Fleet efficiency and productivity are in the data. But can you see it? 

In logistics, coping with fluctuations in demand is all part of the business. That said, 2020 has undoubtedly seen extremes in all areas of the market. Keeping the fleet on the road and operating efficiently has taxed managers all over Europe, and many are facing twin battles of expanding capability and ensuring continued efficiency.

If we question what efficiency means in the logistics sector, then surely “serving more customers better with fewer resources” is the simplest answer. Achieving that goal in today’s highly competitive logistics and supply space is no small undertaking, especially when the market’s larger players have much more to invest in infrastructure and technology than smaller outfits.

However, the nature of fleet management software and hardware is such that smart code and kit (software and hardware, in IT terms) is a great leveler and is used to drive the types of savings that flatten out an uneven playing field.

Single platforms of collected technologies aimed specifically at the sector create more efficient operations across the board. Physical telemetry on vehicles and smart devices at distribution points combine with pure-play technologies like open APIs and powerful databases, for example.

The goals are improved productivity using finite resources, increased profitability, happier staff and drivers, and positive customer experiences. The nature of logistics is such, too, that savings mean less environmental impact. In short, profit margins, the planet, and people (staff and customers) all win when efficiency is realized.

The detail in the connections

For the fleet manager, having access to comprehensive information that’s been collected and processed by logistics solutions is highly empowering. Using the single case of collected telemetry data, operations staff can ensure vehicles spend less time on inefficient routes, pulled-up at the roadside, or in the repair workshop.  The CFO sees more customers served more quickly, at lower fuel costs and drivers feel under less pressure (hence reducing the chance of inappropriate driving).

In-vehicle technologies feedback on driving style, idling times, and fuel consumption so that this information can be used as coaching materials for staff. Some companies even use this data to “game-ify” drivers’ performance; awarding the good, incentivizing the less-good, and improving morale with a little healthy competition.

Stylish driving

Balancing the need for efficiency with individual schedules and performance targets is a contentious area for any supply chain business. Mindful driving lowers fuel costs and pollution levels and means a great deal less wear and tear on tires, engines, and bodywork. Here, too, technology can be used in-vehicle to encourage responsible driving, and overall metrics can be used to better plan when vehicles need to go in for scheduled maintenance. The combination of business data that shows long-term trends in demand can inform those schedules to better effect so that proactive maintenance can be lined up during slower periods.

For drivers, efficiencies similarly come from many smaller factors adding up to more significant gains. A live schedule on a driver’s mobile not only reduces paper waste but has the added advantage of over-the-air updates. Canceled deliveries and additional stops can be created on the fly, so customers get reached faster than they might otherwise, ticking boxes for fewer truck rolls, more efficient staffing, less fuel burnt, and improved customer service.

Today’s fleet management technology creates opportunities like these for companies that can differentiate themselves via quality of care; for their customers, for the environment, and for the bottom line, too.


Most fleet managers will have made any sweeping operational changes already, leading to fundamental changes in the way the company works. But from that point on, gains in efficiency and productivity is a numbers game: attention to detail and small wins add up and multiply over an extended fleet’s operations. Reducing every journey’s idle time by three percent may seem like a small saving, but with fleet management technology, that saving – along with dozens of others – begin to add up.

Even a small operation that utilizes a dozen vehicles or so will find that Verizon Connect‘s technology will create those savings and produce a significant return on investment in short order.

To find out more about how Verizon Connect can change the way your company operates, get in touch here for UK readers or here for Dutch / Belgian readers. Verizon Connect has the technology that opens up competition with the outfits a hundred times bigger.

Thank you for stopping to visit My Local Pages. We Hope you enjoyed seeing this news article about Asian and related news called “Fleet efficiency and productivity are in the data. But can you see it? “. This news update is posted by MyLocalPages as part of our local and national news services.

#Fleet #efficiency #productivity #data

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How a New Collaboration Is Using “Intelligent Efficiency” to Improve Healthcare

 By Catherine Estrampes, GE Healthcare, and Dr. Pedro Rico, Vithas Group

In the healthcare sector, we take great pride in what we do. We have the incredible responsibility of caring for patients and saving lives. This gives us a sense of mission that often translates into a feeling of professional satisfaction and a natural desire for even greater impact.

But today, the challenges that have long faced our industry have never been more critical, as we find ourselves facing one of the biggest crises of our lifetime: a global pandemic.

The increasing difficulty of delivering sustainable, high-quality care to patients poses an existential risk to hospitals and caregivers.

This challenge is often talked about as one of “efficiency.” And we have yet to solve it as an industry. Although many have been focused for a while on improving and increasing efficiency, perhaps it is the way we have been trying to do so that can explain why it remains a work in progress.

As members of a major global medical technology company and a reference healthcare group in Spain, we have lived these challenges firsthand, and we believe that now more than ever is the time to encourage novel thinking and a willingness to try new approaches to rethinking the concept of efficiency.

This is exactly what is behind a collaboration that we have just announced to holistically optimize the medical equipment installed in Vithas Group’s hospital network. In summary, we are implementing a comprehensive asset-management platform that will oversee risks and prevent damage and breakdowns in any of our MRI, CT, mammograph, or X-ray systems, avoiding downtime and the need to reschedule patients, and ultimately providing better care.

Moreover, this platform incorporates artificial intelligence (AI) algorithms for proactive and predictive monitoring, as well as an augmented reality (AR) application to diagnose and remotely repair the systems.

The entire collaboration focuses on enhancing efficiency with a new perspective: addressing issues through a comprehensive approach that involves all relevant stakeholders to solve a diverse set of problems all at once. We have transformed what we call efficiency to what we may now know as intelligent efficiency, a three-key-principle concept focused on:

  • Integrating technology, data, and human capabilities
  • Being patient-centric and provider-centric
  • Taking a comprehensive and holistic approach to find systematic solutions

This method is more integral, with the objective of boosting and uplifting all those who touch the healthcare ecosystem—from patient to practitioner—and by improving the management of our equipment installed all over a hospital’s complex network.

While we expect a healthy skepticism toward such a new approach at first, healthcare professionals and even patients are now more open to applying this new perspective, including this much-needed paradigm shift in how we view and define efficiency.

Using intelligent efficiency helps to actively solve the central healthcare issues that long sat unsolved, letting caregivers spend more time treating their patients and less time struggling with ad hoc systems. It can mean patients spend less time waiting for tests, results, and treatments. Overall, it means empowering physicians and other frontline providers to deliver higher-quality care, with better, more integrated data and tools. And, ultimately, it means better patient experience and health outcomes.

Click here to learn more.

Catherine Estrampes is president and CEO of GE Healthcare Europe/Russia, Middle East & Africa (EMEA). Dr. Pedro Rico is CEO of Vithas Group.


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Advance could enable artificial intelligence on household appliances while enhancing data security and energy efficiency — ScienceDaily

Deep learning is everywhere. This branch of artificial intelligence curates your social media and serves your Google search results. Soon, deep learning could also check your vitals or set your thermostat. MIT researchers have developed a system that could bring deep learning neural networks to new — and much smaller — places, like the tiny computer chips in wearable medical devices, household appliances, and the 250 billion other objects that constitute the “internet of things” (IoT).

The system, called MCUNet, designs compact neural networks that deliver unprecedented speed and accuracy for deep learning on IoT devices, despite limited memory and processing power. The technology could facilitate the expansion of the IoT universe while saving energy and improving data security.

The research will be presented at next month’s Conference on Neural Information Processing Systems. The lead author is Ji Lin, a PhD student in Song Han’s lab in MIT’s Department of Electrical Engineering and Computer Science. Co-authors include Han and Yujun Lin of MIT, Wei-Ming Chen of MIT and National University Taiwan, and John Cohn and Chuang Gan of the MIT-IBM Watson AI Lab.

The Internet of Things

The IoT was born in the early 1980s. Grad students at Carnegie Mellon University, including Mike Kazar ’78, connected a Cola-Cola machine to the internet. The group’s motivation was simple: laziness. They wanted to use their computers to confirm the machine was stocked before trekking from their office to make a purchase. It was the world’s first internet-connected appliance. “This was pretty much treated as the punchline of a joke,” says Kazar, now a Microsoft engineer. “No one expected billions of devices on the internet.”

Since that Coke machine, everyday objects have become increasingly networked into the growing IoT. That includes everything from wearable heart monitors to smart fridges that tell you when you’re low on milk. IoT devices often run on microcontrollers — simple computer chips with no operating system, minimal processing power, and less than one thousandth of the memory of a typical smartphone. So pattern-recognition tasks like deep learning are difficult to run locally on IoT devices. For complex analysis, IoT-collected data is often sent to the cloud, making it vulnerable to hacking.

“How do we deploy neural nets directly on these tiny devices? It’s a new research area that’s getting very hot,” says Han. “Companies like Google and ARM are all working in this direction.” Han is too.

With MCUNet, Han’s group codesigned two components needed for “tiny deep learning” — the operation of neural networks on microcontrollers. One component is TinyEngine, an inference engine that directs resource management, akin to an operating system. TinyEngine is optimized to run a particular neural network structure, which is selected by MCUNet’s other component: TinyNAS, a neural architecture search algorithm.

System-algorithm codesign

Designing a deep network for microcontrollers isn’t easy. Existing neural architecture search techniques start with a big pool of possible network structures based on a predefined template, then they gradually find the one with high accuracy and low cost. While the method works, it’s not the most efficient. “It can work pretty well for GPUs or smartphones,” says Lin. “But it’s been difficult to directly apply these techniques to tiny microcontrollers, because they are too small.”

So Lin developed TinyNAS, a neural architecture search method that creates custom-sized networks. “We have a lot of microcontrollers that come with different power capacities and different memory sizes,” says Lin. “So we developed the algorithm [TinyNAS] to optimize the search space for different microcontrollers.” The customized nature of TinyNAS means it can generate compact neural networks with the best possible performance for a given microcontroller — with no unnecessary parameters. “Then we deliver the final, efficient model to the microcontroller,” say Lin.

To run that tiny neural network, a microcontroller also needs a lean inference engine. A typical inference engine carries some dead weight — instructions for tasks it may rarely run. The extra code poses no problem for a laptop or smartphone, but it could easily overwhelm a microcontroller. “It doesn’t have off-chip memory, and it doesn’t have a disk,” says Han. “Everything put together is just one megabyte of flash, so we have to really carefully manage such a small resource.” Cue TinyEngine.

The researchers developed their inference engine in conjunction with TinyNAS. TinyEngine generates the essential code necessary to run TinyNAS’ customized neural network. Any deadweight code is discarded, which cuts down on compile-time. “We keep only what we need,” says Han. “And since we designed the neural network, we know exactly what we need. That’s the advantage of system-algorithm codesign.” In the group’s tests of TinyEngine, the size of the compiled binary code was between 1.9 and five times smaller than comparable microcontroller inference engines from Google and ARM. TinyEngine also contains innovations that reduce runtime, including in-place depth-wise convolution, which cuts peak memory usage nearly in half. After codesigning TinyNAS and TinyEngine, Han’s team put MCUNet to the test.

MCUNet’s first challenge was image classification. The researchers used the ImageNet database to train the system with labeled images, then to test its ability to classify novel ones. On a commercial microcontroller they tested, MCUNet successfully classified 70.7 percent of the novel images — the previous state-of-the-art neural network and inference engine combo was just 54 percent accurate. “Even a 1 percent improvement is considered significant,” says Lin. “So this is a giant leap for microcontroller settings.”

The team found similar results in ImageNet tests of three other microcontrollers. And on both speed and accuracy, MCUNet beat the competition for audio and visual “wake-word” tasks, where a user initiates an interaction with a computer using vocal cues (think: “Hey, Siri”) or simply by entering a room. The experiments highlight MCUNet’s adaptability to numerous applications.

“Huge potential”

The promising test results give Han hope that it will become the new industry standard for microcontrollers. “It has huge potential,” he says.

The advance “extends the frontier of deep neural network design even farther into the computational domain of small energy-efficient microcontrollers,” says Kurt Keutzer, a computer scientist at the University of California at Berkeley, who was not involved in the work. He adds that MCUNet could “bring intelligent computer-vision capabilities to even the simplest kitchen appliances, or enable more intelligent motion sensors.”

MCUNet could also make IoT devices more secure. “A key advantage is preserving privacy,” says Han. “You don’t need to transmit the data to the cloud.”

Analyzing data locally reduces the risk of personal information being stolen — including personal health data. Han envisions smart watches with MCUNet that don’t just sense users’ heartbeat, blood pressure, and oxygen levels, but also analyze and help them understand that information. MCUNet could also bring deep learning to IoT devices in vehicles and rural areas with limited internet access.

Plus, MCUNet’s slim computing footprint translates into a slim carbon footprint. “Our big dream is for green AI,” says Han, adding that training a large neural network can burn carbon equivalent to the lifetime emissions of five cars. MCUNet on a microcontroller would require a small fraction of that energy. “Our end goal is to enable efficient, tiny AI with less computational resources, less human resources, and less data,” says Han.

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Trump rolls back efficiency standards for fast-cycle dishwashers, pointing to long wash times

A Department of Energy final rule will roll back efficiency requirements on dishwashers with a short cycle.

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The Trump administration has advanced easier dishwasher regulations that exempt fast-cleaning machines from decades-old rules, a rollback lauded by the president at a recent Nevada campaign rally.

The Department of Energy final rule creates a separate “product class” for dishwashers with a short cycle, classifying that setting as the “normal” cycle and setting no limit on energy or water use.

Related: With Trump’s planned rollback, it could soon cost more energy to wash Thanksgiving dishes

Critics of the change said the regulatory shift will do nothing to improve today’s machines, which perform far more effectively than older models even while using less energy and water.

“I called up a great dishwasher company from Ohio — that we saved, by the way. I said, ‘What’s the problem with your dishwasher?’” the president told a Nevada crowd during a campaign stop this month. “‘Well, they don’t give us any water… It’d be nice to be able to get enough water.’”

Read: Markets are driving shift to green energy away from oil and gas dependence regardless of election winner — the difference is how fast

“President Trump has once again made good on his promise to free Americans from ludicrous government regulations — this time bringing a common-sense reform to dishwashers,” Russ Vought, the director of the Office of Management and Budget, told Real Clear Politics.

The administration cited data on dishwasher cycle times, compiled by the Competitive Enterprise Institute, showing that the average wash time had risen from just under an hour to nearly three hours by 2018, with the increase in time due to diminished water use in cleaning cycles.

But some consumer groups felt differently.

“The president and the Department of Energy have given two completely different reasons for why this rule is needed, and neither makes sense,” said Steven Nadel, executive director of the American Council for an Energy-Efficient Economy.

“President Trump says dishwashers don’t work as well as they used to, yet tests find that today’s models clean far better than the old ones,” Nadel said. “The Department of Energy says consumers need options for quick cycles, but those are already ubiquitous. In the end, this rule will neither make dishwashers perform better nor offer quicker cycles.”

Dishwasher water and energy use have declined by more than 50% over the past three decades because of federal standards and manufacturer innovations. Congress set the first energy efficiency standard for dishwashers in 1987; the governing body has updated it once and the Department of Energy has updated it twice since then, most recently in 2012.

Cleaning performance has improved in the same period. Product reviewers at Consumer Reports said in 2018 that “[n]ew models clean better and more quietly” and “already do such a good job at cleaning that new features don’t often change our test results much.”

Read: Can Burger King’s reusable packaging change fast food forever?

The new product class could encourage new dishwashers with short “normal” cycles that use far higher amounts of energy and water but don’t make dishes any cleaner.

“They’re declaring a fix to a problem that never existed,” said Andrew deLaski, executive director of the Appliance Standards Awareness Project.

“And while the Department has been wasting time and taxpayer money making pointless and illegal rule changes, it’s been flouting the law by missing one legal deadline after another for reviewing and updating other efficiency standards,” he said. “Energy Department officials keep telling Congress that they’re focused on meeting legal deadlines and prioritizing standards that will save the most energy, but this dishwasher rule does neither.”

DOE issued a similar proposal in August that would eliminate existing standards for clothes washers and dryers with a short cycle as the “normal” cycle. Both the just-announced rule and the proposal for washers and dryers would violate the appliance standards law’s “anti-backsliding” provision prohibiting DOE from weakening standards, American Council for an Energy-Efficient Economy said.

The administration has also taken steps to allow for stronger water flow from shower heads, with Trump in August saying from the White House grounds that increased pressure is key for his own hair needs.

Read: Trump wants ‘perfect’ hair from stronger water flow in the shower — his Energy Department delivers

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Diminished role for councillors or greater efficiency?


There is some resistance to the efficiency broom being swept through council by CEO Robert Jennings and Director of Corporate Services Sabine Taylor, who both came to their positions late last year.

Example: Councillors have voted with their feet (metaphorically) on a recently introduced measure for managing conflicts of interest. It requires councillors to submit a list of meetings they have attended that could potentially represent a risk for conflict of interest.

For the month of July non-compliance in reporting was total.

In the preceding months of June and May, a majority failed to comply.

However, on the more significant restructure of council meetings, the resistance is more muted.

CEO Jennings is proposing that Standing Committee Meetings – the less formal sort that are held mid-month, with free-flowing debate  – no longer be held at all.

Cr Eli Melky

Instead there would be two Ordinary Meetings a month. These are the meetings bound by tighter meeting protocol where decisions are voted on, giving them formal effect.

The two Ordinary meetings would be time-restricted, starting at 5.30pm, finishing no later than 8.30pm. This would include the time allowed for dealing with confidential business. 

Mr Jennings wants to start this new regime on 14 September.

It would be accompanied by a very significantly reduced frequency for reporting by council directorates and for updates on major projects and strategy.

He proposes a twelve-week cycle for such reports, commencing on 28 September. Financial reports would still be made monthly.

It’s not hard to see how this might be more efficient – and less exhausting than the often protracted meetings to date – but might also see a significant shift in power to the directorate.

A sign of this shift being set in motion already has been seen in recent meetings when councillors have raised issues on behalf of constituents and been told crisply that the matter is operational and therefore will not be further discussed. 

Constituent advocacy has been a feature of what elected members can do in all the time I have reported on council. Now Mr Jennings is telling councillors that there may be new ways of engaging with the community, which he will announce soon.

Cr Jacinta Price 

On the restructure, just one councillor, Eli Melky, saw it as a possible erosion of “transparent democratic local governance”.

He assured the CEO that he didn’t want to interfere with running operations, he didn’t want to “micro-manage”, but he was concerned about the limits to “free and fluent discussion” and the “opportunity for good solid debate” represented by the restructure.

The rules of debate for Ordinary Meetings allow each councillor to speak only once. With the support of a seconder, a councillor can move to take the meeting out of “standing orders” (those debate rules), but if you are a sole dissenting voice, that is obviously not possible.

On occasion Cr Melky has been in exactly that position, but by doggedly sticking to his guns – and sometimes being exhausting, as I have reported – he has eventually won support for significant change.

Cr Melky also argued that the Standing Committees structure provides “leadership experience” for the different councillors elected to chair the committees (at present he chairs Technical; Cr Glenn Auricht, Corporate; and Deputy Mayor Jimmy Cocking, Community Development). This experience would disappear, with the Mayor, and occasionally Deputy Mayor, doing all the chairing of Ordinary Meetings.

Cr Glenn Auricht

Cr Auricht said he made “valid points” but noted that councillors also get to chair a number of advisory committees, though that’s “a little but different to chairing Standing Committees”.

DM Cocking accepted that the way council carries out its business needs reform. For instance, he could see the need to free up staff up for implementation rather than excessive reporting. But he argued for some “co-design” in the process: it would be good to workshop the restructure.

At the least the CEO’s model could be trialled until the end of the year and then reviewed, he said.

Cr Melky also wanted time for the four councillors who have at present resigned to contest the NT election, to return to the chamber and have a say on the proposals.

Cr Jacinta Price was happy with the CEO’s proposals, but suggested that meetings be shifted to Tuesdays, rather than Mondays, allowing more time for elected members to digest their meeting papers. 

Mayor Jamie de Brenni, wearing a suit for this first meeting in his new role, was also supportive, noting that this meeting model has been adopted by Palmerston Council, increasing their productivity by a third.

Deputy Mayor Jimmy Cocking

Mr Jennings emphasised departmental approval of his approach and identified the source of his power to make the change: the by-law that gives the CEO the authority to set the agenda for council meetings.

The main advantages of the change would be “safety” – meaning the protection of all involved from the exhaustion of extended meetings – and being able to move through agenda items more quickly “to get things done for the community”.

He also expressed his preference, rather than going to a forum with councillors to hash out detail, for the changes as he has proposed them proceed to this month’s Ordinary Meeting.

Only if they are not backed by a majority vote would he then make them the subject of a forum to “demystify” them.

With Mayor de Brenni and Crs Price and Auricht essentially backing the proposals, that course of action looks likely to give the CEO what he wants.


In other council news, businesses have taken up council’s waiver of rates, under Covid-19 related hardship schemes,  to the tune of  $106,821.60 as of 30 July 2020.

Residents’ demand for waivers has been much weaker, totalling $4,175.67.

The proportion is similar for deferrals: an amount of $173,639.07 has been deferred for businesses; $5,485.76 for residents.

The cost to council of commercial concessions is offset by the NT Government’s Special Community Assistance and Local Employment (SCALE) Program, while the assistance to residents comes from council’s own $5m Covid-19 Reserve, with $1m allocated for non-commercial hardship.


There is no movement on the relocation of the Hartley Street solar lights. The officer’s report for last night’s meeting says that the NT Government is working with Technical Services “to develop

options for the relocation of the solar lights due to the CBD Revitalisation Project earmarking a shade structure to be constructed over the area. The project is estimated to commence in 12 to 18 months.”

Photo at top: CEO Robert Jennings speaking to councillors via Zoom, with Mayor Jamie de Brenni and Director Sabine Taylor in council’s conference room.

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