“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.
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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.
Get in touch
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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
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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
With a fast moving marketplace and the rapid growth in software and technology, keeping up to date with industry trends can be all too much. But for those who are mindful, staying up to date can help keep your organisation competitive and ahead of the proverbial ‘curb’. Here are 4 tips for Operational and Logistics Managers looking to improve their outbound logistics supply chain.
Investing in outbound logistics technology
We’ve all heard the saying before – ‘the more you put in the more you’ll get out’ but that doesn’t always mean pouring thousands of dollars in the latest tech. Sometimes investing more time can be just what you need. It is important in this ever changing climate for key stakeholders to take a step back and look at their supply chain operation from a holistic view to understand where technology can plug in to replace or improve specific functions. This can be as simple as automating the scheduling and planning process and creating an optimised set of delivery schedules.
Transparency is Currency
Transparency is key! It sounds simple enough but the benefits of having greater visibility over your operations are endless. From being able to pinpoint deliveries and trucks, to having a deeper level of insight into service outputs; an increased level of visibility removes the speculation when making decisions and allows you to make decisions based on the facts and in real time! Manage your costs more effectively and see where in your supply chain there are inefficiencies. But how can you increase your visibility? See point 1 or speak to one of our team members today to find out how!
Ensure consistency with delivery schedules
The dreaded c word – no, not Covid, but consistency! Ensure a greater level of consistency across your delivery schedules through an automated system of planning. Managing consistency across delivery schedules can be difficult to maintain, especially when it isn’t automated or done by one person day in and day out. Creating a level of consistency doesn’t only benefit your truck drivers and your fleet but it also helps with building and strengthening relationships with customers by meeting SLA’s.
Future proof your operations
With what has unfolded in the last couple of years from circumstances outside of our control, we have seen the need for flexibility and a system capable of dealing with last minute changes and updates. Future proof your outbound logistics supply chain with a well organised system and take the digital transformation. A digital transformation will also help with managing transport logs and align communication channels across all departments.
Want to know more about these 4 tips? Enquiretoday and speak to a team member about how you and your business can improve your outbound logistics operation with Scopta Run and Route. From saving you time and reducing operational costs to providing more insight, Scopta Run and Route is the new and efficient way to plan delivery schedules and improve your outbound logistics supply chain!
Run and Route Contact Form
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For Logistic Managers and Delivery Schedule Planners, organising and ensuring the delivery of goods from your warehouse to your customers, within agreedservicelevels, requires a well configured delivery schedule. This was the case with LPG gas distributor Genesis, planning a multi-vehicle delivery operation with constraints and variables became a mammoth task, through ‘manual’ methods and clunky paper-based systems.
Read on to see the common challenges that Genesis faced with their last-mile delivery planning, and how they were able to transform their planning through Scopta’s Run and Route.
By ‘manual’ methods, we mean planning delivery route schedules through traditional methods such as white boards, Excel spreadsheets, bundled sets of consignment notes and Google Maps.
1. Manual route planning is prone to human error and requires time
‘Manual’ planning methods through spreadsheets and mapping tools require a lot of time and effort to do correctly, to ensure all your deliveries are accounted for. But when you have deadlines on when delivery schedules are ready for your fleet, how can you ensure the accuracy and efficiency of your plans?
For the team at Genesis the complexity of a last mile delivery task increased significantly with only a few constraints such as cargo / vehicle compatibility or constrained delivery windows. Even their very experienced planner struggled to manually calculate a highly optimised schedule that satisfied all customer requirements.
Having to go back and forth between Google Maps and delivery lists, while creating a spreadsheet increases the chances of making mistakes that could prove costly. Manual planning or ‘planning by hand’ is complex and prone to human error. This can result in inefficient routes, increased delivery time and cost and worse – losingcustomers because of late or missed deliveries.
Even with a great manual planner on your staff, there is significant key personnel risk when all of the required knowledge is invested in one to two people, rather than systematised in a scheduling application.
2. Manual route planning is inefficient and expensive
We’ve seen how ‘manual’ delivery route planning is a notoriously difficult task and puts at risk your ability to meet customer SLAs. The second issue is the cost and inefficiency that manual planning can drive into your distribution function.
As a direct result ‘manual’ planning can impact your bottom line through poor route planning and mismanagement of resources such as:
Increased capitalinvestment due to operating a larger transport fleet than required;
Increased labourcosts due to drivers travelling longer distances and working longer hours, and
Increased fleet operatingcosts.
Why settle for a competent delivery schedule, when you can have an optimised delivery schedule, that meets your requirements and can see your team save time and reduce operational costs?
3. Managing growth and have the ability to scale is difficult with ‘manual’ route planning
One major challenge with ‘manually’ route planning is dealing with change and scale as was the case with Genesis and their growing demand for LPG gas bottles. The ability to manage growth through adding more deliveries and locations ‘manually’ placed added pressure on their already lengthy and difficult process. The only real solution by way of their current ‘manual’ process, was simply putting in extra effort and time to correctly cater to new customers and businesses.
Another challenge was being able to manually plan for last minute changes and added deliveries. This took the form of specific road closures and re-planning delivery routes, delivery windows and changing availability from customers, and additional deliveries to name a few. The ability to deal with adverse change is restricted and difficult through ‘manual’ planning.
By not having an agile system lead to poor delivery schedules with inefficient delivery routes. Not to mention the stress placed on Logistic Managers to add additional delivery locations and the additional pressure placed on Delivery Drivers to meet their deadlines.
Digitally Transforming Genesis – From ‘Manual’ planning to Scopta’s Run and Route
Scopta Run and Route is an automated solution that helped Genesis deal with the complexities of operating last-mile delivery. As described earlier, manually planning delivery route schedules raises many challenges around the significant amount of time and effort required to create a delivery schedule, the inefficiencies of manual planning and the difficulty of dealing with change and ability to grow and scale.
“Moving to digital made sense as it would improve the processes, and drive cost savings. The bonus from the proof of concept was we were able to use our data to categorically prove that optimisation would realise operational savings, which in turn made the business case an easy sell.”
General Manager of Genesis , LPG operations, Cameron Jardine
AWS Partner Network
Scopta Run and Route leverages the AWS services toolkit. As part of the solution we use AWS EC2 to run the optimisation workloads, AWS S3 to store application information, AWS PostgreSQL RDS to manage the workload data tables, AWS EKS to ensure a highly available and scalable workload, and AWS Elasticache to queue and manage tasks.
Since partnering with AWS and joining the AWS Partner Network (APN), Biarri has been able to identify new opportunities to scale. Through leveraging AWS services, we have greater confidence in developing tools that align with our mathematical and analytical Biarri approach. AWS provides a platform that is not only quick but secure, giving our clients around the world assurance. Biarri will continue to acquire additional AWS data and analytics competencies, as we continue to increase our reach.
For more information around Scopta Run and Route or to start a free trial, get in touch with one of our team members today.
Run and Route Contact Form
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Navigating through last-miledelivery offerings can be a daunting task as you figure out which routeplanningsoftware tool is the most applicable for your team, your businessoperation needs and budget. When considering Biarri’s Run and Route planning software, there can be a range of important factors to consider, such as:
Finding the best tool to reduce your operational costs and improve your bottom line;
Purchasing the necessary functionality to streamline current business planning and supplychainmanagement,
Identifying gaps within your current operations that could benefit from an automated software solution, as you seek to make technological advancements,
Selecting the correctrouteplanningtool that has the capacity to deal with your business intricacies and complexities, such as delivery windows, drop off specifications and vehicle compatibility.
As you work through the finer details of other route planning software providers take a look through our customer profiles to see which one best aligns with you and your business.
Customer Type Summary
Pain Points and Challenges
Reducing operational costs. Managing day to day to operations
Vice President of Logistics and Planning
Meeting customer demand and ensuring a safe and smooth operation. Supply chain management and refining processes
Digital Transformation Manager
Improving business processes through AI solutions. Simplifying and streamlining procedures
Delivery Schedule Planner
Creating a efficient delivery schedule that meets customer demand
Are you a General Manager?
Biarri’s Run and Route’s first customer profile describes General Managers of medium to large sized organisations (200+ employees). They hold a management position within the organisation – and are charged with overseeing the day to day service and deliveries of their distribution centre. With broad industry experience and academic qualifications, this GM drives strategy and performance across his business in revenue growth, cost reduction, operational efficiency and customer experience.
As business and demand continues to grow so does the business’s costs; mindful of their bottom line and an eye into the future, they seek alternative options that can aid the deliveryschedulingteam in being able to meet their customers’ needs by making deliveries on time and on demand, and reducing the man hours spent on creating schedules through old hand method ways like Excel and Google maps.
Does this sound familiar? If so, our Run and Route tool helps solve the above challenges by creating an optimal and efficient set of routes, reducing drivers’ travel distance and accelerating the delivery process.
Are you a Vice President of Logistics and Planning?
Aligned closely with the General Manager, Scopta’s Run and Route next profile holds an executive role within the company and leads the logistics and planningfunctions. Tasked with ensuring the quality, efficiency and scalability of the supply chain, the Vice President of Logistics and Planning is constantly looking for systems, processes and technical solutions to gain and maintain competitiveadvantage.
Overseeing the supply chain processes of a large distribution centre of a goods company, they take a great deal of interest in businessprocesses and deliveryoutcomes and performance, with a desire to stay at the forefront of the industry. Taking a measured approach to how best to improve the service and delivery of their goods, they are on the constant lookout for solutions that can streamline the planning of delivery schedules and achieve efficient and costeffectiveplanning.
Open to technology advancement, they see the implementation of a route planning software program as a great advantage for route planning for more efficient delivery schedules.
Does this sound familiar? If so, our Run and Route tool helps solve the above challenges by being market leaders in route optimisation, and being a functional and agile tool that simplifies the way you plan and deal with last minute adjustments.
Are you a Digital Transformation Officer?
With an eye to the future and a strongunderstanding of current business operations, our next profile looks to advance the company through digitalisation and with the integration of artificialintelligence. As a key decision maker with a background in information technology, they are motivated by efficiency and productivity, constantly looking to refine and improve supply chain processes. With a view to automating and digitising where possible, the Digital Transformation Officer sees potential to improve the last-mile delivery planning process, which currently involves a mixture of data sources, manual processes, online mapping tools and excel spreadsheets. This results in labour intensive processes, a lack of optimisation, barriers to scalability and key personnel risk.
The Digital Transformation Officer plays a vitalrole in the continual refinement of these practices, looking to streamline their processes through optimisation as they look for the best route planning software for their business requirements.
Does this sound familiar? If so, our Run and Route tool helps solve the above challenges through automated route planning solutions. Streamline the way your team plan and organise their trucking fleet, with a single tool containing all the functionality your team needs to deal with your business requirements.
Are you a Delivery Scheduler Planner?
Last but not least, the Delivery Scheduler. They have the critical role of planning the routes and delivery schedules for their 20+ trucking fleet. Equipped with nothing more than a spreadsheet, an online map and vast experience (perhaps having been a delivery driver themselves), they spend the better part of their week organising delivery schedules.
Motivated by meeting customers’ demand and ensuring deliveries are completed, there are many variables and barriers that they face. Dealing with the complexities of considering delivery windows, driver availability, infrastructure and truck requirements and specific load types, all while making sure delivery schedules are ready by the first delivery is a time-consuming and extremely difficult task when completed by hand.
Perhaps a route planning software tool could help?
Does this sound familiar? If so, our Run and Route tool helps solve the above challenges through its powerful optimisation engine. Be enabled to do more with a centralised system that deals with complex scenarios and variables, ensuring that you produce not only an error free delivery schedule but an efficient and cost effective set of routes.
Speak to our team now based on your above persona
If any of the following profiles or challenges resonate with you and your business, please reach out to one of our team members to discuss how Biarri’s RunandRoute can assist, using the below contact form.
With a strong understanding of the constraints and challenges last mile delivery and route planning entail, the team behind Biarri’s Run and Route are dedicated to working along side you to improve your business processes and route planning through automation and optimisation.
Run and Route Contact Form
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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 BiarriWorkforcecontinues to simplify and prepare your workforce scheduler to create optimised workforce rosters and work schedules.
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 exceedCBAregulations.
5. Powerful roster view filters
Improve your userexperience 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 WorkforcePlanners 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
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Getting from point A to point B is a simple enough task to be completed on most devices, through various different apps and software. But what happens when you have to get from point A to point B and now point C with consideration of other factors like availabilitywindows and route preference? Scopta have developed Run and Route a route optimisation software that deals with the complexities of vehicle and delivery routing.
Read on to discover Run and Route and how it is changing route optimisation and vehicle routing.
Barry is the Operations Manager at a warehouse depot for a biscuit company and is in charge of the planning and organising of the distribution of goods sold across Sydney. Barry is tasked with delivering 400 orders to be delivered between 20 trucks, exactly 20 orders per truck. For the last 10 years Barry has used a combination of Excel and Google Maps to figure out their delivery routes. He plans his delivery schedule a week in advance, and spends a large portion of the week carefully mapping out delivery routes. Barry is restricted by both time and cost, trying to figure out the fastest and most efficient route.
Planning out a delivery schedule by hand is notoriouslydifficult and timeconsuming, not to mention subject to humanerror. Manually working between Excel and Google Maps to find the best delivery order is inefficient and also raises challenges such as the TravellingSalesmanProblem or TSP, which is simply finding the best order in which to visit a set of locations. Through traditional methods of Excel and Google Maps it won’t tell you the best way to order those stops to give you the overall shortest or fastest route but instead show you the quickestroute from point A to point B. Now say you throw in point C and point D? An extra level of complexity is added with additional locations. How is Barry to know which location to begin with and the order to complete the rest of his deliveries?
With Run and Route, Barry will have a centralised solution that will allow him to input information about his locations and trucks, and automatically configure the fastest and most cost effective delivery routing schedule. Traditionally, Barry might have begun his route at point A, followed by point B, C and D in that order. By inputting this data in Run and Route, Run and Route will determine the optimaldeliveryschedule that would show that this particular truck should begin his delivery route at point C, then point A, then point D and finishing at point B.
Another challenge Barry and other OperationsManagers face is creating a schedule that considers delivery timewindows and customeravailability. Factoring customer availability and time windows is a crucial and important aspect of determining the optimal routes for a fleet of delivery vehicles. For example one shop in Bondi has a strict 2 hour delivery window between 5:00am and 7:00 am, while another customer in Redfern is a bit more flexible and is open for deliveries from 6:00 am to 12:00 am. Manually working through these intricacies one truck at a time not only requires an incredible amount of thought and time, the level of complexity dramatically increases with scale.
Run and Route is Scopta’s Vehicle Routing offering, designed to simplify the planning process for last-mile delivery. It is useful for businesses with multiple vehicles that each perform multiple deliveries per day. Run and Route can help your business cut variable costs and improve your customer service. Remain efficient and competitive with a quality Vehicle Routing Solver that simplifies the role of your Operations Manager and the way you plan and schedule your deliveries. Be confident and assured with the quality and accuracy of your schedules with Run and Routes powerful engine.
Want to know more? Speak to a team member today and find out how Scopta Run and Route can automate your vehicle routing.
Run and Route Contact Form
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Being able to close the labour and skill gap is a critical factor in sustaining growth and maximising profitability for remote operations. It is imperative that companies have the tools and skills available to unravel the complexity to FIFO management.
FIFO workforces are commonly used by large infrastructure and resource projects in remote regions including rural and offshore. These regions often don’t have adequate infrastructure or an available local workforce with the right skillset which leads to companies requiring the use of workers from interstate and sometimes overseas.
The FIFO problem is complex for many companies. It involves determining efficient ways to move people via aircraft, taking into consideration: multiple projects at various phases over multiple locations, with a dynamic workforce utilising different skillsets on a variety of roster patterns, as well as using a fleet consisting of different types and numbers of aircraft.
Often the goal with FIFO management is to determine the number, and type, of aircraft needed in order to minimise cost whilst working with the opposing objectives of ensuring: the staff arrive before the start of their shift (but not too early), depart after the end of their shift (but not too late) and keeping travel durations to acceptable lengths (to ensure low fatigue).
Analytics to break through the complexity
With this level of complexity, a traditional excel approach lacks the rigour and power to find the most efficient and effective results. As a result we’ve developed a number of different FIFO optimisers at Biarri to help ensure the best outcome for clients.
The reality is that there are often many more factors that need to be considered which complicates the problem further. Each FIFO optimisation problem often turns out to be quite different once the detail of the problem is better understood.
High Level FIFO Requirements
Some companies just want us to help them “define their fleet, or travel requirements” so they can then go out to tender (it also helps to keep the vendors honest), others actually want an operational tool. Others may be looking to see if there is a business case for upgrading an airport (e.g. if the airport is upgraded, then larger aircraft can be used which can reduce the need for bus in bus out (BIBO) which will alter their risk profile due to road km and can dramatically alter travel durations).
Specific FIFO requirements
Our clients often want different levels of detail in the solution. Some are happy with a solution that ensures adequate movements at the week level (e.g. 15 flights of aircraft type A between locations B and C per week), others want very detailed minute by minute schedules which take into account: turnaround time, time between takeoff and landing, number of aircraft gates with solutions showing exactly who is travelling on which flight and aircraft and when.
Across Multiple Projects
Our clients have also had multiple projects which are often on the go at the same time and sometimes different priorities are given to different projects. These priorities can be used to ensure that if all the people movement demands can’t be met, then the lower priority movements are less likely to be satisfied.
Optimising the time horizon
The optimisation time horizon can also vary significantly with some clients optimising over a 24 hour period (or even less if they want to re-optimise in the middle of the day due to unpredictable events such as delays due to weather) through to clients wanting higher level schedules over several years to help them make strategic decisions and determine how their fleet needs to change over time.
Understanding the constraints
Constraints such as: the maximum distance an aircraft can travel before needing to refuel, maintenance schedules and the refuelling locations themselves often also need to be considered. We’ve dealt with both fixed and rotary wing (helicopters) aircraft. Helicopters have the additional complication of sometimes having to take more fuel (and thus weight) to travel further, which results in the reduction of passengers because of the helicopter’s limited total payload capacity.
Finding the right FIFO parameters
We have outlined some of the parameters that our FIFO optimisers have considered. It is by no means comprehensive and we can always include new parameters if a different problem requires them but it gives a good understanding into the different variables that can, and should be considered.
Some of the typical inputs include:
Hours of operation
Availability (i.e. you can specify a start and end date for which the airport is available)
Category (e.g. fixed wing or rotary wing)
Type (e.g. DASH 8-200)
Fuel tank capacity
Fuel burn rate
Availability (i.e. you can specify a start and end date for which the aircraft is available)
Aircraft types able to fly this leg
People Movement Demands
Number of passengers
Arrive Before (i.e. must arrive on their first working day of the roster by this time)
Depart After (i.e. must depart after this time on the last working day of the roster)
Roster Pattern (e.g. 14:14 = 14 days on, 14 days off)
Day of week (i.e. which day of the week can this person travel)
Group (demands can be grouped together to allow the user to specify which demands can be grouped on the same aircraft)
Some of the typical outputs include:
KPIs - There are around 40 KPIs, some of them are listed below
Total distance flown
Total fuel burned
Total number of aircraft required
Total unused pax capacity
Total passenger demand
Total passenger demand satisfied
Resource Summary (i.e. which aircraft are required and when)
Total hours flown
Total distance flown
Total fuel burned
Flight leg details (i.e. which flight legs are required and when)
Available pax capacity (this is < pax capacity if the fuel weight is a limiting factor)
Total used pax
Departure date and time
Arrival date and time
Day of week
Total hours flown
Total fuel burned
Fuel weight at start of leg
Refuel at destination (true or false)
Turn around time
Flight leg pax details (i.e. which people movement demands travel on which flight legs)
Departure date and time
Arrival date and time
Project summary (i.e. which demands from which projects were satisfied)
Total satisfied demand
Total unsatisfied demand (e.g. this will be non zero if there is not enough capacity to transport demand)
Total impossible to satisfy demand (e.g. this will be non zero if a flight path has not been specified in the inputs that results in some demand being impossible to satisfy regardless of aircraft resources available)
Number of instances (i.e. how many times is this flight route flown at the same time – but on different dates)
Date of first flight
Date of last flight
Day of week
Total hours flown
Total fuel burned
Unravel the complexity to FIFO Management
The work we have done for companies such as Arrow, Origin, QGC, BMA, IBS, and Santos has shown us that despite having FIFO problems, they all required different approaches in order to achieve the right result.
This has demonstrated to us that when approaching a FIFO problem, where so many different variables have to be considered depending on the client, a standard approach (Commercial off the shelf product) and excel models will generally struggle with the complexity.
Having a tool built around specific variables demonstrates the benefits to bespoke solutions for FIFO problems.
https://biarri.com/wp-content/uploads/2015/04/Biarri-FIFO-Management.jpg564851Ash Nelsonhttps://biarri.com/wp-content/uploads/2022/05/Biarri-White-Logo-Tag-Line-700-×-700-px-300x300.pngAsh Nelson2015-04-08 10:33:412018-05-27 14:46:59Grounding the complexity to Fly in Fly Out management
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