“The pandemic has changed the industry”

The Corona pandemic has posed major challenges to people around the world and has had a huge impact on all areas of the global economy. Rolf Eiten, President & CEO, Clark Europe, explains how the company is responding to the challenges it faces and how Covid-19 has changed the everyday business of the materials handling vehicle manufacturer.

We have been dealing with the Covid 19 pandemic for more than a year and a half. What has changed in Clark’s day-to-day business?

Home office and virtual collaboration have now become firmly established in our company. And we also communicate with our business partners or colleagues within the Clark organisation via video conference. The project work of our teams is done via digital whiteboards and we use virtual presentation platforms for training our sales partners. The Corona pandemic has shown us that virtual exchange works well and that we can save time and resources with it. However, it does not completely replace face-to-face events in the long run. Especially the cooperation with our business partners and customers thrives on personal contact on site.

How would you describe the economic situation of your company?

After the order situation was very poor in the 1st half of 2020 due to the Corona crisis, it picked up again strongly in the 2nd half of 2020, so that we were able to close the 2020 financial year with growth of just over 3% compared to the previous year. In the first half of 2021, the order situation is better than ever. The market for materials handling vehicles in our market region increased by 70% in 2021 compared to the previous year.

Globally, Clark was even able to increase the order volume by 50% compared to the result of the previous year. In the EMEA region – that is, in Europe, the Middle East and Africa – we were able to increase our order intake by 80% compared to the previous year. I don’t think anyone expected the market to pick up so much speed during the Corona crisis. One downer, however, is that we, like many of our competitors, have problems in the supply chain and in the procurement of components, as many vendor parts come from Asia.

The consequence for our customers is that they have to wait much longer for the delivery of vehicles. However, the supply chain is not the only challenge the market is currently facing. In addition, there is a drastic increase in freight costs, delivery delays due to bottlenecks in sea freight and the constantly rising prices of raw materials. The industry is currently operating in a very difficult environment.

Is the fact that for years not only the procurement of supplier parts, but also entire production processes were relocated abroad, and in particular to Asia, now falling on the manufacturers’ feet?

Trade relations between Europe and Asia have become stronger and stronger over the years. Companies have not only placed orders in the Far East, but also transferred technology. Today, there is almost no company that does not buy material from China. So, we are more or less facing the same dilemma here. I believe that the Covid 19 pandemic is showing us quite clearly how great the danger of this dependence on Asia really is, which we have all accepted so far – and yes, now it is falling on our feet that we have outsourced production to Asia in order to save costs. Now we are paying dearly in every respect.

What measures have you taken to at least counteract this?

To secure our supply chain, we ordered machines for our own stock at an early stage. In addition, we have taken a number of measures in conjunction with the Clark factories worldwide and the transporters: We visited our suppliers to get an idea of the situation on site and to find out how we can support them to speed up material procurement. For example, we have ordered parts in advance to help suppliers, changed suppliers to reduce costs and looked for new carriers to speed up deliveries. At the moment, therefore, production is running in all our factories and the supply chains are also largely intact. It just takes a little longer.

Do you now regret that the assembly production in Duisburg is not yet up and running?

No, on the contrary! I am glad that this project has been put on hold for the time being. Assembly production in Duisburg would not significantly change the current situation, because 95% of the parts would come from the Far East in a modular system. We would be in exactly the same dependency and would perhaps only have more flexibility in assembling the units.

Do you now use the capacities created for assembly production elsewhere?

Absolutely! We have put the focus on the used equipment business and the expansion of our direct business in the Duisburg area.

That means you use the free workshop capacities for reconditioning used equipment?

Exactly. Last year, we started buying used vehicles on a trial basis in order to learn how to refurbish used vehicles and to develop quality standards. Among other things, we invested in the existing paint shop and modernised and upgraded it. We can now paint equipment using the wet paint process, which makes the paintwork much higher quality and more durable. This means that the customer can count on very high-quality vehicles with Clark used vehicles. The first reconditioned vehicles were practically snatched out of our hands. Now we are expanding this business area bit by bit. Since visits to customers have become possible again, our direct business has also picked up strongly. So, we can’t complain.

Clark is also active in expanding the product range? In the field of warehouse technology, the range of vehicles with lithium-ion technology has been expanded?

Yes, that is correct. We expanded our range in the low-platform truck sector last year with the WPio series with load capacities of 1.2 to 2t. Operators will be able to benefit from the advantages of the lithium-ion battery with Clark in future: such as easy handling and freedom from maintenance, the high energy density with correspondingly high driving performance, fast intermediate charging without limiting the service life as well as gassing-free recharging of the battery.

With the COP1, we have brought an order picker to the market that is not only extremely efficient in picking goods, but also – thanks to the elevating work platform – proves itself as a rolling ladder or working platform. We have also expanded the range of hand pallet trucks with the HPT Eco and HPT Premium. Both trucks are characterised by high stability and easy operation and have a load capacity of 2500kg – the HPT Eco is designed for lighter and the HPT Premium for more demanding applications.

Has something also changed with Clark engine technology?

Absolutely! In 2020, Clark has launched a Kubota Stage 5 diesel engine with diesel particulate filter and diesel oxidation catalytic converter for the diesel forklift trucks of the C40-55sD and C60-80D900 series with load capacities of 4 to 8t. This opens up areas of application for Clark diesel forklift trucks that were denied to them in the past due to the lack of a diesel particulate filter.

You also presented a new solution for production logistics?

Correct. Clark brought a tugger train system onto the market at the beginning of the year. With this all-in-one solution, we have specifically addressed the specific requirements of industrial customers. The tugger train consists of the Clark CTX40-70 tractor with a trailer load of 4 or 7t as well as the CTR01 and CTR02 trailers. The CTR01 trailer, designed as a single U-frame, is designed for customers who mainly have to transport Euro pallets. It can accommodate a mobile trolley on which Euro pallets with a maximum load of 1200kg can be transported.

The Double U-Frame trailer CTR02 can accommodate both Euro pallets and other types of pallets commonly used in industry. Via a movable centre beam, the operator can flexibly choose between a trolley with a size of 1600 x 1000mm and a payload of up to 1600kg or two trolleys with a load weight of 800 kg each. We are currently realising a new customer project in this area. For this purpose, we have expanded our range of tugs to include vehicles with a load capacity of 3t.

3T wraps up new contract with PFF Group

Leicester-based 3T Logistics and Technology Group has recently been selected by one of the UK’s leading food packaging manufacturers to streamline its customer deliveries and help reduce the company’s carbon footprint.

Food packaging manufacturer PFF Group has recently invested in a new transport management system to supply packaging products to supermarkets and food manufacturers across the UK. With increasing changes to its logistics needs due to the continuing pandemic, PFF Group selected 3T as the ideal partner to help it fulfil its growing supply chain requirements.

PFF Group currently also manufactures disposable PPE aprons which are despatched from the company’s Washington site to Department of Health and Social Care depots and distributed for use by NHS frontline health and social care workers. This will be an important element of the new transport management system which is based on and delivered by 3T Logistics and Technology Group’s cloud-based software.

3T has a proven track record and enviable reputation in the logistics sector for its supply chain and transport management solutions. PFF Group will benefit from this expertise through the platform’s capability to consolidate loads and optimise vehicle usage, thereby reduce journeys and lower carbon emissions.

Lee Wilkinson, group supply chain manager (pictured, left, with Dan Mahan, transport planner) at PFF, said: “We work with a pool of hauliers and the software enables us to select the best carriers for locations where we can do multiple drop deliveries. By making sure we have filled the vehicles, we are reducing costs and the environmental impact as the amount of carbon per pallet delivered is reduced. The new system also tracks delivery lorries to ensure they arrive on time and manages proof of delivery. This means we are improving service to customers who are increasingly seeking suppliers with reduced carbon usage.”

Rob Hutton, sales and marketing director at 3T Logistics and Technology Group, believes the new partnership with PFF Group will see a reduction not only in costs for PFF Group but also in its environmental impact.

“We see the introduction of our TMS solution as a critical step in PFF’s digital transformation. PFF has a vision of reducing carbon footprint and with our transport management system, we will automate and optimise PFF’s transport operations, giving the firm full control of its transport management and bringing greater levels of transparency and visibility.

“The collaboration between 3T and PFF showcases two fast-growing British businesses with a global outlook that are using technology to drive efficiency and innovation. We look forward to working with PFF and providing the logistics solutions it needs to scale their business and further accelerate growth.”

3T is working with a number of high-profile companies across industry verticals, which value a logistics partner that can help them achieve their service, cost and environmental targets whilst digitising their supply chain.

3T wraps up new contract with PFF Group

Leicester-based 3T Logistics and Technology Group has recently been selected by one of the UK’s leading food packaging manufacturers to streamline its customer deliveries and help reduce the company’s carbon footprint.

Food packaging manufacturer PFF Group has recently invested in a new transport management system to supply packaging products to supermarkets and food manufacturers across the UK. With increasing changes to its logistics needs due to the continuing pandemic, PFF Group selected 3T as the ideal partner to help it fulfil its growing supply chain requirements.

PFF Group currently also manufactures disposable PPE aprons which are despatched from the company’s Washington site to Department of Health and Social Care depots and distributed for use by NHS frontline health and social care workers. This will be an important element of the new transport management system which is based on and delivered by 3T Logistics and Technology Group’s cloud-based software.

3T has a proven track record and enviable reputation in the logistics sector for its supply chain and transport management solutions. PFF Group will benefit from this expertise through the platform’s capability to consolidate loads and optimise vehicle usage, thereby reduce journeys and lower carbon emissions.

Lee Wilkinson, group supply chain manager (pictured, left, with Dan Mahan, transport planner) at PFF, said: “We work with a pool of hauliers and the software enables us to select the best carriers for locations where we can do multiple drop deliveries. By making sure we have filled the vehicles, we are reducing costs and the environmental impact as the amount of carbon per pallet delivered is reduced. The new system also tracks delivery lorries to ensure they arrive on time and manages proof of delivery. This means we are improving service to customers who are increasingly seeking suppliers with reduced carbon usage.”

Rob Hutton, sales and marketing director at 3T Logistics and Technology Group, believes the new partnership with PFF Group will see a reduction not only in costs for PFF Group but also in its environmental impact.

“We see the introduction of our TMS solution as a critical step in PFF’s digital transformation. PFF has a vision of reducing carbon footprint and with our transport management system, we will automate and optimise PFF’s transport operations, giving the firm full control of its transport management and bringing greater levels of transparency and visibility.

“The collaboration between 3T and PFF showcases two fast-growing British businesses with a global outlook that are using technology to drive efficiency and innovation. We look forward to working with PFF and providing the logistics solutions it needs to scale their business and further accelerate growth.”

3T is working with a number of high-profile companies across industry verticals, which value a logistics partner that can help them achieve their service, cost and environmental targets whilst digitising their supply chain.

Telematics overhaul needed to combat driver shortages

As the UK government continues to hold talks with hauliers over driver shortages which are affecting critical supply chains, Trakm8 is urging logistics firms to redouble efforts to drive up efficiency in existing fleets.

The logistics sector has been hit by two crises almost simultaneously; the UK’s exit from the EU’s freedom of movement arrangements and the COVID-19 pandemic, either one of which would have introduced significant headwinds on their own. Indeed, the two crises combined have led to a shortage of up to 100,000 drivers.

Trakm8 welcomes efforts to increase fleet capacity, but as recruitment efforts remain difficult, it is equally important to increase the productivity of existing fleets.

Peter Mansfield, Group Marketing Director at Trakm8, explains: “With a shortfall this significant, initiatives to find and employ new drivers are necessary, but do not go far enough. As specialists in scheduling and optimisation, we have seen time and time again how deploying a route optimisation platform can lead to more deliveries being made with the same assets, and help businesses get more out of their existing fleet.

“These benefits are often significant. Indeed, in real terms Trakm8 Optimisation has provided a 30% increase in delivery driver productivity and a 10% reduction in fuel expenditure to one of the UK’s leading supermarket chains. Of course, the system’s other benefits, such as a reduction in costs due to accidents and servicing, can free up expenditure to allow for a larger overall fleet.

“If we are to ensure that critical supplies are to be delivered and our supermarket shelves will be stocked as we expect, every business must look at its existing supply chains and ensure they are using the technology on offer to maximize their fleet’s potential.

Vision-guided robotics & AI: a guide for the non-technical

The automation industry is experiencing an explosion of growth and technology capability. To explain this complex technology, we use terms such as “artificial intelligence” to convey the idea that solutions are more capable and advanced than ever before. If you are an investor, business leader, or technology user who is keen to understand the technologies you are investing in, this article will help you gain a well-rounded view on vision-guided robotics and enable you to make informed decisions.

Types of vision systems used in warehousing and distribution environments

There are three primary applications of vision systems used in warehousing and distribution environments:

Inspection and mapping

Vision systems for inspection are used in a variety of industrial robot applications, providing outputs of “pass/fail”, “present/not present”, or a measurement value. The result dictates the next step in a process.

Mapping systems, on the other hand, are less frequently used but are similar to inspection systems in that vision maps do not directly translate into machine action. Both systems can be very sophisticated, but they do not require deep learning or artificial intelligence.

Pick-and-place without deep-learning

Pick-and-place vision systems with limited variables are deployed on most robotic cells installed today. The cameras direct the robot’s motion through closed-loop feedback, enabling the robots to operate very quickly and accurately, within their prescribed parameters. These systems do not have a “learning loop” but are instead pre-programmed for a fixed set of objects and instructions. While these systems are “smart”, they do not add intelligence or learning over time.

Pick-and-place with deep-learning

The most sophisticated vision systems employ “deep learning”, also referred to as “artificial intelligence”. However, many non-learning systems are marketed as if they have intelligent (learning) capability, leading to confusion. The deep-learning algorithms, subset of artificial intelligence, learn features that are invariant of the objects, in order to generalise over a wide spectrum of objects.

For example, through such algorithms, robots can recognise the edge of an object no matter the exposure of the camera or the lighting conditions. Something as simple as a change in lighting could affect the results and that is the reason behind deep-learning systems not relying on a single variable like colour.

It is crucial to note that all three types of vision systems include three main elements: an input (camera), a processor (computer and program), and an output (robot). They may use similar cameras and robots, but the difference lies in the program.

Basic building blocks for deep-learning systems

Vision-guided robots using deep-learning algorithms for industrial applications recognise various types of packaging, location, and other variables (e.g. overlapping items) and act based on those variables. Compared to self-driving cars, some variables for industrial robots are not as complex, but the underlying approach to learning and responding quickly is the same.

There are three co-dependent requirements for deep-learning solutions:

  • Computer processing power
  • High-quality and varied data
  • Deep-learning algorithms

Fizyr has optimised these three elements required for deep-learning systems.

Deep-learning vision systems for vision-guided industrial robots

Commercial applications using robots to pick, place, palletise, or de-palletise in a warehouse environment require three basic building blocks: cameras, software, and robots. The cameras and robots are the eyes and arms; the software is the brain. All three components must work together to optimise system performance.

Camera technology enables the flow of high-quality data. Cameras and post-image processing provide a stream of data ready for the deep-learning algorithm to evaluate. Some cameras are better suited for an application, but that itself is not what makes a vision-guided robot capable of deep learning. The camera supplies data but does not translate data into actionable commands.

This is where the role of software comes in, which is the deep-learning algorithm – data in from cameras, process, results out to robots.

The robot and end-effector (a.k.a. gripper) also play a critical role in system performance. They must provide the level of reach, grip-strength, dexterity, and speed required for the application. The robot and end-effector respond to commands from the deep-learning algorithm.

Conclusion

To summarise, there are three points to remember about artificial intelligence and vision-guided robotic systems:

  • Deep-learning algorithms classify data in multiple categories
  • Deep-learning algorithms require both high-quality and varied data
  • Algorithms become more powerful over time

Latest developments in camera technology and computer processing power serve as building blocks to advanced deep-learning software that improves robot performance. The future has arrived!

Read the full article here

 

 

 

Vision-guided robotics & AI: a guide for the non-technical

The automation industry is experiencing an explosion of growth and technology capability. To explain this complex technology, we use terms such as “artificial intelligence” to convey the idea that solutions are more capable and advanced than ever before. If you are an investor, business leader, or technology user who is keen to understand the technologies you are investing in, this article will help you gain a well-rounded view on vision-guided robotics and enable you to make informed decisions.

Types of vision systems used in warehousing and distribution environments

There are three primary applications of vision systems used in warehousing and distribution environments:

Inspection and mapping

Vision systems for inspection are used in a variety of industrial robot applications, providing outputs of “pass/fail”, “present/not present”, or a measurement value. The result dictates the next step in a process.

Mapping systems, on the other hand, are less frequently used but are similar to inspection systems in that vision maps do not directly translate into machine action. Both systems can be very sophisticated, but they do not require deep learning or artificial intelligence.

Pick-and-place without deep-learning

Pick-and-place vision systems with limited variables are deployed on most robotic cells installed today. The cameras direct the robot’s motion through closed-loop feedback, enabling the robots to operate very quickly and accurately, within their prescribed parameters. These systems do not have a “learning loop” but are instead pre-programmed for a fixed set of objects and instructions. While these systems are “smart”, they do not add intelligence or learning over time.

Pick-and-place with deep-learning

The most sophisticated vision systems employ “deep learning”, also referred to as “artificial intelligence”. However, many non-learning systems are marketed as if they have intelligent (learning) capability, leading to confusion. The deep-learning algorithms, subset of artificial intelligence, learn features that are invariant of the objects, in order to generalise over a wide spectrum of objects.

For example, through such algorithms, robots can recognise the edge of an object no matter the exposure of the camera or the lighting conditions. Something as simple as a change in lighting could affect the results and that is the reason behind deep-learning systems not relying on a single variable like colour.

It is crucial to note that all three types of vision systems include three main elements: an input (camera), a processor (computer and program), and an output (robot). They may use similar cameras and robots, but the difference lies in the program.

Basic building blocks for deep-learning systems

Vision-guided robots using deep-learning algorithms for industrial applications recognise various types of packaging, location, and other variables (e.g. overlapping items) and act based on those variables. Compared to self-driving cars, some variables for industrial robots are not as complex, but the underlying approach to learning and responding quickly is the same.

There are three co-dependent requirements for deep-learning solutions:

  • Computer processing power
  • High-quality and varied data
  • Deep-learning algorithms

Fizyr has optimised these three elements required for deep-learning systems.

Deep-learning vision systems for vision-guided industrial robots

Commercial applications using robots to pick, place, palletise, or de-palletise in a warehouse environment require three basic building blocks: cameras, software, and robots. The cameras and robots are the eyes and arms; the software is the brain. All three components must work together to optimise system performance.

Camera technology enables the flow of high-quality data. Cameras and post-image processing provide a stream of data ready for the deep-learning algorithm to evaluate. Some cameras are better suited for an application, but that itself is not what makes a vision-guided robot capable of deep learning. The camera supplies data but does not translate data into actionable commands.

This is where the role of software comes in, which is the deep-learning algorithm – data in from cameras, process, results out to robots.

The robot and end-effector (a.k.a. gripper) also play a critical role in system performance. They must provide the level of reach, grip-strength, dexterity, and speed required for the application. The robot and end-effector respond to commands from the deep-learning algorithm.

Conclusion

To summarise, there are three points to remember about artificial intelligence and vision-guided robotic systems:

  • Deep-learning algorithms classify data in multiple categories
  • Deep-learning algorithms require both high-quality and varied data
  • Algorithms become more powerful over time

Latest developments in camera technology and computer processing power serve as building blocks to advanced deep-learning software that improves robot performance. The future has arrived!

Read the full article here

 

 

 

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