Is mining ready for cloud-based fleet management systems?
Since the 1990s, fleet management systems (FMS) have enjoyed ceaseless innovation, from infrared beacons to ever more agile, smaller sensors. The advent of automation, cloud computing, data analytics and AI – perhaps some of the biggest developments of modern times – are ushering in an era of improved safety and enhanced operational efficiency with the likes of real-time optimisation and predictive maintenance among their capabilities.
'Connectivity via the IoT [Internet of Things] provides precise monitoring capabilities, while integration across systems ensures seamless communication and improved efficiency,' says Jarym Kowalchuk, Hexagon's head of product, load and haul.
Other relatively recent developments are fatigue management and collision avoidance systems (CAS), which are now increasingly common despite still being seen as emerging technologies just five to seven years ago, according to Kowalchuk.
'CAS is in the main adoption phase now in mining, and at numerous sites today entry is not permitted unless you are in one of the operation's vehicles with CAS fitted,' he claims.
These technologies have huge value – making mines safer and more efficient than ever before. However, they come with additional hardware and sensors – a prospect that Hexagon is finding customers are less keen on. Responding to their wishes to declutter, the company says it is removing duplicate hardware where it can; for example, by combining its operator alertness system with CAS, when customers are comfortable to do so.
That desire to rationalise stretches to on-site infrastructure too. Touted as revolutionary, taking FMS into the cloud-based domain is a goal that many mine owner/operators and the supply chain are striving to achieve.
By reducing the need for physical infrastructure and IT maintenance, cloud-based FMS provide scalability and cost-effectiveness.
'They enable remote access and real-time data processing, enhancing operational efficiency and decision-making. They also help cultivate collaboration via centralised data and support integration with other cloud services,' says Kowalchuk.
He stresses the importance of collaboration between mines and suppliers when embarking on that journey. This is critical in ensuring that the reliability and security of data and production systems are not compromised. 'Many smaller mine operators, particularly those that use scaled down or 'lite' versions of FMS, are moving directly to the cloud for their deployments,' he says, which is why support from experts is crucial.
In contrast, larger, more complex operations requiring a comprehensive, feature-rich solution should move to the cloud in stages. 'For these operations it would be unacceptable for the FMS to become temporarily unavailable because of latency issues or lost connection with the cloud.'
Instead, Kowalchuk encourages larger mining operations to take their cloud transition 'one step at a time'; for example, using it for data analytics first, then identifying the services cloud can next deliver.
Will Batty, Geotab's APAC associate vice-president of business development, says that adopting cloud-based fleet management in mining is 'a strategic move' that requires clear goals and cross-functional planning from the outset, and a strong integration process. 'Choosing a platform is just the start; success depends on how well it is implemented, supported and aligned with business needs,' he advises.
Taking a mine's FMS to the cloud can be a complex process, with two – but not exclusively – key elements that are particularly demanding: integration and connectivity.
Integration is a challenge due to existing, local (on-site) systems that may comprise older technologies.
If these considerations are addressed properly, the transition to cloud-based FMS offers considerable physical benefits including decluttering the mine and even individual vehicle cabs, making installation easier with far less cabling.
Connectivity is also an 'important consideration', according to Kowalchuk. In Australia, where mines are often remote and hard to access – both physically and conceptually – connectivity is already an obstacle. So, reducing local hardware to instead rely on cloud-based platforms can be fraught with risk.
This is something Batty is particularly aware of. 'Connectivity is one of the most critical elements in mining fleet management,' he says, 'especially in remote Australia where lack of network coverage can directly impact safety, compliance and operational visibility.'
Telematics, which Geotab specialises in, are now not just a 'nice to have', they are a critical part of modern mining operations – and not just for tracking vehicles. 'What began as basic GPS tracking has evolved into a powerful data platform that touches nearly every part of an organisation... It is an essential tool that enables mining operations to stay competitive, compliant and safe in one of the world's most challenging operating environments,' says Batty.
As mines become more digital with the likes of telematics, CAS and other capabilities, it puts greater bandwidth pressure on the network. For example, nearly all mines using an autonomous haulage system are using an LTE or 4G network.
'As FMS become more capable, they are often used together with CAS, fatigue management and other technologies, which places demands on the network to run effectively,' explains Kowalchuk.
Hexagon is not alone in recognising the dangers posed by poor connection in an industry where safety is paramount and data is fast becoming a vital commodity.
'In telematics, when the signal drops, so does your real-time visibility,' Batty says. 'For vehicles operating in remote regions where network coverage is inconsistent or unavailable, this can pose a significant safety risk to drivers and create operational blind spots for site managers.'
Having the ability to store and then upload data and to track vehicle location without network access are technologies that will likely usher in the adoption of the 'hybrid cloud'. As its name suggests, the hybrid cloud employs a mix of local storage with private and public cloud platforms, offering greater efficiency, easier cross-platform integration and enhanced security features.
Saurabh Daga, project manager at GlobalData, says the rapid evolution of cloud computing, particularly hybrid and multi-cloud models, allows organisations to scale quickly and address the complexities of today's digital landscape. 'Consequently, there is a fundamental shift in how businesses leverage cloud technologies to optimise performance and reduce operational costs.'
Mining companies considering cloud-based platforms should first clearly identify the specific operational problems they face and the required system functionalities, then engage with experienced technology partners, says Kowalchuk.
'Scalability is important to accommodate future operational growth, along with an evaluation of data protection measures and regulatory compliance for security,' he continues.
Batty stresses another area that may sometimes be overlooked but is often critical to successful adoption: training.
'As more mining businesses adopt connected platforms… It is not just about putting technology in place, it is about ensuring the people using it are equipped to unlock its full value,' he says.
Mining operations need to ensure staff understand how to use technologies like telematics platforms, how to interpret vehicle and driver data, and how to make informed decisions that support safety, efficiency and compliance. Without the right skills, Batty warns, return on investment can be severely limited.
'The mining operations that are successful in implementing technology are those that invest equally in people and platforms, enabling their teams to grow with the technology and extract the full value of digital transformation,' he says.
With a new era of FMS upon us, Kowalchuk notes that more advanced sensors, radar, onboard intelligence, telematics and edge computing mean that mines can start to move away from that traditional model based on the centralised management of fleet because 'you now have more smarts and intelligence on the machine itself'.
Cloud-based FMS are becoming increasingly utilised, heralding significant advantages – but can mines implement them to gain the greatest benefit and address the integration and connectivity concerns? The simple answer is they can, but the approach needs to be one where all parties, both at the mine and in the supply chain, leverage their knowledge.
As unique as every mine and its environment is, so too is its migration to the cloud. Using a mix of on-site storage and a hybrid cloud solution can deliver greater cost-efficiencies, improved mine safety, enhanced data security and elevated data insights. This means that although the move to cloud-based fleet management is a complex one, the journey is worth taking.
"Is mining ready for cloud-based fleet management systems?" was originally created and published by Mining Technology, a GlobalData owned brand.
The information on this site has been included in good faith for general informational purposes only. It is not intended to amount to advice on which you should rely, and we give no representation, warranty or guarantee, whether express or implied as to its accuracy or completeness. You must obtain professional or specialist advice before taking, or refraining from, any action on the basis of the content on our site.
Try Our AI Features
Explore what Daily8 AI can do for you:
Comments
No comments yet...
Related Articles
Yahoo
11 hours ago
- Yahoo
Trust in AI is growing in finance, especially behind the scenes
This story was originally published on CX Dive. To receive daily news and insights, subscribe to our free daily CX Dive newsletter. A majority of customers trust the use of AI in behind-the-scenes tasks at financial institutions, according to a TD Bank survey conducted by Ipsos released Tuesday. Among the 2,500 U.S. consumers polled, 70% are comfortable with technology being used for fraud detection, and 64% are comfortable with it being used in credit score calculations. Consumers also believe that AI should offer more ease. Two-thirds believe it can expand access to financial tools, and nearly half expect benefits from AI like 24/7 banking access. As consumers have become more familiar with AI tools, their trust in the technology has slowly grown. Nearly 7 in 10 consumers say they are at least somewhat familiar with AI — a finding seen in other surveys, too. Notably, half of consumers trust that AI will provide reliable, competent information, trusting AI just as much as news stations. But consumers are more comfortable with AI in specific use cases and the more complex or sensitive the matter, the more they want to speak to a human or know that a human will be reviewing AI before making any decisions. Consumers are less inclined to want to only use AI when it comes to tasks that one might typically use a financial adviser for, according Ted Paris, EVP, TD Bank AMCB, and head of analytics, intelligence & AI. When it comes to personal finance, 3 in 5 of consumers were comfortable with the idea of using AI financial tools for budgeting and automating savings goals. But less than half were comfortable with more complex tasks like retirement planning and investing. Banks enjoy high consumer trust — more than 4 in 5 consumers trust banks for accurate information. As they deploy AI, it's important that they maintain that, Paris said. 'What's probably the key piece, is creating and enabling and allowing customers and colleagues to feel that they can trust the outcomes of what this capability then generates,' Paris said. One of the ways TD Bank is approaching this is by always having a human in the loop, meaning that the output of an AI solution will be passed through some internal expert before going to a client. 'We need to make sure that first, anything that we're doing is directed toward a particular need,' Paris said. 'We need to make sure that this is going to meet all hurdles that we would set, legal, regulatory, for security and privacy.' Sign in to access your portfolio
Yahoo
21 hours ago
- Yahoo
Analyst Confidence Grows as Semtech (SMTC) Expands IoT Footprint
Semtech Corporation (NASDAQ:SMTC) is one of the 11 Best Tech Stocks to Buy On the Dip. On May 28, Analyst Christopher Rolland of Susquehanna reiterated a Buy rating on Semtech Corporation (NASDAQ:SMTC) with a price target of $60. The rating comes after the company released its fiscal first-quarter results for 2026. The company reported net sales of $251.1 million, reflecting a 22% year-over-year increase. The analyst noted that the company has demonstrated resilience with its core products FiberEdge and TriEdge by filling the gap caused by a temporary slowdown in its CopperEdge product line. This product diversification supports ongoing growth, with new applications expected to ramp up in the latter half of the year. Moreover, Semtech Corporation (NASDAQ:SMTC)'s LoRa technology, which is central to its IoT strategy, has exceeded expectations. The company is actively pursuing new opportunities in the expanding IoT sector, noted Christopher Rolland. Despite short-term challenges such as the NVIDIA ACC disappointment, the company remains focused on reducing debt and investing in research and development. A technician looking at a circuit board of analog semiconductor products. Semtech Corporation (NASDAQ:SMTC) is a high-performance semiconductor company that provides advanced technology solutions. It operates through three main areas including Signal Integrity, Analog Mixed Signal and Wireless, and IoT Systems and Connectivity. While we acknowledge the potential of SMTC as an investment, we believe certain AI stocks offer greater upside potential and carry less downside risk. If you're looking for an extremely undervalued AI stock that also stands to benefit significantly from Trump-era tariffs and the onshoring trend, see our free report on the best short-term AI stock. READ NEXT: The Best and Worst Dow Stocks for the Next 12 Months and 10 Unstoppable Stocks That Could Double Your Money. Disclosure: None. Error in retrieving data Sign in to access your portfolio Error in retrieving data Error in retrieving data Error in retrieving data Error in retrieving data
Forbes
a day ago
- Forbes
Four Strategies To Outsmart Data Pitfalls In Product Innovation
Ramalakshmi Murugan leads product strategy and operations for the Google Play Analytics team at Google. In the age of "data is the new oil," product teams are increasingly leveraging vast amounts of data to fuel innovation. However, many product initiatives, irrespective of having enough data, fail. The truth is, the very data meant to illuminate the path can become a labyrinth of pitfalls if not navigated with caution and a clear strategy. In the journey of moving from "data-driven" to "data-informed," it is imperative that product leaders understand the data pitfalls that might impede their efforts. Here, I will provide four decisive strategies to circumvent the most common blunders and ensure that your data is indeed guiding you toward groundbreaking innovation. 1. Beyond The 'North Star Metric': Embracing A Holistic Data Ecosystem The Pitfall: Many teams are obsessed with the "North Star Metric," fixating on a single KPI to the exclusion of all else. While a North Star can provide focus, an overreliance on one metric often leads to a myopic view. Optimizing for a single metric can also lead to "local maxima"—perfecting an existing solution but missing out on truly disruptive opportunities. The Strategy: Instead of solely chasing a single North Star, cultivate a holistic data ecosystem. • Balance quantitative and qualitative data. Quantitative data tells you what is happening (e.g., conversion rates, CTRs), but qualitative data tells you why (e.g., usability studies, user feedback). Combining both provides a richer, more nuanced understanding of user needs and pain points. • Track a balanced scorecard of metrics. Beyond the North Star, define a set of complementary metrics (e.g., customer churn, ARR) that cover different aspects of product health and provide a more comprehensive picture. • Contextualize data with market and competitive insights. Your internal product data is only part of the story. Data should be interpreted within a larger context, such as market trends and competitor movements, to inform strategic innovation. 2. Beware The Bias Beast: Actively Combating Cognitive Traps The Pitfall: Human beings are inherently biased, which can affect data collection, analysis and interpretation, leading to misguided product decisions. Some examples of bias that I frequently see teams struggling with are: confirmation bias (seeking data that confirms existing beliefs), survivorship bias (focusing only on successful outcomes) and selection bias (skewed samples). The Strategy: To tame the "bias beast," implement these active combat strategies: • Formulate clear hypotheses (and be ready to disprove them). Before diving into data, define specific, testable hypotheses. This forces you to consider what data would disprove your assumptions, rather than just confirm them. • Diversify your data sources. Relying on a narrow set of data increases the risk of blind spots. Seek out diverse data sources (e.g., surveys, customer support logs, social listening, A/B tests), and foster diverse teams with different perspectives and backgrounds to challenge assumptions. • Prioritize "why" over "what." When analyzing data, constantly ask "why." Why are users behaving this way? Why did this metric change? Don't just report the numbers; dig into the underlying reasons. • Implement structured experimentation (A/B testing with rigor). A/B testing is a powerful tool, but only if done correctly. Ensure proper randomization, sufficient sample sizes and clear control groups to minimize bias and truly understand the causal impact of changes. 3. Turning Data Overload Into Actionable Product Insights: Prioritizing 'Need To Know' The Pitfall: In the quest for data-driven glory, companies often fall into the trap of data hoarding: collecting data without clear objectives or a structured plan for its potential applications. I think this is the main cause of "analysis paralysis," an overload of information that makes it difficult to retrieve valuable insights. The Strategy: Shift from data hoarding to a "need to know" mindset, prioritizing actionable insights. • Define clear objectives first. Prior to data collection, set clearly defined business goals you would like to achieve, along with the questions that need to be answered to accomplish those goals. This will direct your effort in data collection and analysis. • Prioritize data quality over quantity. Wrong, incomplete and inconsistent data is more dangerous than having no data because it can lead to making very bad decisions. Establish policies around data governance, and perform periodic audits on the data to ensure integrity and trustworthiness. • Democratize access (with guardrails) and foster data literacy. Empower product teams to access and analyze data, but provide the necessary training and tools to do so effectively. This includes understanding data definitions, limitations and ethical considerations. Self-service analytics, when properly governed, can accelerate insight generation. 4. Beyond The Numbers: Cultivating Intuition And Storytelling The Pitfall: Some teams become so fixated on data that they lose sight of the bigger picture, neglecting human intuition, creative vision and the ability to tell a compelling story with their findings. This can stifle truly innovative ideas that don't immediately show up in current data. The Strategy: Remember that data is a tool, not a dictator. Enhance its power with the following strategies. • Harness the power of informed intuition. While data is crucial, it doesn't replace the insights gained from years of experience, empathy for users and a deep understanding of the market. Use data to inform and validate your intuition, not to replace it entirely. • Leverage strategic storytelling. Learn to weave compelling narratives around your data, highlighting the problem, the insight, the proposed solution and the expected impact. Visualizations, clear explanations and a focus on the "so what" are key. • Embrace "no data" scenarios with calculated risk. For truly novel innovations, there might be no existing data to guide you. In these cases, lean on expert judgment, strategic partnerships and carefully designed, low-risk experiments (e.g., MVPs, pilot programs) to gather initial feedback and iterate. By proactively identifying and mitigating potential data pitfalls and by diligently implementing these four key strategies, product strategy teams can transition from having a superficial reliance on data to a deep and comprehensive mastery of it. This profound understanding and skillful application of data will empower them to move beyond conventional approaches and unlock their inherent capacity to fuel the creation of truly exceptional products. Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?
