Tag Archives: productivity

MaxMine talks up data-led emission reductions for open-pit mines

Environmental, Mining equipment, Mining services, Mining software, Sustainable mining, , , , , , , , ,

MaxMine has been talking up the potential of data in the quest for reducing emissions and boosting productivity at mine sites, with Tom Cawley, Executive Chair and Interim CEO, arguing that there is still plenty of low hanging fruit for mining companies to leverage on their way to achieving longer-term net zero mining targets.

MaxMine, the company says, is an automated, high-resolution data-based business reporting tool that combines advanced data acquisition technology with AI analysis to fully optimise mobile equipment and operator performance within mining and other mobile equipment-based operations, measuring performance differently and using gamification to change behaviours.

According to Cawley, the average open-pit mine can reduce Scope 1 emissions by up to 10-15% by leveraging data, with MaxMine insights enabling this average open-pit mine to improve productivity while also reducing its carbon footprint by around 15,000-20,000 t of CO2.

IM put some questions to Cawley to find out more.

IM: In terms of your expanding product portfolio, where are clients receiving the biggest and potentially quickest return on investments (ROIs) from your solutions?

Tom Cawley, Executive Chair and Interim CEO of MaxMine

TC: MaxMine has the most extensive dataset in the open-cut load and haul mobile mining equipment sector. We continuously collect data from all sensors across 10 original equipment manufacturers or equipment manufacturers. We have around eight million hours of data, enabling MaxMine to provide unique levels of measurement in the mining sector and equip us with an unparalleled range of potential applications. This enables us to cover a broad scope across all mines while delivering tailored solutions to our clients, depending on their requirements from site to site.

The areas of the highest value proposition for our clients include improved TMM (total material moved) by increased payload, reduced cycle times via driver feedback, improved haul road conditions, enhanced safety and faster incident investigations. Cost reductions are delivered by reduced queueing, off-haul travel, idle time, and improved tyre life. High-resolution data is used to measure the performance of and diagnose mining trucks delivering greater efficiency via engine performance improvements and increased availability via predictive analytics and faster fault resolution.

IM: Although you have a specific product focused on decarbonisation (MaxMine Carbon), would you say the majority of your solutions are providing emission reduction benefits? In terms of adding new clients, is this where a lot of the emphasis is?

TC: Yes, there is a strong correlation between productivity and fuel intensity reduction. The better productivity, the better the carbon intensity.

MaxMine Carbon enables mine sites to measure fuel burn on every asset, every second of the day. MaxMine’s high resolution data allows the difference between business as usual and improved operations to be measured, allowing the fuel saved to be included in the productivity benefits.

MaxMine’s high resolution data is used to:

  • Measure actual truck performance, identify trucks operating below design efficiency and provide diagnostic information; and
  • Provide granular feedback to drivers, which can be used to reduce fuel burn.

MaxMine Carbon isn’t only a separate fuel-saving feature; its capability allows open-cut mining companies to measure, manage and reduce their carbon footprint associated with Scope 1 diesel emissions and reduce operating costs related to diesel consumption.

IM: Where is the development of MaxMine Carbon for underground mining? What timelines do you have around the development and rollout of this solution?

TC: Open-cut mining is a large sector and offers significant opportunities for growth. It is much more energy-intensive at the equipment level compared with underground mining.

MaxMine is growing robustly in this area, and we’ll continue focusing on further penetration in the open-cut mining space in the near and medium term.

IM: Is your market proposition stronger at bulk mining operations than others?

TC: Our market is focused on open cut-mining and larger-size trucks, with a size class greater than 100 t.

IM: Outside of your existing portfolio, where do you see room for growth with different solutions? Are you actively engaged in pursuing such opportunities?

TC: We continue to focus and remain disciplined on our core markets, and our unique selling proposition delivers huge opportunities for MaxMine and the open-cut mining industry.

Schenck Process Mining to become Sandvik Rock Processing Australia

Comminution of minerals, Mineral processing, Mining equipment, Mining mergers and acquisitions, Mining services, , , , , , , , , , , ,

The next step in the integration of SP Mining – the mining-related business of Schenck Process acquired by global, high-tech engineering group Sandvik – will see SP mining entities change their names to reflect their new ownership.

On October 1, Schenck Process Australia Pty Limited, which became a wholly-owned subsidiary of Sandvik in November last year, will become Sandvik Rock Processing Australia Pty Limited. The Australian entity is the largest part of SP Mining’s global business, employing around 450 industry professionals.

Since the acquisition, Sandvik has been focused on bringing together its expertise in crushing with the screening, feeding, weighing and loading know-how of Schenck Process Mining.

According to the company’s President Asia Pacific, Terese Withington, this move is part of an integration process that will eventually see SP Mining become a seamless part of the Sandvik organisation.

“In Australia, we are bringing together our sales and back-office teams with those of Sandvik Rock Processing Solutions to allow our customers to access our combined expertise in crushing, screening, feeding, weighing and loading,” she said. “Together we aim to deliver even better digitalisation, sustainability and productivity solutions to our industry.

“The end goal of our integration is to allow our customers to place combined crushing, screening, feeding, weighing and loading orders with our new legal entity.”

Withington says the scale of Sandvik’s operations and commercial reach will help to accelerate the combined innovation portfolio of Sandvik Rock Processing Solutions and SP Mining.

She concluded: “We look forward to continuing to service the business needs of our customers and remain fully focused on the delivery of high-quality equipment, consumables, OEM spare parts and services to help them achieve their business objectives.”

Komatsu looks for productivity Edge with Microsoft partnership

Automation, IoT, Mining equipment, Mining services, Mining software, , , , , , ,

Komatsu, in order to continue its production momentum in the face of continued market uncertainty, is boosting its manufacturing capabilities and productivity through the use of Microsoft cloud, Internet of Things (IoT) and artificial intelligence (AI).

The company, one of the world’s top makers of excavators, bulldozers, and other heavy equipment, needed help gathering and handling data to boost its own manufacturing capabilities and productivity, turning to the Azure cloud and specialists at Microsoft.

Microsoft said: “Komatsu is an innovative manufacturing enterprise that competes in an increasingly unpredictable international marketplace. Ever-shifting economic and other forces – like booms and busts in resource markets – are constantly pushing demand for its equipment up and down from country to country.

“Maintaining production momentum in the face of this sort of uncertainty can be a big challenge for factory managers.”

Nobuyoshi Yamanaka, General Manager for Komatsu’s Manufacturing Engineering Development Center (pictured above) Production Division, said: “Keeping pace with these fluctuations is our primary issue. The best way to do that is by raising our productivity. And, to do that … we need data.”

With the right data and the right insights, decision makers can visualise situations. From there they can opt to speed up or slow down production runs, manage supply chains, and accommodate factory downtime for retooling and maintenance, Microsoft said.

They can also optimise the use of personnel – a key factor in Japan’s sophisticated manufacturing sector, which is grappling with a shortage of skilled workers as the nation’s demographics age.

Acknowledging that it had a need for data, Komatsu went about seeking advice on what technology and data solutions would be best for its ambitious productivity quest, Microsoft said. “They searched widely and settled on Microsoft.”

Adopting a cloud solution

“Microsoft asked us what we wanted to do and how we wanted to expand the solution in the future, then it gave us exactly the right support,” said Yamanaka whose team is now studying how AI and Intelligent Edge solutions might further boost efficiencies.

The company first set out to collect production data in 2009 by using on-premises servers. Five years later, it went further and launched “KOM-MICS” – an IoT system that collects data from sensors installed on a myriad of machine tools and welding robots.

“Komatsu uses a high-mix/low-volume manufacturing system. Plant equipment is not always operating at full capacity as machines may be down for many hours due to setup changes, and so on,” Yamanaka says. “Visualising this situation and reducing machine downtime increases manufacturing output without extra equipment or personnel. Our ultimate goal is to double productivity while reducing equipment and personnel.”

KOM-MICS was a success. And, soon so much information was coming in that Komatsu realised its on-premises approach to data needed a rethink, Microsoft said. It also wanted to collect and visualise data from a network of outside partners and other factories, both in Japan and abroad, that contribute around 80% of its overall production work.

In 2016, it began looking around for a cloud solution.

A Komatsu worker checks a KOM-MICS screen

Keisuke Tsuboi, from Komatsu’s Numerical Controller Team, Advanced Technology Promotion Office, said: “We needed to roll out KOM-MICS to our partners and overseas manufacturing bases to increase the overall productivity of Komatsu.

“Because KOM-MICS collects 20-30 GB of data from each machine tool per year, adding the required resources to the on-premise system, and increasing the number of connected machine tools, would have been difficult. So, we decided the cloud could overcome these problems.”

Weighing up the options

Komatsu moved its data onto Azure in early 2017. According to Tsuboi, a primary reason behind the choice was trust: Azure has extensive security measures backed by Microsoft’s expertise. It also made Komatsu’s data capabilities immediately compliant with GDPR – the European Union’s new data protection measure.

The flexibility and scalability of Azure were also deciding factors that has allowed KOM-MICS coverage to be ramped up almost seamlessly, Microsoft said.

“We are connecting 100 to 200 extra machines to KOM-MICS per year,” Tsuboi says. “We have around 700 connected machine tools and 350 connected welding robots. Komatsu has around 1,200 machine tools and 700 welding robots that can be connected to KOM-MICS. This scale of data is no problem for our system on Azure.”

Expanding its scope

Komatsu connected its Thai and Indonesian bases to KOM-MICS in 2017. Since then, the number of Komatsu’s partners connected to KOM-MICS has been increasing rapidly.

“The transition to Azure instantly expanded the potential scope of the KOM-MICS rollout. The meticulous support of Microsoft enabled us to complete the migration in a short time,” Yamanaka said.

More data from more machines in more places means the company can improve quality measures, plan and adjust with agility, and better anticipate equipment failure, according to Microsoft.

“Before we started collecting data, we didn’t know to what extent our machines were working within a 24-hour period,” says Tsuboi. “With KOM-MICS, data is visualised so we can work on improving production efficiency by increasing areas with low production conditions to be equal to those that are high.

“By analysing the machine data from a certain production line we have been able to increase the machine operation rate by about 25%.”

AI and the Intelligent Edge

Looking ahead, Yamanaka believes AI on the Intelligent Edge can potentially deliver more productivity dividends, such as freeing up the time of skilled workers and opening the door to predictive maintenance.

“I believe that data can be used in a variety of ways,” he says. “We would like to automatically realise optimal machining conditions and have AI do some tasks that are currently handled by skilled workers.

“Also, there is quality. We would like features that can automatically detect signs of failures before they happen. We need to make use of AI. But because processing data in the cloud takes time, we are thinking about adopting Azure IoT Edge so we can run Microsoft Azure services on IoT devices.”

Hitachi Construction Machinery delves into mining industry’s downtime issue

Equipment maintenance, Mining equipment, Mining maintenance, Mining services, , , , , , ,

Maintenance or mechanical failures are often seen as the root causes of industry downtime, but Hitachi Construction Machinery (HCM), in South Africa, thinks there is more to this loss in productivity and profitability than this.

A deeper look into the actual typical operating conditions in the mining environment reveals there are levels of downtime beyond these two, HCM South Africa said.

“A closer approximation of actual production time can be reached by applying an OEE (overall equipment efficiency) analysis on a calendar time-based approach, as opposed to a loading time-based approach, since the latter is based on theoretical total time and is more likely to give an inaccurate reflection of actual production capacity,” the company said.

“With a calendar time-based OEE analysis, then, a number of additional factors affecting productivity are taken into account:

  • “Unscheduled downtime (breakdowns/failures);
  • “Scheduled and unscheduled maintenance;
  • “Idle time (eg operator lunch breaks);
  • “Waiting time (eg when a shovel waits for a truck to be loaded/unloaded);
  • “Inactivity during moves between sites, and;
  • “Environmental disruptions (e.g. unsuitable terrain, etc).”

HCM said: “From this, it is clear that the true cost of downtime is notably higher in reality than in theory, and since many of these factors are beyond the control of managers and personnel, it clearly illustrates the importance of quality and reliability of the equipment itself to the overall viability of a mining enterprise.”

HCM supplies one third of all the hydraulic mining excavators in the world – a fact due in no small part to the “strength and reliability of Hitachi machines”.

“In fact, it is thanks to the overall longevity of Hitachi’s machines, and the fact that customers get far more than expected from their purchases; the modular designs employed in the newer technology machines, in particular, make for timeous and effortless maintenance routines, which play a significant role in production optimisation.

“Superior horsepower output, efficient engines, ergonomically designed cabs, advanced hydraulics, tough frames, and powerful arm- and bucket-digging forces make for formidable, robust machines that maximise production time to get the job done,” the company said.

This also makes for lower total cost of ownership, as the customer benefits from additional value over time, according to the company. “For example, a renowned mining customer has reported seeing extended life on main components purchased with the equipment such as the rigid dump truck wheel units, on which they have achieved in excess of 25,000 hours through the application of class-leading maintenance tactics in partnership with Hitachi’s site support personnel. With a very closely managed and monitored maintenance plan, they aim to manage these components to 50,000 hours.”

The company concluded: “Ultimately, it is through willingness to receive customers’ feedback and then incorporating it into their vigorous and ongoing R&D processes that Hitachi is able to optimise their machines and offer exceptional value.”