Industry 4.0

Industry 4.0 | an overview

Which technology makes the Internet of Things possible for industry

In this guide we give a comprehensive introduction to Industry 4.0 and the Internet of Things (IoT). Take a look with us at past technological developments and the challenges of tomorrow!
Industry 4.0

contents

  • What is the Internet of Things?
  • What separates the IIoT from the IoT?
  • What exactly is the difference between Industry 4.0 and the Internet of Things?
  • What is the Industrial Internet of Things currently being used for?
  • Why switching to Industry 4.0 is worthwhile
  • What challenges does Industry 4.0 face?
  • IIoT networks and protocols
  • IIoT data protocols
  • frequently asked Questions

What is the Internet of Things?

In short, it is the general term for connecting devices, much more than just PCs, smartphones or other telecommunications devices, to the Internet. These are often referred to as “smart devices” or smart devices, such as fitness trackers or voice assistants.
The abbreviation IoT ( Internet of Things ) is also common in German and English. The term has been on everyone’s lips for years and the industrial sector, in particular, has long since discovered it for itself.

What does IIoT mean?

This brings us to the so-called Industrial Internet of Things, which is often equated with Industry 4.0. The IIoT takes the concept of internet-connected devices and extends it to factories, manufacturing and industrial plants to share data quickly from near or far. For example, sensors can collect information and transmit it to the local network via a gateway and from there upload it to a cloud server so that you can access it from anywhere at any time. Incidentally, the direct, automatic control of networked devices is also referred to as a cyber-physical system.

What separates the IIoT from the IoT?

The main difference between IoT and Industry 4.0 lies in the application. While the former is for comfort, health and entertainment, the latter is all about collecting and processing sensor data in real-time to increase efficiency, optimize processes and save costs. It is not for nothing that one also speaks of a fourth industrial revolution.
The Industrial Internet of Things is based on a well-known form of computer-aided control, the distributed control system, which connects several autonomous devices and assigns them functions. These devices are thus able to continue to adjust and optimize the section of the production line they monitor independently, without the risk that a single fault in the system will bring the entire production to a standstill, as is the case with a centrally regulated controller would. The IIoT takes advantage of modern cloud computing to enable data sharing, visualization, and analysis—all in near real-time.
Within a few years, Industry 4.0 has developed into a huge sector. More than 60 per cent of global manufacturers now use IIoT-Tec.

What exactly is the difference between Industry 4.0 and the Internet of Things?

Although the terms Industry 4.0 and Internet of Things are sometimes used interchangeably, they are not synonymous. In fact, the Internet of Things and IIoT are part of Industry 4.0.
Industry 4.0 is generally used to describe the accelerated use of all advanced automation technologies available to industry and intelligent manufacturing today and the resulting benefits. The key components are:

  • Machine-to-Machine Communication (M2M)
  • Implementation of autonomous systems
  • Seamless cloud computing
  • Artificial intelligence and related ‘cognitive’ technologies such as image recognition

The history of the IoT and Industry 4.0

The Internet of Things may seem like a very modern concept but in fact some of the core technologies that make up Industry 4.0 date back to the 1960s. As early as 1968, programmable logic controllers (PLC’s) – essentially early industrial computers – were developed to fine-tune the manufacturing process. From the 1970s, the first industrial process control systems appeared, which gradually supplemented the manual work in the factories.
The Internet of Things as we know it today first came into focus in the following decade. In the early 2000s, the IoT gradually left research institutions such as universities and laboratories to reach the end-user. The development of enabling technologies such as Bluetooth, Near Field Communication (NFC) and 3G cellular networks accelerated the growth of this market. At the beginning of the millennium, cloud computing technologies, in particular, favoured the development of the IIoT.

What exactly does Industry 4.0 mean?

The term “Industry 4.0” first appeared in public at the Hanover Fair in 2011 to describe the use of information technology in production. The neologism was intended to place the impact of modern technologies on automation and data exchange in the wake of earlier industrial revolutions. These are:

  • The development of steam and water-powered manufacturing technology in the second half of the 18th and the first half of the 19th century,
  • The use of electrical energy, especially in connection with assembly line work between about 1870 and the beginning of the First World War,
  • The third, so-called digital revolution, with the creation of modern IT in the second half of the 20th century and the developments already described above.

What is the Industrial Internet of Things currently being used for?

Industry 4.0 can bring a variety of benefits to a wide range of industries and sectors, including:

  • Smart production facilities and buildings
  • Supply chain and inventory optimization
  • data analysis
  • condition monitoring

The IoT has already found its way into various sectors, of which pure production is by far not the only sector that can benefit from Industry 4.0. The energy industry and retail can also participate in the revolution thanks to ever-smaller smart devices and intelligent solutions.

Why switching to Industry 4.0 is worthwhile

Despite the boom, not everyone is aware of the concrete benefits Industry 4.0 is supposed to bring. Therefore, we would like to give some examples below of how the manufacturing industry has benefited from the implementation of IIoT so far:

  • Production line optimization: Industrial IoT sensors enable continuous monitoring of the production line from start to finished product. This allows operators to continuously fine-tune the manufacturing process, saving time and money.
  • Inventory and Supply Chain Management: Manufacturing depends on the delivery of raw materials and components. ⦁ Radio Frequency Identification ( RFID) tags and similar wireless technologies enable real-time tracking of components and shipments from site to site, making inventory and reconciliation monitoring much easier.
  • Packaging assessment: Industrial IoT sensors enable manufacturers to monitor the condition of packaging during transport and storage, and even assess how customers typically interact with it. The data submitted is extremely valuable because it allows for design improvements.
  • Real-time manufacturing data: By transmitting operational data, suppliers can remotely manage the factory units at any time, conveniently.
  • Maintenance data: Smart devices and sensors can issue alerts as soon as an error occurs and maintenance work is required. In the same way, malfunctions or the exceeding of limit values, such as excessive operating temperatures or excessive vibrations, can be reported. In this way, maintenance can be planned in advance, downtime can be minimized and the risk of accidents can be significantly reduced. When combined with health and safety records, such sensor data can contribute even more to safety.
  • Quality Control: Combining IIoT data from various sources, including suppliers, manufacturing processes and end-users, provides a more comprehensive picture that can be used to drive overall improvements from production and delivery processes to optimized user experience.

What challenges does Industry 4.0 face?

Industry 4.0 is basically an interaction of several network technologies. The three main challenges can therefore be summarized as follows:

  • The selection of strong signal networks, both wireless and wired
  • Adoption of standardized protocols, e.g. OPC UA
  • Network security vigilance to ward off any cyber threats

The technological requirements such as procurement of the devices are therefore the least of the problems in the transition, but there is a high demand for uninterrupted connectivity. In addition, an understanding of IT security and data storage when implementing IoT in industrial operations is essential to ensure smooth and efficient implementation.

What are the risks of the industrial Internet of Things?

As with any other digital solution, cyber security is critical for the IIoT, but with the appropriate precautions such as staff training and encryption of data transmissions, these risks can be minimized.
With this in mind, it is important to stay current with the latest technologies and updates. So you can be sure that you are always keeping up with the new developments regarding Industry 4.0 and derive the greatest benefits from them.

IIoT networks and protocols

Like any other information technology, the Industrial IoT uses a variety of protocols (data communication formats) and network types. Therefore, it is important to get clarity about each individual protocol when planning to create an IIoT infrastructure for your production facilities.

IIoT networks: how to choose the right hardware!

Internet-enabled devices each use different technologies for networks. Which of these offers the best solution depends on a number of factors, such as the distances to be bridged, the amount of data to be transmitted, the location and power consumption.
New networks are constantly being added to the list of networks suitable for Industry 4.0 and IoT. We have compiled the currently most important ones for you:

WLAN

Both in private households and in the industrial sector, WLAN is the common radio transmission standard for PCs, smartphones, tablets and more. WLAN networks are integrated into networks via routers, similar to wired Ethernet networks. Most devices use the 802.11 standards defined by the IEEE Association (Institute of Electrical and Electronics Engineers), also known as Wi-Fi.

Bluetooth

Bluetooth is a connection standard developed by the Bluetooth Special Interest Group, an interest group of more than 34,000 companies, and is also widely used in the consumer sector. It is based on ultra-high-frequency radio waves (between 2.402 GHz and 2.480 GHz) with a relatively short range. The advantage is the extremely interference-free radio transmission. It is, therefore, suitable for a number of different applications.

Zigbee

Zigbee is one of the leading protocols for connecting smart devices. This is a low-power network that is widely used, especially in industry. It is related to the Dotdot protocol developed by the same team and uses the IEEE 802.15.4 standard, which has a transmission range of up to 300 meters under ideal conditions. In buildings, it still reaches an impressive 75 to 100 meters. The current version 3.0 offers 128-bit encryption for secure data transmission.

LoRaWAN

LoRaWAN is the abbreviation for Lo ngRange Wide Area Network, an extremely energy-efficient MAC protocol with a transmission range of up to ten kilometres. It offers secure two-way connections over very large networks and can also be applied to digital radio transmission using FSK modulation.

Sigfox

The French telecommunications company Sigfox uses extremely low-power technology for a comprehensive network, similar to the Low Power Wide Area Network (LPWAN). In this way, small smart devices in continuous operation, such as electricity meters and smart-watches, can exchange data in a particularly efficient manner. The power consumption is only a thousandth of that of other radio technologies

IIoT data protocols

  • MQTT (Message Queue Telemetry Transport) is an open, low-power message protocol used to transfer simple data sets between sensors and applications. It is based on the common network protocol TCP/IP (Transmission Control Protocol/Internet Protocol).
  • AMQP (Advanced Message Queuing Protocol) is an internationally recognized open-source standard for transferring messages between devices.
  • OPC UA (OPC Unified Architecture) is an open M2M communication protocol that combines cross-platform shared data exchange in industrial automation with robust system interoperability.

Frequently asked Questions

Can the IIoT replace MES?

MES (Manufacturing Execution System) is an established hardware-based control system for complex manufacturing processes, typically used to ensure efficiency and improve productivity. This is a closed system. It, therefore, does not have the cloud-based analysis and external network functions that are important for Industry 4.0. An extension of the traditional MES with such makes sense, but a complete replacement with IIoT infrastructure is hardly worthwhile for economic reasons alone

What is the advantage of the Industrial Internet of Things for engineers?

The IIoT enables the collection and analysis of a large amount of data that can be collected in several phases of the manufacturing process. In this way, the continuous optimization and improvement of systems can be promoted.

How does the IIoT work?

An IIoT network consists of multiple sensors connected via different wireless protocols to exchange data with the cloud and each other. The basic structure of an IIoT network is as follows:

  • Devices and hardware equipped with sensors, each connected to the local network,
  • The local network itself, which in turn is connected to the Internet and cloud services,
  • Cloud-connected servers that process relevant data such as operating temperatures, mechanical faults and power consumption. Such smaller amounts of data condense over time into big data, which can be analyzed to gain deeper insights into your operations.

What is the difference between Industry 4.0 and Lean Manufacturing?

Lean manufacturing is a production organization method aimed at minimizing waste and maximizing productivity. The principles go back to the 18th century and were formulated in the early 1990s as part of an MIT study of the Japanese automotive industry. Industry 4.0 can support lean manufacturing but is not absolutely necessary for it.

How much does it cost to implement an Industry 4.0 solution?

The costs depend on how large and type of manufacturing processes you want to optimize. Therefore, there is no definitive answer to this question.

Industrial IoT platform as a pioneer for medium-sized companies

Thyssenkrupp Materials Services, together with Intel as a partner, has not only networked its own production landscapes in one platform: the project resulted in toii, a digitization platform for medium-sized manufacturing companies.

“The pace of innovation has increased significantly in recent years and processes are increasingly being digitized and networked. In this environment, it is becoming increasingly important for companies to work with competent partners they can rely on for innovative solutions to secure competitive advantages and gain a foothold in Industry 4.0.” – Sebastian Lang, Managing Director of thyssenkrupp Materials IoT GmbH 
Industrial IoT platform

The networking of machines and systems is still one of the greatest challenges in the Industry 4.0 environment. It is necessary to network heterogeneous landscapes from new and sometimes decades-old systems (by means of retrofitting) within a common data model based on IoT. As one of the world’s largest suppliers of materials, ThyssenKrupp Material Services has over 4,500 production machines and systems from a wide variety of manufacturers in use in its plants. The goal was to network these assets flexibly in order to increase plant productivity and create more transparency for more efficient production.

At the same time, the respective individual production environments were to be retained. The company did not find what it was looking for on the market: the offers from the major platform providers proved to be too complex and inflexible. That’s why the decision was made to build our own, tailor-made IIoT platform with Intel as a partner for the hardware. thyssenkrupp Materials IoT GmbH (tkMIoT), a subsidiary of thyssenkrupp AG, was responsible for the project.

The modular IIoT platform incorporates edge and cloud analytics

A whole range of tasks was mapped with the toii platform, from machine data collection (including connection, transmission and storage), to production data collection from users and devices, to machine automation with bi-directional communication. The platform enables the visualization of production data, for example for benchmarking, and organizes the complex data integration from different data sources, including ERP systems.
The Manufacturing Execution System was also integrated. A particularly important part was the topic of edge analysis for production optimization and quality assurance with real-time production screening. The end-to-end platform enables AI and machine learning to be deployed at the edge, on-premises or in the cloud.

Optimally coordinated hardware and software

Thanks to the individual modules, the platform maps many application scenarios and is easily scalable. The solution consists of Intel servers and Industrial PCs (IPCs) with the necessary storage and network resources, including gateway technology for connectivity.
With IIoT, the interaction between software and hardware determines the efficient real-time processing of data. Thanks to the intensive cooperation, tkMIoT was able to rely on the optimal combination of hardware technologies: The solution consists of Intel servers and industrial PCs (IPCs) with the necessary storage and network resources, including gateway technology for connectivity.

Making success accessible to other companies

Since 2017, the platform has been successfully implemented at more than 30 locations and the entire range of machines and multi-stage production systems, but also the IT systems, have been connected to toii. ThyssenKrupp Materials Services was able to achieve clear advantages: process automation reduced downtimes by up to 50 per cent and increased production by 20 per cent compared to the previous year. In addition, many error-prone, paper-based processes have been eliminated.
ThyssenKrupp Materials IoT decided to use the solution to pave the way for production digitization and automation for other companies and to market toii as part of the Intel IoT Market Ready Solutions program. External customers include GGK, a subsidiary of the Grün Group, which relies on the platform to extensively network its production. Steel Service Krefeld introduces toii.
Lights to digitally network analogue machines and collect data for further processing.

Industry 4.0 | Economy in Digital Upheaval

After ten years of Industry 4.0, many German companies have achieved more in terms of networking and digitization than they think. Where is the journey going now?

Industry 4.0
Industry 4.0

Angela Merkel was immediately enthusiastic. Shortly before the Hanover Fair 2011, the President of the German Academy of Science and Engineering (acatech), Henning Kagermann, the Director of the German Research Center for Artificial Intelligence, Professor Wolfgang Wahlster, and the current State Secretary in the Federal Ministry of Education, Professor Wolf-Dieter Lukas, described the networking of machines via the Internet of Things (IoT) and the digitization of the processes running on them for the first time as Industry 4.0. The Chancellor found the concept so conclusive that she spontaneously used it in her opening speech at the industry show as proof of the performance of German technology providers.

Ten years later, in April 2021, a full 94 per cent of the companies surveyed by the digital association Bitkom on Industry 4.0 stated that they could only remain competitive by networking their processes. However, 65 per cent of the survey participants fear that they have missed the boat when it comes to digitizing their production or are laggards. “Medium-sized companies in particular answer the question of the status of Industry 4.0 in their companies with relentless honesty,” explains Dominik Rüchardt, Head of Business and Market Development in Central Europe at the provider of technologies for Industry 4.0, PTC. “They complain that they don’t have the IT know-how for the change and don’t know how to network and monitor their existing systems and machines.”
It’s different in larger industrial companies: Two out of three of the companies surveyed by Bitkom now use intelligent robots or systems for real-time communication between their machines. Four out of ten large companies also network their systems with IoT platforms on which they evaluate the data transmitted by the machines.

Today all future technologies of the year 2011 are in use

In addition, basically, all technologies have found their way into their production and development, their sales and purchasing, from cloud computing and augmented reality (AR) to digital twins, artificial intelligence (AI) and software solutions for product lifecycle management (PLM). held, with which acatech President Kagermann and his colleagues wanted to connect products and their manufacture in 2011 to so-called “cyber-physical systems”.
“The individual technologies interact with the physical product such as the nervous system, brain, memory and thinking abilities with the human body,” . The digital twin serves as a kind of brain. Because it reflects all the properties and behaviour of the product. “The IoT serves as a nervous system that connects it to the physical product, registers how it behaves and transmits signals between the analogue and the virtual world,” adds Rüchardt. With these impulses, the behaviour of the product can also be influenced.

AI is the mind of Industry 4.0

At the same time, AI and machine learning could correlate the received signals and recognize when changes in the behaviour of the product are necessary – for example, to achieve better manufacturing quality by adjusting the parameters of the process running on a machine. “So AI is, so to speak, the thinking ability of the cyber-physical system. As such, she recognizes at an early stage when repairs or the replacement of wearing parts will be necessary and thus helps to increase the availability of machines through predictive maintenance.
The PLM, in turn, remembers the findings of the AI. Similar to the human memory, it not only knows how the product should work but also how it has behaved and changed over time. The information it provides helps development engineers to optimize future product versions.
“Finally, augmented reality makes all this data usable and helps people to optimally influence devices and systems – for example, by providing a maintenance worker with the data needed to repair a machine on his tablet or showing him where with the help of AR glasses he has to lend a hand,” adds. This means that maintenance work can also be carried out by less experienced technicians. Machines fail for a shorter period of time if an employee from their manufacturer does not have to travel to repair them first.

The digital transformation of industry needs a reliable set of rules

“Anyone who wants to assess what German companies have achieved in the first ten years of Industry 4.0 must not only think technologically,” warns Rüchardt. In order for the smart networking of production, development and procurement processes to succeed, numerous participants must be integrated into a digital ecosystem – from the manufacturing company to its suppliers and clients to logistics service providers. “Industry 4.0 is therefore also a far-reaching structural change that changes the relationships between manufacturers, suppliers and customers and pushes new providers into established markets.
In order for this to be legally secure, a set of rules consisting of laws, contract models, reliable payment systems and active business relationships is needed that everyone involved can rely on. Only such a reliable framework for business relationships in Industry 4.0 allows new business models to be developed. According to the Bitkom survey, three out of four industrial companies are striving for this.

“Machine as a Service” – the supreme discipline of Industry 4.0

Above all, “as a service” business models are a profound disruption for providers. “A mechanical engineering company, for example, then no longer collects the price for its product when it is sold, but spread over many years. This puts a lot of stress on his balance sheet. In order to be able to survive economically, the manufacturer would need new partners, such as financial holdings or leasing companies, who buy their machines and rent them out as operators according to an “as a service” model.
To do this, they must be able to guarantee customers that the devices will perform as promised in the rental or leasing contract. This presupposes that operators continuously monitor the machines and know at all times what condition they are in, what quality they are producing and how their economic operation is. The machine builder’s new partners must also be able to maintain their devices and, in the event of a fault, repair them reliably and, if possible, without a time-consuming journey by a service technician.
The manufacturer of a machine can sell all these services in addition to the machine. Because their rental company must be able to access the technical expertise of the machine builder as well as the complete technology stack of Industry 4.0. Otherwise, his business model will fail. In order for the interaction between the manufacturer of the machine, its operator and its customers to work, all those involved also need a legal framework in which liability issues and the respective rights and obligations in their business relationships are reliably regulated. “Machine as a Service is thus becoming the supreme discipline of Industry 4.0,” summarizes Dominik Rüchardt.

Industry 4.0 in brownfield environments – individual solutions lead to chaos

Companies encounter similar problems when they want to network and monitor older machines and systems in order to increase their availability and performance. In order to be able to develop its potential, Industry 4.0 also needs a framework in such brownfield environments – in this case, however, a technological one.
“Older machines and systems were mostly developed in a downstream process and trimmed for operational excellence. This makes it very easy to monitor and optimize each machine individually. However, the approach is not sufficient to use the existing systems to react quickly and flexibly to changes in demand for the products manufactured with them.
To do this, the machines must be networked via an IT architecture that can extract meaningful information from the data supplied by the devices even if the data follows the logic of the operating systems from different manufacturers and is available in different formats.
“This requires clearly structured and comprehensible IT architecture concepts that can deal with the heterogeneity of brownfield environments. Otherwise, there will be a lot of individual solutions and thus chaos,”. Networking of manufacturers, their suppliers and customers are then hardly possible.

The digital platform economy is not far away

“However, the first corresponding sets of rules are now available – for example, the reference architecture model Industry 4.0, RAMI,” assures Rüchardt. At the same time, the first consortia would implement these frameworks in practical cooperation between companies from sectors such as mechanical engineering, the process industry or automotive production.
“If things continue to develop like this, in three to four years the economic interaction between companies and their partners will mainly take place on digital platforms.

How to work more efficiently with Robotic Process Automation

Robotic Process Automation
Robotic Process Automation

This case study shows which criteria you should definitely know for the successful use of Robotic Process Automation

A manufacturer of machine systems and special machines ensures more efficient work processes in the company with Robotic Process Automation (RPA). Together with the management consultancy ROI-Efeso, it determines areas of application for RPA in eight business areas. After only three months, the software bots complete 14 processes independently and reliably. You can find out how the experts from ROI-Efeso proceeded here.
The case study: initial situation and measures in brief
Starting position:

⦁ Data maintenance and information exchange in different formats cost time and generate errors
⦁ The process landscapes in operations, quality management, controlling and other business areas are to be redesigned.

Method:

⦁ Determination of fields of application and tasks for software bots (Robot Process Automation) including detailed recording and development in all departments involved
⦁ Immediate use after testing in practice

Solution approach:

⦁ Test phase with the real and the automated process running in tandem
⦁ Calculation of the ROI of the software bot for the sum of all use cases

Robotic Process Automation: What software bots can do

For work tasks that are very repetitive, time-consuming and error-prone, there are now smart technology assistants: In the context of digitization and Industry 4.0, software bots take on a wide range of tasks in the operations area.
Under the term Robotic Process Automation, they provide relief for simple but time-consuming work processes. In this case study, one objective was to centralize bookkeeping from five locations at the company’s headquarters without hiring additional staff. Instead, bots should take over tasks such as invoicing, dunning or the preparation of data from different sources for reporting from the first day of the changeover. The focus in the selection was on regular activities which are on the one hand so specialized that their automation in systems like SAP is not worthwhile – but which on the other hand tie up considerable time resources. 

Industry 4.0: Award from ROI-Efeso and the magazine Production

Digital assistance systems, data analytics, artificial intelligence or machine learning are changing the value creation processes in the manufacturing industry at breakneck speed. Companies that manage to successfully integrate these digitization technologies, tools and systems into their value creation processes are among the pacesetters of Industry 4.0. Together with the trade newspaper PRODUKTION, it has been honouring ROI-Efeso with the Industry 4.0 Award since 2013 – one of the most important benchmarks for digitization projects and Industry 4.0 best cases.
The second objective and special challenge of the project was to expand the change from manual to RPA-controlled work processes in just a few months to include operations, R&D, production and logistics. Together with ROI-Efeso, the company quickly implemented the appropriate bots. Just two weeks after the start of the project, the first bot was already saving half an hour a day with a previously manual inventory correction of shortfalls in SAP.

Testing and deploying

The project kicked off with an information event at which all employees involved got to know the deployment options of RPA. ROI-Efeso explained the limits of the technology very clearly – after all, those involved should be able to develop a realistic picture of the effort involved in introducing RPA to its benefit.
After this kick-off, the project team gathered ideas and suggestions on work processes that the employees believe could adopt RPA tools. In the days that followed, it selected 14 suitable processes from over 40 ideas in one-on-one discussions, and a detailed analysis was then carried out on each of them.

Industry 4.0 – An RFID module guides you through the Future Factory – Source: ROI-Efeso

On the basis of this knowledge, the project team created an automation concept for all areas involved, in which it prioritized the most attractive processes. The selection of the RPA software suitable for the company’s requirements and the assignment of authorizations were completed within two weeks in coordination with the IT department, so that implementation of the identified work processes could start immediately afterwards.
The project team now brought the RPA into everyday life – department by department, process by process – always via the detailed recording, development, and test stations. In the test phase, the previous and the automated process ran simultaneously according to the “tandem” principle. In this way, errors could be corrected or hurdles overcome without delays in day-to-day business. Once handed over, every work process ultimately ran through the RPA, which immediately relieved the workload.

If you hate it – automate it!

In the project, three experiences proved to be particularly valuable for the implementation of RPA projects:

  1. Identify “processes in the middle”! The maxim “If you hate it – automate it!” Is a good starting point for RPA projects. It is important to classify the intended use correctly: Which use cases are so simple that the programming effort for a system adaptation is not worthwhile – but automation? Software bots are intended for precisely these processes in the middle. In sum, this is where the greatest leverage for savings lies. 
  2. Exploit software bots 24/7! The license model of most RPA providers is designed in such a way that a software bot is available around the clock on every work and public holiday. As with employees, payment is made per bot. If the bot is not busy with tasks, the invoice is still due. Therefore, you should constantly look for other, suitable work tasks for the bot.
  3. Make your expectations realistic! RPA is not a ready-made software that takes care of all extensions independently after purchase. If the process that the software bot has taken over changes, this change must be adapted. This should be clearly communicated in RPA projects in order to achieve realistic expectations of the possible uses of the bots.

4 consultant tips for your successful RPA use!

⦁ Operate targeted stakeholder communication! An RPA introduction is only successful if it is (pro) actively driven forward by the specialist department – in cooperation with IT.
⦁ Clearly define roles and responsibilities! Determine at an early stage who will monitor the bots, make adjustments or pay attention to compliance requirements.
⦁ Look beyond the existing rules and limits! RPA is a technology that helps with process optimization – but only for those who are able or willing to think process-oriented.
⦁ Build up internal RPA competencies! Nobody knows the processes as well as your employees. You should therefore create a Center of Excellence with internal consultants for the (further) development of RPA as well as for support with its application.

Careful testing saves expensive mistakes

In this as well as in other RPA projects by ROI-Efeso, controlling the previous manual process alongside the new automated process proved to be an important success factor. This is the only way to compare whether the desired results can be achieved. This also makes special cases and sources of error visible that were not yet present in the process recording. With this start-up support for each process, the RPA introduction takes a little longer – the transferred bot then works more reliably than with an implementation that is too fast. And that in turn saves time and money for troubleshooting later.

Storage technology | where automation really pays off

How the right automation strategy in intralogistics can save money and significantly increase efficiency in Storage technology

Storage technology
Storage technology

When building or redesigning logistics systems, the question of the degree and type of automation inevitably arises at the beginning. Because a warehouse that is automated with foresight can be operated much more cost-effectively in the long term and also offers other advantages compared to conventional manual warehouses.
Basically, the type of warehouse is always a compromise between safety and efficiency: In a theoretical world in which every part is always available, warehouses would be unnecessary. Logistics concepts such as Just in Time and Just in Sequence are approaching this ideal, but they also show the limits, as the automotive industry recently had to painfully experience the border closings with the Czech Republic due to Corona: Here arose due to delivery delays Bullwhip effect, which ultimately messed up the supply chains from OEM to 3rd tier supplier.
Such effects can, in their two extremes, lead to a complete overload of the system on the one hand. On the other hand, it can happen that entire shifts have to be sent home due to missing orders. Both situations are sometimes associated with enormous increases in costs.

Intralogistics: How can my warehouse work more cost-effectively?

But smart, automated intralogistics can be a worthwhile alternative. In general, automation always pays off if the number of storage and retrieval is constant and at a high level. This is often the case in the B2B area, since here the flow of goods is more calculable compared to the B2C area. In addition, it must be taken into account that automation costs more money: In addition to the hardware components such as storage and retrieval machines and driverless transport systems, experts estimate that around 40 to 50 per cent on top must be calculated in order to optimally network such a logistics solution and integrate it into the existing IT and infrastructure to involve.

Industry 4.0: Award from ROI-Efeso and the magazine Production

Digital assistance systems, data analytics, artificial intelligence or machine learning are changing the value creation processes in the manufacturing industry at breakneck speed. Companies that manage to successfully integrate these digitization technologies, tools and systems into their value creation processes are among the pacesetters of Industry 4.0. Together with the trade journal PRODUKTION, it has been honouring ROI-Efeso with the Industry 4.0 Award since 2013 – one of the most important benchmarks for digitization projects and Industry 4.0 best cases. You can find out more about the award here.
But in addition to the classic automation solutions mentioned here, systems such as Autostore are now also available. With the help of picking robots, storage space can be saved in addition to full automation.
Of course, it should not be forgotten that these systems are also connected to the Internet and must therefore be secured against unauthorized access from outside.

If automation solutions are implemented across the group, synergies and thus significant cost savings can be achieved through standardized interfaces. In this way, a not inconsiderable savings potential can be created with a higher-level solution.

Tip: Is a combination solution worth it?

The big advantage is, of course, that with an optimally coordinated system, all costs are quickly amortized. Therefore, hybrid solutions are also recommended for systems with certain fluctuations, ie an automated area for the base load and a manual area for peak loads.

What influence does the load carrier used have

Another point that can be of crucial importance when automating a warehouse is the use of standardized load carriers: the fewer different boxes and pallets that have to be stored and retrieved, the easier it is to implement automation. It should be noted that many components in logistics, from storage and retrieval machines to driverless transport systems to conveyor and storage technology, are mostly designed for standardized transport containers. This makes planning a new system much easier and the individual components are cheaper to obtain and combine.
Such standardized systems can then be used in the automotive industry as well as in e-commerce or in the pharmaceutical industry. Of course, automation solutions are also feasible for individual load carriers. Examples of this can be found in furniture production or metal production. There, however, more engineering effort and higher costs can be assumed. But even this additional effort can ultimately pay off.

Returns management included

Finally, you should definitely take a look at the tiresome subject of returns management. You have to differentiate how far the product range of the returns to be processed goes: with large full-range stores, the individual warehouse worker is certainly not in a position to determine the quality of each individual return right away. Here, too, a certain degree of automation combined with smart technologies can help. An example of this is data glasses or special display systems at the workplace that provide the employee with important information for a return. For example, an employee who is responsible for processing returns is able to decide whether or not the product can return to the first cycle for jeans or a smartphone.

Industry 4.0 – An RFID module guides you through the Future Factory – Source: ROI-Efeso

Of course, such smart solutions are of crucial importance for an effectively working system not only in returns processing but also in the order picking process in general. Here, too, automation technology can help ensure that the systems are ergonomically optimally connected to the respective employees. This has now gone so far that the employee no longer comes to the shelf, but the shelf to the employee (moving shelves), which can be achieved through fully networked transport systems. In this way, in addition to increasing efficiency, picking errors can also be avoided, ie making a return in advance superfluous.
This is the only way to achieve a high level of acceptance and the resulting high efficiency of the logisticians. It is also crucial that the employee is provided with the information they need quickly at all times. Instruments from Industry 4.0 such as 3D glasses, voice systems, picking aids and monitors, which are networked via intelligent systems, are of crucial importance and will become more and more essential in the future of Storage technology

Industry 4.0

Industry 4.0
Industry 4.0

Industry 4.0 is the digital transformation and automation of traditional production companies. Industry 4.0 is often referred to as the “fourth industrial revolution” or “smart manufacturing”. By digitizing machines and systems, including the Internet of Things (IoT), traditional processes and procedures should be made more intelligent and autonomous. Industry 4.0 affects a whole range of sectors such as construction, food and beverage, aerospace and the automotive industry.
Industry 4.0 increases productivity, lowers costs and improves international competitiveness. Read on to find out more about Industry 4.0. Learn more about the new technologies that are transforming traditional manufacturing operations into smart factories and get an idea of ​​the advantages and challenges facing production areas.

What is Industry 4.0?

Under Industry 4.0 means the optimization of traditional manufacturing industries through the Internet of Things (IoT) and other technologies. In addition to automating production, these new technological possibilities enable complete digital transformation. The aim is an optimized and data-driven production environment.
The driving factor of Industry 4.0 is data obtained through networking. Technologies from the field of Industry 4.0 record and collect data from production independently of company or machine systems. IoT, cloud computing, data lakes and other computer-based technologies are used. These technologies can improve traditional manufacturing industries through data-driven process optimization. Manufacturing facilities are becoming more efficient and flexible and can produce high-quality products at lower costs.

Industry 4.0 is the next after the three well-known and successful industrial revolutions. The first industrial revolution (around 1760) came about as a result of the invention of the steam engine and enabled the transition from manual to machine production. The second revolution (around 1870) is also known as the technological revolution and included numerous technical improvements such as electricity and railways. The third revolution (the 1960s) brought computerization and was characterized by the use of computer systems for mass production and automation. We are now experiencing the fourth revolution, which is driven by data and the IoT.

The term Industry 4.0 has existed since 2011 and came up as an idea and strategic initiative of the German federal government and German industry. The German economy is heavily dependent on traditional sectors such as mechanical engineering or the electrical, automotive and pharmaceutical industries. In order to remain internationally competitive, Industry 4.0 technologies are increasingly being used in German factories. 
The digitization of the manufacturing industry is also being promoted. In Germany Industry 4.0 is also associated with the term “Work 4.0”, a conceptual framework for the discussion about the future of work.
Let’s take a look at the technologies Industry 4.0 offers for smart production lines:

12 Industry 4.0 technologies

Industry 4.0 knows twelve essential industrial technologies and trends.

Additive manufacturing

3D printing and digital manufacturing to create lighter, better performing parts and systems. Additive manufacturing enables quick and individualized implementation. Manufacturing companies can manufacture the parts they need themselves, precisely when and in the required design.

Advanced Robotics

Modern autonomous robots will work side by side with humans. Robots enable new forms of collaboration, higher speed and higher efficiency. They also reduce costs and improve workplace safety.

Augmented Reality

Augmented Reality technologies can be used to control robots, for maintenance, assembly and repair work, for training purposes and quality controls or for monitoring production processes and for auditing systems. With augmented reality, companies can reduce errors, improve security, save time and cut costs.

Big data and data analysis

This means the acquisition and analysis of data from various sources, e.g. data from conventional machines and sensors or from customer relationship management software (CRM) or enterprise resource planning systems (ERP). In most cases, Industry 4.0 solutions enable manufacturing companies to systematically collect data from parts of the supply chain for the first time. With big data analyzes, managers can make data-based decisions, which leads to optimization and increased productivity.

Cloud

The implementation of cloud computing technologies enables the storage, management and sharing of data. For the growing amount of data generated by smart factories, the cloud is essential. It enables scalability and improves collaboration.

Cyber security

Cyber security ensures secure communication between machines, devices and industrial systems, especially in systems that are being networked for the first time. A high level of cyber security is essential to maintain confidentiality and data protection. Cyber security prevents data breaches that could endanger the entire plant.

Horizontal and vertical system integration

This is about the complete integration of all data from all departments, teams, functions, production and components and that across the entire value chain. Complete integration is essential for end-to-end monitoring and for building a reliable database (SSOT-Single Source of Truth). This database serves as the basis for decision-making, the allocation of resources and the organization of cooperation in order to enable lean manufacturing. This is also one of the greatest challenges of Industry 4.0.

Simulation / digital twin

A digital twin is a virtual copy of a real process with real data. Before a decision is made, simulation tools are used to test and optimize processes. In this way, error rates can be reduced, costs lowered and processes optimized.

Sensors

Sensors connected to machines enable performance, system and environmental characteristics to be recorded. Manufacturing companies can use sensors to collect data from production. This data can be used for monitoring purposes, for predictive maintenance, to optimize processes and improve machine availability, or in general for data analysis.

AI (Artificial Intelligence)

AI enables the optimization of tools, technologies and processes through intelligent machine algorithms. In principle, AI technologies can always be used to improve Industry 4.0 solutions, for example, to improve monitoring, maintenance, robotics or operational management.

11. ML (machine learning)

With machine learning, a branch of AI, manufacturing processes can be optimized on the basis of recorded and collected data. This also includes calculation models and algorithms from the field of operational excellence.

12. Industrial Internet of Things (IIoT)

This means the digitization of all devices and machines used in industry. Older machines without their own control can also be digitized. The IIoT enables collaboration, data acquisition and optimization in real-time. The industrial Internet of Things forms the basis for generating data for most other Industry 4.0 technologies.

Advantages of Industry 4.0 for manufacturing companies

Industry 4.0 is useful in many ways for manufacturers in a wide variety of industries, from food production to automotive suppliers. The following benefits are mentioned as examples:

Productivity and efficiency

The main benefit of Industry 4.0 and smart factories is that the respective industries can increase their productivity and improve their efficiency. How does it work? With Industry 4.0 technologies, production companies can collect data and use the analysis of this data to improve their key business figures. The new processes ensure data-based and therefore more intelligent decision-making. Bottlenecks can be identified and resource allocation optimized. Barriers are removed and, as a result, machine availability is increased, so that performance and production quality increase.

Cost reductions

As a result of improved productivity and efficiency, costs decrease and profits increase. The costs also decrease thanks to improved resource allocation, shorter production times and fewer downtimes, thanks to improved quality and less waste or rejection. In addition, so-called predictive and preventive maintenance enable optimized machine maintenance. Wear and tear are reduced and depreciation is increased. Although Industry 4.0 naturally requires a certain initial investment in new technologies, the return on investment (ROI) is still significant.

Traceability and transparency

The networking of all machines and systems enables data to be collected. This information can be used to transparently track the performance and output of the machines. This enables managers to better monitor and control operational processes. This enables the optimization of production processes and the improvement of product quality. Key figures can be set and monitored. In addition, the data can also be used to comply with legal regulations more easily.

Agility & Innovation

Industry 4.0 enables deeper insights into production. You will then better understand where the bottlenecks are, introduce scalable technologies and work together better on processes. Production companies are thus given the flexibility and agility they need to reorganize the allocation of their resources, introduce new processes and implement ideas.

competitiveness

Many manufacturers are still competitive due to lower costs and increasing product quality, even without cuts in wages or even outsourcing to low-wage countries.

Industry 4.0 challenges

Even if Industry 4.0 brings many advantages, there are also some challenges

Initial investments

Industry 4.0 offers a very advantageous return on investment (ROI), but also requires a certain investment that not all companies may be able to shoulder. This includes, for example, investments in new technologies, training courses and machines. Adjusting business models can also result in costs.

Data protection and security risks

When communicating between devices (D2D – Device to Device) and machines (M2M – Machine to Machine), personal data can also be exchanged. Data breaches in the company’s own production facility or at third-party providers could make this data accessible to attackers and endanger data protection with regard to PII (Personal Identifiable Information). Furthermore, there are fundamentally new attack surfaces and weak points from networking via IoT. These two topics require appropriate government regulations and also new cybersecurity technologies.

Training of the workforce

Operating a conventional machine requires different skills than operating a conventional machine connected to a computer. Managing traditional industrial production is different from managing data-driven, automated production. Industry 4.0 brings new technologies and functions that require a change in the mindset of employees and other skills. For this reason, companies have to train their employees and also ensure a new corporate culture. This takes time, effort and resources.

Dependence on technology

Industry 4.0 technologies can “free” older processes, but companies are still restricted by existing technologies and their operating personnel. For example, networked devices and machines require IT teams in the factories. Companies have to analyze large amounts of data and are therefore dependent on AI algorithms and computer scientists. While Industry 4.0 is gradually gaining ground, many of these technologies are still in development. Therefore, not all challenges of today’s modern factory can be answered by Industry 4.0.

Interoperability

The connection of different systems and machines to a common business process requires standardization and technologies that are not always available.

An entry into Industry 4.0

Switching to Industry 4.0 is not always easy. In the beginning, an initial investment is always required and you have to find the right technological solutions for yourself. Employees have to be trained and given confidence in the process of collecting and evaluating data.
A simple way to get started with Industry 4.0 is to implement a universal, non-invasive technology.
3d Signals offers an IoT solution that can be set up easily and non-invasively on your machines (without interfering with the machines). You can use it to collect data and gain usable insights immediately. Increase the availability and productivity of your machines right from the start. View data from your machines in real-time, optimize your production processes and increase productivity.

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