Process Optimization: Techniques And Tools

October 5, 2021

Discover how process optimization allows the company to be more efficient and effective. We live in a highly changing market. New technologies and changing consumer tastes mean that companies must have agile organizations to cope with these changes. To achieve this, businesses must have good process optimization.

What Is Meant By Process Optimization?

Process optimization is a technique by which the company can analyze all business processes to eliminate errors and, most importantly, make these more efficient and effective by reducing time.

It is possible that, without your knowing it, you can optimize processes within your company that are considered routine. It is essential to carry out an in-depth analysis that we will deal with later in the text. This study can help us see points where the company is ineffective and wastes a lot of time and, therefore, money.

Thanks to this type of work, companies will integrate external and internal information more quickly. In such a way that the analysis capacity will be improved, they will act more rapidly, and, consequently, the business losses produced from the loss will be minimized. Time and unnecessary mistakes.

How To Work On Process Optimization

Once you know the importance of optimizing processes in the company, it is time to understand how to carry out this task. The steps to follow would be the following.

Step 1: Identify Problems Or Weaknesses

Now is the time to ask yourself all the doubts you have. Where is the company failing? Where do you spend the most time? Are our customers, suppliers, or workers unhappy with a specific process? These and other questions of this nature are necessary to know where to start the analysis. In addition to asking yourself these questions, you must list all the processes, rank them based on their importance and objectives within the business process and estimate the time used in each one of them.

Step 2: Reframe The Situation

Now it is your turn to think about how you could rethink the processes in which you have detected points of improvement. Do you think there is something that can be improved? Brainstorm with all the parties involved in the process so that you will achieve objective points of view that will provide you with good ideas for improvement.

Step 3: Implement

Once you are clear about what to improve and how it is the process of implementing these changes, it is possible that, at this point, you can use process automation tools to generate greater agility.

Step 4: Control

As always, you can’t just stay on implementation. It is essential to keep track of the processes and the changes that have been made to them. Ask yourself if the changes you have made really meet your objective and, if not, go back to step 2 and rethink the situation again.

Valuable Tools To Carry Out Process Optimization

Depending on the processes you want to optimize and automate, you will have a series of tools or others on the market at hand. However, we want to recommend some that can be very useful to start carrying out this optimization:

NSA Auto Store: The ideal tool to transition from paper documents to the electronic plane in an agile way and without wasting time.
ABBYY FineReader: The ideal tool to transform documents into editable documents, thus saving time and the possibility of implementing modifications continuously.
Smart Fax: The ideal way to use fax and scanner, minimizing sending errors.
Document management programs to manage all documents from the same platform.

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

Enterprise Cloud Index : Healthcare Industry Relies On The Hybrid Cloud

Techno Rumours

October 4, 2021

For the new study “Enterprise Cloud Index ” by Nutanix, companies in the healthcare sector were asked whether they use private, public or hybrid cloud. Seventy percent of surveyed people confirmed that the Covid 19 pandemic has given the IT infrastructure a strategically important role and accelerated their digital transformation projects.

With the pandemic outbreak, the healthcare industry was looking for ways and means to effectively master the essential technical requirements triggered by the Covid 19 crisis – from setting up home workplaces to supporting telemedicine procedures to coping with the growing number of patients. Against this background, the digital transformation in the healthcare industry has top priority. The healthcare sector is showing the most significant interest in an IT model based on a hybrid cloud. Ninety-five percent of those surveyed see this as the model of their choice.

Hybrid Cloud: Replacement Of Traditional IT Architecture

More than half of respondents in the healthcare industry have increased their use of the public cloud and the hybrid cloud , while 46 percent have increased their investments in private cloud environments. The industry aimed to give the newly arrived teleworkers access to IT resources in the shortest possible time. Before the crisis, 77 percent of the companies surveyed had employees who worked from home. At the time of the survey, it was 93 percent.

The future of the healthcare industry depends on the replacement of traditional architectures: 27 percent of those surveyed only use standard, non-cloud-enabled data centers. That is more than in any other industry with an average of 18 percent. But the gap will become smaller: The share of traditional data centers in the industry will decrease by 21 percentage points by 2025, while the share of hybrid cloud implementations will increase by 32 percentage points.

Hyper-Converged Infrastructures As The Basis For Hybrid Cloud

To support the modernization of IT and pave the way towards the hybrid cloud, the healthcare industry is turning to hyper-converged infrastructures: Hyper-converged infrastructures (HCI) are often viewed as the basis for hybrid cloud infrastructure, the HCI of the next decade. Because hyper-converged infrastructures massively reduce the time required to set up a software-controlled infrastructure necessary to support a private cloud. At the same time, they offer the scalability of cloud technology. Around 64 percent of respondents in the healthcare industry said they have already introduced hyper-converged infrastructures or are in the process of doing so. Compared to the global average of 50 percent, this is a significantly higher proportion.

Security And Compliance As The Most Significant Challenges

Security, data protection, and compliance, in general, represent a significant challenge for the digital transformation of the healthcare industry: 58 percent of respondents from the healthcare industry see the issue of security as a significant challenge compared to 51 percent on the global average. In addition, 45 percent of respondents from the healthcare industry named the issues of cost control and business continuity as significant challenges more often than their colleagues from other industries.

In the healthcare industry, cost advantages are increasingly becoming a decisive factor influencing the provision of IT infrastructures: the healthcare industry sees the respective strengths of technology solutions concerning security, data protection, and compliance as essential factors influencing the decision-making process for specific infrastructure. On the other hand, in the health sector, cost advantages were cited as a decisive criterion more often than the issue of safety. A similar finding was otherwise only found in the services for consumers and the energy industry.

Hybrid Cloud As A Critical Factor For Digital Transformation

“The healthcare industry is in a critical phase. It needs to accelerate its digital transformation to meet the needs of patients and staff better. The powerful trigger for this acceleration was the pandemic. IT decision-makers agree that the hybrid cloud is a critical factor for digital transformation, “.

“Now, it is important that companies and organizations in the healthcare sector identify the IT solutions that will help them on this path. They need to invest in private cloud environments based on hyper-converged infrastructures and find ways and means to connect their private and public cloud environments. With all this, the issues of safety and costs must never lose priority,”.

ALSO READ: A Turbo For Artificial Intelligence In Production

A Turbo For Artificial Intelligence In Production

Techno Rumours

October 4, 2021

A Turbo For Artificial Intelligence In Production

Artificial intelligence (AI) in Industry 4.0 has a lot of potential – but there is technology barriers that make the use of AI more difficult. Researchers at Fraunhofer IKS are developing a framework that supports and optimizes the data and AI life cycle. This significantly increases the added value through AI.

Industry 4.0 describes the advancing digitization in production – machines and processes are intelligently networked, which means that more and more data can be generated. Artificial intelligence (AI) can yield information from this data to improve products and services. Possible application scenarios are predictive maintenance, optimization, and automation of processes and quality checks. However, this potential cannot currently be fully exploited in Industry 4.0 because several technical barriers restrict the generation and processing of information.

The first obstacle is the multi-vendor landscape in today’s production facilities: machines from different manufacturers from different technology generations with other – and often proprietary – communication interfaces and protocols. Due to this heterogeneity, uniform data access is not possible. Instead, there are many technology-specific island solutions for which domain knowledge is required.

Incomplete Data Sets Cause Problems

The second obstacle is the lack of support for the data scientist. He has no domain knowledge, which is why he needs help in obtaining real-time or historical data. There is also the problem of incompatible, inconsistent, and incomplete data sets and missing metadata. As a result, the data processing process is often tedious, lengthy, manual, and complex in coordination.

The third obstacle is the inflexible AI operation. AI applications are often operated rigidly in the cloud or on a local server. As a result, the applications do not have the opportunity to make optimal use of the available resources. In addition, updates of the AI applications are necessary – to react appropriately to changes in the production facility or the processes – for which, however, there is still no general solution.

The Solution

A Framework For The Data And AI Lifecycle

To overcome these problems, researchers at the Fraunhofer Institute for Cognitive Systems IKS are working on an open, interoperable, and technology-neutral project as part of the project “REMORA – Multi-Stage Automated Continuous Delivery for AI-based Software & Services Development in Industry 4.0″Framework that supports and optimizes the data and AI lifecycle. The aim is to ensure an automated, continuous, and dynamic process that consists of

Data acquisition
Data aggregation
Data preparation
AI development
AI training
AI integration
AI operation
Data analysis
AI monitoring
AI update

In detail, the framework should achieve the following goals :

Support of the data scientist,
Automated and flexible AI integration as well
Automation of AI processes.

It all starts with developing an interface for the data scientist to support the AI development process. This interface makes it possible to query data easily and uniformly without considering technology-specific aspects such as communication interfaces and protocols. The interface then takes on – internally – the mapping to the technologies and the required data transformations. In addition, the interface provides an overview of the topology, the metadata, and an interface for training and operating an AI model. This interface can be used not only by a data scientist but also by a layperson in connection with, for example, an Auto ML framework.

An application management component enables automated and flexible AI integration – from the component level to the cloud-based on the required resources and optimization goals. In addition, the AI application manager, together with the data interface, ensures that the AI applications are networked to provide the flow of data.

Finally, an AI management component should enable the automation of AI processes, i.e., the automatic retraining and redeployment of an AI model to ensure the continuous improvement of the data analysis. For example, new training data can be automatically collected to train a new AI model when machines are exchanged. Furthermore, automated operations can be carried out in response to the data analysis (e.g., cooling down in the event of overheating) or to increase the efficiency of the real-time AI analysis (e.g., adjusting the sampling rate).

For the production of the future, this means: With this framework, the potential of AI in Industry 4.0 can be better exploited – through simplified and technology-neutral data access, support for AI development, flexible and automated AI integration and updates – and thus efficiency can be increased in AI operation.

Also Read: What Is The Security Of Cloud Services?

What Is The Security Of Cloud Services?

Techno Rumours

October 4, 2021

In this overview, you will find – without claiming to be exhaustive – introductory information about the Security of cloud services.

General

Cloud computing offers numerous potentials in terms of flexibility, cost advantages, and other factors. The possible uses range from simple data transfers to data backup in the cloud to the use of software as a service, where an external IT service provider operates software and IT infrastructure. In this way, depending on the constellation, costs can be reduced, for example, by requiring fewer local IT resources.

In addition to the pure IT infrastructure, the cloud also forms the basis for entirely new process designs or business models. A typical example is collaboration tools, applications that enable various actors to work together on specific data or projects stored “in the cloud.” Further examples can be found in faster image processing in the medical field, more efficient order processing in agriculture, or in logistics – the possible uses are ultimately virtually unlimited.

As with any IT application or infrastructure, cloud services are associated with various security issues. In addition to technical and organizational aspects, these also include legal issues such as protecting personal data. In the public discussion, the range of cloud security assessments ranges from blanket rejection to the consideration that a single company can only achieve the high level of Security of a specialized cloud provider with great effort.

Cloud Security And Standards

Accordingly, cloud providers are also required to monitor and implement existing and changed legal requirements continuously.

In recent years, various approaches have developed on this basis, detailing the security requirements for cloud services and providers. The typical requirements range from compliance with basic IT security standards such as the ISO / IEC 2700x series to the use of state-of-the-art encryption methods and protection against, for example, DDoS attacks to ensure availability.

The basic idea behind the corresponding standards is, on the one hand, the definition of state of the art, to which reference is made in various legal bases and which can thus be relevant as a benchmark concerning questions of liability or decisions on fines. On the other hand, standards can also be contractually agreed upon between the provider and user if required, which is particularly common for projects in the B2B area.

Labels And Certification

In areas ranging from product quality to organic food, numerous certifications and labels are intended to increase customer confidence in certain products. In most cases, these are awarded by private-sector organizations based on previously defined catalogs of requirements. Corresponding offers also exist concerning the Security of cloud services.

However, in cloud services, in particular, the security requirements are comparatively extensive and complex. Therefore, in connection with labels, it should be noted that providers or services without a specific brand are not automatically “unsafe.” A title can be the first indication, but the actual security requirements and measures require an in-depth analysis on a case-by-case basis for all cloud projects that go beyond standard applications.

In this respect, the respective standards and catalogs of requirements can be used in several ways, for example:

For cloud providers as a basis for implementing and documenting security measures according to the state of the art
As a basis for a corresponding certification or the receipt of a label as a cloud provider
Independent of labels in the sense of a checklist for the consideration of security aspects in the context of cloud project.

Also Read: Cybersecurity In Companies- To Protect Themselves When Introducing 5G

What Is Artificial Intelligence?

Techno Rumours

October 4, 2021

Artificial intelligence is already one of the most critical technologies of the future. But what is it explicitly about, what benefits does the technology bring for companies, and where are possible application areas?

What Is Ai

When artificial intelligence (AI) is mentioned, so-called weak AI is usually referred to: Individual human capabilities – such as recognizing text, image content, or specific patterns – are transferred to machines. One sub-area is “machine learning”: Mathematical techniques enable a device to independently recognize relationships based on large amounts of data and project the knowledge gained onto future work steps. However, these methods usually require large and high-quality data sets and can only be used to a limited extent in many areas. In addition, machine learning processes have only been able to make predictions and not provide any explanations for relationships.

Vital artificial intelligence aims to create an intellect capable of anything that a human would also be capable of. So far, this form of AI is still a vision of the future. Algorithms have long been part of mathematics, and artificial intelligence has been worked on for 20 years. But only today’s enormous computing power makes it possible to understand vast amounts of data, draw conclusions from data patterns, learn and change results, and, last but not least, interact with systems or customers. However, whether a strong AI can ever be created from this is controversial.

What Can AI Do?

Swarm Intelligence

A population of autonomous software programs cooperates to solve problems. For example, based on this principle, a swarm of autonomous robots can be developed that has collective perception. This means that the individual swarm robots collect their data about their environment and have access to the data of other swarm members. In this way, the swarm has collective knowledge, and tasks can be solved through cooperation between the swarm members.

Language Understanding

Machine learning enables software programs to read from a spoken sentence what language it is and the content of the sentence. In addition, the speech recognition algorithms allow the creation of answer sentences and thus dialogue with the technology used. Such applications of artificial intelligence are often used in everyday life for voice control of technical devices.

Emotional Skills

Systems that are developed to recognize and interpret human emotions fall into the research area of affective computing. Based on factors such as the pitch of the voice or the facial expression, such systems can conclude a person’s emotional state. The purpose of such applications is that machines develop a better understanding of humans and become capable of social interaction.

Artistic Creativity

In art, too, patterns can be recognized through machine learning processes, among other things. These can then be automatically assembled into entirely new works of art. By analyzing the results of an artist, contemporary pieces can be created in his style. In the meantime, artificial intelligence works have already won poetry competitions and raised large sums of money at art auctions.

Image Understanding

AI is also suitable for recognizing patterns in image material if enough images are made available as a reference in the machine learning process. This enables visual quality control to be automated in logistics, for example. Artificial intelligence now usually achieves better results in this area, and this has been constant over time.

Robotics

Robots can use artificial intelligence to learn independently to solve new tasks and to react to their environment. This means that more complex tasks can be automated. Other application possibilities for AI listed here, such as image or speech recognition, also come into play in robotics. Artificial intelligence enables robots to better support people outside of a controlled environment, such as private households or public facilities.

Logical Reasoning

As soon as human knowledge has been formalized and thus made readable for machines, logical conclusions can be drawn from the lowdown based on algorithms. Such procedures are used, among other things, to automate mathematical proof procedures. Some mathematical laws could only be proven with such methods and the high computing power of modern computers.

Automatic Planning

Planning and optimization problems can be solved by artificial intelligence based on collected data. The procedures are used, among other things, in logistics, production planning, or the automated setting of prices. The AI makes its decisions based on optimization algorithms and predicts future events.

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

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

Techno Rumours

October 2, 2021

This Is How Companies Can Set Up Their IT Infrastructure To Be Future-Proof

In times of increasing data traffic and increased business-critical applications, companies should prepare for digital transformation. Many are still a long way from a future-oriented, cloud-compatible IT infrastructure. For them, converged infrastructure systems can be a quickly implementable alternative to conventional solutions.

It sounds like a relic of the old days, but servers based on RISC and Unix systems are still common practice in some companies; they continue to prevail, particularly in the financial and health sectors. This does not mean that the companies are modern and future-proof. A particular obstacle is that the systems are not suitable for integrating cloud environments due to their proprietary system software. At the same time, new applications and the know-how among IT professionals for the old platforms are increasingly dying out.

Companies that continue to operate such an outdated IT infrastructure despite everything quickly lag behind the competition. Because the advancing digitization “forces” companies to set up their IT environments in an agile, flexible, and decisive manner. A healthy thought-out IT infrastructure solution that ideally supports all cloud variants can be the solution here.

Rethink The Foundations Of The IT Infrastructure In The Company

The new cloud-based IT environment should support existing applications and processes and design agile development and deployment models. Because a future-proof IT infrastructure acts as a strategic partner for a company, automating and simplifying (existing) business processes. An alternative to the outdated solutions is cloud-compatible converged infrastructure systems that meet all the criteria of sustainable corporate IT. They include selected and validated server, storage, and network components. These are combined in an optimized IT system – including management and virtualization software.

Converged Infrastructure:

Where Does It Make Sense?

While a Hyper-Converged Infrastructure (HCI) primarily appeals to companies that want to set up a standardized platform for exclusively virtualized workloads and microservices with little effort, a Converged Infrastructure (CI) is particularly suitable for companies with more than 500 employees. These have high demands on their IT infrastructure regarding scalability, performance, availability, and reliability.

The advantages of a CI solution: Thanks to the integrated architecture, it can be used in virtualized and non-virtualized customer environments and hybrid cloud scenarios. Furthermore, it easily adapts to different requirements. On the one hand, this minimizes the business risk and, on the other hand, increases the efficiency of the data centers.

Datacenter Architecture: Modern And Custom-Made

The FlexPod-CI is designed to increase IT responsiveness while reducing computing costs. It consists of four main components: Unified Computing System (UCS), Unified Management software, storage components, and data center switches – all coordinated with one another. Therefore, the implementation effort is deficient; ideally, it takes less than a day to get the CI up and running.

Since the UCS systems have a programmable infrastructure, users can centrally manage the server resources using unified management software and flexibly assign workloads. Different automation functions simplify operation. Users also can centrally manage FlexPods working in different data centers and automating the entire stack. Further advantages for companies with demanding business applications: Fast flash memory for applications that require short response times, central storage management, automated data management, and high performance of the data center switches with data rates of up to 400 Gbit / s.

IT Infrastructure In The Company: FlexPod In Practice

In practice, this can mean: Increase reliability, create data backups. The IT service provider Logicalis implemented a FlexPod system with a NetApp Metro cluster with 30 terabytes for the medium-sized office manufacturer. Thus, Palmberg can save CIFS (Common Internet File System) data and secure virtual machines ten times faster. Thanks to the deduplication of the CIFS data, the company could save about 40 percent of the storage volume.

Reason for the implementation: a complete renovation of the data center. In collaboration with Logicalis, the university’s IT department developed a concept based on the FlexPod CI. It ensures that the IT infrastructure in the company is flexible and expandable as required. The higher reliability, especially of the SAP systems, is another advantage of the solution.

IT Infrastructure In The Company: Little Effort, Significant Effect

With competent support for implementing a converged infrastructure, companies can quickly set up their IT infrastructure for the future. Such a solution, which can be flexibly adapted to the respective requirements, relieves the IT department. This can again concentrate on its core business and easily control demanding business applications.

ALSO READ: Hyperscale: How Companies Get The Most Out Of The Hybrid Cloud

Mobile Robots: How AI And Edge Computing Are Accelerating Their Use

Techno Rumours

October 2, 2021

Autonomous things are changing many industries in which processes based on artificial intelligence and edge computing were so far more of a marginal phenomenon. Intelligent devices such as mobile robots are advancing into fields of application that require physical interaction with people and the environment.

Companies that want to benefit from the great potential in automating tasks should closely follow the current trends in robotics and edge computing. Mobile robots, for example, offer a multitude of potential applications that are no longer a dream of the future. But what determines the new autonomy of things? Autonomous things interact with each other and with people in an extended ecosystem without human supervision.

The use of autonomous devices is made possible primarily by advances in artificial intelligence, network technology, and cloud and edge computing. They are used in a wide variety of areas: from household appliances to driverless transport systems and drones in warehouses to the maintenance and monitoring of systems and buildings and self-driving automobiles.

Mobile Robots In Use During The Vehicle Inspection

A concrete example from practice is the mobile robot “Spot” from Boston Dynamics. He can climb stairs, take high-resolution photos with the help of machine image processing and collect valuable data for preventive maintenance. Among other things, it is used for vehicle inspections. Equipped with computer vision technology and the necessary computing power onboard, Spot can independently walk around a vehicle and register its condition. With additional dashboards, employees can see the overall status at a glance via the app.

Further future-oriented application scenarios already exist, for example, Reply projects in building information management and system monitoring showed. With the help of predictive maintenance models and deep learning algorithms, potential threats to health, safety, or the environment can be identified. The mobility and autonomy of the robot enable precise visual and acoustic measurements or gas detection in areas that are difficult to access.

Data Collection Through Wireless Connectivity And Edge Computing

Thanks to object and pattern recognition, mobile robots can navigate and continuously record data with various sensors – from cameras to microphones and GPS to temperature, humidity, gas, or radiation detectors. Wireless connectivity with intelligent system architecture and cloud edge computing makes it possible to use the captured data with AI and machine learning algorithms that give the mobile robot autonomy to decide how it best performs a task.

The AI is divided over several levels in an intelligent network, from a central cloud to an edge cloud to the individual robots. This means: As edge devices, mobile robots analyze the data directly before it is sent to the cloud. This preprocessing can, among other things, drastically reduce the data volume and save energy when transferring data to the cloud. This method is particularly suitable for tasks that would overwhelm the device’s storage capacity. This approach can also be followed to protect sensitive data from being accessed in the best possible way.

Mobile Robots: Adapting Artificial Intelligence To The Required Performance

The demands on computing power and energy consumption are pretty considerable for autonomous things. In principle, such devices can be equipped with exactly the degree of intelligence that they need for the performance they require. So-called “weak AI” is sufficient for a robot that does simple tasks. For more demanding activities, on the other hand, more robust variants are required. Implementing the AI in the right place is imperative so that the corresponding robot is not overloaded and the high speed of decision-making is maintained.

Smooth Communication Between Man And Machine

When using autonomously moving robots, communication between humans and machines must run smoothly. Voice interfaces are increasingly gaining acceptance here as a medium of interaction. Machine learning models for speech recognition and technologies such as sentiment analysis, semantic networks, ontologies, and self-learning chatbots help applications understand natural language as well as possible.

The world of new autonomous things opens up many new perspectives. It is now a matter of setting the technical parameters correctly for each application. In case of doubt, a competent partner familiar with the many individual aspects is constructive. In addition, the partner can optimally configure the equipment of the desired solution.

ALSO READ: Transformation In A Crisis: Automation And AI Are Changing World Of Work

Transformation In A Crisis: Automation And AI Are Changing World Of Work

Techno Rumours

October 1, 2021

Automation and AI are changing the world of work. The Covid-19 pandemic and the resulting uncertainty are causing loss to companies, to convert their work even faster. In this situation, managers focus in particular on their employees and their further development. How artificial intelligence and automation will change the world of work in the future.

Every third employee does not have enough time for training and education in their working life.
The use of predictive analytics in companies has increased fivefold.

Even before the Corona crisis and its effects, 99 percent of companies were transforming. According to Mercer’s new study “Global Talent Trends 2020”, 42 per cent of employees assume that their job will be replaced by artificial intelligence and automation within the next three years. Seventy-one percent believe that their employers prepare them well for the future of work, and 77 per cent trust that their company will train them accordingly if their job changes due to increasing automation.

Automation And AI Transformation: Bringing Profitability And Empathy Together

Automation and AI are changing the world of work. The study offers comprehensive insights into nine industries and 16 regions of the world; 450 people were interviewed.

“It is important to reconcile economic efficiency and empathy, especially in uncertain times like these. Companies need both a financial model and a cultural mindset that allows them to prepare for the future and position themselves accordingly,”. “Rethinking the purpose and priorities – that is important for the entire company, but especially for HR managers. This year’s study results clarify that the HR function plays a key role in building a sustainable organization. “

Trend 1 In The Automation And AI Transformation: Focus On Futures

Ninety-six per cent of company managers believe that the purpose of an organization, i.e. its purpose, should go beyond the requirements of the shareholders. However, only 28 percent of companies meet this requirement today. According to the study, every third employee would prefer to work for an employer responsible to all stakeholders, not just to shareholders and investors.

The design of a sustainable business model is a topic that is definitely on the agenda of many executives – 80 per cent want to focus more on sustainability in the areas of the environment, social affairs and corporate governance. While 71 percent of employees trust that their employer will prepare them for the future of work, 69 per cent feel threatened by a burnout risk.

The way we look at career paths is also changing: 84 per cent of the employees surveyed state that they can imagine working beyond retirement age. On the other hand, 73 percent of companies do not have active programs for dealing with employees shortly before the statutory retirement age. “Dealing with older employees and personnel planning that does justice to all generations are becoming more important,” .

ALSO READ: AI Systems: How New Technology Is Changing IT Professions

Trend 2 In The Transformation: Race To Reskill

Ninety-nine per cent of the companies surveyed are currently transforming and at the same time report significant skills gaps. But even though 75 percent of employees say they are ready to learn new skills, 33 percent say they don’t have enough time for training.

Additionally, only 37 per cent of HR leaders invest in reskilling employees as part of their strategy to prepare for the future of work. In addition, 41 per cent do not know what skills their workforce has today. “When it comes to transformation, the question is not whether, but how. To stay at the top, companies have to train their employees on a large scale, quickly and across all generations,”.

But which skills will be most in-demand over the next twelve months? When asked about the top 3 skills, HR managers named entrepreneurship and a global mindset in second and third place. Employees, on the other hand, named innovation and problem-solving skills as the top two. At the top for both groups is digital marketing.

Trend 3: Sense With Science

Machine learning is constantly evolving and permeating more and more industries and lifestyles. The use of predictive analytics, i.e. predicting future developments by evaluating data, has increased almost fivefold in five years . Still, only 51 percent of organizations use metrics to identify employees who are likely to quit.

Automation and AI are changing the world of work. After all, 49 per cent have an eye on when key employees are likely to retire, 18 per cent know the effects of salary strategies on employee performance, and 14 per cent use analyses to correct and prevent inequality. 13 per cent can determine whether it is better to hire employees externally, build them up internally or use freelancers. Other forms of data collection on employee engagement are also increasing: 61 per cent of companies are already using tools for pulse checks or regular feedback, and 33 per cent are planning to invest in them this year.

While machines outperform humans in tasks that focus on speed and scalability, humans are still superior when it comes to verifying meaningfulness and judgment – both central elements of ethical decision-making. Sixty per cent of HR managers are confident that they can ensure that Artificial Intelligence is free from bias and that no prejudices are institutionalized. However, codes of ethics on the collection, application and impact of staff reviews are still in their infancy.

In talent assessment, in particular, it is important to combine digital methods and human intuition. Today, only about every second employee has had positive experiences with assessments and found them useful. “Companies today know more about human behaviour and cognition than ever before. How they collect this information and react to it requires serious consideration of moral and ethical aspects,”.

Trend 4: Energize The Experience

Automation and AI are changing the world of work. The topic of employee experience, i.e. the experiences that employees have in their job, has found its way into the HR function. Seventy-four per cent of companies are redesigning their work to focus more on their employees. Yet only 28 percent of C-suite executives believe that investing in employee experience in the company will pay off. And while 67 percent of employees trust their employer to take care of their wellbeing, only 26 percent of HR leaders have a health and wellbeing strategy in place. Only three per cent say that they offer an outstanding employee experience.

The topic plays a role here. Employees whose businesses are focused on health and wellbeing are twice as likely to be motivated. And motivated employees are essential to realizing a company’s transformation plan: They are more likely to stay with the company, are more resilient, and are more willing to learn accordingly.

ALSO READ: Cloud Strategy: 3 Tips On How Cloud Security Becomes Business Enabler

Cloud Strategy: 3 Tips On How Cloud Security Becomes Business Enabler

Techno Rumours

October 1, 2021

The term security encompasses more than just protecting a company. A holistic security strategy supports all business processes up to DevOps projects instead of restricting them. To implement a holistic cloud strategy, suitable security tools must be integrated and responsibilities assigned to avoid misunderstandings, and to be able to defend the complex cloud environments against cyberattacks. Complexity is increasing as more and more companies realize that a single cloud environment is not the right approach in the long run. Whether private or public, every cloud service offers different tools and options, from advanced machine learning tools to low prices for storage space.

For most companies, this means that sooner or later, they will pursue a cloud strategy that takes a multi-cloud environment into account. This requires a uniform security platform that performs security controls and compliance for hosts and containers in an automated form, regardless of the cloud provider or the deployment model used, to meet the requirements of DevOps and the various clouds. For cloud security to succeed, companies should consider three key components: unification, automation, and integration.

Cloud Strategy: Standardization Of Security Solutions

If you look at today’s security situation, you see that the threat actors are the same, but the environment that needs to be protected has changed significantly. Traditional security tools cannot save a cloud because they were not developed for dynamic cloud environments and have gaps in visibility and security. And even if they have been retrofitted, they have become unusable for the types of attacks targeting cloud environments. Overcoming these current cybersecurity challenges is untenable for security teams who want to keep up with the realities of a cloud-native world with selective solutions.

When the limitations of these stand-alone products become obvious, this often leads to ad hoc approaches aimed at fixing blind spots and a lack of integration. The solution is simple: Protect the cloud by using the cloud. A cloud-native security platform is the best way to eliminate the gaps in invisibility and scale it to the needs of a company, from containers to microservices.

Armed with full visibility and continuous workload detection, these platforms support vulnerability identification efforts and ultimately help DevOps teams weave security into CI / CD workflows so that issues can be resolved before they reach production. IT security needs to keep pace with DevOps and work across all clouds to maintain security and visibility as workloads are moved to be truly effective.

Automation Is Critical To Any Cloud Strategy

Another characteristic of a multi-cloud environment is its fast pace. A good example of the dynamics of a cloud environment is microservices, which can be set up quickly and are often very short-lived. Therefore, companies need to know which processes are being carried out where and who is carrying them out. This is where automated detection and monitoring of assets come into play. Companies can use it to get an overview of everything without slowing down a business process.

By interlinking security with CI / CD, the guarantee can be increased by enabling a “shift left” approach. Thanks to automation, the security system can be orchestrated more effectively to remedy weaknesses and security risks early in the development life cycle. However, care must ensure that security gaps are not introduced using Infrastructure-as-Code (IaC) templates. Automation prevents security from being an obstacle for developers. Instead, it reduces complexity and enables rapid deployment, providing organizations with the visibility and security orchestration needed.

Integrated Security Solutions Are Scalable

When a company renews its security strategy, it is important to consider that it cannot work in isolation, especially when working with DevOps. Its integration enables the security department to work seamlessly with applications, cloud instances and cloud workloads. Only the integration turns an average security strategy into an effective one. When examining non-cloud-native tools, it becomes clear that they are not designed to protect dynamic cloud environments.

The latter are often not optimized for cloud-native applications and make monitoring more difficult. They also require additional manual intervention. In contrast, cloud-native solutions offer consistency across the entire cloud environment and maintain the level of security and compliance without incurring as much overhead as on-premise tools that were previously relied on.

Cloud Strategy: More Transparency And Control Through A Security Platform

Only the interaction of the three components described above results in a cloud strategy, including a security platform that can support companies in their growth. Cloud-native security platforms offer visibility and control across public, private, hybrid and multi-cloud environments. Complemented by automation, this enables security teams to focus on more important tasks instead of identifying cloud misconfigurations that can be used for cyberattacks. Many problems are avoided much earlier, and success for the company is achieved more quickly.

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AI In Mechanical Engineering: This Is How Companies Get Started

Techno Rumours

October 1, 2021

Fraunhofer spin-off plus10 works with mechanical engineering companies to develop industry-specific use cases for artificial intelligence, making it easier to get started with AI. The workshop provides systematic road maps for machine manufacturers. The workshop format has already been successfully implemented at the plant manufacturer Hosokawa Alpine.

Some machine manufacturers have already tackled the integration of Artificial Intelligence (AI) in their machines and systems and in-house processes. Due to the novelty and versatility of these approaches, appropriate expert knowledge is usually not available or only partially available in the company. Therefore, mechanical engineers look for and find the missing AI expertise externally. So it happens that many system manufacturers enter into cooperation with start-ups.

As a current study by the VDMA Startup-Machine shows, there is often a lack of a systematic approach to such collaborations, although this can be decisive for success. That is why the Fraunhofer spin-off has a plus10an offer specially developed for mechanical engineering companies. This makes it easier for companies to take the first steps towards AI in mechanical engineering. As a provider of AI software for machine and production optimization, the experts at plus10 know the potential that arises from using artificial intelligence. They are also informed about what is technically possible and where problems lurk.

Integrative Workshop To Get Started With AI, Especially For Mechanical Engineering Companies

As the study by VDMA Startup-Machine shows, more than 50% of the manufacturers of machines and systems have already entered into cooperation with various start-ups. For 84% of the surveyed plant manufacturers, the motives are to develop new products or improve existing ones. To remain competitive, companies get external input. But for cooperation to be a success, it is essential that it is strategic and aligned with a previously defined target. It is precisely for this reason that plus10 has developed an integrative workshop format, especially for machine and system manufacturers. This workshop systematically develops the introduction to AI that is individually tailored to the company.

It shows that there is a willingness and openness for new technologies in mechanical engineering. Often the only thing missing is the knowledge or experience of what would be technically possible with AI and what prerequisites are necessary for this. Identifying these points of contact and potential use cases in the company is the goal of plus10’s AI introductory package. This is not a piece of one-sided advice. The use cases are developed together: Employees from the company bring valuable business knowledge from different perspectives to the workshop.

The Necessary Expertise For AI Applications In Mechanical Engineering Companies

At the same time, AI and automation technology specialists from plus10 provide the necessary expertise on AI basics, existing solutions and best practices in mechanical engineering. After the organized and systematic derivation of use cases, these are technically assessed with the AI experts. In this way, the company develops AI use cases that are individually suitable for them with the associated requirements, benefits and challenges. As a result of the workshop, machine builders receive a specific use case preselection, including a technical assessment, so that they can move on to implementation promptly.

The joint development of possible applications in the company has further advantages: On the one hand, it creates an understanding of new technologies among employees and thus supports the development of knowledge and skills in this area. On the other hand, the acceptance of digital projects in the workforce is decisive for whether the deployment is successful. The VDMA Startup Machine Study also confirms this: Projects involving employees as supporters are more successful . It is therefore worth promoting the support of internal experts by integrating them into digital measures.

AI In Mechanical Engineering: Workshop At The Plant Manufacturer Hosokawa Alpine

The collaboration between mechanical engineering companies and AI start-ups also showed successes at the plus10 workshop and the plant manufacturer Hosokawa Alpine. Christian Riendl, Head of Electrical Engineering of the Film Extrusion Division of Hosokawa Alpine AG, reports on the joint seminar: “Together with plus10, within two days we worked out specific applications of how artificial intelligence can be integrated into our systems in a way that adds value and at the same time is technically realistic. With its concrete examples for implementation in mechanical and plant engineering, plus10 made the topic very tangible. That helped us extensively to identify AI applications for our systems. “

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