MB-320 Microsoft Dynamics 365 for Finance and Operations- Manufacturing (beta) dumps with free pdf made up good pass marks

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MB-320 Microsoft Dynamics 365 for Finance and Operations, Manufacturing (beta)

Skills Measured
Set up and configure manufacturing (30-35%)

Implement and test the production control module
• identify components of unified manufacturing
• validate the interconnectivity between General Ledger and the production control modules
• implement parameters, production orders, and life cycle
• implement and manage subcontracting processes
• configure and manage Costing sheets
• implement and manage work Calendars
• configure Inventory dimensions in Production
• implement and manage resources and resource groups
• create and manage operations and routes

Configure and manage a product configuration model
• build and manage product configuration model components
• create and manage products
• configure and manage constraints
• configure and manage BOM lines and route operations
• configure and manage pricing for production configuration models
• describe the purpose and capabilities of the product configuration models

Create and manage production and lean orders (25-30%)
Create common components of production and lean orders
• create and configure catch weight items
• create production flows
• create and manage Kanbans
• create and manage formulas
• create and process batch, production, and lean orders
• set up and maintain commodity pricing
• apply product compliance standards
• identify items and substitute items within a bill of material (BOM) or formula

Manage scheduling and subcontracting
• implement processes to manage Scrap and Waste for a Discrete order
• implement production scheduling and subcontracting
• implement activity-based subcontracting
• create and maintain project items and item tasks

Create, process, and manage production batch orders (40-45%)
Manage the Production batch order lifecycle
• process Batch orders
• implement containerized packaging
• set up and maintain commodity pricing
• manage product compliance
• implement and configure rebates
• implement lot and batch control processes
• create planned production batch orders by using the Master Planning module
• implement processes to manage Scrap and Waste of a Batch order
• perform a batch Rework
• configure batch reservations and expiration dates
• identify and configure batch attributes for processes
• implement integrated batch processing with warehouse management system (WMS),also known as Advanced Warehouse Management
• complete production processes for co-products and by-products

Manage and maintain formulas
• create and manage co-products and by-products
• create and manage planning items
• create and manage formulas with scalable and percentage-based calculations
• create and manage formulas with co-products, by-products, and planning items
• create and manage formulas with active ingredient-based calculations
• implement step consumption and batch consumption

Configure and manage manufacturing executions
• identify the capabilities and limitations of the manufacturing executions module
• identify the responsibilities of the security role managing the production processes
• process Production orders by using manufacturing execution processes
• process lean orders by using Kanban boards
• identify the process workflows for managing a production environment

Microsoft Dynamics 365 for Finance and Operations, Manufacturing (beta)
Microsoft Manufacturing learner
Killexams : Microsoft Manufacturing learner - BingNews https://killexams.com/pass4sure/exam-detail/MB-320 Search results Killexams : Microsoft Manufacturing learner - BingNews https://killexams.com/pass4sure/exam-detail/MB-320 https://killexams.com/exam_list/Microsoft Killexams : Microsoft is driving digital transformation of the manufacturing sector: Peter Gartenberg Microsoft is driving digital transformation of the manufacturing sector: Peter Gartenberg In conversation with ETtech, Peter Gartenberg,General Manager, Enterprise and Partner Group, Microsoft India talks about their cloud strategy in India, their play in the manufacturing sector, cloud security among others.

Is Microsoft testing the waters by bringing the local datacenters to India? And who owns the datacenters?

India is an important market for us and to support our customers’ digital transformation, cloud services offered through local datacenters will be pivotal to our continued growth. We announced the availability of our commercial cloud services from three local data centers in India at the end of September 2015. These three datacenters are located in Chennai (South India region), Pune (Central India region) and Mumbai (West India region).

We have invested more than $15 billion in building a resilient cloud infrastructure. Our global cloud infrastructure currently includes more than 100 datacenters located in over 40 countries and we are rapidly building capacity to serve our customers worldwide.

Today, we operate more than 100 datacenters including both owned and leased facilities.

What are the various types of cloud which Microsoft offers, and what is the difference?

Microsoft offers hyper-scale public cloud as well as hybrid cloud solutions which provide high-end security and computing services around big data analytics, intelligence and the Internet of Things (IoT) – a key differentiator for us in the market.

When Microsoft says Security, how do you ensure that customer’s data remains secure?

Microsoft adopts a three-pronged approach to enable a secured IT environment for businesses

• Protect all endpoints – from sensors and datacenters to identities and SaaS applications
• Move faster to Detect threats using the scale and intelligence of the cloud, machine learning and behavioral monitoring
• Respond more quickly and comprehensively.

There are three, key important differentiators in Microsoft’s security approach: Platform, Intelligence and Partners.

• Under Platform, Microsoft secures our own technology as well as third parties, whether through identity, device, apps & data or infrastructure. This is done by security teams across Microsoft, Windows, Microsoft Azure and Office365. No other cloud provider offers the breadth of trust features that Microsoft does across cloud platforms.

• Under Intelligence, Microsoft has an intelligent security graph which takes information from across all our services and products, and brings them together to be able to be proactive around security. This allows us to monitor for user authentications and updated devices, and check for spam and malware.

• Under Partners, Microsoft understands this is a broad ecosystem in which we need to work with everyone. We partner with peers, industry, industry associations, government bodies and others in the ecosystem.

Who are adopting the cloud at the highest rate? SMBs, start-ups or big enterprises?

In the first six months of our launching our local cloud services, 52 of the top 100 companies on BSE have embraced Microsoft cloud.

Here are some examples.

o Luminous Power Technologies in association with iBoT Control Systems has launched its next generation Connected Solar Inverter, powered by iBoT’s iQu Internet of Things (IoT) platform. This is built on Microsoft Azure Hyperscale cloud platform.

o One of India’s largest AlcoBev manufacturers was facing the issue of stolen / misplaced Visicoolers (display refrigerators) which was not only leading to reduced availability of chilled beer to customers, but also brand visibility for the company. An integrated hardware (iBoT Connected Processor) + Software (Azure IoT Hub/Storage/App Services) mechanism helped keep track of the Visicoolers leading to increased profitability, prevention of theft and enhanced consumer experience.

o Manufacturing: A large diversified Indian conglomerate leverages the iBot (iQu) platform with Azure IoT Services to integrate existing instrumentation & sensors that monitors resource (electricity, steam & gas) consumption in near-real-time.

• BFSI Sector: These include Financial institutions like IDFC and ICICI Lombard, Kotak Mahindra Bank’s payroll processing and portfolio analytics and HDFC Bank’s customer experience analytics work on Office 365.

• Healthcare Sector: Leading hospitals like Fortis, Apollo Hospitals, AIIMS, L V Prasad Eye Institute (LVPEI) and Narayana Health have adopted the local cloud services to Excellerate their operations, patient care and drive overall business efficiency.

• Government: The Andhra Pradesh government uses Azure Machine learning to predict which students will struggle/drop out of school across its 10,000 schools. Earlier in the year we signed a memorandum of understanding (MoU) with the Punjab government to provide our cloud computing infrastructure for providing better facilities in education, agriculture and healthcare. We signed a similar MoU with the Government of Telangana, in November 2016.

What part of Cloud computing technology has been adopted by the manufacturing industry overall?

Microsoft is driving digital transformation of the manufacturing sector at every stage, starting with Azure IoT Suite available through the Microsoft Azure cloud platform, to the rich insights possible through the Cortana Intelligence Suite, to presenting information in new, natural ways. Microsoft solutions enables

How do you beat AWS?

Microsoft Cloud’s core differentiation is three-fold: Hybrid Approach, Intelligent Platform Services and Trusted Cloud.

• Hybrid Approach - While providers are focused only on Public Cloud Infrastructure as a Service (IaaS), Microsoft has strong focus on, both, on-premise private cloud solutions as well as Public cloud platform (Azure). This allows customers the choice and flexibility of selecting the right cloud for their applications.

• Intelligent Platform Services – Microsoft has been the leader in Intelligent Platform as a Service (PaaS) service like analytics, machine learning, media services, etc. which makes it easy for our customers to move seamlessly from IaaS to advanced high value platform services on the cloud. In a world where agility is an important competitive factor for most companies, this enhances the value that they can derive out of the cloud.

• Trusted Cloud – As enterprises move their applications to cloud, they are looking for cloud services providing security, transparency and compliance. Microsoft offers the highest compliance levels in the industry including “Government of India MeitY Empanelment”.

Your initiatives with the government of India?

Agriculture: Microsoft has collaborated with ICRISAT (International Crops Research Institute for the Semi-Arid Tropics) and the Andhra Pradesh government in creating a Sowing App and Personalized Village Advisory Dashboard to provide powerful cloud-based predictive analytics. The app empowers farmers with crucial information and insights, helping the farmers to determine the most appropriate time to sow seeds. This in turn helps reduce crop failures and achieve optimal harvests.

Education: To boost the Andhra Pradesh’s education ecosystem, Microsoft is working with the Government of Andhra Pradesh on a machine learning based model to analyze student data across its 10,000 schools to predict which students will struggle/drop out. This helps them take preventive action to increase student retention. Officials have created more than 600,000 predictions using Azure Machine Learning, revolutionizing how Indian local governments can increase student retention.

Last Mile Connectivity: Addressing the issue of last mile connectivity, we have piloted last-mile access projects in Srikakulum in Andhra Pradesh and Varanasi in Uttar Pradesh. We have also partnered with the Government of Maharashtra to create the first smart village in Harisal.

Healthcare: L V Prasad Eye Institute is using Azure Machine Learning and Power BI to predict the final surgical outcome of eye surgery patients and provide insights into how blindness spreads across the country, helping health officials develop strategies to fight the issue. Using a predictive model that helps predict regression rates for eye operations, doctors can pinpoint the procedures needed to prevent and treat visual impairments. Using the data, doctors can also better understand the risks involved for a patient, which leads to more effective treatment while reducing costs.

We have also entered into partnerships with the State Governments of Telangana, Punjab and Tamil Nadu to help the Government explore cloud, machine learning and mobile based solutions.

What lacks in terms of infrastructure and governance with respect to datacenters in India?

The government recognizes the transformative power of technology and sees it as an enabler to amplify the change that we all seek in delivering better citizen services. Harnessing the power of innovation to address big societal challenges, requires more than technology itself. It is critical for stakeholders across the ecosystem to come together.

India is seeing massive investment in datacenter and cloud infrastructure recently driven by companies like Microsoft and others. But datacenters are only one aspect of the country’s digital infrastructure. Reliable and ample network connectivity is still not available everywhere and is not available at competitive cost compared to global rates.

It is also equally important to support policy initiatives like open technology standards, intellectual property rights, freedom of expression, and addressing evolving challenges in areas such as data security and privacy. MeitY is a step in the right direction to create a cloud adoption framework.

You recently said, "With the help of the local datacenters, we have been able to address key customer concerns related to public cloud such as security, privacy control, transparency, and compliance." Explain How?

There are two major concerns that enterprises have while adopting cloud – latency for their mission critical applications and data residency. By providing cloud services from local datacenters both these concerns are addressed. We have seen many restricted sectors adopting cloud services in the last one year since we went live from India. Insurance is one example. We have 22 insurance companies using our cloud services. We also have a dominant share of the healthcare industry. Most of the organized healthcare providers – hospitals and pharma companies - are already using our cloud services.

One of the main concerns from potential customers is that Azure only works well for Windows shops. Comment?

You can run operating system, any database, any development framework on any device using Azure. Microsoft Azure is one of the most open platforms in the world. It supports first party workloads (from Microsoft), third party workloads (from IBM, SAP, Oracle), applications from ISVs in Azure Marketplace and a lot of open source products. In fact, a high percentage of virtual machines running on Azure in India run on Linux. Microsoft not only supports open source, but also heavily contributes to open source and uses open source for delivering services on Azure.

Wed, 16 Nov 2022 10:01:00 -0600 en text/html https://cio.economictimes.indiatimes.com/news/strategy-and-management/microsoft-is-driving-digital-transformation-of-the-manufacturing-sector-peter-gartenberg/55867177
Killexams : Leading vehicle manufacturing companies in the IoT theme

The future of the automotive industry will be shaped by a range of disruptive themes, with the internet of things (IoT) being one of the themes which will significantly impact the potential growth of leading companies in the industry.  

The automotive sector faces four synchronous disruptive threats: electric vehicles (EVs), autonomous vehicles (AVs), connected car, and transport as a service (TaaS). IoT refers to the use of connected sensors and actuators to control and monitor the environment, the things that move within it, and the people that act within will play a key role in advancing these four disruptive themes.  

The global IoT market was worth $622bn in 2020, and GlobalData forecasts that the market will reach $1,077bn by 2024. The market value for connected car is expected to climb to $42bn by 2024 from $27bn in 2020. Enterprise IoT dominates the overall IoT market, generating 76% of total revenue in 2020. 

IoT is one of the primary enablers of digital transformation in the automotive sector. It brings together various technologies, like AI, 5G, edge computing, and cloud computing, which helps reduce latency levels to allow real-time decision-making and reduce the need for human intervention in IoT ecosystems. The connected car will increase data generation exponentially and establish itself as a key component of the IoT ecosystem, and eventually, an extension of consumers’ automated homes. Automakers must use this abundance of data to pivot their business models towards software and reduce their dependence on Big Tech to meet the needs of their customers. 

However, not all companies are equal when it comes to their capabilities and investments in the key themes that matter most to their industry. Understanding how companies are positioned and ranked in the most important themes can be a key leading indicator of their future earnings potential and relative competitive position.  

According to GlobalData’s thematic research report, IoT in Automotive, leading adopters of IoT include: BMW, BYD, GM, Honda, SAIC Motor, Toyota, and Tesla.   


Toyota is investing more than $3bn in its Toyota Research Institute and a spin-out new company TRI-AD (Toyota Research Institute – Advanced Development), focused on auto tech. It insists the world is “not close to achieving level 5 autonomy.” It is developing LiDAR with US start-up Luminar. In February 2020, Toyota spread its AV bets a little further with a $400m investment in Chinese company Pony.ai with the intention of co-developing mobility services. Toyota has created a separate mobility company to focus on future mobility, including AVs and smart cities, called Woven Planet. In April 2021, Woven Planet agreed to purchase the level 5 (full-self driving) business from ride-hailing company Lyft. 


BMW was one of the first carmakers to rebrand itself as a mobility service provider. It’s been the first to bring to market some of the most innovative connected vehicle technology features, such as Here maps that provide real-time traffic information. It works closely with Harman at the design stage to package Human-Machine Interface (HMI), connectivity, and telematics services as seamlessly as one might expect of a premium vehicle brand. The company also worked with Microsoft to launch the Open Manufacturing Platform, an open industrial IoT platform to accelerate production and logistics optimisation efforts. BMW has also worked with IBM to explore how Watson’s machine learning services can Excellerate the driving experience. 

To further understand the key themes and technologies disrupting the automotive industry, access GlobalData’s latest thematic research report on IoT in Automotive

  • Nissan
  • Changan Automobile
  • Honda
  • Volkswagen
  • Daimler
  • Ford
  • Hyundai Motor
  • BAIC
  • Dongfeng Autos
  • Tata Motor
  • Mazda Motor Corp
  • Mitsubishi Motors
  • Renault
  • Stellantis
  • Mahindra & Mahindra
  • Suzuki
  • Subaru Corp

GlobalData, the leading provider of industry intelligence, provided the underlying data, research, and analysis used to produce this article.

Thu, 08 Dec 2022 21:46:00 -0600 en-US text/html https://www.just-auto.com/data-insights/top-ranked-vehicle-manufacturing-companies-in-internet-of-things/
Killexams : Microsoft acquires Lumenisity®, an innovator in hollow core fiber (HCF) cable

Today, Microsoft announced it has acquired Lumenisity® Limited, a leader in next-generation hollow core fiber (HCF) solutions. Lumenisity’s innovative and industry-leading HCF product can enable fast, reliable and secure networking for global, enterprise and large-scale organizations. The acquisition will expand Microsoft’s ability to further optimize its global cloud infrastructure and serve Microsoft’s Cloud Platform and Services customers with strict latency and security requirements. The technology can provide benefits across a broad range of industries including healthcare, financial services, manufacturing, retail and government.

Organizations within these sectors could see significant benefit from HCF solutions as they rely on networks and datacenters that require high-speed transactions, enhanced security, increased bandwidth and high-capacity communications. For the public sector, HCF could provide enhanced security and intrusion detection for federal and local governments across the globe. In healthcare, because HCF can accommodate the size and volume of large data sets, it could help accelerate medical image retrieval, facilitating providers’ ability to ingest, persist and share medical imaging data in the cloud. And with the rise of the digital economy, HCF could help international financial institutions seeking fast, secure transactions across a broad geographic region.

Lumenisity HCF benefits

Lumenisity’s next generation of HCF uses a proprietary design where light propagates in an air core, which has significant advantages over traditional cable built with a solid core of glass, including:

  • Increased overall speed and lower latency as light travels through HCF 47% faster than standard silica glass.[1]
  • Enhanced security and intrusion detection due to Lumenisity’s innovative inner structure.
  • Lower costs, increased bandwidth and enhanced network quality due to elimination of fiber nonlinearities and broader spectrum.
  • Potential for ultra-low signal loss enabling deployment over longer distances without repeaters.

Lumenisity was formed in 2017 as a spinoff from the world-renowned Optoelectronics Research Centre (ORC) at the University of Southampton to commercialize breakthroughs in the development of hollow core optical fiber. In 2021 and 2022, the company won the Best Fibre Component Product for their NANF® CoreSmart® HCF cable in the European Conference on Optical Communication (ECOC) Exhibition Industry Awards. As part of the Lumenisity acquisition, Microsoft plans to utilize the organization’s technology and team of industry-leading experts to accelerate innovations in networking and infrastructure.

To learn more visit https://lumenisity.com/.

[1] Speed of Light in Transparent Materials, Matthew J. Parry-Hill and Michael W. Davidson – National High Magnetic Field Laboratory, 1800 East Paul Dirac Dr., The Florida State University, Tallahassee, Florida, 32310, Sept. 10, 2018

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Fri, 09 Dec 2022 01:30:00 -0600 en-US text/html https://blogs.microsoft.com/blog/2022/12/09/microsoft-acquires-lumenisity-an-innovator-in-hollow-core-fiber-hcf-cable/
Killexams : Machine Learning in Manufacturing Market Geographical Segmentation By Forecast Revenue 2023-2028

The MarketWatch News Department was not involved in the creation of this content.

Nov 25, 2022 (The Expresswire) -- Final Report will add the analysis of the impact of COVID-19 on this industry.

"Machine Learning in Manufacturing Market" Insights 2022 - By Applications (Automobile, Energy and Power, Pharmaceuticals, Heavy Metals and Machine Manufacturing, Semiconductors and Electronics, Food and Beveragess), By Types (, Hardware, Software, Services, ), By Segmentation analysis, Regions and Forecast to 2028. The Global Machine Learning in Manufacturing market Report provides In-depth analysis on the market status of the Machine Learning in Manufacturing Top manufacturers with best facts and figures, meaning, Definition, SWOT analysis, PESTAL analysis, expert opinions and the latest developments across the globe., the Machine Learning in Manufacturing Market Report contains Full TOC, Tables and Figures, and Chart with Key Analysis, Pre and Post COVID-19 Market Outbreak Impact Analysis and Situation by Regions.

Machine Learning in Manufacturing Market Size is projected to Reach Multimillion USD by 2028, In comparison to 2021, at unexpected CAGR during the forecast Period 2022-2028.

Browse Detailed TOC, Tables and Figures with Charts that provides exclusive data, information, vital statistics, trends, and competitive landscape details in this niche sector.

Considering the economic change due to COVID-19 and Russia-Ukraine War Influence, Machine Learning in Manufacturing, which accounted for % of the global market of Machine Learning in Manufacturing in 2021


Moreover, it helps new businesses perform a positive assessment of their business plans because it covers a range of courses market participants must be aware of to remain competitive.

Machine Learning in Manufacturing Market Report identifies various key players in the market and sheds light on their strategies and collaborations to combat competition. The comprehensive report provides a two-dimensional picture of the market. By knowing the global revenue of manufacturers, the global price of manufacturers, and the production by manufacturers during the forecast period of 2022 to 2028, the reader can identify the footprints of manufacturers in the Machine Learning in Manufacturing industry.

Machine Learning in Manufacturing Market - Competitive and Segmentation Analysis:

As well as providing an overview of successful marketing strategies, market contributions, and accurate developments of leading companies, the report also offers a dashboard overview of leading companies' past and present performance. Several methodologies and analyses are used in the research report to provide in-depth and accurate information about the Machine Learning in Manufacturing Market.

The Major players covered in the Machine Learning in Manufacturing market report are:

● Intel
● Siemens
● GE
● Google
● Microsoft
● Micron Technology
● Amazon Web Services (AWS)
● Nvidia
● Sight Machine

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Short Description About Machine Learning in Manufacturing Market:

The Global Machine Learning in Manufacturing Market is anticipated to rise at a considerable rate during the forecast period, between 2022 and 2028. In 2020, the market is growing at a steady rate and with the rising adoption of strategies by key players, the market is expected to rise over the projected horizon.

This report focuses on global and United States Machine Learning in Manufacturing market, also covers the segmentation data of other regions in regional level and county level.

Due to the COVID-19 pandemic, the global Machine Learning in Manufacturing market size is estimated to be worth USD million in 2022 and is forecast to a readjusted size of USD million by 2028 with a Impressive CAGR during the review period. Fully considering the economic change by this health crisis, by Type, Machine Learning in Manufacturing accounting for % of the Machine Learning in Manufacturing global market in 2021, is projected to value USD million by 2028, growing at a revised % CAGR in the post-COVID-19 period. While by Application, Machine Learning in Manufacturing was the leading segment, accounting for over percent market share in 2021, and altered to an % CAGR throughout this forecast period.


The global Machine Learning in Manufacturing market is projected to reach USD million by 2028 from an estimated USD million in 2022, at a magnificent CAGR during 2023 and 2028.

Report Scope

This report aims to provide a comprehensive presentation of the global market for Machine Learning in Manufacturing, with both quantitative and qualitative analysis, to help readers develop business/growth strategies, assess the market competitive situation, analyze their position in the current marketplace, and make informed business decisions regarding Machine Learning in Manufacturing.

The Machine Learning in Manufacturing market size, estimations, and forecasts are provided in terms of output/shipments (K Units) and revenue (USD millions), considering 2021 as the base year, with history and forecast data for the period from 2017 to 2028. This report segments the global Machine Learning in Manufacturing market comprehensively. Regional market sizes, concerning products by types, by application, and by players, are also provided. The influence of COVID-19 and the Russia-Ukraine War were considered while estimating market sizes.

For a more in-depth understanding of the market, the report provides profiles of the competitive landscape, key competitors, and their respective market ranks. The report also discusses technological trends and new product developments.

The report will help the Machine Learning in Manufacturing manufacturers, new entrants, and industry chain related companies in this market with information on the revenues, production, and average price for the overall market and the sub-segments across the different segments, by company, product type, application, and regions.

Key Companies and Market Share Insights

In this section, the readers will gain an understanding of the key players competing. This report has studied the key growth strategies, such as innovative trends and developments, intensification of product portfolio, mergers and acquisitions, collaborations, new product innovation, and geographical expansion, undertaken by these participants to maintain their presence. Apart from business strategies, the study includes current developments and key financials. The readers will also get access to the data related to global revenue, price, and sales by manufacturers for the period 2017-2022. This all-inclusive report will certainly serve the clients to stay updated and make effective decisions in their businesses.

Get a sample Copy of the Machine Learning in Manufacturing Report 2022

Machine Learning in Manufacturing Market 2022 is segmented as per type of product and application. Each segment is carefully analyzed for exploring its market potential. All of the segments are studied in detail on the basis of market size, CAGR, market share, consumption, revenue and other vital factors.

Global Machine Learning in Manufacturing Market Revenue Led By Product Type Segment:

● ● Hardware
● Software
● Services

Global Machine Learning in Manufacturing Market Leading End-Use Segment:

● Automobile
● Energy and Power
● Pharmaceuticals
● Heavy Metals and Machine Manufacturing
● Semiconductors and Electronics
● Food and Beverages
● Others

Machine Learning in Manufacturing Market is further classified on the basis of region as follows:

● North America (United States, Canada and Mexico) ● Europe (Germany, UK, France, Italy, Russia and Turkey etc.) ● Asia-Pacific (China, Japan, Korea, India, Australia, Indonesia, Thailand, Philippines, Malaysia and Vietnam) ● South America (Brazil, Argentina, Columbia etc.) ● Middle East and Africa (Saudi Arabia, UAE, Egypt, Nigeria and South Africa)

This Machine Learning in Manufacturing Market Research/Analysis Report Contains Answers to your following Questions

● What are the global trends in the Machine Learning in Manufacturing market? Would the market witness an increase or decline in the demand in the coming years? ● What is the estimated demand for different types of products in Machine Learning in Manufacturing? What are the upcoming industry applications and trends for Machine Learning in Manufacturing market? ● What Are Projections of Global Machine Learning in Manufacturing Industry Considering Capacity, Production and Production Value? What Will Be the Estimation of Cost and Profit? What Will Be Market Share, Supply and Consumption? What about Import and Export? ● Where will the strategic developments take the industry in the mid to long-term? ● What are the factors contributing to the final price of Machine Learning in Manufacturing? What are the raw materials used for Machine Learning in Manufacturing manufacturing? ● How big is the opportunity for the Machine Learning in Manufacturing market? How will the increasing adoption of Machine Learning in Manufacturing for mining impact the growth rate of the overall market? ● How much is the global Machine Learning in Manufacturing market worth? What was the value of the market In 2020? ● Who are the major players operating in the Machine Learning in Manufacturing market? Which companies are the front runners? ● Which are the accurate industry trends that can be implemented to generate additional revenue streams? ● What Should Be Entry Strategies, Countermeasures to Economic Impact, and Marketing Channels for Machine Learning in Manufacturing Industry?

Customization of the Report

Our research analysts will help you to get customized details for your report, which can be modified in terms of a specific region, application or any statistical details. In addition, we are always willing to comply with the study, which triangulated with your own data to make the market research more comprehensive in your perspective.

Inquire more and share questions if any before the purchase on this report at -https://www.360researchreports.com/enquiry/pre-order-enquiry/17766920

Detailed TOC of Global Machine Learning in Manufacturing Market Insights and Forecast to 2028

1 Study Coverage
1.1 Machine Learning in Manufacturing Product Introduction
1.2 Market by Type
1.2.1 Global Machine Learning in Manufacturing Market Size by Type, 2017 VS 2022 VS 2028
1.3 Market by Application
1.3.1 Global Machine Learning in Manufacturing Market Size by Application, 2017 VS 2022 VS 2028

1.4 Study Objectives
1.5 Years Considered

2 Global Machine Learning in Manufacturing Production
2.1 Global Machine Learning in Manufacturing Production Capacity (2017-2028)
2.2 Global Machine Learning in Manufacturing Production by Region: 2017 VS 2022 VS 2028
2.3 Global Machine Learning in Manufacturing Production by Region
2.3.1 Global Machine Learning in Manufacturing Historic Production by Region (2017-2022)
2.3.2 Global Machine Learning in Manufacturing Forecasted Production by Region (2023-2028)
2.4 North America
2.5 Europe
2.6 China
2.7 Japan

3 Global Machine Learning in Manufacturing Sales in Volume andamp Value Estimates and Forecasts
3.1 Global Machine Learning in Manufacturing Sales Estimates and Forecasts 2017-2028
3.2 Global Machine Learning in Manufacturing Revenue Estimates and Forecasts 2017-2028
3.3 Global Machine Learning in Manufacturing Revenue by Region: 2017 VS 2022 VS 2028
3.4 Global Machine Learning in Manufacturing Sales by Region
3.4.1 Global Machine Learning in Manufacturing Sales by Region (2017-2022)
3.4.2 Global Sales Machine Learning in Manufacturing by Region (2023-2028)
3.5 Global Machine Learning in Manufacturing Revenue by Region
3.5.1 Global Machine Learning in Manufacturing Revenue by Region (2017-2022)
3.5.2 Global Machine Learning in Manufacturing Revenue by Region (2023-2028)
3.6 North America
3.7 Europe
3.8 Asia-Pacific
3.9 Latin America
3.10 Middle East andamp Africa

4 Competition by Manufactures
4.1 Global Machine Learning in Manufacturing Production Capacity by Manufacturers
4.2 Global Machine Learning in Manufacturing Sales by Manufacturers
4.2.1 Global Machine Learning in Manufacturing Sales by Manufacturers (2017-2022)
4.2.2 Global Machine Learning in Manufacturing Sales Market Share by Manufacturers (2017-2022)
4.2.3 Global Top 10 and Top 5 Largest Manufacturers of Machine Learning in Manufacturing in 2022
4.3 Global Machine Learning in Manufacturing Revenue by Manufacturers
4.3.1 Global Machine Learning in Manufacturing Revenue by Manufacturers (2017-2022)
4.3.2 Global Machine Learning in Manufacturing Revenue Market Share by Manufacturers (2017-2022)
4.3.3 Global Top 10 and Top 5 Companies by Machine Learning in Manufacturing Revenue in 2022
4.4 Global Machine Learning in Manufacturing Sales Price by Manufacturers
4.5 Analysis of Competitive Landscape
4.5.1 Manufacturers Market Concentration Ratio (CR5 and HHI)
4.5.2 Global Machine Learning in Manufacturing Market Share by Company Type (Tier 1, Tier 2, and Tier 3)
4.5.3 Global Machine Learning in Manufacturing Manufacturers Geographical Distribution
4.6 Mergers andamp Acquisitions, Expansion Plans

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5 Market Size by Type
5.1 Global Machine Learning in Manufacturing Sales by Type
5.1.1 Global Machine Learning in Manufacturing Historical Sales by Type (2017-2022)
5.1.2 Global Machine Learning in Manufacturing Forecasted Sales by Type (2023-2028)
5.1.3 Global Machine Learning in Manufacturing Sales Market Share by Type (2017-2028)
5.2 Global Machine Learning in Manufacturing Revenue by Type
5.2.1 Global Machine Learning in Manufacturing Historical Revenue by Type (2017-2022)
5.2.2 Global Machine Learning in Manufacturing Forecasted Revenue by Type (2023-2028)
5.2.3 Global Machine Learning in Manufacturing Revenue Market Share by Type (2017-2028)
5.3 Global Machine Learning in Manufacturing Price by Type
5.3.1 Global Machine Learning in Manufacturing Price by Type (2017-2022)
5.3.2 Global Machine Learning in Manufacturing Price Forecast by Type (2023-2028)

6 Market Size by Application
6.1 Global Machine Learning in Manufacturing Sales by Application
6.1.1 Global Machine Learning in Manufacturing Historical Sales by Application (2017-2022)
6.1.2 Global Machine Learning in Manufacturing Forecasted Sales by Application (2023-2028)
6.1.3 Global Machine Learning in Manufacturing Sales Market Share by Application (2017-2028)
6.2 Global Machine Learning in Manufacturing Revenue by Application
6.2.1 Global Machine Learning in Manufacturing Historical Revenue by Application (2017-2022)
6.2.2 Global Machine Learning in Manufacturing Forecasted Revenue by Application (2023-2028)
6.2.3 Global Machine Learning in Manufacturing Revenue Market Share by Application (2017-2028)
6.3 Global Machine Learning in Manufacturing Price by Application
6.3.1 Global Machine Learning in Manufacturing Price by Application (2017-2022)
6.3.2 Global Machine Learning in Manufacturing Price Forecast by Application (2023-2028)

7 Machine Learning in Manufacturing Consumption by Regions
7.1 Global Machine Learning in Manufacturing Consumption by Regions
7.1.1 Global Machine Learning in Manufacturing Consumption by Regions
7.1.2 Global Machine Learning in Manufacturing Consumption Market Share by Regions

8.1 North America
8.1.1 North America Machine Learning in Manufacturing Consumption by Application
8.1.2 North America Machine Learning in Manufacturing Consumption by Countries

9.2 United States
9.2.1 Canada
9.2.2 Mexico

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10.1 Europe
10.1.1 Europe Machine Learning in Manufacturing Consumption by Application
10.1.2 Europe Machine Learning in Manufacturing Consumption by Countries
10.1.3 Germany
10.1.4 France
10.1.5 UK
10.1.6 Italy
10.1.7 Russia

11.1 Asia Pacific
11.1.1 Asia Pacific Machine Learning in Manufacturing Consumption by Application
11.1.2 Asia Pacific Machine Learning in Manufacturing Consumption by Countries
11.1.3 China
11.1.4 Japan
11.1.5 South Korea
11.1.6 India
11.1.7 Australia
11.1.8 Indonesia
11.1.9 Thailand
11.1.10 Malaysia
11.1.11 Philippines
11.1.12 Vietnam

12.1 Central and South America
12.1.1 Central and South America Machine Learning in Manufacturing Consumption by Application
12.1.2 Central and South America Machine Learning in Manufacturing Consumption by Countries
12.1.3 Brazil

13.1 Middle East and Africa
13.1.1 Middle East and Africa Machine Learning in Manufacturing Consumption by Application
13.1.2 Middle East and Africa Machine Learning in Manufacturing Consumption by Countries
13.1.3 Turkey
13.1.4 GCC Countries
13.1.7 Egypt
13.1.6 South Africa

14 Corporate Profiles

14.1.1 Corporation Information
14.1.2 Overview
14.1.3 Machine Learning in Manufacturing Sales, Price, Revenue and Gross Margin (2017-2022)
14.1.4 Machine Learning in Manufacturing Product Model Numbers, Pictures, Descriptions and Specifications
14.1.7 accurate Developments

15 Industry Chain and Sales Channels Analysis
15.1 Machine Learning in Manufacturing Industry Chain Analysis
15.2 Machine Learning in Manufacturing Key Raw Materials
15.2.1 Key Raw Materials
15.2.2 Raw Materials Key Suppliers
15.3 Machine Learning in Manufacturing Production Mode andamp Process
15.4 Machine Learning in Manufacturing Sales and Marketing
15.4.1 Machine Learning in Manufacturing Sales Channels
15.4.2 Machine Learning in Manufacturing Distributors
15.7 Machine Learning in Manufacturing Customers

16 Market Drivers, Opportunities, Challenges and Risks Factors Analysis
16.1 Machine Learning in Manufacturing Industry Trends
16.2 Machine Learning in Manufacturing Market Drivers
16.3 Machine Learning in Manufacturing Market Challenges
16.4 Machine Learning in Manufacturing Market Restraints

17 Key Finding in The Global Machine Learning in Manufacturing Study

18 Appendix
18.1 Research Methodology
18.1.1 Methodology/Research Approach
18.1.2 Data Source
18.2 Author Details
18.3 Disclaimer

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Fri, 25 Nov 2022 10:19:00 -0600 en-US text/html https://www.marketwatch.com/press-release/machine-learning-in-manufacturing-market-geographical-segmentation-by-forecast-revenue-2023-2028-2022-11-25
Killexams : 3 High-Yield Tech Stocks to Buy in December

High-yield tech stocks aren't easy to find.

After all, tech companies tend to be growth-oriented, which means they reinvest any profits in growing their business rather than sharing them with investors. However, some tech stocks are able to do both, while others are a good source of quarterly dividend payments for investors looking for income.

Keep reading to see three high-yield tech stocks you can buy today.

1. Broadcom (3% yield)

Broadcom (NASDAQ: AVGO) is a chipmaker, but it's been able to escape much of the malaise in the sector because it focuses on specific niche industries, making chips for set-top boxes of broadband connections, data centers, servers, storage systems, mobile phones, and industrial uses like factory automation and motor controls.

The company has grown throughout its history, both organically and through acquisitions. It recently agreed to acquire VMWare, a provider of multi-cloud services, though that deal has not yet closed.

Broadcom's accurate results show why the company has outperformed the semiconductor sector this year. In its third quarter, revenue jumped 25% to $8.46 billion, and adjusted earnings per share rose 40% to $9.73. Broadcom generates huge margins with an adjusted net margin of 50%, and even on a generally accepted accounting principles (GAAP) basis, it kept 36% of its revenue as profit. That's a sign that the company faces relatively little direct competition, and it's been able to outgrow its peers because it's insulated from the slowdown in the PC industry.

The VMWare deal will help accelerate its software ambitions, and the company looks set to continue delivering growth and income for investors as it has guided to 20% revenue growth in its upcoming fourth-quarter earnings report. 

Broadcom has a strong history of dividend growth as well, as its payout has increased by more than 2,000% over the last decade.

2. Taiwan Semiconductor (2.2% yield)

For investors unfamiliar with Taiwan Semiconductor (NYSE: TSM), Warren Buffett just helped put it on the map as Berkshire Hathway bought more than $4 billion worth of the world's leading chip foundry, and it's easy to see why.

First, TSMC has a massive economic moat as the leading semiconductor foundry in the world. Most chip companies don't manufacture their own chips. Instead, they design them and count on foundries like TSMC to produce them.

Taiwan Semiconductor has tremendous market power, with an estimated 53% of the global foundry market, making it a vital link in the supply chain for myriad industries.

That positioning has enabled TSMC to enjoy monopoly-like profits. In its most accurate quarter, the company posted revenue of $9.27 billion on $20.3 billion, giving it a net profit margin of 36%. Taiwan Semi is also growing quickly, with revenue up 48% in its most accurate quarter.

With chip demand only likely to continue to grow as more products become digitized, TSMC is well positioned for long-term growth, and it's currently building a massive factory in Arizona.

As a longtime dividend payer, the stock currently offers a 2.2% yield.

3. Comcast (3% yield)

Comcast (NASDAQ: CMCSA) may not be thought of as a typical tech stock, but the company's strength in broadband means tech investors should consider it even as it's diversified in industries including cable, telephony, and its entertainment business through NBC Universal, which includes the Universal movie studios and theme parks.

Comcast's broadband segment is now its biggest, and that should help make up for the declines in businesses like cable. In its third quarter, revenue declined 1.5% to $29.8 billion, though that was primarily due to lapping the Olympics in the third quarter of 2022. Profits continued to grow as adjusted earnings per share increased 10.3% to $0.96, and the company also saw gains in free cash flow (FCF), with FCF up 4.7% to $3.39 billion.

While the company took a large write-down in the quarter on its acquisition of Sky, the British video entertainment giant it acquired several years ago, its other businesses continue to deliver steady growth and attractive profitability, allowing Comcast to aggressively buy back shares and reward investors with dividends.

The stock looks cheap at a price-to-earnings ratio of 10, and its payout ratio is still low at just 30%, giving the company room to raise its dividend. With growth set to continue in areas like broadband, studios, theme parks, and business services, Comcast looks like a good bet for a solid yield and a steadily growing dividend.


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Jeremy Bowman has no position in any of the stocks mentioned. The Motley Fool has positions in and recommends Berkshire Hathaway and Taiwan Semiconductor Manufacturing. The Motley Fool recommends Broadcom, Comcast, and VMware and recommends the following options: long January 2023 $200 calls on Berkshire Hathaway, short January 2023 $200 puts on Berkshire Hathaway, and short January 2023 $265 calls on Berkshire Hathaway. The Motley Fool has a disclosure policy.

Mon, 05 Dec 2022 23:16:00 -0600 en-US text/html https://www.msn.com/en-us/money/topstocks/3-high-yield-tech-stocks-to-buy-in-december/ar-AA14XV17
Killexams : Microsoft releases new book that takes an in-depth look at the role of AI in manufacturing

Artificial intelligence (AI) has been a significant area of interest for Microsoft in the accurate past. A number of partnerships have been undertaken by the tech giant with specific regards to this field. These include the involvement of firms like General Assembly, platforms such as OpenClassrooms, collaborations with higher education institutions, and more. As recently as yesterday, the company detailed how its AI-centric applications have been deployed by scientists to confront environmental challenges.

Today, Microsoft has announced the release of a new book, The Future Computed: AI & Manufacturing. This is the second book of its The Future Computed series, with the first one, namely The Future Computed: Artificial Intelligence and its role in society being launched last year. The new one has been penned by Greg Shaw, co-author of Microsoft CEO Satya Nadella's Hit Refresh - which was released in 2017.

The primary focus, as is apparent from the title, resides upon the transformative nature of AI in the manufacturing sector, and how it has helped drive innovation in it through a number of ways. These include the enabling of safer work environments, new products and services, and optimizing of digital operations. The six key learnings from the book have been identified as follows:

  1. Manufacturers are already seizing and, in some cases, leading the industrial AI opportunity.
  2. AI is a journey.
  3. Central to digital transformation is cultural transformation.
  4. Those closest to the workforce are the most sensitive and attentive to AI’s impact on it.
  5. But there will be disruption and dislocation.
  6. Next-generation policies and laws are needed for next-generation technologies.

Microsoft believes it is important to view the complex questions raised by the increasing use of AI in high regard. This series of books is aimed toward encouraging broader conversations surrounding AI, and hopefully helping empower people in a respectful and mutually beneficial environment. Particular focus has been laid upon manufacturing in the second book, as industry holds a central role as the forefront of adoption of new transformational tech.

Çağlayan Arkan, Global Lead, Manufacturing and Resources Industry, Microsoft - who has also written the foreword for the book -, notes some of its significant features in the following words:

"The book also looks at how manufacturers will need to engage with governments and civil society to help craft new regulatory frameworks, guiding the use of this new technology as the industry transforms. These frameworks should address key societal challenges, including the need to retrain workers to take on new roles and be part of a talent supply chain capable of realizing the potential of AI in manufacturing. They should also address how to store the data generated from connected supply chains and digital factories safely, securely, and in ways that respect privacy and ensure that AI is used ethically. Drawing on insights from customers and policymakers from around the world The Future Computed: AI and Manufacturing offers Microsoft’s perspective on how we might move forward on these important issues."

Various manufacturing customers including ABB, Colfax, Jabil, Tetra Pak, Toyota Materials Handling and ZF, have provided their thoughts about their journey through AI. Similarly, workforce experts, union leaders, and policymakers have also been interviewed. The importance of cultural change with respect to digital transformation in today's age has been specifically remarked upon as well.

As such, the book is noted to be a useful guide for essentially anyone interested in learning more about the significant role AI will play in the future of manufacturing. It is available for free obtain in the form of a PDF file here. Additional resources related to its content can be accessed through Microsoft's recently introduced online course, AI Business School, while the people and companies mentioned in the book can be read more about here.

Sun, 20 Nov 2022 04:00:00 -0600 Hamza Jawad en text/html https://www.neowin.net/news/microsoft-releases-new-book-that-takes-an-in-depth-look-at-the-role-of-ai-in-manufacturing/
Killexams : Leading vehicle manufacturing companies in the autonomous vehicles theme

The future of the automotive industry will be shaped by a range of disruptive themes, with autonomous vehicles (AVs) being one of the four megatrends impacting the growth of the industry for the next decade. 

Fully autonomous vehicles are vehicles that can drive themselves without human input. There are five levels of vehicle autonomy ranging from Level 1 to 5. The promise of AVs models is great – they could bring mobility to sections of the market that have never before had access such as children, disabled, or those otherwise unable to drive themselves. In addition, they could enable a brace of new businesses in the commercial space such as ride-hail services using robotaxis, on-demand freight logistics services, or mobile retail and service spaces that can be brought directly to the customer.  

However, the challenge of commercialising autonomous vehicles has proven equally great. The leap taken from level 1 autonomy to level 2, for example, is small compared to the jump in complexity needed for level 3 ‘eyes-off’ AV operation. These systems then look laughably simple compared to the level of complexity that will be demanded by truly self-driving level 4 and level 5 models, which might not even include controls for human drivers. 

Nevertheless, not all companies are equal when it comes to their capabilities and investments in the key themes that matter most to their industry. Understanding how companies are positioned and ranked in the most important themes can be a key leading indicator of their future earnings potential and relative competitive position.  

According to GlobalData’s thematic research report, Autonomous Vehicles, leading adopters of AVs include: BMW, GM, Tesla, BYD, and Toyota. 


Tesla is an audacious, PR-savvy agenda setter in the auto and alternative energy sectors. The company is at the forefront of AV technology through its Level 2 Autopilot and Full-Self Driving features. Unlike many rivals, Tesla has based its AV systems on 360-degree camera vision and ultrasonic sensors, using its own AI image processing to determine the safe path forward.  

Furthermore, Tesla can credibly claim to sit alongside Waymo, Baidu and Cruise in the vanguard of auto brain building. Using its network of cars equipped with Autopilot, Tesla claims to have racked up more than one billion miles of data which can enhance its machine learning system – although it’s unclear how many of those miles were covered with Autopilot actually engaged. 


Toyota competes with Volkswagen for the title of world’s largest motor company by volume. The company is sceptical about the arrival of level 5 vehicles before 2040 but, has invested $2.8bn to develop self-driving software in conjunction with Denso and Aisin Seiki. It has also set up the Toyota Research Institute (TRI) in Silicon Valley, with over $1bn of funding to research robotics and AI. TRI-AD is the ‘advanced development’ offshoot of the TRI focused on autonomous driving technology.  

Toyota has created a separate mobility company to focus on future mobility including AVs and smart cities called Woven Planet. In April 2021, Woven Planet agreed to purchase the Level 5 (full-self driving) operations of ride-hail company Lyft for a total of $550m to help it get closer to its AV development goals. 

General Motors (GM) 

General Motors acquired Cruise for more than $1bn in 2016 and this is generally considered in automotive circles to have been a good move, allowing it to leapfrog into autonomous driving. In January 2021, Cruise announced it had secured an additional $2bn through an investment round led by Microsoft along with existing partners GM and Honda. At the start of 2020, the company revealed the Cruise Origin self-driving shuttle concept. Like other pod-type AVs, this has no human controls and is fully self-driving within a geofenced area – meeting level 4 AV standards. Honda has also expressed interest in bringing the Origin to Japan to form a robotaxi service there. Cruise expects the Origin to go into production in 2023, built at GM’s Detroit Hamtramck plant. 

To further understand the key themes and technologies disrupting the automotive industry, access GlobalData’s latest thematic research report on Autonomous Vehicles.   

  • Nissan
  • Changan Automobile
  • Honda
  • Volkswagen
  • Daimler
  • Ford
  • Hyundai Motor
  • BAIC
  • Dongfeng Autos
  • Tata Motor
  • Mazda Motor Corp
  • Mitsubishi Motors
  • Renault
  • Stellantis
  • Mahindra & Mahindra
  • Suzuki
  • Subaru Corp

GlobalData, the leading provider of industry intelligence, provided the underlying data, research, and analysis used to produce this article.

Thu, 08 Dec 2022 21:58:00 -0600 en-US text/html https://www.just-auto.com/data-insights/top-ranked-vehicle-manufacturing-companies-in-autonomous-vehicles/
Killexams : Maximize on productivity. Join 4IR with Tarsus on Demand

4IR (4th Industrial Revolution) has the potential to revolutionise the manufacturing sector as we know it. Your company can Excellerate on product development, workforce productivity and mitigate supply chain issues as they occur. Get your business onto the latest technology, differentiating your company from your competitors as you capitalize on real time visibility of your supply chain, agile planning, business continuity and collaboration on the Microsoft ecosystem.

What does 4IR have to offer your manufacturing company?


Artificial Intelligence (AI)

The 2019, Microsoft report stated that manufacturing companies that had adopted AI performed 12% better than their competitors. AI offers companies predictive capabilities that monitors their equipment’s condition, almost eliminating reactive maintenance that leads to downtime or equipment failures that compromise your supply chain’s efficiency. AI can limit this because it forecasts when a specific asset might fail, so that you can plan and schedule maintenance in advance.


Machine Learning (ML)

Machine learning can save your manufacturing company thousands of man hours annually with supply chain and inventory management. ML has the capabilities to run the entire supply chain, boosting efficiencies and reducing costs by automating routine tasks through algorithms that streamline your resources. Over and above supply chain and inventory management, ML plays a vital role in security, it regulates access to valuable platforms, and critical information, giving you control to who has access to sensitive data.

Internet of Things (IOT)

The IoT can help manufacturers create new streams of revenue, reconstructing their business models to keep them competitive. The IoT through smart factory initiatives offers production monitoring in real-time, giving your company the opportunity to make decisions based on the identified trends and areas of improvement recommended by this system. With the IoT you can remotely monitor and control the whole manufacturing process, because all your machines and equipment are wirelessly connected to the internet, helping you increase on productivity.

Enhance your manufacturing company with efficiency, minimizing operational expenses while maximizing on revenue growth.

Contact us now to find out how to adopt next generation solutions.

Wed, 07 Dec 2022 16:00:00 -0600 en text/html https://www.miningweekly.com/article/maximize-on-productivity-join-4ir-with-tarsus-on-demand-2022-12-08
Killexams : Additive Manufacturing
  • Additive Manufacturing at ATTC Dayton
  • This video shows our AM capabilities at the Advanced Technology and Training Center in Dayton, Ohio.

UDRI offers comprehensive capabilities and research services in additive manufacturing (AM), otherwise known as 3D printing, in which a computer-driven printer deposits layers of polymer, metal, or other media to create simple or intricate objects from a three-dimensional, digital design file.

AM is typically used to print spare parts for system sustainment or to create very large tooling and molds for traditional manufacturing. In many cases, these parts, tooling, and molds do not yet exist or no longer exist and cannot be produced by any other means. In addition, AM usually costs significantly less because no material is wasted in the production process.

Our scientists and engineers are conducting large-scale, high-level research and operations for multiple customers in both the commercial and government sectors. Contact us today and put our knowledge to work for your organization.


  • Advanced, Printable Composites - UDRI performs R&D in advanced, printable composites, specifically to help reduce the time and cost of manufacturing composite structures for aircraft.
  • Cold Spray Repair Technologies - UDRI offers expertise in this solid state metal surface repair technology, allowing the repair of high-dollar assets in a fraction of the typical lead time and cost.
  • Computer-Based Models - UDRI develops models that allow manufacturers to better predict the cost of developing a printed part, which can be difficult for small- and mid-sized businesses.
  • Integration of AM into Production - Small- to mid-size manufacturers are often unsure how to adopt additive manufacturing. UDRI helps create a “recipe” including development and analysis of materials and sample parts, production trial runs, and quality control data collection.
  • Manufacturing Tooling - Our researchers develop advanced additive manufacturing processes that produce complex geometry tools at a fraction of the cost of conventionally machined metal tools.
  • Materials Optimization - UDRI has experience in characterizing powder for creating build parameters leading to optimized material performance.
  • Printed Metals - UDRI has in depth experience in designing parts for AM, printing in a myriad of powders such as 17-4 stainless steel, titanium, aluminum, and nickel-based alloys, and developing innovative solutions to minimize residual stress.
  • Printed Polymers - UDRI has the capability to print standard polymers such as ABS, nylon, and Ultem® (polyetherimide), as well as advanced polymers.
  • Product Development - UDRI has the capability to perform new product design or reverse engineering of products via 3D digitization and geometric analysis, 3D printing of hardware and prototype models, and computer design and optimization of components, molds, and tools.
  • Training - UDRI offers education and training to provide technicians and engineers with the tools and mindset to maximize the potential of AM and Cold Spray.
Mon, 14 Nov 2022 19:11:00 -0600 en text/html https://udayton.edu/udri/capabilities/advanced_manufacturing/additive_manufacturing/index.php
Killexams : Microsoft Dynamics vs. Salesforce | Money

Customer relationship management (CRM) systems manage a complex global customer base and help businesses Excellerate their bottom line and capabilities. Here are two of the most popular CRM applications to help you choose.

© Provided by Money.com

What is Microsoft Dynamics?

Microsoft Dynamics 365 is much more than just a customer relationship management application — it’s a piece of a larger software suite that can be used for customer relationship management and enterprise resource planning.

Features and functionalities

Microsoft Dynamics analyzes data using artificial intelligence and machine learning (AI/ML). Some of the use cases for Dynamics are marketing and sales, commerce, supply chain and finance, but it can be used in most industries, such as manufacturing, financial services, healthcare and retail. Further, it will be easy to use if you’re familiar with Microsoft Office products.

Dynamics 365 allows you to build custom applications and websites to meet your needs. With the suitable modules loaded up, it can simplify territory management by automating many tasks. Some of these are:

  • Microsoft Dynamics Marketing automation: This function manages and improves task completion time. It can generate leads, identify audiences, design content and manage workflows and events. It also enhances marketing team productivity and marketing accuracy by helping identify possible opportunities.
  • Marketing campaign metrics: Metrics can help you see how effective your campaign is, and adjust accordingly.
  • Salesforce automation: Automatic contact tracking can organize your contacts for scheduling follow-up sessions by showing you who has spoken with a client and their results. It also develops real-time metrics to create sales visuals.
  • Customer service: An administrative center and service hub, the CRM application allows you to keep customer profiles and Excellerate support and experiences.
  • Field service: Optimizes field representatives and resources. This uses resource scheduling and GPS functionality.
  • Finance: Generate reports, fill ledgers and manage accounts payable and accounts receivable.
  • Human resources: Manages tasks, workflow and benefits, which are automated through an employee self-service website.
  • Supply chain: Identifies and tracks resources and automates orders.
  • Project operations: Connects all loaded modules. Gives leaders real-time insights on progress, resource management and expense tracking.

Microsoft Dynamics pricing

Microsoft Dynamics 365 offers several modules based on your needs. Each module is priced per tenant, user and app.

The Data module provides customer insights and customer voice, with one module for each. Insights pricing is $1,500 per month per tenant and $1,000 per qualified app. The Sales module has five tiers designed for different user needs — Professional, Enterprise, Premium, Relationship and Viva, which range in price from $40 to $162 per month with a fee of $20 for each additional user.

The Dynamics Service module has four tiers. The Professional tier is $50 per user per month and $20 per user per month for each additional user. The Enterprise is $95 and $20, Field Service is $95 and $20, and the Remote Assist is $65 and $20.

There’s a wide variety of additional modules to choose from that range from $50 per month to $1,500 per month. These include marketing, commerce and fraud protection. Supply chain and intelligent order management modules automate logistics and ordering. A finance module and small and medium-sized business modules are available as well.

The user reviews

Most users state that Microsoft Dynamics CRM features an easy administrative experience, and integrates well with Microsoft’s other applications. Average ratings are generally high, giving four out of five stars.

Dynamics is expensive, however, because you need to pay for each tenant, user and module. For this reason, it is best suited for businesses with the extra capital to invest in the CRM/ERP applications they need.

© Provided by Money.com

What is Salesforce?

With Salesforce, customer service is the focus of the customer relationship management application — it works to Excellerate customer relationships. This is different than Dynamics, which has modules for many different business functions.

For instance, the Dynamics 365 Finance package is designed to assist financial departments with accounting and managing costs while also streamlining payment and collection processes. Salesforce doesn’t have a dedicated financial package: Payment, collections and invoicing are included in its Sales Cloud and are oriented on improving customer experiences.

Features and functionalities

Salesforce designed its application to be customizable per client. The interface can be changed to suit the needs of whoever uses it. It also created a platform to Excellerate your sales teams’ performance and customer experiences.

Salesforce CRM can also be used in most industries. It provides support, sales, marketing and commerce tools. All applications are cloud-based. This allows authorized users to access information from anywhere at any time. Salesforce operates four different clouds, each with a different fucntion — Sales, Service, Commerce, and Salesforce Marketing Cloud.

  • The Sales Cloud is where sales data is kept. Automated opportunity management, daily task automation and forecasting also exist in this cloud.
  • The Service Cloud stores information about customer concerns, which helps businesses organize, track and resolve issues.
  • The Marketing Cloud automates customer data, engagement, intelligence and loyalty programs.
  • The Commerce Cloud handles payment methods and performs order management tasks.

Each cloud provides data analysis services to help identify areas that need improvement, allowing users to create customizable reports. You can also opt into receiving notices from an automated analytics platform.

One of the most valuable outputs is customer interests and perceptions, as this allows more involvement with customers, since it gives you insight into what they need and are thinking without talking to them.

Salesforce pricing

There are affordable packages for small businesses based on each cloud type:

  • Marketing Cloud Account Engagement: $1,250 per month for up to 10,000 contacts
  • Service Professional: $75 per month, any size service team
  • Sales Professional: $75 per month, any size team
  • Essentials: $25 per month for sales and a support app

There are also several different pricing models for the services provided based on the cloud the service belongs to. Sales Cloud pricing is as follows:

  • Unlimited Edition: $300 per month, with account, lead, contact and opportunity management. Integrate your email integration, mobile app, lead registration and scoring. Collaborate with forecasting and workflow management with 24/7 support.
  • Enterprise Edition: $150 per month with account, lead, contact and opportunity management. Integrate your email, mobile app, lead registration and scoring, collaborative forecasting and workflow management.
  • Professional Edition: $75 per month with account, lead, contact and opportunity management. Integrate your email, mobile app, lead registration, scoring and collaborative forecasting.
  • Essentials Edition: $25 per month with account, lead, contact and opportunity management. Integrate your email and mobile app.

The Service Cloud Prices are as follows:

  • Unlimited Edition: $300 per month sales, support, customizable CRM, web services, 24/7 support
  • Enterprise Edition: $150 per month sales, support, customizable CRM, web services
  • Professional Edition: $75 per month sales, support, and CRM for any team size
  • Essentials Edition: $25 per month all-in-one sales and support

The Salesforce Marketing Cloud includes data and account engagement services that are expensive. They range between $1,250 and $50,000 per month.

The CommerceCloud packages focus on business-to-client commerce and order management. Prices are based on a percentage of merchandise value sold and the number of orders. The B2C Commerce packages are:

  • Plus: 3% of Gross Merchandise
  • Growth: 2% of Gross Merchandise
  • Starter: 1% of Gross Merchandise

Order management also falls under the Commerce Cloud, with growth, starter, and order visibility packages.

Salesforce can be expensive if you need more than one of the cloud-based services. On the other hand, a small business can benefit from the packages designed specifically for its needs. Individual cloud services are best for mid to large-size enterprises.

The user reviews

Reviews are generally positive, with four or five stars out of five-star ratings. Most users say they don’t know how they functioned before implementing Salesforce. The interface is generally easy to use, but it takes effort to bring it to the point of usability. Users also need to be trained, since it’s a completely new platform.

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The pros and cons of Microsoft Dynamics 365


  • Data centralization
  • UI that reflects all Microsoft products, making it familiar for those that use their software
  • Integrates with Microsoft office suite
  • Modular
  • Native integration with existing products


  • Dynamics can become very expensive as modules are added
  • Those unfamiliar with Microsoft UI may struggle at first
  • Modularity and scalability require more spending
  • Learning the system takes time

The pros and cons of Salesforce


  • Compatible with all browsers, allowing for user preference
  • Focused on customer relationship management
  • Modern and easy-to-use user interface (UI)
  • Mature and easy-to-use marketing and e-commerce platform
  • Manages time more efficiently
  • Simplifies account plannin
  • Improves team collaboration


  • Too complex for smaller business
  • Salesforce is expensive; the rates are all per user
  • Salesforce software has a steep learning curve
  • Scalability allows you to overbuild solutions
  • Fewer integrations in Salesforce

How these solutions enhance the customer experience

Businesses face fierce competition — new ideas emerge, existing products and services evolve, and companies enter and exit the markets live waves on a beach. The ones that want to last must find ways to attract customers and clients with excellent offerings and keep them coming back.

The best way to do that is through user experience. CRMs such as Dynamics and Salesforce can Excellerate that experience as they each analyze the customer process, from lead generation to post-delivery follow-up services.

Businesses that use these CRM platforms receive data on the whole customer experience. This allows them to quantify their performance with metrics related to customer satisfaction. Solutions can then be created to develop customer loyalty and preference.

The importance of customer relationship management (CRM) software

Improving the customer experience is the ultimate goal for many businesses. More businesses are using artificial intelligence and machine learning (AI and ML) to Excellerate their internal and external business processes. The competition will eventually tighten significantly more than it is. Only those with the best offerings and experiences can compete in their chosen industry and marketplace.

It’s no secret that many businesses close within one to five years of opening. Many more shut their doors after a decade of operations. Those that make it through this gauntlet must find ways to continue growing — which requires funding and revenues. Customer relationship management software isn’t cheap, and many newer and small businesses cannot afford it.

Thus, a CRM becomes a critical tool for businesses that make it through their first decade. They have more capital to budget toward CRM software solutions and growth. If clients and customers are dissatisfied with a business, they will find another one that offers the same product. The key to keeping a customer coming back is the purchasing experience, and that is what CRM is designed for.

Which CRM software application is right for your business?

If you use Microsoft products currently and have the budget for it, Dynamics integrates with Outlook, Microsoft 365, LinkedIn and more. While you can add or remove modules as needed, you must pay for each additional module and user.

Salesforce is also expensive, but it is overall less costly than Dynamics. It’s geared more toward customer relationship development rather than being a one-size fits all modular CRM/ERP system like Dynamics. Salesforce can be integrated with other applications and services, but you’ll need to purchase additional apps on its AppExchange.

Salesforce benefits smaller businesses because it has solutions designed for them, eliminating having to pick and choose. If you need something the small business package doesn’t have, you can talk to Salesforce and customize it.

Dynamics is all about picking and choosing your services. It can also be used in a small business, but you’ll need to know what you need and what each module does.

Both CRM applications have the potential to change the way your business runs. The critical difference between them is how they are priced. To decide between Microsoft Dynamics and Salesforce, you’ll need to take inventory of what you have. From there, consider what you need now and what you believe you’ll need in the future. For additional products that meet your growing business needs, check out our recommendations for tax software and recruiting software.

© Copyright 2022 Money Group, LLC. All Rights Reserved.

This article originally appeared on Money.com and may contain affiliate links for which Money receives compensation. Opinions expressed in this article are the author's alone, not those of a third-party entity, and have not been reviewed, approved, or otherwise endorsed. Offers may be subject to change without notice. For more information, read Money’s full disclaimer.

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