Customer Relationship Management: 3 Opportunities Use Of AI in CRM

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.

ALSO READ: How Companies Can Set Up Their IT Infrastructure To Be Future-Proof

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.

ALSO READ: Digital Marketing: This Is How SMEs Implement Effective Measures

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.

ALSO READ: Enterprise Cloud Index : Healthcare Industry Relies On The Hybrid Cloud

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.

ALSO READ: Cloud Strategy: How Companies Successfully Introduce A Multi-Cloud

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.

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

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.

ALSO READ: Metadata: How This Can Secure Messenger Services

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.

ALSO READ: Data Integration: Digital Transformation In Retail

Data Integration: Digital Transformation In Retail

Techno Rumours

October 6, 2021

Data Integration Digital Transformation In Retail

The trade is constantly changing. If you want to be successful as a retailer, you have to react to new trends and developments in good time. The main focus is on digitization and the associated integration of data into the various systems. The trick is to automate the processes as much as possible and avoid manual intervention.

The Challenge In Retail: Many Systems Without A Direct Connection

Anyone who is not confronted with the processes in retail daily usually imagines entry into the world of e-commerce to be straightforward. After all, what is problematic about getting the right software for setting up an online shop from the many reasonable solutions available on the market, adding the related products, and then simply starting online trading?

Well, for smaller companies, this picture is at least partly accurate. Even if the entry has its pitfalls here too. But access is much more complex, especially with larger dealers. In most cases, the difficulty lies in the fact that many different software solutions are already in use in the company. Although these autonomously serve their purpose well, they are not automatically networked with one another.

As a result, data from third-party systems such as ERP, PIM, or CRM systems are often integrated manually into the online shop using Excel tables. The disadvantages of this manual approach are apparent:

Valuable working time is required for this
The data is not available in real-time
The source of error is much higher than with automatic import

The larger and more complex the system architecture, the stronger the effects mentioned. But what options do retailers have today to meet this challenge efficiently and thus operate efficiently in contemporary e-commerce?

The Solution: A Digital Ecosystem

The expectations of customers when shopping online are very high these days. The prominent players in the industry, such as Amazon, are responsible for this. With these, everything works smoothly, from selecting the products to the delivery to the eventual return of the items. Customers therefore no longer understand if this is not the case with another retailer.

You can expect detailed information on the individual products that you see in the online shops. In addition to basic information such as size, weight, color, and price, images and, increasingly, videos also contribute to the purchase decision. In addition, they want to be recognized as customers in the online shop and expect features such as printing out a label for return shipping or an overview of the items they have purchased so far.

This information is available in some software solutions in the company, for example, in its web development for the preparation of images and videos or in a cloud-based CRM system. Still, the merging prepares those responsible for sales, marketing, and IT gray hair.

The solution is to build a digital ecosystem in which all the different information and data sources from the most varied channels can be brought together and subsequently output in real-time. Because only with automated synchronization of the data from the different systems is it possible to optimize the internal workflows and processes accordingly and thus exploit the full potential of the individual software solutions fully. So far, these ecosystems in retail have mainly been built up through the use of appropriate middleware. But now, so-called iPaaS solutions are moving more and more into the foreground.

IPaaS Enables Complex Connections With Little Effort

The abbreviation iPaaS stands for “Integration Platform as a Service” and is thus a modification of the already known SaaS (Software as a Service). State-of-the-art platforms enable data consolidation from the different systems to be set up via a simple web interface.

The software solutions in use, such as ERP, PIM, or CRM, are linked via plug & play connections or web interfaces or file transfers. Thanks to the low-code approach, even complex relationships can be implemented with minimal effort.

A significant advantage of iPaaS compared to previous middleware solutions is primarily the lack of maintenance. The operator makes necessary adjustments and updates to the software. This means that the system is always up to date. Another advantage is the scalability of iPaaS solutions. A flexible expansion is possible at any time without great effort.

iPaaS is particularly interesting for retailers who, on the one hand, want to design their sales processes themselves, but on the other hand, do not want to burden their team with the time-consuming tasks associated with it. Since critical issues such as data protection and security are in the hands of the respective iPaaS provider, the crew can concentrate fully on their core tasks.

For Automated Data Integration In Just A Few Steps

Anyone who has long been a thorn in the side of manual data consolidation in the company can change this situation very quickly. Usually, only the following six steps are required:

Analysis of exactly where the individual data comes from, i.e., from ERP, PIM, CRM, etc.
Clarify which structure the respective data is available, such as Excel, Edifact, SAP, etc.
Define the desired target structure: data mapping
Select the appropriate iPaaS solution and obtain reasonable offers
Carry out data integration in the iPaaS solution
Begin with the data transfer according to the set specifications for the data integration

The complexity of the current system landscape determines the period on the way to automated data integration. In austere software environments, the changeover can be accomplished within a few days. With more complex solutions, it usually takes several weeks for all systems to communicate with one another smoothly.

ALSO READ: IIoT Is Driving Industrial Innovation With 5G And Edge Computing

IIoT Is Driving Industrial Innovation With 5G And Edge Computing

Techno Rumours

October 6, 2021

Even if the number of connected IoT devices in the consumer goods sector exceeds the number of devices in the industrial sector, investments in industrial IoT (IIoT) are growing strongly, especially in cross-industry solutions and customized machines.

Ripley’s new report “Industrial IoT: A Reality Check” examines two key areas driving the growth of IoT in the industry: intelligent factories and intelligent solutions in transport & logistics. IIoT enables manufacturers to improve production transparency by networking machines and tools in real-time. The enormous amounts of data that devices generate at IIoT drive the optimization of production and the improvement of delivery quality. In addition, the implementation of systems for predictive maintenance or the automation of the supply chain.

“Without Industrial IoT, Industry 4.0 is not possible. Data is the fuel for all intelligent use cases in the industry. IIoT is the crucial infrastructure element for data collection, its transfer to the cloud, and analysis. Companies benefit from many advantages here,”.

IIoT: Markets On A Growth Path

The study was carried out using the data collected on the Trend Sonar platform and with the support of Technology. She explores the significant markets for both intelligent factories and intelligent solutions in the transportation and logistics industries.

Despite the challenging economic climate in 2020, both clusters recorded slight growth in investments in smart factories and the area of transport & logistics. However, analysts forecast much more substantial growth until 2025. Overall, the market for intelligent factories of the “Big 5” cluster – led by the USA with high investments in appropriate platforms, predictive solutions, and remote monitoring – is expected to contain more than 86 billion euros by 2025. The market for intelligent solutions in transport & logistics is expected to be more than 15 billion euros.

In the “Europe 5” cluster, on the other hand, the market for intelligent factories is expected to almost triple in all countries and reach a total of over 23 billion euros in the five countries, with taking the top position. The platforms are growing exponentially, and the companies are investing in improving quality management and reducing costs. remains the leader in the field of transport & logistics. The other countries in the cluster are also recording considerable growth. According to the forecast, this group will generate a total volume of 3.6 billion euros in 2025.

Growth Spurt Through 5G And Edge Computing

The introduction of low-cost sensors and 5G networks, in which telecommunications companies are investing heavily, contributes to the spread of Industrial IoT. Experts expect that the improved communication between autonomous vehicles/robots, artificial intelligence, and machines in connection with increased computing power and very low latency will improve the efficiency of the systems and increase their safety.

In addition, the establishment of private, high-density networks enables the broad use of Industrial IoT and the connection of numerous sensors, machines, vehicles, and robots. This is supplemented by increased use of augmented and virtual reality to support “networked employees.”

Cybersecurity Is Critical To IIoT

The constant increase in networked devices and their heterogeneity require good security management, especially with devices and networks’ setup and maintenance guidelines. Companies need to set up robust and micro-segmented environments (local and cloud-based). These can react to threat scenarios with suitable technologies and techniques. This reduces the chances of success for new types of cyberattacks.

The analysis of IoT architectures, industrial components, and entire infrastructures helps companies eliminate gaps, weak points, and threats in advance. This is far more than just a technical question: training programs for employees and continuous testing of all equipment used are also crucial.

IIoT: From The Factory To The Consumer

In recent years factories and logistics centers have introduced IIoT technologies to optimize efficiency. Investments during the pandemic were primarily aimed at improving worker safety. The success of so-called “networked products” accelerates investments in solutions in which the collection and processing of usage data affect production and the finished products. The redesign of the design, display and sales processes for IoT-networked products enables new value-added services. In addition, IoT makes it easier to update and maintain household appliances, cars, robots, electronics, and entertainment devices without being on-site.

The study “Industrial IoT: A Reality Check” is part of the Reply Market Research series, which already includes the following reports: From Cloud to Edge, New Interfaces, Zero Interfaces, and Beyond Digital Marketing.

Reply specializes in the development and implementation of solutions based on new communication channels and digital media. Consisting of a network of specialized providers, Reply supports European industrial groups in the fields of telecommunications and media, industry and services, banks and insurance companies, and public administration in the development of business models made possible by technologies such as AI, big data, cloud computing and the Internet of Things.

ALSO READ: Metadata: How This Can Secure Messenger Services

Metadata: How This Can Secure Messenger Services

Techno Rumours

October 6, 2021

Crime fans know the scene from many films: suspects are caught with the tracing of telephone calls. The investigators use metadata for this, which provides information on who is communicating with whom and when. These are more easily accessible for secure communication than expected.

Metadata is structured data that describes other data – in a sense, data about data. They contain important information about websites or even pictures and videos, including the place and time of a recording. Metadata is also used in software development, for example. There you can describe various processing rules and programming instructions, which can be used to implement more complex applications. In the example of telephone usage, metadata describes who communicated with whom and when. It is important to note that they alone do not provide any information on the exchanged content.

No Protection Through End-To-End Encryption

Depending on the area of application, it can make sense to capture metadata. In the case of messaging services, among other things, they secure the functionality. Many therefore assume that the services can be encrypted and thus protected to a similar extent as the message content itself. However, this is often a fallacy since the end-to-end encryption often used does not cover metadata. Therefore, both messaging providers and, in some cases, third parties can read and analyze them.

Such analyzes can be interpreted in many ways, for example, to reconstruct groups of friends and acquaintances. Daily routines can also be traced. The time of the first sent message of a day provides information about when to get up. If a messenger logs in from Monday to Friday via the IP address of the same company, this is almost certainly the employer. This is no illusion, but backed up with scientific evidence: In a study, researchers from Ulm reconstructed daily routines, including deviations, from the presence status on WhatsApp alone, and at the same time disclosed who was in contact with whom at what time.

The obvious question is: How can such conclusions be prevented from the outset? Specific critical data in chat messaging are essential. This includes the sender, recipient, and the times for sending and receiving. Can metadata be dispensed with here? No, but the amount of metadata can be reduced. Furthermore, access to them can be limited, and their combination with other (meta) data can be prevented.

Approaches To Protecting Metadata

There are several approaches to protecting metadata. First and foremost, “Sealed Sending” should be mentioned here. Messages can be sent without the sender being identified – practically a digital equivalent of a letter with an empty sender address. However, even with this method, metadata can be read out under certain circumstances – conclusions about the person cannot be completely ruled out. If the IP address is read out, it can be traced to who is communicating with whom. For example, if IP 1 sends 5,372 bytes to a messenger server and forwards 5,372 bytes to IP 2 directly at the connection, IP 1 is likely in contact with IP 2.

But this is not the only reason why IP addresses are problematic. In addition, they can often be assigned to a fixed geographical area and thus restrict the potential whereabouts to a certain extent. Messenger services pass this information on to the provider – possibly also to service providers.

If the metadata protection provided by “sealed sending” is insufficient, further measures can be taken. These include messenger services that anonymize the IP address involved when exchanging messages. However, this variant does not offer absolute security either but instead harms the user experience. The reason? To communicate with each other, both the sender and the recipient must be online simultaneously with this method.

The Key: Shredding Metadata

So-called metadata shredding is recommended if neither content nor metadata is to be recognized. This procedure protects by mixing metadata in “anonymity sets” and makes them unrecognizable. As a result, service providers and third parties can neither analyze activity patterns nor connect senders with the respective recipients.

In this way, the privacy of both sender and recipient is fully protected. Conclusions about the respective persons are no longer possible due to the anonymity sets. So far, providers have mainly implemented this concept for messenger services. Potential future application scenarios can also be payment systems. In any case, this technology has the potential to prevent conclusions from being drawn from metadata finally.

ALSO READ: Digital Marketing: This Is How SMEs Implement Effective Measures

Sales: New Level Of Digital Development Due To The Pandemic

Techno Rumours

October 5, 2021

Sales New Level Of Digital Development Due To The Pandemic

During the corona crisis, companies had to use the digital turbo, especially in sales. Because in times of social distancing and remote work. face-to-face is a thing of the past. Technology now takes on administrative tasks – and determines the value chain in sales.

Attributing the digitization success of companies exclusively to the Covid-19 pandemic does not fully reflect reality. Many companies have been investing in digital business models and the corresponding infrastructure for years. And yet: Digital tools have seen a quantum leap in cross-company and cross-functional acceptance in the past fifteen months. Even industries that live and benefit primarily from human contacts have developed into true pioneers of digitization due to lockdowns and social distancing. Sales are particularly affected by this development.

Sales Is A Pioneer In Digitization

The “State of Sales” survey conducted by CRM provider Pipedrive on the status quo of the sales industry also shows that sales are one of the pioneers in digitization. In total, the company surveyed over 1,700 sales employees worldwide. Only four percent of sales employees stated that they use pen and paper to track sales. Instead, spreadsheet programs predominate in 17 percent of cases. And four out of five salespeople use a CRM solution. In addition, the study shows that technologies are not only purchased by employers but are also increasingly used and accepted by employees. Three-quarters of those surveyed are happy about the existing CRM tools to support their work.

Sales: New Understanding Of Digital Tools

Digital tools have long been seen and used in sales – as little helpers for tedious or repetitive administrative tasks. But that falls into the “nice-to-have” category and, if so, indirectly creates value. However, during the pandemic, there was no alternative to integrating software into integral parts of the sales value chain.

Even old-school salespeople, who have always sought customer contact and visited customers face-to-face for face-to-face meetings across the sales territory, had to consider new exchanges and product demonstrations options. Because face-to-face doesn’t work if personal customer contacts are not allowed.

The same applies to lead generation: If trade fairs and networking events no longer occur, business cards can no longer be exchanged. Lots of canceled or postponed face-to-face events, reduced budgets, and office closings suddenly determined the everyday life of sales employees worldwide in the pandemic.

Virtual Product Demos Instead Of Face-To-Face Meetings With Customers

The alternatives are online product demos or zoom calls instead of personal customer appointments and lead-gen software instead of trade fair visits. What sounds striking at first but quickly led to the conclusion that with the help of technologies, sales can scale significantly better:

More video calls can be made every day than booking appointments with customers, including travel time.
The software flushes more leads into the databases than a fair trade date.
Potentially interested parties can be better qualified if the artificial intelligence has checked beforehand whether the right contact person is at all or whether there is any interest at all.

Of course, sales remain a face-to-face business – and appointments with customers come back, of course. But sales have learned that digital tools can take over routine activities and create value if used well. Technologies and automation tools for generating and qualifying leads will be used eleven percent more often in 2021 than in the previous year, at 63 percent. The sales results confirm this trend.

Competitive Advantage Through Digital Technologies

And so, in the past year, virtually the entire value chain in sales was digitized. Many decision-makers created technological alternatives to the analog processes and thus kept sales going. Because of the positive consequences, even the last refusal to digitize should slowly run out of arguments. Because of the sales employees who were able to fall back on automated technologies for lead generation and qualification, 63 percent achieved their sales targets despite the problematic framework conditions.

In contrast, just over half of those who did not have technology and automation tools at their disposal fell short of expectations in the same year. These numbers fill many salespeople with confidence that they will be able to increase sales further. Nine in ten salespeople believe their professional skills will positively impact the economy this year, not least because of a new digital understanding.

ALSO READ: Process Optimization: Techniques And Tools

1...525354...56 Page 53 of 56