Efficient and effective logistics is vital to large Oil & Gas projects – equipment must be transported to work sites and raw products need to be processed and then delivered to customers, often using multiple modes of transport in a complex supply chain. In addition to this complexity, another important part of the overall logistics problem is people logistics – How do we move personnel to and from a worksite both safely and efficiently?

Why should I invest in personnel?

Particularly in Australia, where many work sites exist in remote areas, it is not always feasible to relocate employees and their families to a town near the work site. As a result, many workforce personnel are employed on a fly-in fly-out (FIFO) or bus-in bus-out (BIBO) basis, where the employer is responsible for moving personnel to and from the work site.

2013 report from Ernst & Young indicated that human capital deficit is one of the top ten risks facing the global Oil & Gas industry, affirming that

as the sector develops technologically . . . companies that can retain and mobilize people will be able to sustain their competitive advantage.

Effective and efficient personnel mobilization is crucial to any significant oil and gas project. Research published in 2012 by the Australian Centre of Excellence for Local Government (ACELG) into the prevalence of FIFO in Australia predicts that percentage of Australian mining sector jobs filled by FIFO roles will only increase over the coming years.

Improper workforce utilisation increases susceptibility to many internal and external issues, such as: a high employee turnover, high ongoing operational costs to the business, and under- or over-allocation of staff to specific jobs.

Analytics to cut through complexity

People logistics for a large project requires the coordination of travel and accommodation for hundreds, sometimes thousands of people in various remote locations. As with traditional logistics, several crucial decisions have to be made including BIBO vs FIFO, choice of airports or bus routes and staff roster schedules. In addition to this complexity, working with personnel often requires much greater scrutiny – we don’t always require a minimal-cost solution – we need to balance cost, project risk, employee safety and employee satisfaction.

Trying to weigh up all these variables and make decisions in an intelligent way for all of the personnel involved in a project very quickly becomes complex. A 2014 interview by Mining Australia notes that “for many companies [workforce management] is man­ual, or only semi-automated, which increases the risk of error or processing in­efficiencies, increasing costs”. Managing a large workforce is difficult, and only with the right technology can companies provide efficient and effective workforce management.

Through analytics companies can cut through the complexity involved in workforce management. By considering specific variables and how they affect outcomes companies can make better decisions.

Analytics empowers organisations through quantitative justification so that decision makers can be certain that employees will be spending less time in transit, and working on the right jobs at the right times, with the right people.

Biarri and People Logistics

Biarri delivers consulting and SaaS solutions to provide the right technology to enable effective workforce management.

Biarri has a proven track record, delivering people logistics and FIFO solutions for Oil & Gas and Mining companies in Australia including Santos, BMA, and Arrow Energy.

To learn more about how Biarri can help your organisation benefit from analytics and optimisation for people logistics get in touch today!

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“AI expends a lot of energy being polite especially if the user is polite saying ‘please’ and ‘thank you’” tripscan Dauner explained. “But this just makes their responses even longer expending more energy to generate each word.” For this reason Dauner suggests users be more straightforward when communicating with AI models. Specify the length of the answer you want and limit it to one or two sentences or say you don’t need an explanation at all. Most important Dauner’s study highlights that not all AI models are created equally said Sasha Luccioni the climate lead at AI company Hugging Face in an email. Users looking to reduce their carbon footprint can be more intentional about which model they chose for which task. “Task-specific models are often much smaller and more efficient and just as good at any context-specific task” Luccioni explained. https://tripscan.biz tripskan If you are a software engineer who solves complex coding problems every day an AI model suited for coding may be necessary. But for the average high school student who wants help with homework relying on powerful AI tools is like using a nuclear-powered digital calculator. Even within the same AI company different model offerings can vary in their reasoning power so research what capabilities best suit your needs Dauner said. When possible Luccioni recommends going back to basic sources — online encyclopedias and phone calculators — to accomplish simple tasks. Why it’s hard to measure AI’s environmental impact Putting a number on the environmental impact of AI has proved challenging. The study noted that energy consumption can vary based on the user’s proximity to local energy grids and the hardware used to run AI models. That’s partly why the researchers chose to represent carbon emissions within a range Dauner said. Furthermore many AI companies don’t share information about their energy consumption — or details like server size or optimization techniques that could help researchers estimate energy consumption said Shaolei Ren an associate professor of electrical and computer engineering at the University of California Riverside who studies AI’s water consumption. “You can’t really say AI consumes this much energy or water on average — that’s just not meaningful. We need to look at each individual model and then examine what it uses for each task” Ren said. One way AI companies could be more transparent is by disclosing the amount of carbon emissions associated with each prompt Dauner suggested.

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Study reveals how much energy AI uses to answer your questions [url=https://tripscan.biz]трипскан вход[/url] Whether it’s answering work emails or drafting wedding vows, generative artificial intelligence tools have become a trusty copilot in many people’s lives. But a growing body of research shows that for every problem AI solves, hidden environmental costs are racking up. Each word in an AI prompt is broken down into clusters of numbers called “token IDs” and sent to massive data centers — some larger than football fields — powered by coal or natural gas plants. There, stacks of large computers generate responses through dozens of rapid calculations. The whole process can take up to 10 times more energy to complete than a regular Google search, according to a frequently cited estimation by the Electric Power Research Institute. https://tripscan.biz tripscan войти So, for each prompt you give AI, what’s the damage? To find out, researchers in Germany tested 14 large language model (LLM) AI systems by asking them both free-response and multiple-choice questions. Complex questions produced up to six times more carbon dioxide emissions than questions with concise answers. In addition, “smarter” LLMs with more reasoning abilities produced up to 50 times more carbon emissions than simpler systems to answer the same question, the study reported. “This shows us the tradeoff between energy consumption and the accuracy of model performance,” said Maximilian Dauner, a doctoral student at Hochschule Munchen University of Applied Sciences and first author of the Frontiers in Communication study published Wednesday. Typically, these smarter, more energy intensive LLMs have tens of billions more parameters — the biases used for processing token IDs — than smaller, more concise models. “You can think of it like a neural network in the brain. The more neuron connections, the more thinking you can do to answer a question,” Dauner said. What you can do to reduce your carbon footprint Complex questions require more energy in part because of the lengthy explanations many AI models are trained to provide, Dauner said. If you ask an AI chatbot to solve an algebra question for you, it may take you through the steps it took to find the answer, he said.

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