Biarri S&OP Agribusiness

Leading to Greener Pastures with S&OP in Agribusiness

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.

Textbook S&OP  

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

Forward selling 

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.

Allinace S&OP in 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 Biarri introduced 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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The Economics of AI: Delineating the economic limits to AI adoption

by Evan Shellshear, Brendan Markey-Towler and Leonard Coote

An abridged copy of this article was published on The Conversation. Follow the link to read the shorter article ‘A simple calculation can stop artificial intelligence sending you broke‘. We present the full article here.

Mike’s story: AI in the outback

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 [1]. 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 [2]. 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 [3]. 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 [4]. 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 [5]: 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 [6].

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 [7]. 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 [8]). From the firm’s perspective this means:

  1. We have better allocation/utilisation of the firm’s inputs
  2. 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: 

  1. The up-front cost of installation and setup
  2. The ongoing operating cost of the AI system, and 
  3. 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.

Figure 2: The economic principle of AI adoption unpacked, what are the changes in benefits and costs relative to the alternative?

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.


Figure 3: A simple, three-step decision tree for any AI adoption problem. Note the high bar that AI must meet to be economically valuable.

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.

Near cure' for rare disease costs $2.1 million but makers say it's better  than ongoing treatment - ABC News

Zolgensma is part of a new wave of drugs that promise to usher in a revolutionary era of personalised medicine [9]. 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.

Conclusion

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:

  1. What is the dollar value or percentage gains created by the technology?
  2. What is the dollar value of setup and ongoing costs?
  3. 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.

References

[1] 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.

[2] 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.

[3] Sullivan, Joshua and Zuvatern, Angela (2017). The Mathematical Corporation. New York: Public Affairs.

[4] Wooldridge, Michael (2020). The Road to Conscious Machines. London: Penguin.

[5] Robbins, Lionel (1932). Essay on the Nature and Significance of Economic Science. London: MacMillan.

[6] 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.

[7] 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.

[8] https://www.gartner.com/en/documents/1964015/best-practices-in-analytics-integrating-analytical-capab

[9] https://www.novartis.com/news/media-releases/zolgensma-data-including-patients-more-severe-sma-baseline-further-demonstrate-therapeutic-benefit-including-prolonged-event-free-survival-increased-motor

Creating team friendly rosters and the benefits for everyone

Introduction

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

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

A Friendly Roster

An example of what makes a team friendly roster.

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

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

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

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

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

This gets complex quickly!

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

Benefits

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

Better work-life balance 

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

Staff retention 

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

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

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

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

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Biarri Workforce New Feature: Updated Shift Swaps

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

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

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

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

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

 

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Agribusiness Optimisation Solutions

Maths and Machine Learning for Agribusinesses

Mathematics powered by computers is changing the world we live in. At Biarri we see this everywhere, across every industry, and I’m sure you do too. Recently we have delivered a number of Machine Learning and Mathematical Optimisation solutions for Agriculture businesses in Australia and were fortunate enough to be invited to speak at the recent Case IH agri-business conference in Mackay.

Ash Nelson, Biarri’s co-founder, presented on Maths and Predictive Analytics for better business decisions. He described how our everyday lives are being changed by corporations leveraging large data sets, advanced statistical analysis and powerful computing resources. Ash then outlined how these same set of technologies can be utilised to improve business decisions in agriculture. This includes optimising agricultural supply chains and port operations, reducing unplanned equipment failures by using intelligent predictive maintenance algorithms or to improve health and safety outcomes for farm workers by better identifying areas of best practice to inform injury prevention initiatives.

Are you interested in leveraging your data using advanced maths to make better business decisions? Don’t hesitate to get in touch with our friendly team.