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Top 5 Challenges of Implementing Industrial IOT

IIOT INDUSTRIAL INTERNET OF THINGS

IoT solutions have not only conquered our homes in recent years, they are also being used more and more frequently in industry. IoT deployment is diversifying from consumer-based applications to mission-critical applications in the industrial sector and playing a critical role in the next phase of factory automation, which has been called Industry 4.0. The industrial IoT (IIOT) offers companies the full digitization of production processes, cooperating the digital and physical worlds within the factory. The most common scenarios for IIOT include predictive maintenance, intelligent measurement technology, asset management, and fleet management.

 

During this global financial crisis of Covid19, more and more industrial companies are turning to the IIOT to remotely monitor their systems and prevent unplanned downtime. A survey by Microsoft found that 85% of companies have at least one IoT project in the works, and a recent forecast by Million Insights predicted that the Industrial Internet of Things (IIoT) could reach a whopping $992 billion in global spending by 2025. Even though IIOT is transforming the future of manufacturing, it does bring some serious challenges as companies start to accelerate their digital transformation. Here below are listed top 5 challenges that companies have to face in their implementing phase of IIOT.

 

High-Investment and Ownership Cost

The cost of industrial IoT products and their deployment are obviously very high. Sure, one of the main promises of industrial ITO is to improve manufacturing efficiencies and reduce costs through better asset management, access to business intelligence, and productivity gains. However, not only development but support should be considered along with high skill resources who are expert in IoT. So, the overall cost of industrial IoT application implementation is very high. And it’s hard for organizations to justify the cost when they’re not entirely sure what kind of ROI to expect at first.

 

Connectivity

One of the main requirements for adopting the IIOT is having reliable data networks with sufficient capacity. IIoT connectivity should be a forethought before deployment, not an after thought. Studies shows that in 85% of worldwide factories, machines are not connected and are unable to collect / provide data and transmit back to the centre for analysis. One of the main reasons is because legacy devices have a lifespan of 30-60 years and do not support data-driven tools and offer few connectivity options. On the other hand, the number of connected devices is growing at a much higher rate than the network coverage, which creates monitoring and tracking problems. Also, because the Internet is still not available everywhere at the same speed, connectivity issue is the one of the biggest challenges in IIOT deployment. Thus, having scalable IoT network to connect devices and servers is critical for a large scale industrial IoT app.

 

Cybersecurity

Securing industrial IoT devices is a challenge in itself for a number of reasons. With this expansion of the industrial IOT, the attack surface for companies also increases. In case of any successful attack on industrial IOT, not only sensitive data is realised, but it can also cause massive physical damage to machines and bring the entire production of manufacturing companies to a standstill. Thus, security challenges for IIoT technologies are the biggest concern as the breaches affect both individuals and organizations vulnerable to financial and operational damage.

 

When it comes to protection, most cybersecurity protection tools only focus on network and cloud but misses the endpoint and OTA vulnerabilities. “According to IDC, 70% of security breaches originate from endpoints. While organizations may not be able to eliminate all industrial IoT attacks from occurring, OTA vulnerabilities and potential point of entries into endpoint devices should be identified, and devices should be tested using a regularly-updated database of known threats/attacks to monitor device response and detect anomalies”. The ultimate goal for companies and their industrial processes is not only need to adapt to this rapid change but also avoid being target for hacking groups.

 

Data Analysis

A common method for implementing IoT solutions in industrial environments is to expand manufacturing facilities with tools for data acquisition, analysis and visualization. These include sensors, IoT gateways, human-machine interfaces and cloud-based analysis tools that transform raw data from devices into usable insights.

The analysis of data is important to make this voluminous amount of data being produced in every minute via the rapidly growing number of sensors, embedded systems and connected devices as well as the increasing horizontal and vertical networking of value chains, useful. In multiple organizations, this valuable data isn’t being used in a structured and sufficient manner. It is important for business organizations to hire a data scientist having skills that are varied as the job of a data scientist is multidisciplinary and the critical business decisions should be taken effectively.

 

Skill Gap

Industrial IOT project owners realise that one of the most challenging issues with industrial IoT is the skills gap and how to address this issue. Industry is undergoing a rapid change right now and companies have been raising worries of a lack of technical staff. Absence of qualified staff is impacting many areas within the company. According to Microsoft’s 2019 IoT Signals report, 29% of organizations reported that a lack of resources was one of the top reasons for holding off on IoT adoption. For many manufacturers, finding qualified staff to design, deploy and maintain modern industrial networks and the urgent need to update and transform business operations is huge challenge.

 

Sources:

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Industry 4.0: Future of Manufacturing

Industry 4.0: Future of Manufacturing

The concept of Fourth Industrial Revolution was first introduced in 2015 by Klaus Schwab, executive chairman of the World Economic Forum. It’s characterised by the increasing digitization and interconnection of products, smart machines, value chains and business models. Since connected machines collect a tremendous volume of data that can not only analyse that data to identify new patterns and insights but also inform maintenance, performance and other issues, that are impossible for a human to do in a reasonable timeframe. Today, Industry 4.0 offers the opportunity to factories across the globe, the opportunity to optimize their operations quickly and efficiently by knowing what needs attention.

 

Here below technologies that are the significant driver for the advance of Industrial Internet Solutions aka, IIOT:

  • The Internet of Things (and services)
  • The Digitization and Interconnection of products and services
  • Virtual and Augmented reality
  • Cloud Computing
  • Intelligent machines and autonomous Systems
  • Big data and Analytics
  • Cybersecurity

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The Internet of Things is nothing new. It is already present all around us and is growing strongly. Every day new things are linked together. This growing list of objects includes everything from your thermostat to the parking meters, household appliances, heating and cooling systems, vehicles, health and fitness monitors, environmental sensors, and more. Many of these devices can already observe their surroundings, exchange information over internet and report this data back to the user.

 

The adoption of IOT in the world of production is also increasing at an exponential rate. By 2025, Industry 4.0 –the Fourth Industrial Revolution–is expected to generate close to $1 trillion in economic value, much of it is unlocked by continuous operational efficiencies through traditional cost-cutting measures, significant transformation in the way goods are produced and delivered and large manufacturers are becoming more streamlined, efficient, and agile.

 

Whether we call it Smart Industry, Factory Automation or Industry 4.0, the goal remains the same, enable companies to produce more efficiently, which translates into an increased efficient manufacturing and production. The aim of Industry 4.0 is to make factories “smart factories”, producing “smart products with autonomous machines that will self-monitor and organize their own maintenance.

 

More and more companies are using the IIOT to capture new growth through increasing production and creating new hybrid business models, exploiting intelligent technologies to fuel innovation, and transform their workforce. It’s quite clear that the Fourth Industrial Revolution represents a fundamental change in the way we live, work and relate to one another. There is huge potential for digitization and innovation to add value to factories and to contribute to the environment and biodiversity.

 

However, to unlock the true potential of this revolution, companies must digitise and integrate their processes vertically across their entire organisation, from product development and purchasing, through manufacturing, logistics and service. Factories and warehouses must adapt and implement a digital mindset, leverage the IIoT and digitalization to become much more agile and efficient in their production lines around the world.

4 Basic Tips for a Successful Transition to the Cloud

4 Basic Tips for a Successful Transition to the Cloud

IT managers nowadays have to deal with a wide variety of challenges that comes with migrating to the cloud. Although cloud usage has become widespread in recent years, some companies still feel that they have not yet reached the full potential of the cloud.

However, the reasons for this are easy to identify, and cloud usage can be optimized using a few basic measures. Transaction to the cloud successfully means having an experienced partner who know exactly your industry requirements and can answer the following questions before the move. Such as how large and complex is company’s data? How important are regulatory considerations? Are company’s current business applications cloud ready? How much your day-to-day operations can tolerate downtime depends on the type of the application involved and what service level agreement does the company require for a cloud environment? If the company decides to change the cloud provider in the future, can the data and applications migrate with them?

 

Once these questions are answered, IT team can choose their cloud partner who can provide a migration plan and offer a cloud customized solution. Keep in mind that performance, security and reliability must be maintained when moving to clouds. Approach the migration in smaller chunks and stay in close coordination with your cloud provider. The goal is for the entire migration to cause minimal disruptions. Here below are few basic tips for a successful cloud migration and management.

 

Prioritize security

In the cloud age, the security of IT applications plays a particularly important role. Before any move to the cloud, IT managers must go through a list of business applications and identify those that they want to migrate. Planning is the key in order to recover any disaster, risk management and other potential situations. As company’s highly sensitive data, which is also used regularly, is moved to these infrastructures or is already stored on the complex architecture of cloud infrastructures, it makes many IT managers sweat.

IBM’s Cost of a Data Breach Report 2020  has shown that despite a nominal decline from $3.92 million in the 2019 study to $3.86 million in the 2020 study, the average total cost of a data breach  was much lower for some of the most mature companies and industries and much higher for organizations that lagged behind in areas such as security automation and incident response processes.

With the right security measures, however, risks and financial losses can be significantly minimized. While you might expect it’s your cloud provider’s responsibility to take all security measures, it’s also one of the biggest responsibilities of the customer to ensure their data is secure. Here are some of the methods we recommend at Storm to keep yourself safe when using the cloud. IT managers can ensure their data is secure by using methods such as multi factor authentication, strong passwords, data encryption and regular backups.

 

Understand and Enforce your Cloud Governance Plan

When implementing cloud services, many companies fail to develop a clear governance plan from the start and then consistently adhere to it. Governance, may be defined as an agreed-upon set of policies and standards, which are based on a risk assessment and inclusive of audit, measurement, and reporting procedures, as well as enforcement of policies and standards. Most security leaks in the cloud are due to weak corporate governance practices. In a multi-enterprise or multi-platform cloud environment, a lack of governance can not only lead to the loss of highly sensitive data, but also to considerable financial losses.

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Therefore, from the start, companies must not only establish and implement chains of responsibilities, authority and communication to empower people but also establishing measurement, policy and control mechanisms to enable people to carry out their tailored roles and responsibilities towards the respective cloud infrastructure.

 

Prepare your IT teams for cloud

Another challenge that IT departments have to face is the lack of knowledge of employees on the subject of cloud infrastructures. Just like any new technology, your employees need to learn specific skills that allow them to successfully work with the cloud solutions you plan to integrate.  For IT departments, the switch to cloud computing requires not only a different skill set but a different mindset. In order to take all the benefits cloud has to offer, it’s impossible for companies to dive into it without prior training and intelligent strategy. A proper training has a significant impact on cloud adoption, and this is especially true for organizations that invest in more comprehensive training. Once employees undergo training, they can understand where their skills fit and where they can contribute.

 

Optimize the cloud performance

Performance optimization is one of the main reasons why companies switch to the cloud in the first place. Performance optimization on key areas including scalability, concurrency, response time and throughput optimization can help you run better on Cloud. In this optimisation company can correctly select and assign the right resources to a workload or application. Simply put, cloud optimization can help you reduce cloud infrastructure cost and improve your application performance. Once the workload performance, compliance, and cost are correctly and continually balanced against the best-fit infrastructure in real time, efficiency is achieved.

 

Conclusion

The change to the cloud does not happen overnight, nor does it happen with the flick of a finger. You have to invest time, resources, and fund to migrate your applications and data to the cloud successfully. Security risks, a lack of governance, a lack of expertise and performance problems are all challenges that discourage many companies from taking this step. However, as long as companies take a few basic measures, they are well on the way to a successful and secure migration to the cloud.

 

Source :

Cost of a Data Breach Report 2020

How can Digital Marketing benefit from Artificial Intelligence?

AI in Digital Marketing xorlogics

 

AI is now more accessible than ever with it’s potential to change business forever. With its positive impact, and being a lot more affordable than before, both big and small companies are able to benefit from the insights and automation options it provides. With the integration of AI brands can not only leverage customer’s data but also anticipate their customer’s next move, understand sales cycles better, correlate their strategies for converting prospects into paying customers and improve the overall customer’s journey with machine learning efforts.

 

As a subset of AI, machine learning involves the analysis of historical data from various business interactions with customers / prospects (for example: when and what was the last time the customer ordered?). As ML has the ability to analyse extremely large sets of data, its being used in the digital marketing departments around the globe to identify sales patterns and increase success factors. Algorithms for ML generate insights via predictive analytics, based on these insights, marketers can either take actions individually or automate AI to do the job. For example, they can automate emails that are aimed at re-targeting your audience, giving you a better chance of a higher ROI.

 

ML is also playing a major role in SEO. Even tough SEO algorithms change across major search platforms, with AL and ML tools, the insights from searchable content may become more relevant than specific keywords in the search process. In order to maintain a high-ranking place on search engine result pages, consider the quality of your content rather than simply the keywords included. By doing so, you’ll be ahead of the game when it comes to future-forward content creation and SEO.

Marketers can also benefit from ML tools to analyse what type of content, keywords, and phrases are most relevant to your desired audience. Once they have the key insight, they can optimize their dialogue and develop engagement across multiple online platforms, to drive brand awareness and create meaningful relationships with leads, prospects, and customers alike

 

The key difference between modern AI-based and traditional outbound marketing strategies is the integration of contextual data, i.e. information that results from the interaction with the customer is not stored in labour-intensive and time-consuming data warehouses but is available in real time and can be used for decision-making. With these customer profiles and interests recorded in parallel to the interaction, the previously defined decision strategies are fed in order to achieve high planning security for high, profitable conversion rates.

 

Artificial intelligence opens up possibilities in marketing domain far beyond currently available functions. PwC estimates that business could save $2 trillion globally by applying intelligent automation to many activities that were previously processed by humans and making employees more productive. In addition to that, AI, robotics and other forms of smart automation will bring great economic benefits and contribute up to $15 trillion to global GDP by 2030.

 

With AI integrated marketing, business can forecast customer behaviour and run data-based campaigns to have remarkable results. It helps them to save a lot of money on marketing and sales efforts by bringing them valuable leads. Not only business can achieve valuable leads and turn them into customers but they can also maintain a good relation and provide a better service to these customers by introducing a 24/7 customer service with the help of AI equipped chatbots. These chatbots are able to handle customer enquiries and provide customer support on time and appropriately, based on the needs of customers. Business are creating value through transforming customer journeys by providing immediate response to consumer’s queries or issues. According to McKinney’s study, 75% of customer demand NOW service within 5 minutes of online contact. If business can beat this time, they can convert a ‘visitor’ into a ‘paying’ customer.

Here below are some interesting statistics of AI in marketing:

 

  • According to research from Callcredit, 5 hours and 36 minutes is the amount of time that the average marketing professional spends collating data and getting it ready for presentation. An AI integrated marketing dashboard can do the collating in minutes and give you more time for reporting process. Statwolf
  • 97% of leaders believe that the future of marketing lies in the ways that digital marketers work alongside machine-learning based tools. QuanticMind
  • By 2020, 30% of companies worldwide will be using AI in at least one of their sales processes. Gartner
  • By 2020, 85% of customer interactions will be handled without a human. Gartner
  • For 61% of marketers AI is the most important aspect of their data strategy. MeMSQL
  • 80% of business and tech leaders say AI already boosts productivity. Narrative Science
  • When AI is present, 49% of consumers are willing to shop more frequently while 34% will spend more money. PointSource
  • Large businesses with more than 100,000 employees are most likely to have an AI strategy – but only 50% of them currently have one. MIT Sloan Management Review
  • Netflix is saving $1 billion per year by using machine learning to make personalised recommendations- Artelliq
  • 44% of consumers don’t even realise they’re already using AI-powered technology platforms. Pega
  • 45% of end users prefer chatbots as the primary mode of communication for customer service inquiries. Grand View Research

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The ways that ML is being used in digital marketing practices helps organizations to expand their understanding of their target consumers and how they can optimize their interactions with them.  So, when developing appropriate AI applications for marketing purpose, the collaboration between software developers and domain expert’s must not be neglected. The ultimate decision-making competence when it comes to the all-important question of which next best actions are to be implemented in order to achieve the greatest possible marketing success usually belongs to the domain experts. You should have the experience and expertise to incorporate the right NBAs into the self-learning models for decision-making strategies.

 

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What is Edge Computing and why does it matter for the Internet of Things

 

What is Edge Computing and why does it matter for the Internet of Things

Possibilities are that you have already heard the term “edge computing” as many organizations use it in their daily business when it comes to handle, process and deliver data from millions of devices around the world. Multiple definitions of edge computing can be found on the internet, but I like the one from Gartner.

 

Gartner defines edge computing as “a part of a distributed computing topology in which information processing is located close to the edge – where things and people produce or consume that information.” “The emerging category of edge computing has been steadily rising in importance and maturity, and 2020 will be the most interesting year yet for vendors and users in this exciting space,” Forrester  said on Forbes.

 

The IoT is having a significant role in the digital transformation of organizations and industries. The explosive growth of IOT, mobile computing along with new applications that require real-time computing power, either to receive information from the cloud or deliver the data back to the cloud are the main reason of edge-computing systems’s growth.

The amount of data generated by digital devices is escalating day by day as 127 new IoT devices are connected to the web every second. The installed base of active Internet of Things connected devices is forecast to reach 21.5 billion units by 2025. IoT applications important growth place challenging demands on the IT infrastructure. It requires the lowest latency with the highest scalability, reliability and availability. This explains the fact that edge computing has become a special and increasingly important discipline within a network infrastructure.

 

One of the biggest benefits of edge computing is the ability to process and store data faster, enabling for more efficient real-time applications that are critical to companies. As the distributed, open IT architecture of Edge Computing processes a lot of data in real time directly on site and thus drastically reduces the bandwidth requirements and the latency times that inevitably arise during data transmission and data processing in a data center. And regardless of whether an in-house or a cloud data center is used.

 

At the same time, the high and constantly growing data volume and the increase in critical applications at the endpoints (edge) mean that the quality features typical of a data center such as scalability, reliability and high availability are also indispensable for edge infrastructures. Therefore, edge computing and the IOT are the perfect match for several reasons.

Apart from simplifying the data processing at the edge of a network, close to the sensors, and thus avoids the issues of sending all the data directly to the cloud, edge computing in the IoT environment is characterized by these four advantages compared to cloud and data center-centered approaches:

 

Speed

The longer it takes for data to be processed, the less relevant it becomes. Not only, edge computing reduces the total volume of data traffic and thus increases the performance of applications and services, but also makes latency-sensitive applications possible, for example autonomous driving, which would not be possible with data center-based data processing or in areas with insufficient network coverage.

 

Security

The number of IoT devices is constantly growing. This makes them increasingly targets for potential network attacks. Due to the centralized structure, cloud computing is vulnerable to DDoS attacks and failures. Edge computing, on the other hand, distributes applications and processes across different devices, making it much more difficult and complex for attackers to infiltrate the network. Edge computing can also filter sensitive information and, if necessary, only transmit uncritical data in order to meet security and compliance requirements. This means that less data can be intercepted, which makes compliance with security standards easier.

 

Cost

By its nature, the edge is closer to the IoT device than the core or cloud. For IoT use cases, connecting thousands or even millions of devices directly to the cloud is often not feasible due to costs, privacy, and network issues. But, with edge computing, the data can be filtered at the point of origin and does not have to be sent to a data center. Companies therefore have the choice of using the perfect mix of local services and cloud-based applications for a cost-effective IoT solution. Data processing and storage in edge devices reduces the expensive bandwidth requirements and thus optimizes the overall costs.

 

Scalability

Edge computing enables companies to expand their capacity requirements at any time and efficiently by combining IoT devices and edge data centers. The use of edge devices optimizes the scaling costs because processing is decentralised and each additional device is associated with a far lower bandwidth with less load placed on the network.

 

Source :

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Twitter’s Massive Hack 2020 : Reminder on Social Media Security Tips

One of the biggest twitter hack happened last Wednesday in which several large Twitter accounts  belonging to the billionaire Bill Gates, former US President Barack Obama, Tesla boss Elon Must, Apple, Joe Biden among other were hacked. Similar tweets soliciting donations via cryptocurrency Bitcoin were sent from hacked accounts.

 

Barack Obama Joe Biden Elon Must

 

PVV leader Geert Wilders’ Twitter account was also hacked, although it is not clear whether the people behind this hack belongs to the same group or not. Wilders’ profile picture was replaced with a caricature of a black man and his account has been retweeting conspiracy theories. So far no money has been asked for on his page.

In addition to that, accounts from multiple companies sent tweets that were not originally from the company itself. For example, the account of Apple and Uber has been asked to transfer bitcoins. If people would transfer 1000 dollars bitcoins in half an hour, they would receive $ 2,000 in exchange. “Giving back to the community,” was the headline.

 

According to Twitter, the hackers gained access to Twitter employees who have access to the platform’s internal systems. Through that they got access to the verified accounts and hijacked them. Once Twitter became aware of the hack, it immediately locked down affected verified accounts and removed the tweets posted by the attackers. As a precaution, Twitter also limited the functionality of all verified accounts. “This was disruptive, but it was an important step to reduce risk. Access will be restored to original account holders only when we are certain we can do so securely.”

 

In a tweet on Wednesday, CEO Jack Dorsey said « it was a tough day for us at Twitter. We all feel terrible this happened. We’re diagnosing and will share everything we can when we have a more complete understanding of exactly what happened.”

Twitter Support twitter hack

 

It brings us to think how we can stay untouched if hackers can get access to the verified accounts. Just imagine if hackers would also have access on everyone’s DMs how much personal information that they shared via private messages would also have been leaked.

 

So, whether you use your social media account to stay in touch with family/friends or for business purpose, such as brand awareness, grow a large audience, increase your website traffic, generate more leads, connect with your audience in a better way, make more sales and more money, social media may become a critical part of your everyday life. With nearly half of the world’s population using social media platforms, they’re a natural place to reach new and highly targeted potential customers. According to Global social media research summary 2020, social media users are now spending an average of 2 hours and 24 minutes per day multinetworking across an average of 8 social networks and messaging apps.

summary of global social media users around the world

In today’s tech-savvy world, every business is showcasing what they’ve got and make the most of it on social medias. But unless they don’t optimally utilize the online security tools, they are going to be vulnerable to social media security risks like hacking and phishing attacks. Twitter has a large security department and they takes security very seriously, but even with proper training, a hack via social engineering can hardly be prevented. So here below are few reminder safeties tips to deal with social media security issues:

 

  • Use auto-updates to get the latest versions for apps, software and operating systems.
  • Don’t ignore the 2 factor authentification. It is much more difficult to hack your account if you’re using two-factor authentication on it.
  • Change your password frequently. Opt for a strong password instead of using your date of birth, your nick name, your favorite dessert or your pet name.
  • Use unique password for each social media account. Using same password for your twitter/Facebook LinkedIn or other social tools can be dangerous.
  • If you receive any specious link from your followers via email or inbox, don’t just ignore / delete it but always report and spam those accounts.
  • Run a monthly check to verify and block fake accounts as platforms like Facebook and Instagram are full of fake profiles. Those fake accounts can be a hacker, a suspicious organization who wants to monitor your activities.
  • Make a habit of checking your inbox and reading mails received from social media accounts. Many people often ignore those emails, but it might be a notification to alert you about a login attempt by a hacker.
  • Be aware before giving access to any third part apps. It should be clear that before you give an app access to an account, it’s important to understand the permissions it requests and what it does with the data it can access.
  • Create an informed social media policy for your staff against security risks and legal issues. Obviously, your team members will be careful with company information online but it’s important to remain vigilant and watch out for phishing and other social engineering attacks.

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COVID-19: Why RPA alone is not enough

COVID19 Why RPA alone is not enough

 

Robotic Process Automation (RPA) is demonstrating remarkable potential in enterprise deployments, saving thousands of hours each year, reducing wages costs and enabling people to focus on higher-value work. However, recent experience has shown that intelligent and agile automation of business processes requires more than RPA. The biggest problem with enterprise operations today is the simple fact that most firms only implemented RPA and believe that this technology is the only good solution for optimizing their routine processes. An RPA application often has to coexist with other applications supporting different workflows for handling huge digital workload while increasing productivity and reducing costs.

 

RPA is relatively easy and quick to implement technology and promises a strong ROI and is defiantly a helpful element for automating simple, repetitive tasks, especially in connection with legacy systems or documents. However, the current pandemic has shown the limits of this technology.

RPA imitates human behaviour that is repetitive and doesn’t require special knowledge, such as filling in the same information in multiple places, re-entering data, or copying and pasting content, screen scraping, opening emails and attachments, carrying out checklists, vetting contracts, moving files and folders, or reconciling processes as codified rules instruct the computer software ‘robots’ to perform human tasks within a process.

 
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As RPA directly integrates into existing / legacy infrastructure, the most common pitfall in its integration is the lack of strategy, governance, and planning. Robots, which act as digital assistants on end devices in the company and sometimes require the intervention of the employees, can only be made available for the home office with a lot of configuration effort. This is often not even possible due to the presence of legacy infrastructure. One the other hand, holistic, intelligent process automation offers the necessary flexibility and agility that goes beyond RPA and requires additional technologies such as artificial intelligence, case management and business process automation.

 

With the combination of RPA and BPA, companies can comprehensively digitize their entire customer processes, handle and orchestrate flexible units. Human and digital actions can be coordinated and controlled to create seamless end-to-end processes and this control can be quickly and flexibly adapted to new conditions at any time. As RPA does not stand on its own, organizations will need tools such as BPA for overall management of a business process. RPA cannot itself change process or optimize it, it only executes a process, but on the other hand BPA enhance the performance of an implementation of RPA by ensuring that the process being automated is optimized and delivering the required results. The goal of RPA and BPA is to create more efficient and effective workflows. However, both use different forms of automation.

 

With such flexible approach and technology stack, companies have significantly more options in situations like COVID19. For example, there would be the possibility of redesigning the process flows so that they integrate the home offices. Business applications can be adapted very quickly to the new working environment, since the underlying processes and system connections remain constant. This flexibility will not only pay off in crises but will also offer companies the possibilities benefit significantly from this in normal times. For example, when they have to react to new market requirements, with acquisitions and mergers or when certain departments can no longer be filled because the employees have retired and no young people can be found.

 

Therefore, firms need to set a clear vision, governance, and performance objectives when integrating and using RPA solutions to scale up their operations. stakeholders from IT, business, support, compliance, and HR must also be involved in order to ensure strategic alignment across the organization.

RPA – Robotics Process Automation Trends and Statistics for 2020

RPA Robotic Process Automation XORLOGICS

 

This year, many companies found their business growth through the smart interaction between human resources and supporting software robots as the direction is set towards digitalization and automation. The combination of RPA with artificial intelligence (AI) and machine learning (ML) is playing a huge role in the global economic environment.

According to a new report by Grand View Research, Inc. the global robotic process automation market size is expected to reach USD 10.7 billion by 2027, expanding at a CAGR of 33.6% from 2020 to 2027. In addition to that, in Gartner Top 10 Strategic Technology Trends for 2020, hyper-automation, autonomous things and AI security were at the top 3.

 

RPA is a type of IT solution that allows organizations to automate many of their tasks through the use of specialized software programs. Many business executives believe that RPA enables their companies to automate structured tasks that take just a few work steps and repeat themselves frequently – like transferring data from one IT system to another. RPA can relieve employees of tasks that consume a lot of time but do little to add value and are prone to errors – keyword typing errors. It also helps to automate routine jobs on the computer and processes to increase efficiency, improve service and save costs.

 

One should not think that RPA is similar to BPM. RPA only simulates human data entry and helps with simple and recurring tasks, to save time because the software does things in parallel that an employee can only work through one after the other. Such an application needs simple rules with few exceptions – and the biggest advantage is that, unlike humans, it runs around the clock. Unlike BPM which stands for describing, contolling, modeling, and optimizing the processes that are present in an organization.

 

Here below are the statists and trends of RPA that shows it’s worth:

 

    • RPA as an industry is growing exponentially– the global robotic process automation market size is expected to reach USD 10.7 billion by 2027, expanding at a CAGR of 33.6% from 2020 to 2027 – Grand View Research

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    • The RPA industry will grow from $250 million in 2016 to $2.9 billion in 2021. This is one industry that is growing at a lightning speed. It was already worth $1.7 billion in 2018 – Forrester

 

    • RPA will achieve “near universal adoption” in the next 5 years – Deloitte

 

    • By 2025, the market for collaborative robotics is expected to reach $12 billion – MarketsAndMarkets

 

    • By 2024, organizations will lower operational costs by 30% by combining hyperautomation technologies with redesigned operational processes. Gartner

 

    • The RPA fast adoption is helping business to reduce the operational costs and enhance overall customer satisfaction, improve transparency and visibility for service functions and reduce of manual efforts – Reportlinker

 

    • 11,214 results This is the number of open positions produced by a recent search for “robotic process automation”on LinkedIn’s jobs site. Titles vary within this growing IT jobs category, but “RPA developer” (and variations of the same) is an increasingly common one – reflecting the need for IT pros who can build the bots that enable organizations to offload repetitive, time-consuming tasks – LinkedIn

 

    • RPA deals with the application of advanced technologies including artificial intelligence (AI) and machine learning (ML), to increasingly automate processes and augment humans – Gartner

 

    • RPA is offering a lot of benefits to the business by giving access to collaborative intelligence where humans and technology works side by side so that they can perform their roles optimally. As employees don’t need to perform repetitive tedious tasks, they can be educated to work with automation tools and learn the latest business and marketplace information through machine learning – Xorlogics

 

    • The market for RPA in Healthcare is driven by the increasing demand to automate claims and process management. RPA vendors are focusing on developing best-in-class intelligent process automation bots – Research and Markets

 

Demand Forecast Powered by Machine Learning

Demand Forecast Powered by Machine Learning

 

The business landscape is rapidly becoming more global. Largely due to improvements in communicationsand increasing globalization which are dramatically impacting the way business is managed. No area of a business is more affected by the trend of a global business environment than the supply chain. Supply chain logistic, known as the backbone of global trade, is a network of many partners involved such as customer, dealers, manufactures,transportation, external warehouse,suppliersand inventory. Sometimes a delivery comes along with delay, sometimes there is something wrong in a package, delivered article is different to ordered article and sometimes a shipment is lost. This is annoying for all sides. It costs time, energy, money and sometimes even the customer. Challenges for decision-makers in supply chain management are growing due to the widely networked supply chains and the constant change in the environment of companies.

 

In fact, many companies are facing hurdles in their existing business processes and technologies that aren’t flexible enough to deal with “large and global” business environments. Therefore, areas such as manufacturing, distribution, sourcing of materials, invoicing and returns are impacted by the increased integration of a global customer and supplier base.

Supply Chain specialist must deals with long-term planning in terms of location, make-or-buy decisions, supply relationships, capacity dimensioning, logistics strategy and general tasks along with cost optimization in structuring of the logistics and production processes. Hence, in order to initiate the demand forecasting, it’s highly recommended to understand the workflow of machine learning modeling. This offers a data-driven roadmap on how to optimize the development process.

 

Operational inefficencies in SCM often lead to potential revenue losses, increasing costs, and poor customer service, ultimately diminishing profits. With the help of AI, machine learning techniques are able to forecast the right number of products or services to be purchased during a defined time period. In this case, a software system can learn from data for improved analysis. Only good data produces good results!

Data interpretation is a vital part of supply chain management and demand forecast as it’s used to improve your ability to estimate future sales, reduce shortages and overstock. Once the data is interpreted correctly, both in national and international trade results in having the right products at the right time in the right number at the right place.
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So, for demand forecasts that are generated by self-learning algorithms require data that is closely related to sales. However, in order for machine learning to achieve a high quality of forecasting, a certain amount of quality data is required. The result of ML process depends solely on the quality and quantity of data provided.

To ensure that the data is up to date, the input data should not be older than 5 years. Data selection can be a special hurdle before using machine learning methods, because it can be very time-consuming. In connection with the data quality, it must be ensured that there are only a few missing values of the data records in the input data, otherwise the machine learning model may generate incorrect results. Data preparation is necessary for successful implementation and definitely pays off later. If the data record does not have sufficient data quality, it must be prepared through an intensive process and carry sufficient information for qualitative algorithms and for a good forecasting performance.

 

The goal of ML is to develop algorithms which can learn and improve over time and can be used for predictions. Therefore, ML fed with qualitative data can generate precise forecasts and thus ensure a secure basis for planning. The resulting benefits, such as reducing inventory levels and simultaneously optimizing the ability to deliver, also improve the operating result. ML uses learning algorithms to recognize patterns and regularities in data and is able to adapt automatically and independently through feedback and thus react to changes.

 

Compared to traditional demand forecasting methods, machine learning not only accelerates data processing speed but provides a more accurate forecast, automates forecast updates based on the recent data in order to create a robust system.

COVID-19: What Can Artificial Intelligence Contribute to Healthcare Industry

COVID19 What Can Artificial Intelligence Contribute to Healthcare Industry

 

One can hardly escape the mention of Artificial Intelligence, or AI for short, today. AI is reshaping economies, promising to generate productivity gains, improve efficiency and lower costs. We see AI in the movies, books, news, human vs computer games and online. AI is part of robots, self-driving cars, drones, medical systems, online shopping sites, and all sorts of other technologies that affects our daily life in so many ways. It contributes to better lives and helps people make better predictions and more informed decisions. AI is also altering the professional world and this also affects the IT specialists themselves, as their routine activities, sometimes even programming, are beingcarried out by algorithms.

 

AI technologies are seeing rapid acceptance in multiple sectors, such as, healthcare, criminal justice, transport, agriculture, finance, marketing and advertising, science, security the public sector, as well as in augmented and virtual reality (AR & VR) applications. As AI systems can detect patterns in enormous volumes of data and model complex, interdependent systems to generate outcomes that improve the efficiency of decision making, save costs and enable better resource allocation, it’s gaining greater public awareness during the corona pandemic.

 

A Canadian company’s advanced artificial intelligence system Toronto-based BlueDot, was among the first in the world to notice the coronavirus disease emerging from China, by using AI-driven algorithm to go through more than 100,000 articles every day in 65 languages looking for news about more than 150 different diseases. Around 10 a.m. EST on Dec. 31, their system spotted an article in Chinese about a “pneumonia of unknown cause” with 27 cases.

 

The corona crisis confirmed to be a motor for the further development of artificial intelligence. The idea of creating an artificial machine is as old as the invention of the computer. Alan Turing in the early 1950s proposed the Turing test, designed to assess whether a machine could be defined as intelligent. Learning algorithms have long since found their way into everyday life – in the form of navigation systems, voice assistants or vacuum robots.

 

Today AI technologies are playing a huge role to fight back and limit the damages caused by COVID-19 outbreak by detecting and diagnosing the virus and predicting its evolution. Once the virus is studied in detail, it’s medical research on drugs and treatments can be accelerated. Further than that, AI technologies and tools are playing a key role in detecting and diagnosing the virus and predicting its evolution and understanding it in every aspect to accelerate medical research on drugs and treatments. AI is already being used for a range of healthcare and research purposes, including detection of disease, management of chronic conditions, delivery of health services, and drug discovery. Artificial intelligence has the potential to transform how care is delivered and to help address important health challenges, without the right data it has its limits.

 

When talking about AI, many people still think of process optimization, such as replace many routine processes, repetitive tasks that are tiring for people. However, AI can do much more than that, through machine learning, the generation of knowledge from experience, the algorithms are able to deal with unknown data, find patterns and independently derive actions. Chatbots, for example, which are increasingly used in customer service, are getting better and better over time as a result of supported learning. Deep learning based on neural networks enables predictions based on very complex relationships. In order to recognize and use the full potential of AI, IT users not only need development and implementation services, but also comprehensive advice. Because the use of AI elements is not comparable to the introduction of new software. As consulting skills, knowledge of AI systems and industry knowledge are becoming more important, IT consultants and business analysts need to have a knowledge background in order to familiarize potential users with the possibilities of artificial intelligence.

 

Source:

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