Daily Archives: March 27, 2022

Summary

Automating factories is the way of the future, but it needs to be completed holistically, thoughtfullyand thoroughly to realize all of the benefits.

Adding automation to factories is a multi-faceted initiative. Both the benefits and the challenges are plentiful. The issues associated with the workforce alone are varied and complex. Additionally, there are processes associated with hidden factories, those that may not be documented, but do, indeed take place to flow value through an operation.

How to characterize the return on investment also requires rational and sometimes creative justification depending on the goal one is hoping to achieve through the automation project(s). In spite of these challenges, the benefits of automation cannot be ignored, and may, indeed, be just what is needed to have a competitive factory.

Hidden Factories":Implementing automation may be one of the only ways to eliminate tribal knowledge and ensure that processes are captured and performed consistently without the intervention or dependence on the workforce members that know how to progress parts through experience and work-arounds. This means that factors creating variation must be dealt with, instead of compensated for.

Product quality:Automated technology will lead to less variation in people-dependent processes, equating to improved quality.For example, older machining equipment typically requires very skilled operators to manually set and check axes multiple times during a process.There may also be some creativity required to properly set up different part numbers.Loading programs into automated equipment requires the operator to choose the right program, but not depend on that operator to set up each and every parameter correctly.This will greatly reduce the incidents in which the dreaded operator error arises in problem-solving meetings in which rework or scrap decisions must be made.

These are but a few of the benefits associated with automating a factory.Based on these alone, it begs the question as to why every company isnt investing in automation now.Or, why werent investments made in previous years when the gap in skilled labor was first recognized.There can be many answers to these questions; however, in our experience it has been a combination of incomplete knowledge on best application, navigating the capital expenditure process, and, at times, adverse reactions among the workforce, concerned about losing their jobs.

Purchasing new automation equipment requires investment, and it competes with other use of limited cash. Unless there is a clear growth path for the business revenue in which automated equipment is a pure capacity increase, extra people are usually generated through the implementation process.If people are not being exited from the business, it can be hard to justify the expenditure.However, if people are being let go, automation projects can be met with resistance.People who remain employed may not have the skills required to maintain or program the automated equipment.Additionally, some of these newly required skillsets are not readily available in the marketplace.It is a difficult position businesses find themselves in.Also, just because the variation of the process is greatly reduced, does not mean that the quality or availability challenges of incoming material are automatically solved. Many times this must be resolved to fully experience the complete benefits of the automation.

Create a long-term plan.Three to five years would be an excellent place to start. Depending on the equipment needed, planning capital projects and acquiring the funds, combined with the lead time of the equipment, could take 18 months or longer in some cases. Planning horizons that exceed five years are subject to change as technology progresses rapidly. Understanding what equipment will be retired and what will replace it, the cost and rough benefit of each will be necessary. We suggest replacing unsafe equipment first, then moving toward equipment with higher customer demand to gain the most benefit.

Examine each piece of equipment and understand which part families could go onto the new automated equipment.Since the cycle times should be significantly less, or the yield significantly better, you may be able to combine demand from multiple pieces of legacy equipment ultimately requiring less machinery and floor space in the new configuration.Perhaps the highest-end, fastest equipment you are considering will ultimately leave too much excess capacity because of the characteristics and demand that can go onto it.In this case, maybe a lower-level piece is a better option?

Understand the impact on people.Again, with faster cycle times and better quality, fewer people should be required to produce the same value.It would be good if freed up human capacity could be trained to perform other functions in the enterprise such as continuous improvement.

These are a few examples of the challenges and benefits that automation can bring to factories.Automating factories is the way of the future, but it needs to be completed holistically, thoughtfullyand thoroughly to realize all of the benefits.

Shannon Karels (pictured on the right) is a senior operations manager who has led multiple lean transformations and run operations for two large publicly traded corporations across various industries and business models. She started her career in supply chain management and progressed through lean and operations leadership roles. Through this journey, Shannon has improved cultures in numerous manufacturing facilities by leading employee empowerment and engagement, and building highly successful teams, with a focus on business results. Shannon holds a Bachelors Degree in Supply Chain Management from Western Michigan University.Kathy Miller(pictured on the left) is a senior operations executive who has held numerous global vice president and director roles both in manufacturing and lean enterprise leadership. Kathy is a Shingo Prize Recipient for Large Businesses as a Plant Manager. She started her career in Operations as a 17-year-old co-op student at a vehicle assembly plant, and progressed through engineering, marketing, lean, and operations leadership roles, working for four large publicly traded corporations in executive roles. Kathy is a transformational leader who consistently delivers impressive business results through team development, process discipline, and continuous improvement. Kathy holds a Bachelors in Industrial Systems Engineering from Kettering University, MBA from Ball State University and a Masters in Applied Positive Psychology from the University of Pennsylvania. Kathy was inducted into the Women in Manufacturing Hall of Fame in 2021.

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Adding Automation to Factories: A Multi-faceted Initiative - Automation.com

Call for change: a wider scope of automation

Self-Optimizing Network (SON) is not a sufficient answer to the new demands:

The Ericsson Intelligent RAN Automation portfolio, shown in Figure 1, features end-to-end network automation that includes centralized and distributed SON solutions and new capabilities that support the transformation to a more open environment enabled for AI/ML, which empowers innovation and support for wide range of use cases, shorter time to market and is highly adaptable supporting existing and future networks.

Figure 1 Transformation of current offerings into a more open environment for innovation

The objective of RAN automation is to boost RAN performance and operational efficiency by replacing the manual work of developing, installing, deploying, managing, optimizing and retiring of RAN functions with automated processes. The AIs role is to unlock more advanced network automation performance to make RAN network functions more autonomous and replace manual processes with intelligent tools that augment humans. Furthermore, it makes both AI/ML powered RAN network functions and tools more robust for deployment in different environments.

Ericsson AI and automation foundations gives service providers the platforms, and evolved life cycle management of RAN SW and services to evolve networks efficiently to successfully meet ever-changing demands. The aim to deliver improved network performance, accelerate time to market for new capabilities, target right investment for improved ROI and enhanced operational efficiency.

Ericsson Intelligent RAN Automation solutions provide the right automation where it makes sense, gives most bang for the buck. The Figure 2 illustrates how the task of efficiently operating a RAN to best utilize the deployed resources can be divided into different control loops acting all together according to different time scales and with different scopes. Intelligent RAN Automation solutions utilize AI/ML algorithms interacting and integrating with engineered algorithms and existing processes, where applicable, in all these control loops.

Figure 2 Holistic take on RAN Automation

The two fastest control loops are related to traditional Radio Resource Management (RRM). Examples include link adaptation in the fastest control loop and cell supervision in the second fastest) control loop. Functionality in these control loops is mostly autonomous, although mostly driven by engineered algorithms requiring complex configurations in a timeframe ranging from milliseconds (ms) to several hundred ms. In many cases AI/ML makes it possible to enhance the functionality in the fast control loops to make them more adaptive and robust for deployment in different environments. This, in turn, minimizes the amount of configuration optimization that is needed in the slow control loops.

The slower control loops shown in Figure 2 are related to traditional Network design, optimization and management. Examples include RAN coordination and network power management. In contrast to the two fast control loops these slower loops are today to a large degree manual. The slow control loops encompass the bulk of the manual work that will disappear as a result of RAN automation, which explains why AI/ML is especially attractive in these loops.

In the near term, we expect the AI/ML powered solutions to be more accurate and efficient for certain use-cases or applications than the rule-based powered solutions. In the long-term AI/ML powered solutions will be ubiquitous in the RAN and AI/ML just another tool to achieve best performing and cost-effective network.

Compared to traditional software, AI/ML technology introduces the elements of training, model concept drift, federated learning, and a stronger need for access to data. The life cycle management (LCM) processes set the roles of suppliers, integrators and CSPs in essence, who is responsible for what and who sells what to whom. As an industry, we must adjust the LCM of software to include AI/ML-LCM and technology to reach its potential as it evolves, maintaining a clear separation of concern and, with a minimum of variants, to avoid industry fragmentation. A very high-level AI/ML LCM process is captured in the figure below.

Figure 3 A high-level life cycle management (LCM)

We recognize four main LCM alternatives as shown in the Figure 3:

AI/ML lends itself much better to choosing level of global vs local adaptation in comparison with traditional rule-based solutions. Globalization of AI/ML model can be described, as a model that is trained once, e.g. in vendor environment and deployed in many networks and situations or it has ability to adapt correctly to new previously unseen data, etc. There is also a need to do local adaptations and train or re-train the AI models with unique local data.

Data collection is probably the biggest challenge to scaling the AI/ML. Public data (e.g. performance monitoring) is standardized exposed data available from a product or service supplied by a vendor to CSPs for the purpose of product operations and/or service delivery. Non-public data (e.g. AI model debug trace), on the other hand, is data containing sensitive information relating to Intelligent Property Rights (IPR) and is used by the vendor for innovation, and/or service development, verification and deployment. Generated non-public data is typically hundred thousand times larger in volume than the public data. Ericsson has therefore developed mechanisms to bring out just the data that is needed for the relevant use case from specific network elements.

A simulated environment is often used as our first development step with AI/ML-based algorithms, regardless of whether we use public and non-public field data or simulated data to train the final model.

The AI/ML algorithm may be improved over time, or complemented with other algorithms, to make the predictions more accurate, or by re-training the model with local data in the network where it operates. In a longer perspective, this iterative development may result in centralization of certain AI/ML resources as the system architecture and capabilities evolve. Data-driven development is important components in evolution of life cycle management of RAN SW.

We make a distinction between initial training of AI/ML algorithm, here defined as creating and training an ML algorithm in design phase, or training and re-training in maintenance phase. Once the AI/ML features are identified for the initial training of the AI/ML model, we know what data is needed for re-training when the model starts to drift, which might impact efficiency of network function or a process. The re-training can either be done off-line in data-driven development at Ericsson or within the operators network. In the latter case, the re-training is done with customer-unique data and often with the purpose of adapting to local environment that are hard to generalize with the data available off-line.

The industry has recognized that in order to transition to an industrialization phase and enable mass adoption of AI/ML, industry alignment is required. This results in all the major industry bodies trying to work out how they can leverage the technologies and claim their stake in the AI/ML landscape, leading to multiple and somewhat diverging directions being taken. To accelerate the coming industrialization phase and mass adoption, the industry must do more to align standards between 3GPP, ORAN, ONAP and ETSI by:

Objective of AI is to unlock more advanced network performance and automation and ultimately it is about delivering the value to customers. Data sciences are combined with telecom knowledge to create use-case driven and business driven approach to implementing AI where it makes most sense.

Table 1 Few of the latest use cases being industrialized

AI powered link adaptation is network optimization solution targeting improved spectrum efficiency. The feature introduces a neural network Ericsson compute to enhance link properties giving an improved spectral efficiency and increased throughput. Current link adaptation is optimized for high loaded systems. By utilizing information from adjacent cells, we enhance link adaptation for medium loaded systems with significant improvements in the spectrum efficiency.

AI powered advance cell supervision is network healing solution. Locally executed, self-learning, on RAN Compute, and self-retraining on the Centralized Training System, Machine Learning algorithms allows for continuous full network supervision that continuously looks for anomaly in cells performance. Capable of instantaneous and predictive detection and immediate recovery actions with minimal impact.

This offers Instantaneous detection and recovery of cells with degraded KPI, resulting in improved In-Service Performance (ISP). At model drift RBS triggers AI model re-training and deployment of new model by Centralized Training System in the cloud.

AI powered inter-DU coordination is network deployment solution where NR carrier aggregation (CA) between e.g., low-band/ high-band, provides enhanced peak rate and coverage extension. Selecting and configuring the most optimal DU partners, on a network-wide basis, can be challenging and time-consuming. Advanced RAN Coordination optimizes and automates this task, via a central application for optimal partner selection over the entire network. Machine Learning algorithms are used to predict the cell load to secure an optimal selection removing the need or manual selection and configuration.

Downlink Power Optimization is network optimization solution that uses Deep Reinforcement Learning technology to identify if cell TX power can be reduced without compromising coverage or performance. Equally the solution identifies cells where power increase is required for performance improvement. Power optimization saves energy and allows maximizing radio capacity in markets with strict RF emissions regulations. Continuous closed-loop optimization automatically maintains the optimum settings as the network evolves and traffic distributions change. Resulting in DL power reduction on coverage layer while maintaining traffic volume and improving DL and UL performance.

Ericsson is well on the way to innovate and significantly change the way the automation of RAN is done. Leveraging thought leadership in the intersection between data-driven AI/ML principles and RAN automation expertise, AI/ML is applied in the Ericsson Intelligent RAN Automation solution. The Ericsson Intelligent RAN Automation portfolio, which is the next step in SON, features end-to-end network automation and new capabilities that support the transformation to a more open environment enabled for AI/ML, empowering innovation, support for wide range of use cases, shorter time to market and is highly adaptable supporting existing and future networks. This innovative solution transforms RAN SW life cycle enabling AI operations and provides AI functions where it makes most sense. Ericsson AI and automation foundations give our customers the platforms, and evolved life cycle management of RAN SW and services to evolve networks efficiently to successfully meet ever-changing demands. Applying AI in RAN enables to industrialize a wide range of use cases working across various control loop time frames. The use cases will enable our customers to create business value in terms of improved performance, higher efficiency, enhanced customer experience and ultimately create new revenue streams.

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How best to apply AI in the Intelligent RAN Automation - Ericsson

Panasonic Factory Solutions Europe has introduced its NPM G Series, an integrated range of Surface Mounted Technology production systems designed to respond in real-time to customer supply and demand changes through continuous, autonomous updates helping to make the autonomous factory a reality.

The Panasonic NPM G Series offers flexibility and customisable options to address production needs and extend automation in manufacturing.

Many production sites still rely on their employees knowledge and manpower the so called 5M (Human, Machine, Material, Method, Measurement) method. The new Panasonic NPM G Series uses Artificial Intelligence (AI) and automated production to improve the process.

The use of the auto setting feeder (ASF), the NPM-GP / L stencil printer and the NPM-GH pick and place machine enables customers to set-up individual, flexible, efficient, and economical production lines.

Automated material supply is delivered by the ASF smoothly inserting components into the production line, without the requirement for an operator to physically connect the old component reel with the new reel using splice tapes or clips. It automatically peels off the cover tape for feeding surface-mounted components with a width of 4mm to 104mm.

The loading unit then automatically feeds the next tape and refills as required. Existing Panasonic machines from the NPM and NPM-X Series can also be adapted and equipped with the new ASF.

In addition, the NPM-GP / L stencil printer offers high-precision printing and solder performance combined with a high degree of integrated automation. Up to 10 masks can be stored and switched autonomously by a mask changer.

An automatic solder supply and remove function, in combination with autonomous replacement of the underlay pin that supports the PCB, enables efficient and high-speed production.

The printer achieves a printing performance with a mechanical repeat accuracy of 3.8 m and a cycle time of 12 seconds, which includes the cleaning process after each printing operation.

Further in the production line, the compact and lightweight placement head of the NPM-GH pick and place machine delivers high-level productivity (max. 41,000 chips per hour total number of assembled chips per hour under optimal conditions) with outstanding accuracy of 15 m, as well as optional ultra-precise placement at 10 m.

Thanks to simultaneous front and rear operations, operability has been improved. The NPM-GH increases overall quality, output and ultimately boosts automation within the production line.

The entire production process and machines are monitored through feed-forward and feed-back communication technology, called APC-5M. Adaptive Process Control (APC) tracks the correct placement of components based on the solder printing position and transfers feedback to the stencil printer in case of misalignment.

APC-5M detects 5M variations as well as line changes in real-time and ensures a smooth production process without downtime. Thanks to AI, the control system improves and specifies detections and feedback after each production process.

The sequential release of NPM G Series has started with additional solutions being released throughout the year.

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Panasonic launches new factory automation systems - Robotics and Automation News

This DIY Home Automation Market report studies the diverse and growth picture of the DIY Home Automation industry. It is an executive summary of the DIY Home Automation industry highlighting its major trends, other findings, and recommendations. The report studies the DIY Home Automation industry by highlighting the individual companies, investors, producers, distributors, and providers of raw materials. The report details the risks, opportunities, mature segments, and emerging segments in the market. The report forecasts the revenue growth of the global DIY Home Automation market at regional, global, and country level and analyzes the latest trends in the industry in the every sub-segment in the industry.

Key Players in the DIY Home Automation market:

Icontrol Networks, Inc., Nortek, Inc., Smart labs, Inc, Nest Labs, Inc., Ismartalarm, Belkin International, Inc., Wink, Crestron, HomeSeer, Frontpoint, Savant, Honeywell International, Inc., Johnson Controls, Inc., Schneider Electric SE, Legrand SA, Ingersoll-Rand PLC, ABB Ltd., Acuity Brands, Inc, and Samsung Electronics Co., Ltd.IGR Competitive Quadrant

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DIY Home Automation Market Types:

by Product Type, Technology, Application, and Geography, IGR Competitive Analysis,

DIY Home Automation Market Applications:

Application I,Application II,Application III

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The report studies the global DIY Home Automation industry covering the growth, challenges and digital disruption in the industry.

The growth prospects of the DIY Home Automation industry are studied in the report.

The leaders that have historically held a dominant position and are expected to remain dominant in the future as well are listed in the report.

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Orbis Research (orbisresearch.com) is a single point aid for all your market research requirements. We have a vast database of reports from leading publishers and authors across the globe. We specialize in delivering customized reports as per the requirements of our clients. We have complete information about our publishers and hence are sure about the accuracy of the industries and verticals of their specialization. This helps our clients to map their needs and we produce the perfect required market research study for our clients.

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DIY Home Automation Market Key Players Change the View of the Global Face of Industry by 2028: Icontrol Networks, Inc., Nortek, Inc., Smart labs, Inc,...

This Semiconductor Robotic Automation Market report studies the diverse and growth picture of the Semiconductor Robotic Automation industry. It is an executive summary of the Semiconductor Robotic Automation industry highlighting its major trends, other findings, and recommendations. The report studies the Semiconductor Robotic Automation industry by highlighting the individual companies, investors, producers, distributors, and providers of raw materials. The report details the risks, opportunities, mature segments, and emerging segments in the market. The report forecasts the revenue growth of the global Semiconductor Robotic Automation market at regional, global, and country level and analyzes the latest trends in the industry in the every sub-segment in the industry.

Key Players in the Semiconductor Robotic Automation market:

FANUCKUKAABBYaskawaKawasakiDENSONachi-FujikoshiOTCCOMAUOmron Adept TechnologiesSIASUNHIWIN(TW)YamahaGSKTriowinNanjing Estun AutomationStubliRobostar

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The yearly production volumes, amount of products and goods consumed annually, the market of Semiconductor Robotic Automation industry across the globe is studied in the report. Moreover, the diverse consumer base of the Semiconductor Robotic Automation industry, investors, previous data, and financial value of the market is determined in the report. The diversity of demand for the products and services available in the market and the robust nature of the Semiconductor Robotic Automation industry that is enabling market players for investing in the market are studied in the report. The government-developed policies and Semiconductor Robotic Automation industry standards are detailed in the study. The policies developed by the governments, international agencies, and policy-makers that have reduced the barriers and increased access to the market are elaborated in the study.

Semiconductor Robotic Automation Market Types:

Assembling RobotTransfer RobotTesting RobotOthers

Semiconductor Robotic Automation Market Applications:

Raw Silicon WaferIntegrated CircuitOthers

The Goals of the Semiconductor Robotic Automation Industry Report Are To:

Report the demand dynamics, changing profit dynamics, market and distribution trends, new product innovations and technology interventions, and new business strategies.

To study the global patterns in the Semiconductor Robotic Automation industry goods and products production.

To highlight the leading firms in the market across the world.

To present the factors that are influencing the global Semiconductor Robotic Automation market.

To highlight the risks, opportunities, mature segments, and emerging segments in the market.

Highlights of the Report:

The report studies the global Semiconductor Robotic Automation industry covering the growth, challenges and digital disruption in the industry.

The growth prospects of the Semiconductor Robotic Automation industry are studied in the report.

The leaders that have historically held a dominant position and are expected to remain dominant in the future as well are listed in the report.

The domestic Semiconductor Robotic Automation industry and its vulnerabilities like cheap imports and demand fluctuations are focused in the report.

The factors leading to growth and profitability of the industry are given in the report.

The present situation of all the sectors in the global Semiconductor Robotic Automation market is detailed here.

Do Inquiry before Accessing Report at: https://www.orbisresearch.com/contacts/enquiry-before-buying/6641966?utm_source=PoojaGIRM

About Us:

Orbis Research (orbisresearch.com) is a single point aid for all your market research requirements. We have a vast database of reports from leading publishers and authors across the globe. We specialize in delivering customized reports as per the requirements of our clients. We have complete information about our publishers and hence are sure about the accuracy of the industries and verticals of their specialization. This helps our clients to map their needs and we produce the perfect required market research study for our clients.

Contact Us:

Hector CostelloSenior Manager Client Engagements4144N Central Expressway,Suite 600, Dallas,Texas 75204, U.S.A.Phone No.: USA: +1 (972)-362-8199 | IND: +91 895 659 5155Email ID: [emailprotected]

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Semiconductor Robotic Automation Market Expected To Surge Significantly By 2028: FANUC, KUKA, ABB, Yaskawa, Kawasaki, ChattTenn Sports - ChattTenn...

GMDs analysis indicates that the cumulative revenue of global industrial automation equipment (IAE) market will reach $1,717.8 billion during 2019-2025, resulting from a continuous growth of 4.55% per annum over the forecast years.

Highlighted with 64 tables and 76 figures, this 209-page report GlobalIndustrial Automation Equipment (IAE) Marketby Equipment Type, Industry Vertical, and Region 2014-2025: Growth Opportunity and Business Strategy is based on a comprehensive research of the entire global industrial automation equipment market and all its sub-segments through extensively detailed classifications. Profound analysis and assessment are generated from premium primary and secondary information sources with inputs derived from industry professionals across the value chain. The report provides historical market data for 2014-2017, revenue estimates for 2018, and forecasts from 2019 till 2025. (Please note: Before delivery, the report will be updated so that the latest historical year is the base year and the forecast covers the next 5-10 years over the base year.)

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In-depth qualitative analyses include identification and investigation of the following aspects: Market Structure Growth Drivers Restraints and Challenges Emerging Product Trends & Market Opportunities Porters Fiver Forces

The trend and outlook of global market is forecast in optimistic, balanced, and conservative view. The balanced (most likely) projection is used to quantify global industrial automation equipment market in every aspect of the classification from perspectives of Equipment Type, Industry Vertical, and Region.

Based on equipment type, the global market is segmented into the following sub-markets with annual revenue included for 2014-2025 (historical and forecast) for each section. Automation Equipment Sector (further split into Discrete Controllers & Visualization, Process Control, Switchgear) Power Transmission Equipment (further split into Rotary Products and Linear Products) Motors and Motor Controls (further split into Motors & Generators and Motor Controls)

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Based on application in industrial verticals, the global market is segmented into the following sub-markets with annual revenue included for 2014-2025 (historical and forecast) for each section. Oil & Gas Automotive & Transportation Mining & Metals Machine Manufacturing Energy and Power Electrical & Electronics Aerospace & Defense Chemical Industry Pharmaceuticals Food & Beverages Other Industries

Geographically, the following regions together with the listed national markets are fully investigated: APAC (Japan, China, South Korea, Australia, India, and Rest of APAC) Europe (Germany, UK, France, Spain, Italy, Rest of Europe) North America (U.S. and Canada) Latin America (Brazil, Mexico, Argentina, Rest of Latin America) RoW (Saudi Arabia, United Arab Emirates, Iran)

For each of the aforementioned regions and countries, detailed analysis and data for annual revenue are available for 2014-2025. The breakdown of all regional markets by country and split of key national markets by Equipment Type and Industry Vertical over the forecast years are also included.

The report also covers current competitive scenario and the predicted manufacture trend; and profiles key vendors including market leaders and important emerging players.Specifically, potential risks associated with investing in global industrial automation equipment market are assayed quantitatively and qualitatively through GMDs Risk Assessment System. According to the risk analysis and evaluation, Critical Success Factors (CSFs) are generated as a guidance to help investors & stockholders identify emerging opportunities, manage and minimize the risks, develop appropriate business models, and make wise strategies and decisions.

Key Players:ABB Ltd.Azbil CorporationBharat Heavy Electrical LtdDanaher CorporationEmerson Electric Co.Fanuc Corp.General Electric Co.Hitachi, Ltd.Honeywell International Inc.Johnson Controls IncMetso CorporationMitsubishi Electric Corp.Nextnine LtdNovaTech Process Solutions LLCOmron CorporationRockwell Automation Inc.Samsung ElectronicsSchneider Electric SESiemens AGToshiba Machine Co., Ltd.Yaskawa Electric Corp.Yokogawa Electric Corporation

(Please note: Before delivery, the report will be updated so that the latest historical year is the base year and the forecast covers the next 5-10 years over the base year.)

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Industrial Automation Equipment (IAE) Market size is expected to reach US$ XX Mn by the end of 2030 ChattTenn Sports - ChattTenn Sports

In a post-pandemic working environment that has not only prompted the move towards hybrid working but has also seen enterprises increasingly looking for flexible and agile management tools that can be delivered in the cloud, unified communications (UC) and collaboration services software developer Kurmi Software has launched Kurmi as a service (KaaS).

The platform is fundamentally designed to deliver simplified provisioning of unified communications and contact centre services to enterprises worldwide from the cloud. KaaS pairs all the capabilities of Kurmis on-premise platform with a hosted model that is intended to deliver faster and easier access to the tools required to streamline and optimise the day-to-day administration of unified communication platforms, including Avaya, Cisco, Microsoft and Zoom.

Outlining reasons for the launch, Kurmi said research had shown that the Unified Communications market is expected to reach $344bn by 2028, driven by digital transformation, a hybrid workforce environment and a surge in enterprise cloud adoption and migration. It also pointed out that the dynamic had in no small part been accelerated by the pandemic, and that a move towards hybrid working has also seen enterprises increasingly looking for flexible and agile management tools that can be delivered in the cloud.

Enterprises are always looking at ways to cut costs and the volume of hours required to support critical UC solutions is a big part of this, said Thibaut Felgeres, CEO at Kurmi Software. This is why we have developed Kurmi as a service, to extend the delivery model options for our UC automation services beyond on-premise into the cloud. KaaS enables users to benefit from all the existing Kurmi Software features, but with the added flexibility of total cloud management, instantaneous updates and a predictable upfront monthly cost.

Developed to meet this rising demand for UC automation, KaaS is attributed with accelerating the implementation of UC management and creates an improved customer experience, automatically delivering the latest and most up-to-date release of the platform, without the IT overhead. It also offers a transparent and predictable monthly cost model with a range of additional benefits for enterprise customers.

Kurmi added that with KaaS, the ability to create templates by site or function and set up repeatable processes could reduce the amount of time needed to onboard new users by up to 80%, and transforms provisioning UC into a fast, one-click activity.

Additionally, automated moves, adds, changes and deletes (MACDs) workflows and processes, including flexible Role Based Access Control (RBAC) and advanced features such as rollback and scheduling, means that IT admin teams can assign day-to-day UC management tasks to employees without advanced technical skills, including helpdesk staff, local administrators and HR assistants.

With the latest in data encryption and cloud-based systems maintenance, KaaS is said to offer benefits with security software, removing the need for manual integration or security expertise by automatically keeping up-to-date and compliant with the latest regulations and threats, with all platform management handled by Kurmi. All KaaS users will receive access to the latest Kurmi support for new features from their platform suppliers without delay.

Providing an alternative delivery model to Kurmis on-premise service, KaaS will complement the existing Kurmi UC Provisioning suite, giving customers the same experience, but with what the company assures will be the added flexibility and peace of mind that comes with a SaaS model.

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Kurmi brings UC, CC provisioning automation to the cloud - ComputerWeekly.com

Strata, Mubadala Investment Companys aerospace unit, plans to expand its advanced manufacturing capabilities to include more segments in the industrial sector as part of its new long-term strategy unveiled on Wednesday.

The company will focus on a broad range of high-tech production opportunities covering biopharma, advanced materials manufacturing, digitisation, automation and more, it said in a statement.

Today we embark on a new journey where we will witness a period of significant improvements and growth for Strata. We will not only double down on our aerospace manufacturing capability but also intend to have a larger contribution on the UAE manufacturing and socioeconomic growth by diversifying our manufacturing portfolio, said Ismail Ali Abdulla, Strata's chief executive.

The announcement comes after The National reported in November about the company's plans to expand into advanced manufacturing technology such as robotics and artificial intelligence with the aim of developing local production.

Benefits will include establishing new specialities within the UAE economy, providing high-skill jobs for Emiratis, attracting more companies to carry out their activities from the Emirates and offering small and medium enterprises (SMEs) new opportunities to work with Strata within these areas, Mr Abdulla said at the time.

Strata's new long-term strategy is in line with the UAE's ambition to more than double its industrial sector's contribution to national gross domestic product to Dh300 billion ($81.6bn) by 2031 from Dh133bn in 2021. The Operation300bn strategy also seeks to support 13,500 SMEs over the next decade.

By investing in national talent and vision, fostering vibrant commercial and industrial clusters, and forming strong partnerships with global entities who share the same vision of sustainable development, Strata aims to dynamise future growth and investment in new and exciting ways, Mr Abdulla said.

In 2020, the aerospace company outlined its plans to diversify into health technology as the Covid-19 pandemic boosted demand for medical equipment. Strata began producing N95 masks in partnership with Honeywell in May 2020, before seeking export markets.

Strata's pivot to HealthTech is also in line with the UAE's plan to localise high-tech manufacturing capabilities.

The company also aims to transform local businesses into global champions with a shared goal of industry disruption, sustainability and growth, it said on Wednesday.

Strata has promised to create numerous opportunities for local companies and SMEs, all while supporting national industry growth and enhancing their global competitive edge, it said.

Set up by Mubadala in Al Ain a decade ago to position the UAE in the global aerospace supply chain, Strata has billion-dollar contracts with Boeing, Airbus, Leonardo in Italy and Pilatus Aircraft in Switzerland.

The company expected 2021 revenue of $80 million to $100m, similar to the levels of 2020, Mr Abdulla said in November.

Updated: March 23, 2022, 2:33 PM

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Strata's new plan to focus on advanced manufacturing covering biopharma and automation - The National

Aviation instrument maker uAvionix said it has added digital autopilot and external magnetometer interface capabilities to its AV-30-E multimode indicator for experimental aircraft.

The Bigfork, Montana, company said the AV-30-Es ability to control an autopilots heading, altitude, and vertical speed makes it a well-integrated replacement for traditional attitude indicators and directional gyros. The AV-30-E works with modern digital autopilots, which simplifies its installation.

The company said a no-cost software upgrade is available now for the non-certified AV-30-E. The upgrade supports TruTrak Vizion 385 and Vizion PMA, and BendixKing xCruze 100 and AeroCruze 100 autopilots. Other autopilots, including the Trio Pro Pilot, are undergoing testing.

Certification is also underway for the autopilot interface to be used in the AV-30-C, uAvionixs multimode indicator for certificated aircraft. The company said it is also continuing to develop interface adaptors for analog autopilots.

In addition to autopilot interface capabilities, the AV-30 series will soon be able to support an external magnetometer from uAvionix called the AV-Mag. The magnetometer helps increase performance and reduce the pilots workload through magnetically slaved DG operation.

Earlier this month uAvionix, which is also known for its years of work in unmanned aircraft systems and their integration in national airspace, announced its acquisition by private equity firm DC Capital Partners. The move was meant in part to help uAvionix intensify its development programs.

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uAvionix Adds New Automation Interfaces to AV-30-E Instruments - FLYING