IIoT In The Service Area: 5 Scenarios For A Quick Start

October 9, 2021

When it comes to projects to implement IIoT in the industry, many people think of long terms and high IT budgets. But IIoT initiatives don’t have to be complex and expensive. A lot of potential lies dormant, especially in the service area, which can easily be exploited.

Higher service quality, lower maintenance costs and better spare parts availability: The advantages of IIoT in the service environment are manifold and can often be implemented with relatively simple means. A suitably selected, scalable and secure IoT platform and some connectivity – and you’re ready to go. ECS, the provider of IoT and PLM solutions, has put together five use cases that are particularly suitable for rapid implementation.

IIoT: Condition Monitoring

The condition monitoring of machines and technical products provides manufacturers with a database for various IoT applications. But even as a stand-alone solution, condition monitoring offers many advantages. Because the data obtained employing sensors help to understand machines and products better, for example, progressive wear and tear or the malfunction of individual components can be quickly identified and remedied at an early stage – thus improving overall equipment effectiveness (OEE).

Data on the operating conditions also provide essential information for guarantee and warranty cases. If specific symptoms accumulate, this is an opportunity to develop products further. Condition monitoring also opens up the opportunity to offer better or new services, such as the direct replenishment of consumables.

Remote Diagnosis And Remote Maintenance

Experience shows that around 35 per cent of all service cases can be resolved remotely across industries with IIoT: an enormous cost saving that leads to a quick ROI (return on investment) for IIoT projects. Thanks to remote access, manufacturers can also comply with service level agreements (SLAs) more easily or offer improved service levels, thus generating additional sales. Customers, in turn, benefit from faster troubleshooting and higher system or product availability. With the solution architecture, companies have to pay attention to solid security mechanisms – right up to the encrypted handshake during the automated registration of a device in the network.

If a service case cannot be resolved remotely, it can be identified or narrowed down. But even then, a specialist does not always have to be on site. Thanks to augmented or mixed reality technologies, even less experienced service technicians or customers can directly solve many problems. For example, step-by-step instructions or additional information are shown on a tablet or data glasses display.

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Software Updates

Software and firmware can be found in more and more devices and machines today: whether in sensors, networked vehicles or production systems. So it’s all the more important to keep it up to date. Patches and updates can be distributed and installed automatically and precisely via remote access, depending on the operating system, region or processor load. For mobile devices, an over-the-air update via WLAN or cellular network is ideal. If errors occur, they can be rectified immediately. Companies not only extend the life cycle of globally distributed IoT devices. You can also subsequently increase the performance of delivered products, address security issues and even activate new functions temporarily or permanently.

Spare Parts Management With The Support Of IIoT

Those who procure, store and provide spare parts as needed can significantly improve their first-time fix rate. The service technician can only solve the problem if the necessary spare parts are available at the first appointment. To ensure this, all spare parts processes should run automatically, from requirement request to provision. Ideally, a networked device reports the status of wear parts or consumables and the necessary replacement is automatically ordered or even sent – even before the customer contacts support.

Another aspect: By forecasting future spare parts requirements, inventories and suppliers can be better controlled, and storage costs can be reduced – with maximum availability at the same time. In addition, IoT-based spare parts management also provides essential information for optimizing pricing. Integration with the existing warehouse management, disposition, and ERP systems is recommended to exploit all facets of this IoT scenario fully.

Field Service Control With IIoT

Inefficient service calls unnecessarily drive up costs and annoy customers. This is precisely where an intelligent customer service control comes in: It automatically assigns service cases to the most suitable technicians – according to know-how, location and availability; it ensures that the demand for spare parts or consumables supplied by the machines is at the right place on time, and it continuously optimized route planning so that travel times are shortened, and travel costs are reduced.

The field service control thus makes a decisive contribution to improving the first resolution rate. Companies and customers benefit from faster service processing and higher service quality. A little connectivity and a suitable industrial IoT platform are sufficient for implementation. However, it can make sense to integrate existing ticketing, scheduling, and route planning solutions in individual cases.

IIoT: Scenarios Provide The Basis For Innovative Business Models

There are, therefore, many starting points for digitization in service. If use cases and roadmap are defined cleanly, and future-proof together with experienced consultants, significant savings, process improvements and even new sources of income can be quickly realized. “In addition to the cost advantages, it is precisely these scenarios that form an essential basis for innovative business models that differentiate themselves from the competition,”. In his many years of practical experience, it has been shown time and again that if you have managed to get started with a more straightforward use case, more complex IoT initiatives such as predictive maintenance or product-as-a-service offers are much more manageable.

ALSO READ: AI Solutions: Companies Are Lagging In The Implementation

AI Solutions: Companies Are Lagging In The Implementation

Techno Rumours

October 9, 2021

The Covid-19 pandemic puts the power of AI solutions in the spotlight as companies use them to predict trends in real-time, personalize customer experiences and even research vaccines against the coronavirus. However, the crisis also shows the limits of the previous use of AI in business.

A new study by ESI Thought Lab and several manufacturers, including Cognizant, examines the prevalence of AI solutions in companies in 15 countries.
Half of the companies surveyed are in the early stages of developing AI solutions.

Artificial intelligence models had to be reprogrammed to continue functioning in a time of dramatic change. As the pandemic catapults companies into a digitized world, AI is becoming one of the most important drivers for growth and competitiveness. As digital executives know, this technology is not a silver bullet or a one-size-fits-all solution. AI solutions can fail if the wrong business case is chosen, the correct data is not identified, processed incorrectly, or the model is not designed to scale. This is why AI results vary so widely today.

AI Solutions: More Than Half Of Companies In The Early Stages

To help executives drive the ROI of AI solutions, ESI Though Lab and providers of AI solutions, including Cognizant, conducted a benchmarking study with around 1,200 companies in 15 countries. In Europe, more than half of the companies surveyed are in the early stages of AI development and are classified as either beginners or implementers.

AI Solutions Play An Essential Role In Performance

The most significant advantages of companies are reflected in their more human-centered approach to AI. In addition to higher productivity , increased customer satisfaction and greater employee engagement also play an essential role. In return, however, executives also expect these advantages to increase profitability .

Companies Need To Accelerate To Meet AI Goals

Companies are lagging behind their options in the field of AI. They must accelerate their efforts to implement the ambitious public sector plans and catch up with America and the Asia Pacific competition. Compared to other regions.

Europe Is Lagging When It Comes To Implementing AI Applications

Companies are also lagging behind those in America and Asia-Pacific when implementing almost all AI applications. They are weakening, especially on the personal side: developing and recruiting AI talents , using the ecosystem and coordinating AI -Experts and business teams The only area where companies are ahead is in defining business scenarios and implementation plans, which reflects their thoughtful approach to AI.

Challenges In Using AI Solutions

For companies, the main issues are the implementation of AI solutions and project management , regulatory restrictions , risk and ethics management , data management and limited AI capabilities and -Talents . Some relate to internal restrictions in managing projects and data. Others, such as regulatory constraints and limited talent, reflect the realities of doing business.

Make Investments In AI Technology

To catch up with competitors in North, Central and South America and the APAC region, companies wisely invest considerable sums in AI . In terms of sales, they invest almost as much as Asian companies and significantly more than their competitors in America . Companies are increasing their spending faster than American companies and expect their budgets to double in growth over the next three years.

“For companies, the level of development of AI determines the next step. The lessons learned from your predecessors can help you move forward. When starting, it’s important to get the foundation right. That is why two-thirds of AI beginners emphasize ensuring that their IT architecture and data management system can support AI. A further 57 percent advise providing a sufficient budget. As the company matures, these basic IT, data and budget hurdles decrease. At the same time, other organizations and personnel challenges emerge,”.

“AI leaders understand the benefits of working together, both inside and outside their company. You value the collaboration between AI specialists and business teams to identify use cases. They are also taking proactive steps to expand their ecosystem of AI partners, suppliers and consultants. To scale AI, executives understand how important it is to have an entrepreneurial mindset and set up a workforce plan to get employees on board. “

Cognizant is a service and consulting company that aims to transform companies’ business, operating, and technology models into the digital era. The industry-based consulting approach helps them to build and manage a more innovative and efficient company.

ALSO READ: AI Systems In Accounting: Companies Can Use The Enormous Potential

AI Systems In Accounting: Companies Can Use The Enormous Potential

Techno Rumours

October 9, 2021

Finance and accounting face many challenges in the digital transformation. New technologies can help cope with these, as they can take on more complex tasks. Despite the enormous efficiency potential, many companies still hesitate to use them.

Suppose RPA and AI systems are used in accounting. In that case, this enables, among other things, data processing in real-time, automated checking for anomalies, forecast calculations and benchmarking as well as the automatic reading and further processing of invoices and receipts. The challenges for finance and accounting have increased in times of globalization, networking, and rapid technological progress.

AI Systems Offer An Efficient Analysis

The global increase in digital data and the associated increased risk and compliance requirements, and the acute shortage of skilled workers make it impossible to record all information manually. The automation of individual processes in accounting helps to analyze and prepare data in a targeted and efficient manner. The use of new technologies relieves employees of their routine tasks and creates freedom for higher-quality and more demanding tasks.

While business transactions have mainly been automatically recorded, processed and stored in ERP systems for several years, new innovative technologies such as Robotic Process Automation (RPA) or artificial intelligence (AI) are also used in finance and accounting. These promise enormous efficiency potential, as they can be used around the clock 365 days a year without additional human, manual intervention and only require a fraction of the human processing time.

With the help of these technologies, processes can be accelerated and more reliable, and higher quality results can be achieved by processing a higher volume of data. The use of RPA and AI systems enables, among other things, data processing in real-time, automated checking for anomalies, forecast calculations and benchmarking as well as the automatic reading and further processing of invoices and receipts.

Companies Hesitate To Use AI

However, the application of the new technologies in finance and accounting is still in its infancy, so many companies hesitate to use them to process sensitive data in finance. This is also shown by the PwC study “Digitization in Finance and Accounting 2020” results. Currently, only 15 percent of companies use robotics or AI in accounting. However, the results also show that the benefits have intensified for companies that have already used AI. It is to be expected that the use of RPA and AI systems will also gain importance in the future in finance and accounting.

AI Systems Are A Case For The Strategic Plan

However, digitization is not just a technical question. And it is a strategic and organizational challenge. The best technologies will not help if managers and employees are not taken on the journey into the digital age from the start. It is necessary that the digitization of finance and accounting is included on the companies’ strategic agendas and that the departments are actively involved in the implementation.

The sustainable design of digital change is a decisive factor in securing growth potential and remaining competitive in the long term. How quickly and to what extent new technologies will find a permanent place in finance and accounting remains to be seen. These will undoubtedly change fundamentally due to the increasing use of technology.

ALSO READ: Container Technology: 6 Essential Developments In 2021

Container Technology: 6 Essential Developments In 2021

Techno Rumours

October 9, 2021

With the beginning of the corona pandemic, many companies stepped up their digitization efforts. container technology is most important, When migrating to the cloud,. Those responsible for IT should always take these six significant developments into account.

The corona pandemic and the associated developments such as the strong growth in e-commerce or working from home have led many companies to accelerate their digital transformation and thus also their migration to the cloud. A central aspect is the transformation processes towards a cloud-native architecture based on container technology, which promises to maximize the advantages of the cloud.

Not only companies but also developers can benefit from cloud-native technologies and thus significantly increase their efficiency. Because cloud-native architectures are mapped using container technology, they have become the simplest variant of the best possible cloud usage. Cloud experts have summarized six critical trends for container technology this year

Container Technology Is Developing Into The New Interface In Cloud Computing

The new interfaces will further adapt the essential functions of cloud and edge computing to one another. This further promotes the standardization of edge products and accelerates container applications in Edge, IoT and 5G. This is important because these technologies have to develop further in the course of high-density and high-frequency scenarios. A continuous revision of the cloud computing architecture to prepare for the techniques mentioned earlier is also essential.

High Automation Of Cloud-Native Applications

Cloud-native architectures naturally enable a high degree of automation. This advantage can be used to the full when developing cloud-native applications; this also applies to maintaining the number of replicas, version consistency, error repetition and asynchronous event control. As a result, we will see a significant increase in automation in application deployment, risk prevention and control and operator runtime in 2021.

Application-Centric And Highly Scalable Upper-Layer Platforms

An easy-to-use and scalable upper-layer platform based on standard application models will replace traditional platform-as-a-service solutions in the long term and develop into the new mainstream. Since application-centric software is complex and its use brings many challenges, platforms that are easy to use will prevail.

Fast Cloud Edge Integration Through Container Technology

With the integration of AI, IoT and Edge Computing, more and more companies will be confronted with more significant scaling and complexity scenarios. In addition, edge computing will continue to establish itself as an extension of cloud computing in hybrid cloud scenarios. This requires an infrastructure that enables decentralization, autonomous edge facilities and edge cloud hosting. The development of infrastructures such as 5G and IoT will also significantly accelerate the growth of edge computing.

Cloud-Native Data Transformation

Nowadays, data is a company’s most valuable asset. In the next few years, cloud-native technologies will increasingly promote data-driven applications that support companies’ intelligent IT transformation. For example, the be-all and end-all will be the development of a uniform cloud-native basis to support AI and significant data processes.

Container Technology: Security Becomes The Top Priority

Containers may have become the standard for the delivery of applications and computing resources in the cloud-native era. Nonetheless, container technology still faces significant challenges in the cloud computing environment. The topic of security, in particular, will become a top priority this year: Different aspects from the container runtime, which has to guarantee optimal security isolation, to the so-called “lightweight” virtualization.

ALSO READ: Customer Relationship Management: 3 Opportunities Use Of AI in CRM

Customer Relationship Management: 3 Opportunities Use Of AI in CRM

Techno Rumours

October 8, 2021

To maintain and expand customer relationships, most companies use customer relationship management (CRM) systems. But many are not yet using the full potential of their data to understand and use the needs of their customers ideally. AI helps here, even on a tight budget, to be more successful.

After more than a year in the pandemic, we can say without a doubt that the past year presented us with new and unexpected challenges in both private and professional areas. And not least because of these changes, the demands on customer service are also increasing sharply. Companies currently have to work particularly hard to maintain a good relationship with their customers even during the pandemic. To support and expand customer relationship, the majority of companies use CRM systems for customer relationship management.

These programs often come with the promise of providing a uniform solution that combines data, insights and analyzes and puts the customer at the centre of corporate goals – and thus secures corporate success in the long term. But this promise of performance is not always kept by a long way. This is shown by a look at a recent Forrester survey, which reveals that over half of users are disappointed with their CRM systems after only two years due to poor data quality.

Customer Relationship Management: Breaking Down Data Silos, Enabling Increases In Efficiency

This dissatisfaction is primarily due to user-unfriendly tools or complex multi-stack solutions that impede efficient work. They often lead to data silos, i.e. to individual storage locations of information that do not allow joint evaluation. Many companies are still a long way from exploiting the full potential of the available data and miss the opportunity to get to know and use the needs of their customers.

Small and medium-sized companies, in particular, are increasingly using older legacy CRM or CRM-1-0 solutions, which meanwhile require at least considerable adjustment and integration work to meet today’s requirements. CRM 2.0 solutions, on the other hand, have been developed from scratch and have an integrated data platform that enables companies to access a standardized and normalized data set across the various touchpoints. This alone dramatically increases user-friendliness. In addition, through AI, customer relationships can be expanded and improved with limited know-how and budget. Before doing this, however, it is imperative to look at which specific added value should be achieved through AI.

AI In Customer Relationship Management – Opportunities And Advantages

All Information Is Equally Important

The basis for meaningful use is primary data. To offer an improved customer experience, the first thing to understand is that no individual data points are more important than the others. According to the principle: the whole is greater than the sum of its parts. In analysis, it is essential to bring all the information together in a common data platform. This allows data scientists to focus on identifying which data is responsible for which results.

Once this is understood, companies will realize that it is fascinating to collect additional data from customers, explaining, for example, why they took a particular action like opening an email or clicking a button.

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Accurate Predictions And Recommendations

To create forecasts and better understand customer behaviour, AI-supported CRMs must be trained with relevant data. Based on this training data, recommendations can be made on how businesses can best be driven forward. If, for example, a company suddenly loses a whole series of customers for no apparent reason, artificial intelligence can search for specific schemes that connect these customers by analyzing data. In this way, even complex causes of problems can be identified and remedied. Based on intelligent predictions of what customers will expect in the future, the sales team, for example, can plan its measures more effectively.

Better Scalability Through AI

Scalability is increasingly becoming a challenge for medium-sized businesses because more customers also mean more data records at the same time. Artificial intelligence can help analyze business metrics across the customer cycle and identify and uncover anomalies that would otherwise go unnoticed. Artificial intelligence can help identify and analyze suboptimal customer journeys and recommend corrections concerning content, timing, or frequency of contacts.

This basis enables better, fact-based decisions, and the building feel gives way to solid prognoses. In addition, employees are encouraged to acquire customers more efficiently, take better care of them and thus bind them to the company in the long term.

Last but not least, intelligent algorithms can increase the satisfaction of the users of CRM systems.

Customer Relationship Management – The Three Stages Of CRM Implementation

Analysis Of Needs

Before the preparatory phase for the introduction of a new CRM system, the status quo is determined. From this, the required range of functions of the new system is determined. Specifically: where are new leads generated? How are these qualified and enriched? And under what conditions is a qualified lead handed over to sales as an opportunity? This primary research helps to define the functional scope of the new system.

Preparatory Phase

The introduction of a new CRM system should not be underestimated. It is therefore essential to take enough time and develop a concrete implementation plan. It is also advisable to test different providers to test the defined range of functions in different environments.

Implementation

To use the full potential of the new CRM system, it is essential not to forget to train employees accordingly. Even if the systems are exceptionally user-friendly, there is no avoiding employee training. In this way, old processes can be revised, and the acceptance of the new system among employees can be increased.

ALSO READ: Edge Artificial Intelligence: When Is It Worthy For Your IoT Projects?

Edge Artificial Intelligence: When Is It Worthy For Your IoT Projects?

Techno Rumours

October 8, 2021

Artificial intelligence and the cloud: this combination has been established for a long time. While the cloud provides resources, for data training for IoT solutions, AI improves algorithms that make IoT systems autonomous.

The changing digital environment brings new challenges that the new Edge AI trend is much better at. That’s why large corporations like Google, Apple and Amazon have already invested millions in edge computing so that their solutions can respond quickly to incoming inquiries. The next article answers the following questions: How does edge computing work in conjunction with AI? What are the advantages of this combination for companies? And: when is the use of Edge AI even worthwhile?

What Does Edge Artificial Intelligence Mean?

Edge AI means that AI algorithms are executed locally – directly on the device or on the server near the machine. These AI algorithms use data captured now from the device. Appliances can make independent decisions within milliseconds without having to connect to the Internet or the cloud. In contrast to Cloud AI, data is processed locally on Edge AI devices. And only the results of this processing are sent to the cloud.

Edge AI: What Does It Include, And How Does It Work?

An Edge AI system includes the following components:

Sensors that collect data
Processors/chips that perform data analysis with AI
Communication interfaces to send the determined metadata to a server

A machine learning model is trained based on a data set to perform specific tasks. The model is programmed to recognize patterns in the training data set and then in several training models with similar properties. It can be used for inferences – deriving new facts from an existing database – in a particular context to make predictions.

As soon as the model works as intended, it can draw insights from absolute sensor data that can be used to improve business processes. Usually, the model works through an API. The model output is then transmitted to another software component or visualized on the app front end for the end-user.

Why Is The Cloud Alone No Longer Enough?

According to a report, the Edge AI software market alone will grow an average of over 25% per year over the period 2019-2024. Industry 4.0 requires real-time computing functions. “The need for real-time insights and immediate action, the current network constraints, the high volumes of data and the speed at which sensors and endpoints generate this data are forcing IT managers to use edge computing solutions to manage the Process data closer to the source of its origin,”

The digital environment is changing rapidly. And the cloud alone can no longer meet the new demand, as the following prerequisites show:

Shorter latency times: With cloud computing, the data is sent to the cloud before being analyzed. This can lead to delays. These delays are, in some cases, unacceptable. For example, when connected cars need to recognize an obstacle and brake, fractions of a second can be decisive. Activating a robot too early or too late on the assembly line can result in a damaged product. If the error goes unnoticed, the defective product ends on the market or causes damage in later production phases.

Independence from connection problems: With Edge AI, intelligent devices are no longer dependent on communication with the cloud. Therefore there are no problems if the internet connection is not stable. Edge AI can be an advantage, especially in rural areas or for weather stations exposed to extreme conditions.
Scalability: According to forecasts by IDC, there will be 41.6 billion IoT devices worldwide by 2025, generating 79.4 zettabytes of data. This amount of data requires new ways of efficient data analysis and processing. When you need to process video data from hundreds or thousands of sources simultaneously, transferring data on a cloud service is not a viable solution. However, if most data processing occurs at the Edge, there will be no bottlenecks in data transfer.
Cost savings: Of course, Edge AI requires local computing power and investment in hardware. Even so, it is often the most cost-effective solution. Since the need for data transfer is reduced, Edge AI can save bandwidth. Edge AI also makes devices more energy efficient. The connection to the cloud does not have to be maintained permanently. This can significantly extend the runtime of the devices.
Data security: The less data is sent to the cloud, the fewer opportunities for online attacks. Edge makes information theft more difficult because the processing takes place in a closed network on site. After the analysis, data is deleted, with only selected metadata being forwarded to the cloud at specified intervals.

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Intelligent Algorithms With Edge AI: Successful Use Cases

Industrial Manufacturing: Prevent Errors And Simplify Production

Errors in production can lead to production downtimes and increased costs due to error elimination. They can easily be overlooked as they can be so small and inconspicuous that the human eye has no chance of detecting them. The theBlue.ai GmbH has developed an edge AI solution that enables complete automation of quality management processes. Using unique video cameras and sensors, she checks every product in real-time for deviations and errors. The system also classifies and assigns product types. As a result, the product is directed to a selected conveyor belt corresponding to the classification to be further processed there.

Automotive: More Security And Faster Reactions

Connected cars produce a lot of data while driving. With some decisions, you can’t wait for the data to be transferred to the cloud and back.

Every Formula 1 racing car is equipped with more than 200 sensors that produce 100 gigabytes of data on a racing weekend and transmit more than 100,000 data points per second. McLaren engineers and crew needed instant, real-time access to the captured data to decide when to change a tire and assess the track’s safety.

McLaren has combined edge computing with the edge core cloud strategy. While team members receive actionable information in real-time, the same data is sent to the central McLaren Technology Center. There they are analyzed and processed in an ML racing simulator to optimize the AI algorithms.

Retail: Analyze customer flow

Shops can integrate Edge AI in video cameras to analyze customer frequency and buying behavior, for example, with a camera system from the Finnish company Adrian. The solution shows which areas of the shop the customers visit at what times, which routes they take, what they look at, where they stop, what they buy and even what mood they are in. These insights enable retailers to identify the areas of the store that are low in traffic or predict when the checkouts will queue. Plus, they can streamline the facility and deploy store staff where they can make the most impact.

Without Edge AI, this process would be too slow and expensive: you would have to send videos from several cameras to the cloud, edit them with software and then send the recommendations back. The amount of data would be too large, and centralized computers would be overwhelmed with processing this amount of data. In addition, data transmission would require more bandwidth than we will ever have. Edge AI can also minimize data protection risks – videos are analyzed locally, and only the necessary information is transferred to the cloud.

When Is Edge AI Worthwhile?

The use of Edge AI is associated with increased development costs, tradeoffs between price and performance, and the complexity of executing AI algorithms on edge devices. To assess whether Edge AI is worthwhile in your specific case, ask yourself the following questions. If your answer is “yes” several times, Edge AI is most likely worth it for you.

Do Sensors At The Edge Produce More Data That Can Be Sent For Processing In The Cloud?

There are three main reasons why data cannot be sent to the cloud:

The costs for data transfer to the cloud are too high.
The performance of the devices is insufficient for data transmission.
Connectivity does not support the transfer of the required amount of data.

Is A Stable Connection To The Cloud Ensured?

A stable internet connection is required when a device takes actions based on sensor data and the processing is running in the cloud. In this case, it is advisable to transfer part of the data processing for essential functions to the device.

Are Our Fast Response Times Critical To Your Solution?

The decisive factor is whether the period is acceptable for your application, during which the sensor data is sent to the cloud, processed and sent back to the device. Or does your solution have to make decisions in a split second? Will latency negatively impact user experience?

Do You Have Complete Data Sets For AI Training?

For AI algorithms to work well, they need large amounts of data to train the AI model. This data must be complete and unbiased. Otherwise, you will get false and pointless results.

Is There A Stable And Flexible Solution Architecture In Place?

If you want to move to Edge AI, you need a stable solution architecture that uses edge computing in conjunction with a cloud backend. Only then can AI be integrated. Also, an Edge AI architecture requires some flexibility to implement and update models over time. The architecture must support additional devices, new versions of chips and operating systems, and migration to other cloud providers.

ALSO READ: Data Hubs: Why They Should Be At The Centre Of The System Landscape

Data Hubs: Why They Should Be At The Centre Of The System Landscape

Techno Rumours

October 8, 2021

In most companies, the ERP system still functions as the central hub to bring all systems together. However, it was not initially developed for this role, which often leads to innovation backlogs.

Today nothing works in any company without data. Whether financial accounting, digital sales, production or logistics – every department depends on the rapid transfer of information from the other corporate divisions to work strategically and efficiently. The internal flow of data employing data hubs plays a critical role today for the success and innovation of the company. How is data managed in companies nowadays? In most cases, so-called Enterprise Resource Planning, or ERP systems for short, act as the central transshipment point.

The Misunderstood Role Of ERP Systems

ERP applications have initially been developed as software for material requirements planning in the 1980s. Their only task was to connect the areas of purchasing, production and sales. Numerous expansions and interfaces to new areas followed over the decades. These included customer and supplier management, business intelligence and e-commerce. So it came about that ultimately more and more data ran through the ERP, and it moved from the edge to the center of the system landscape. With the increasing complexity of corporate structures and their data pools, however, negative experiences with ERP applications at the center of the data flow are increasing simultaneously. Many companies have found that their dependency on one provider is expanding.

How do these noticeable problems come about? On the one hand, slow databases stand in the way of ERP systems for their increasingly complex role as a data hub for the entire company. In addition, the ERP cannot fundamentally harmonize the incoming and outgoing data streams. Instead, the system has to transform these at both interfaces. If that wasn’t enough, there is also the generally poor integration ability: If individual plans have to be renewed or replaced, this automatically affects the data path to all other systems. Development teams, therefore, often have to set up new, improvised data flows in brutal ways to ensure that the existing applications can continue to run smoothly.

Data Hubs Prevent Additional Effort And Innovation Backlog

However, the disadvantages of the ERP system as a data hub result in restrictions for IT and the entire company. Because the inefficient detour of the data via the ERP acts as a brake on company dynamics and innovation since many processes have to adapt to the system due to the high level of integration effort, they can no longer be freely designed; the system architecture limits itself. In addition, the additional effort drains valuable resources and can often nip promising initiatives in the bud.

However, to solve these problems from the ground up, it is hardly sufficient to replace the ERP application as the core system with another system, for example, for Product Information Management (PIM). Instead of an alternative application, the primary data architecture has to be rethought and adapted to the modern requirements of internal company data management. This is where the new concept of data hubs comes into play.

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Data Hubs – New Paradigm Of System Architecture

The primary idea behind a data hub is to place a central layer that is like a spine between the other system layers of the company. This layer has the complete data structure sovereignty, including the status changes of all data records and bundles the role management and the interfaces. All production, financial accounting, commerce or customer relations systems are connected to the hub via standardized interfaces and can quickly and easily exchange data via the hub API.

In contrast to the linear and branched communication in the ERP system, the hub internally uses a harmonized standard data structure that does not depend on the other systems. In this way, data can be transferred as required, and flexible connections can be established with other IT systems. The resulting great independence of the data hub also simplifies its implementation at the same time. The corner is initially set up entirely separately from the rest of the IT. In contrast to the monolithic structure of the ERP, it consists of three components:

a system for API management
an event handler
a powerful database system

Move The Data Hub To The Cloud

Once these components have been put together, data hubs can still be tested and optimized separately from other applications. Only when the services meet the respective requirements will the corner be gradually connected to all systems. The significant advantage: In this phase of the gradual migration, the old and new data architecture can work in parallel. This means that the data layer can also be introduced during ongoing operations. As soon as the hub is connected to all applications, the old infrastructure can be switched off.

To ensure that the hub and the data it contains are optimally protected against failure and loss, it is advisable to relocate it to a cloud. In contrast to on-premise solutions, large cloud providers such as Azure or AWS can secure the performance of the database to a much greater extent and absorb and compensate for increased loads. The data hub in the cloud offers maximum reliability and scalability even with the company’s most varied requirements.

Intelligent Data Links For More Innovations

Similar to the limitations of the ERP system, the positive effects of the data hubs are now impacting the entire company. In this way, new links can be established through the central data system that was previously difficult to achieve. This makes intelligent shopping based on user behavior, marketing or external weather data easier. But also the recognition of process-related correlations or possibilities for increasing efficiency. The data required for this are already available beforehand. The only difference is that they are now stored centrally, harmonized and not redundantly in a pool to which all systems have direct access.

It turns out that the flow of data in a company also has a decisive impact on its flexibility and ability to innovate. However, for their data to be used intelligently and in a way that adds value, companies must first overcome old and monolithic structures for data storage and processing. Only a neutral and harmonized data hub can companies regain control and sovereignty over their data treasure.

ALSO READ: Expense Management: 4 Key Business Expense Trends

Expense Management: 4 Key Business Expense Trends

Techno Rumours

October 7, 2021

Expense Management 4 Key Business Expense Trends

The corona pandemic has also changed the way companies handle business expenses. Digital tools open up entirely new possibilities, for expense management – if they are used correctly.

The Covid-19 pandemic has permanently changed the business world: everyone must work from home, video calls instead of face-to-face meetings, new tools for digital collaboration are being introduced – the list is long. The changing framework conditions also result in a change in business expenses, which also affects expense management. Companies have only reduced their business expenses by an average of two percent. Still, these are no longer mostly travel expense reports, but mainly costs for IT equipment for employees and cell phone and internet contracts or food deliveries.

The provider of professional expense management, has analyzed how the spending behavior of companies has changed in the wake of the corona pandemic. The business expenses of over 35,000 medium-sized and large companies around the world were evaluated. Four critical trends emerged:

Customer Contact As A Decisive Factor

The Covid-19 pandemic has divided companies into two groups: those were sales (primarily online) have skyrocketed and those where consumer interest in their products and services has decreased every day. It also showed that companies spent their money differently during the crisis. Compared to the time before Corona, companies spent an average of only two percent less, but differently. Instead of business travel and expenses, the money was spent on remote employee setup.

Companies whose sales had declined also spent between 33 and 50 percent less on business expenses. These are companies whose services are based on direct contacts, such as banks and insurance companies, and companies in the catering, telecommunications, and industrial services sectors. It can therefore be concluded that customer contact is the deciding factor in whether sales have plummeted and how business expenses were incurred during the pandemic.

Expense Management: Less Money Spent On Business Travel

The world came to a standstill: Travel around the globe and business trips came to a halt, meetings and events had to move quickly to virtual platforms. As of March 2020, 80 percent of companies have not booked any flights for over a year and a half. Overall, 30 percent of companies continued to spend the same amount of money on business travel as they did before the pandemic. At the same time, some companies stopped traveling at all. Since the beginning of the Covid-19 pandemic, hardly any money has been spent on business travel.

More Money For Location-Independent Work

In return, the expenses for location-independent work increased drastically in 2020. This includes payments for (minor) purchases related to remote work, including the technical equipment of the employees. As part of the obligation to work from home, many companies have invested in the specialized equipment of their employees: laptops, cell phones, and telephone and internet contracts have been purchased.

The Digitization Of Expense Management

The pandemic has brought digitization forward by three to four years. Customer and supply chain interactions, as well as internal processes, are now more digital. The advanced digitization is also reflected in the type of expense management. With a suitable platform for expense management, companies can digitally map, manage and automate their entire expense management. This makes company expense management more efficient and more accurate. Digital and automated expense management ensure error-free processing of receipts and invoices. The finance departments have access to consistent data in real-time, which can be displayed in detail or presented visually.

This enables accounting staff to understand the impact of the expense policy, gain insight into corporate spending behavior, and identify potentially unnecessary expenses. Digital expense management solutions notice abnormalities and inform the responsible clerk about a possible fraud case and prevent the company from spending more money than necessary. The accounting staff and the employees who submit their expense reports are demonstrably more satisfied with such a solution.

Manage Expenses Better With Expense Management

Using a digital expense management solution during the pandemic allowed companies to manage their expenses better. Even if the employees traveled less than before, costs were still incurred, for example, for IT equipment or a suitable remote setup. Without an expense management tool, employees have to send their receipts to the office, but they all work from home, so it can take a long time for companies to get their money back.

With a digital billing of business expenses, employees could settle their bills from home without delay, even during the pandemic. It also gives companies better control over overspending. Even if they don’t see their teams in person, thanks to digital expenditure management, they can manage their budget more efficiently, even compared to the time before the pandemic.

Expense Management: Outlook Into The Future

The pandemic has significantly changed the way companies spend their money. Digitization is accelerating and changing the expense management market. Companies have to adapt, digitize their spend management, and look for a better solution with fewer processes and more flexibility. It can currently be observed that the transition to digital techniques is accelerating sharply during the pandemic.

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Cloud Strategy: How Companies Successfully Introduce A Multi-Cloud

Techno Rumours

October 7, 2021

It explains the essential steps for implementing a cloud strategy: from administration to security and data protection in an interview. A modern and adaptable cloud infrastructure offers companies a multitude of advantages. Sometimes it serves as a complete replacement for the usual data center, and sometimes it is used as a supplement to on-premises solutions in the form of a private or public cloud. More and more companies are implementing a cloud strategy, but also in a hybrid or multi-cloud architecture. While many IT managers appreciate the advantages of a multi-cloud environment, there is also concern that moving to a multi-cloud will increase administrative overhead and that costs cannot be calculated precisely.

More and more companies are seeing the advantages of a multi-cloud solution. These include increased flexibility and agility as well as improved performance and availability of IT resources. Ultimately, companies can set up a cloud strategy the way they need it and adapt it to changing needs at any time. In the rapidly changing IT world, this is a convincing argument. Often, however, the path to a multi-cloud is also creeping. In cooperation with other companies, further cloud services are added to the existing infrastructure. Cloud solutions that have already been implemented for specific test systems migrate unnoticed into productive operation after success.

It is undoubtedly apparent that implementing a multi-cloud solution also brings challenges. This includes the administration of the structures but also the issue of security. The topic of compliance and liability law and internal and external data protection should not be underestimated because the requirements for data security have increased once again due to the GDPR.

Where data is located and who has access are all questions that users need to clarify at the beginning. Overall, it is essential to implement governance frameworks for these structures and to monitor them continuously. The most important are identity and access management, metering and billing, and cloud tagging.

Identity And Access Management: A Central Element Of Corporate IT

Identity and access management is one of the most central elements of corporate IT and is now standard almost everywhere. Finally, Identity & Access Management (IAM) must be used to manage a large number of accesses required to administer access rights to various resources, systems, and applications. Particular identity and access management tools can manage these easily and centrally. The tools ensure that there are fewer security risks and that resources can be saved. Overall, the IT administration is managed holistically with the help of these tools, and numerous individual decentralized approval and authorization processes are avoided. In this way, IT managers always have an overview.

It is also important to mention that companies naturally also take compliance and liability aspects into account when managing identity and access. After all, as a rule, both your company data and customer data can only be viewed and processed by authorized parties – but this is ultimately a circumstance that should be taken into account when making any adjustments to the cloud strategy. Since the GDPR means that both cloud providers and users are obliged to provide evidence at all times of where personal data is stored and processed, it is advisable to ensure that the cloud provider has all the data, especially with multi-cloud solutions stores held in data centers within the EU. Then you are on the safe side.

A classic server almost always causes the exact costs – even if it is not always fully utilized. In contrast to this, billing models in cloud computing are primarily based on the intensity of use. It is, therefore, imperative to precisely monitor the use of the individual services. Those who rely on multi-cloud models should be careful not to use excessive resources that remain unused most of the time but still cause costs.

In advance, users can simulate and calculate various cloud scenarios and the associated costs based on assumptions. Different metering and billing software tools also support this. Multiple factors are considered in the calculation, such as the number of calls or transactions, the supply and demand for certain services, and times of the day or regions in which a particular workload occurs.

Overall, it makes sense to strategically consider which workloads will be moved to which cloud in advance. Because in addition to certain services, which for technical reasons are better off in a particular cloud than in another, the different cost structures of the various providers play a decisive role. With the right cloud strategy, you are on the safe side here.

Cloud Tagging Enables Control Of Resource Usage

Many choose a multi-cloud environment because it increases the degree of automation and autonomy in administration. This allows employees – within the framework of specifications – to claim resources independently, which creates freedom and flexibility. Cloud tagging is required for this to work in reality. Cloud tagging makes it possible to control the use of resources and assign corresponding accesses within the framework of the selected security concept. By tagging resources with resources, for example, the models of a cloud strategy can be systematically analyzed and monitored.

Cloud tagging can also be used to assign who is responsible for a budget overrun or whether a particular abnormality indicates an IT security incident in a home office network. All of this used to be managed in external tools . A tagging strategy that is implemented across resources is much more elegant and efficient and can be used in parallel across different clouds.

Because that is particularly crucial in the context of a multi-cloud solution: An alert should not only be issued for a specific resource in a specific cloud but should also affect all public and private clouds that the company uses following the rules. The use of various tools for cloud tagging, metering, and billing or identity & access management enables the multi-cloud solution to be efficiently controlled and monitored. All the advantages of a multi-cloud infrastructure for Wear can come. Most challenges can be overcome by implementing a congruent multi-cloud governance model.

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Industrial Edge: Updates For New Functions At The Push Of A Button

Techno Rumours

October 7, 2021

Edge computing technology is on everyone’s lips. Rightly so because it closes the gap between the world of automation and the cloud. It offers manufacturing companies the opportunity to meet the constantly increasing demands on flexibility and individualization in production. Concrete use cases show where the interaction between automation, industrial edge, and the cloud can show its strengths and the most significant potential.

Anyone who wants to benefit from the ever-increasing digitization needs to be aware of one thing: as great as its advantages in industrial production are, at least as remarkable, are the challenges in dealing with the resulting amounts of data. More and more companies are now realizing the importance and necessity of processing and analyzing their production data on a larger scale to exploit optimization potential in their production. Edge-based solution approaches are crucial because they allow production-related data to be collected and processed – directly on the machine.

Device Management Is Easier Than Ever

With all the challenges that can be overcome with edge solutions, there is excellent potential in device management. The number of automation devices located in a production facility and that communicate with one another will continue to increase. So far, there has been no ready-to-use and user-friendly solution for managing many automation systems, operating and signaling units, drive controls, network devices, and many more. With the help of Siemens Industrial Edge, machine and system operators can now fill all their systems with updates for new functions and firmware updates from a central point with the Industrial Edge Management System.

So-called edge-enabled devices have another significant advantage. This is understood to be a conventional automation device that also has the edge functionalities already mentioned. In this way, only a single physical machine has to be installed, connected, and put into operation in a system, which would have been needed anyway. This is particularly advantageous when there is only a little space for additional devices in a system, or the administration and cabling effort is to be kept low. But even for existing machines and systems, there is already the option of retrofitting an industrial PC to obtain the edge functionality.

In the past, production lines and their infrastructure were designed to run for several years to decades. During this time, there were only minimal changes to the products to be manufactured. With the increasing demand for flexibility and customer-specific solutions, the focus has shifted: Today, the most important thing is to expand a system with new functions as quickly as possible to do justice to the constantly changing product range. Edge-enabled devices have a different operating system, the so-called Industrial Edge Runtime, which enables the modernization of designs by adding new functions in the form of so-called Industrial Edge Apps.

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Industrial Edge In Action – An Application Example

Fine paper dust on the cardboard blanks poses a significant challenge for the handling process. It leads to uncontrolled clogging of the vacuum injector, which is responsible for generating the vacuum for sucking in the cardboard blanks. As a result, the cardboard boxes fall off the suction cup during the travel to the folding device, and the entire packaging process comes to a standstill. To prevent this, Schmalz programmed its app for Siemens Industrial Edge, which collects process data from the vacuum grippers at high frequency and calculates key wear indicators. These, in turn, allow conclusions to be drawn about upcoming service and maintenance work by indicating when a vacuum filter needs to be cleaned or a suction cup needs to be replaced.

In the future, all other component suppliers of the packaging machine will be able to access the Industrial Edge Platform in the machine and install their apps on it. The trick is: All applications run on the same Edge device.

But that was just the beginning: The knowledge gained is ultimately of little use if you cannot reach the right people, namely the service staff, who can repair the system in the event of failures. By connecting Siemens Industrial Edge to MindSphere, maintenance work and the ordering of spare parts can be arranged on demand worldwide. Machine builders and machine operators can work together even more efficiently. The efficient interaction between edge and cloud opens up opportunities to finally implement new solutions and business models that have long existed in people’s minds.

Edge Or Cloud?

As the previous example shows, edge and cloud do not compete with one another; they complement one another. High-frequency data can be recorded, managed, and preprocessed close to the machine using edge devices and then made available globally through the cloud. Due to the enormous computing power available on-demand in the cloud, high-performance machine learning tasks can be implemented in the future. Thanks to the seamless combination of the automation world with the IT world, high-level language applications can run even closer to the actual production process in the future. In the application example with the vacuum grippers, the Schmalz developers put their energy into programming the app.

The Use Of Industrial Edge In The Future

The use of Industrial Edge and MindSphere makes it possible to master the significant challenges that the ever-increasing trend towards customized solutions and production flexibility brings with it. The number of devices, sensors, cameras, or control elements will continue to increase, making comprehensive data management necessary. But data processing must not be ignored here either: What to do if there is no ready-made app for your application? To meet the conditions and requirements here, too, Siemens has integrated the Mendix low-code platform. This even allows users without programming knowledge to create their applications and incorporate them on Siemens Industrial Edge and MindSphere.

For the example of predictive maintenance at the Amberg electronics plant, a clear maintenance dashboard for all systems could in the future be the solution for displaying the immediate need for action for machines – from simple milling machines to complex assembly lines. In addition, new business models with a “pay per use” model could be developed for the entire industry in the future, which, however, requires a comprehensive implementation of integrated and connected edge cloud solutions. In concrete terms, this means: First of all, the basis for seamless connectivity in the factory must be created before profits can be made with data.

Main Advantages Of Cloud And Edge Solutions

The automotive industry is an excellent example of an industry that can benefit significantly from edge and cloud technologies. Your long assembly processes and supply chains can be better interlinked and controlled more efficiently by linking machine statuses, the order backlog, and the delivery status of components.

Faster adaptation of system functions using apps
Greater flexibility and openness for automation
Machine-level preprocessing of high-frequency data
Building machine learning and AI applications
shorter innovation cycles for machines and systems
central device management
global access to system data and machine statuses.

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