept is useful as it also helps explain why the situation faced by Mike arises. When pricing their AI products and services, producers analyse their potential customer’s gains and then choose a price point to maximise their producer surplus.
Figure 1: The basic economic principle of AI adoption – of which the right-hand side is extremely easy to forget.
Where this thinking can go wrong, as with Mike, is that producers fail to factor in the total cost of ownership, which includes all the other costs which arise with such a system such as maintenance, repair, installation, failures, lower than expected results, etc. Mike needs to consider all these factors, as well as the chance that the system could fail altogether, if he is to make an optimal decision. The above formula is valuable as a “cue” for the economic mindset, a habitual thought to always call to mind when considering AI adoption. Alone, however, it is not enough guidance for decision makers; it is too abstract. We need to be more specific.
The basic economic principle of AI adoption can be restated in what economists famously call “marginal” terms. What will be the change in profit obtained by AI adoption? Adapting this, we can unpack the basic economic principle of AI adoption. It now becomes something more specific: an AI should be adopted if and only if, relative to the next best alternative, the marginal benefit of its adoption is greater than its marginal cost. The next best alternative (the opportunity cost) will, again, typically be the status quo, but it could also be hiring another employee or outsourcing some tasks.
To make this as useful as possible, let us be still more specific. AI adoption can generate gains by improving the quality of our judgement (see Footnote 6). This improvement of our judgment leads to better quality decisions (typically by better quality predictions and prescriptions ). From the firm’s perspective this means:
We have better allocation/utilisation of the firm’s inputs
We have better quality/delivery of the firm’s outputs
In the first case this leads to lower costs, in the second case this would lead to greater revenue. In other cases where we improve the quality/delivery of the firm’s outputs it can simply lead to a better delivery of services, for example, in a hospital scenario this leads to more lives saved, hence an increase in the value of statistical lives saved. In addition, these outcomes may be generated directly or indirectly. In the latter case, the integration of AI may generate greater returns on existing assets by creating synergies that boost their productivity. For example, in Mike’s case, the AI offered a cost saving. By integrating AI into his irrigation, pest control, and soil management systems, he would have reduced wastage and enhanced the productivity of his existing assets, potentially reducing his required capital expenditures in the future.
On the other hand, the marginal cost of AI adoption consists of at least three main components:
The up-front cost of installation and setup
The ongoing operating cost of the AI system, and
The ongoing cost of maintenance and repair of the AI system.
When these components are converted into expected net present value, we have the basis for an economically informed decision as the following diagram illustrates (see Figure 2). To make the diagram more applicable, instead of the general inputs and outputs perspective mentioned above, we focus on specific value drivers to make the framework clearer.
These are the economic principles of AI adoption, and they provide simple available cues for building and triggering a habitual economic mindset when thinking about AI adoption in organisations. To complete the system and expand these cues for a habitual mindset into the basis for a habit of behaviour, let us set down a simple decision tree for AI adoption (see Figure 3).
The decision tree consists of three simple questions, two are the responsibility of an AI salesperson to answer, one is a question that can be posed internally to the organisation. The first question is for the salesperson: what is the dollar or percentage gain that your AI generates? If the salesperson cannot answer in terms of revenue, cost reduction, or value of a statistical life, a conservative rule of thumb is to not adopt the AI. If the salesperson gives a sufficient answer, the second question may be posed to the salesperson: what are the dollar costs of installation/setup, operation, maintenance, and repair? Again, if the salesperson cannot answer, a conservative rule of thumb is to not adopt the AI. If the salesperson gives a sufficient answer, however, we proceed to the third question: given the next best alternative to this AI, are the marginal benefits of adoption (in expected net present value terms) greater than the marginal costs? If no, the alternative ought to be pursued, if yes, the AI ought to be adopted.
This might seem like common sense and relatively straightforward economic thinking, but as the saying goes the funny thing about common sense is that it isn’t that common. Notice how, when we put the economic process for making decisions about AI adoption in a decision tree, any given AI needs to meet a high bar to be adopted. Three out of the four endpoints conclude in rejection. Keeping these three elements of an economic attitude in mind, practicing them regularly and habituating them, are important for getting economics back into the driver’s seat when it comes to organisational AI adoption, and putting engineering under the hood. The problem is one of hype and expectations getting ahead of the reality of AI’s value proposition. The solution is to build good habits with simple heuristics that send us into the mindset of economics whenever assessing it.
Resolving Mike’s AI investment challenge, and others
Applying this simple heuristic to Mike’s situation, we can readily understand why he couldn’t make business sense of the salesperson’s pitch of AI-enabled precision farming.
As we discussed in the introduction, the salesperson suggested that Mike would achieve cost savings of 80 percent; the salesperson got past the first decision point. We saw that if Mike’s farm was roughly average, the relevant expenditures would have sat somewhere around $100,000. The dollar value of Mike’s savings would have been around $80,000 in the best-case scenario. The salesperson was also upfront about the dollar cost of the systems: $500,000 for installation $80,000 per annum in ongoing costs; thus, the salesperson got past the second decision point.
However, we can immediately see why the salesperson failed on the third decision point: the best-case marginal benefit of adopting the AI ($80,000) was less than the marginal cost ($80,000 plus the installation cost). Mike would have made less profit than his opportunity cost (e.g. doing nothing) if he adopted the AI and would have eroded the meagre 1.6 percent rate of return on his assets he was accruing. He may have even bankrupted his family by incurring a significant debt to purchase a profit-neutral technology. The technology (engineering) may have been amazing, but the economics was not.
Mike’s example is based on a real life situation experienced by the authors, and one but many of the real examples that we encounter every day in practice. Because it is based on economic reasoning, our heuristic applies equally to these other cases. Let us look at an example from medicine.
In May 2019, the Food and Drug Administration made a decision that created headlines around the world by approving the world’s most expensive drug treatment to date, Zolgensma. This medicine treats spinal muscular atrophy in infants replacing annual lifelong treatments with a once off cure. The minimum price is (only!) $2 million (USD) for a single treatment. To many this price point makes no sense, however, when we apply our economic heuristic, we can better understand why Novartis chose this fee.
Zolgensma is part of a new wave of drugs that promise to usher in a revolutionary era of personalised medicine . This form of medicine uses AI to leverage Big Data and discover treatments bespoke to individual genetic profiles. Zolgensma works by replacing the defective SMN1 gene that expresses itself in infant spinal muscular atrophy with a normal copy. To discover this technology for bespoke genetic medicine, Novartis had to mine terabytes of genomic data to find the right compound which, when delivered, would introduce a highly specific change to a highly specific point in highly specific individual genomes.
Does this AI-enabled technology make economic sense? Let us apply our heuristic. In this case, the direct benefit of the technology is to save (quality-adjusted) statistical lives by improving the quality of life for infants debilitated by spinal muscular atrophy. The value of a statistical life used by governments and corporations across the world in daily policymaking is typically between $4 million and $10 million (USD). Novartis’ AI-enabled drug costs around $2 million (USD). Given we are talking about infants with an expected life of up to 80 years, there are a wide range of statistical lives that could be saved by the drug that would justify adoption. The marginal benefit (statistical lives saved) is greater than the marginal cost of adoption, the profit greater than the opportunity cost of doing nothing or even other drugs. This calculus may sound hard-hearted, until we remember that opportunity cost may also very well be the value of allocating funds to research infant oncology.
Putting economics back in the driver’s seat and engineering under the hood allows us to resolve investment decision as specific as whether a given farmer should adopt AI and as general as deciding among which biomedical priorities to allocate scarce research funds. Using an economic heuristic of worth, rather than an engineering heuristic of possibility, guides us to make better decisions that not only mitigate against the chance of a new AI winter, but also promote a more prosperous and healthier world.
There is no denying that AI is a powerful technology with the potential to not only automate but supercharge many things, from menial labour to biomedical data analytics. It at least therefore offers a vast expansion to human capability. There is, however, a risk involved in the understandable hype generated by AI. The risk is the expectations of researchers and industry can get way ahead of the reality of the technology. This invites the potential for yet another AI winter which delays the development and implementation of this extraordinary technology. Our argument has been that this problem can be traced back to an old problem in technology adoption whereby the engineering mindset of possibility dominates the economic mindset of value. In AI as with so many technologies, economics must be put in the driver’s seat and engineering under the hood to avoid expectations getting ahead of reality and the advent of disillusionment.
We proposed a simple heuristic to habituate the economic mindset when assessing AI-enabled technologies. Businesses, governments, individuals; all can profit from adopting the three simple questions we propose for arbitrating whether the value of an AI technology exceeds opportunity cost:
What is the dollar value or percentage gains created by the technology?
What is the dollar value of setup and ongoing costs?
Relative to the next best alternative, are the marginal benefits of adoption greater than marginal costs?
In short: don’t ask what AI can do. Ask what it is worth.
 Ashton, D, Martin, P, Frilay, J, Litchfield, F, Weragoda, A & Coelli R, 2021, Farm performance: broadacre and dairy farms, 2018–19 to 2020–21, ABARES research report, Canberra, March, DOI: https://doi.org/10.25814/ycy6-3p65. CC BY 4.0.
 von Neumann, John (1958). The Computer and the Brain. New Haven: Yale University Press; Turing, Alan (1950). Computing Machinery and Intelligence. Mind. 59(236):433-460.
 Sullivan, Joshua and Zuvatern, Angela (2017). The Mathematical Corporation. New York: Public Affairs.
 Wooldridge, Michael (2020). The Road to Conscious Machines. London: Penguin.
 Robbins, Lionel (1932). Essay on the Nature and Significance of Economic Science. London: MacMillan.
 Here we build on the work of Joshua Gans who has pioneered the economic analysis of AI systems with a series of papers, and a summarising book: Agarwal, Ajay, Gans, Joshua and Goldfarb, Avi (2018). Prediction Machines. Cambridge, Massachusetts: Harvard Business Review.
 This confusion is often invited by economists using “profit” as shorthand for “economic profit”. Economic profit is “accounting” (i.e. standard) profit minus opportunity cost.
https://biarri.com/wp-content/uploads/2022/01/Biarri-Agribusiness-Banner-3.png18903780Evan Shellshearhttps://biarri.com/wp-content/uploads/2022/05/Biarri-White-Logo-Tag-Line-700-×-700-px-300x300.pngEvan Shellshear2022-01-11 18:12:032022-01-17 08:42:42The Economics of AI: Delineating the economic limits to AI adoption
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