Category Archives: Data Mining

Wenco expands on a more open digital mining future – in architecture, analytics and autonomy – International Mining

Posted by Paul Moore on 30th April 2021

The future of autonomy in mining is set to include much more open and interoperable platforms than exist today. And the evolution of fleet management systems or FMS as they are known in the industry is a key part of that enabling mining customers to get the elusive single source of the truth across the on the ground reality of mixed fleets and contractor machines. Ahead of an in-depth article on the future of FMS in the May 2021 edition of IM, Editorial Director Paul Moore caught up with Wencos Reid Given, Senior Product Manager & Patrick Ligthart, Principal Product Manager to explore the topic of Open Autonomy and where the latest FMS functionality

Q How important is the FMS system to achieving true open autonomy and how has your open autonomy approach been received so far by the mining industry?

RG FMS is only one component in achieving Open Autonomy. Whats more important for Open Autonomy than any individual component is establishing open standards that break down the current closed approach and, instead, allow customers to mix and match components from their preferred vendors. This way, customers can choose technologies that drive the best ROI for them in their unique circumstances the most efficient trucks, the smartest and safest autonomous drivers, the FMS most tightly integrated with their systems and processes, and so on. Now that weve introduced this vision of Open Autonomy, its gathering a lot of momentum. Wenco and other industry contributors are making progress on ISO 23275 and proposing new standards for other components. Were also working with several customers and industry thought leaders to bring the Open Autonomy approach commercially to market. Non-traditional mining OEMs are especially excited about the prospects of Open Autonomy, as it gives them a path to enter our market. Open Autonomy enables new mining strategies to become profitable, such as swarm mining a tactic that uses trucks previously considered too small for our industry. As a result, were being engaged by companies from the automotive, long-distance trucking, and military industries looking to apply their autonomy technologies to mining use cases.

Q Is FMS interoperability still an issue in mining in enabling mines to access the technologies that they want to use; what progress is Wenco making in this regard?

PL Interoperability can still prove a challenge when mines rely on critical technologies that remain siloed. Without the ability to exchange data freely between their operational systems, mines struggle to optimise their decision-making that is, have the right decisions made at the right time by the right person. Mines typically have vast volumes of data to support these decisions, but theyre not treating their operational data as the asset it is. Too much data is left untapped in huge databases with only limited connection to other systems at best. Wenco has always taken care to make our database as accessible as possible, allowing mines to turn their data into actionable intelligence with the least amount of overhead. Were continuing to expand our capabilities in this area with our own technologies and with other vendors in the pit-to-port landscape. We currently have projects working to integrate solutions from various OEMs and aftermarket vendors that enforce stricter material compliance, facilitate ISA-95 automation, and strengthen management of unexpected events using cameras and other sensors. All these projects are aimed at extending our interoperability with others to help mining customers extract more unrealised value.

Q Automation aside, what role do todays FMS systems in enabling highest levels of mining efficiency such as high precision and asset health systems?

RG The real power of any data system comes from its improved decisions. FMS and other operational mine technologies deliver greater control, yes, but they also create synergies and enable more robust insights than are possible otherwise. The contextual data about equipment behaviour that comes from an FMS allows these other technologies to make much more accurate decisions around ore/waste determination (and, therefore, enable selective mining) and predictive maintenance. It works both ways as well. With the FMS serving as the orchestrator for in-pit operations, data from high-precision and asset health systems gives dispatchers and mine controllers the ability to act on deviations that occur within a shift. For example, access to messages and events from third-party systems allows our FMS to make smarter assignments, such as diverting a truck that was in the process of being loaded when a ground-engaging tool alarm was generated away from the crusher.

Q What potential is there in teaming Wencos FMS technology with Hitachi tech such as ConSite to achieve best results for customers?

PL Wenco is creating ConSite Mine for Hitachi Construction Machinery (HCM) on a digital IoT platform, to be delivered this year, with the intention of integrating Wenco, HCM, and third-party technologies into solutions that deliver the best results for customers. Of course, this platform will ultimately integrate Wenco FMS capabilities with advanced technologies from Hitachi and other ecosystem partners. The digital IoT platform being created by Wenco on behalf of HCM is designed to serve as a one-stop shop for capture, storage, processing, exchange, and analysis of data through an open architecture and with common interfaces. This digital IoT platform is not only intended for our current customer base of Tier 1 and Tier 2 mines, but also for customers in markets such as quarries, construction, and beyond who understand the efficiency gains possible from digital technologies. There is huge demand from these sectors for an integrated, cloud-based fleet management solution that isnt tied to a specific location. This platform will be able to deliver certain cross-functionalities that are difficult to establish with single purpose on-premises technologies, while also bringing capabilities normally reserved for top-tier mining companies to a whole new series of customers. It also offers new ways to scale and manage FMS functions in a much more tailored way, so our customers can invest discretely in solutions that really drive their operation forward.

Q How can long term existing Wenco FMS customers benefit from the latest functionalities how easy is it for them to upgrade or is it effectively like putting in an entirely new system?

RG Were very careful about ensuring our long-term customers can take advantage of our latest functionalities. Its top of mind for us as we build our new solutions, including our digital IoT platform. Our philosophy is to make the transition to our new platform as seamless as possible as we gradually release new capabilities. We know the impact a hardware replacement can have on our customers, so were very careful about designing our technology to avoid cases where a hardware upgrade is required to derive optimal value. We obviously strive to avoid the change management requirements that come when a new solution is implemented. As such, our pathway to a new platform is much more evolutionary, rather than revolutionary.

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Wenco expands on a more open digital mining future - in architecture, analytics and autonomy - International Mining

Puget to Introduce Proprietary Software that Utilizes Artificial Intelligence to Optimize Distribution and Transportation Systems –

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BOCA RATON, Fla., April 30, 2021 (GLOBE NEWSWIRE) -- Puget Technologies, Inc. (Puget; OTC PINK: PUGE), a Nevada corporation subject to reporting pursuant to Sections 13 and 15(d) of the Securities Exchange Act of 1934, as amended, announces that the companys Chief Technologies Officer (CTO), Victor Germn Quintero Toro has contributed proprietary software to Puget, subject to retained royalty rights, designed to improve the functioning of logistics in transportation and distribution systems. The methodology involved is believed to be unique and subject to protection as trade secrets, however, Puget may elect to reinforce such protection through patents in the near future.

The solutions currently available in the marketplace to manage distribution and transportation logistics are limited to just a few specifically customized applications. In contrast, Pugets software can solve extremely complex problems for its end users by customizing the myriad of variables not currently included in out-of-the-box modular software. It does so in a seamlessly integrated environment without the need for additional capital expenditures. By data mining in big data environments with advanced artificial intelligence algorithms and other proprietary trade secrets, Pugets newly acquired software is the only technology on the market today, in my opinion, that supports the majority of variables that affect these end users, commented Mr. Quintero Toro.

Designed specifically to seamlessly integrate functionality within the big data environments of existing distribution and transportation systems, the software does not replace existing technology. One of the main advantages of this solution is the optimization of companys operations since this software complements and enhances existing platforms to deliver efficiencies, enabling cost reduction without the need for a significant capital outlay. Im looking forward to commercializing this technology with Pugets assistance, Mr. Quintero Toro explained.

Mr. Quintero Toros past experience working to solve similar problems at Walmart distribution centers around the world contributed to the domain expertise needed to come up with such an innovative, integrated solution.

The software has already been beta tested in the public transportation system of the City of Manizales in the Republic of Colombia, where it achieved a 30% reduction in hydrocarbon emissions as a result of better route management. The beta test results were presented at the Congreso Latino-Iberoamericano de Investigacion de Operaciones (CLAIO), and a summary was published in the publication Annals of Operations Research and in the Journal of Heuristics.

Puget intends to commercialize this technology through licensing agreements, leveraging Puerto Rico as a springboard for rollout to Latin America and other parts of the world. The transportation and distribution problems on the Island, aggravated by unfortunate recent weather disasters, provide an opportunity for the technology to make a significant positive impact there. In addition, because of the substantial incentives provided by the Puerto Rico Incentives Code (Law No. 60 of July 1, 2019), Puget believes that the Commonwealth of Puerto Rico would be an ideal site as a worldwide research and development center, which will enable Puget to have a local presence as the team works directly with local business and government leaders to improve the Islands infrastructure.

For additional information, please contact Puget at 1-561-210-8535, by email at or visit our website for continuing updates at

About Puget Technologies, Inc.Puget Technologies, Inc.( aspires to evolve into an innovation-focused holding company operating through a group of subsidiaries and business units that work together to empower ground-breaking companies to reach their next stage of growth. With a strategy that combines acquisitions, strategic investment strategies, and operational support,Pugetintends to provide a one-stop shop for growing companies who need access to both capital and growth resources, while enablingPugetand its stockholders to generate synergies and derive profit through pooled resources and shared goals. Pugetscurrent investment focus ranges from traditional industries like health care that are ripe for business model innovation to new markets that strive to solve big societal problems such as climate change. Publicly traded on the Pink Open Market under the ticker symbol PUGE,Pugetis committed to delivering a competitive return to investors.

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The Intersection of Big Data and KM: An Update for 2021 – CMSWire

PHOTO:Jin xc | unsplash

This week a person I never met contacted me on Twitter to ask if I had done any further research on the integration of two subjects big data and knowledge management based on a 5-1/2-year-old article I wrote on the subject.

I had to admit I had not really done much more research on the topic. At the time I was director of knowledge management for the very large legal and compliance group of one of Canadas largest banks, so I wrote it from the KM point of view. Since then, I moved to another bank as product manager for enterprise search, and now I am one year and nine months into my director of product management role at a SaaS information management vendor. Oh, yeah, and we had a global pandemic ....

The big data world has gone through almost as many changes in the intervening years.

The pace of change in the KM world is somewhat more pedestrian I dont mean that in a bad way. I see KM as a management discipline. You can have a KM strategy, but I dont believe anyone can sell you a KM system." KM practitioners and academics who study and do research in the field sometimes take time to catch up with the technology, working practices and social changes that can be integrated into a KM strategy. In that nearly 6-year-old article I argued we should integrate big data into our KM processes, to be treated as another source of information from which knowledge could be derived, in order to provide actionable insights for decision making and creation of organizational value:

The diagram above encapsulates my high-level thinking from 2015, but the question from Twitter was really asking whats new?

Related Article: The State of Knowledge Management in 2020

Deloitte create a regular report called Global Human Capitol Trends, which includes insights into KM. As we started with an article I wrote in 2015, I thought it would be interesting to paraphrase its KM trends from the past six years:

However, the 2020 report provided this interesting commentary:

For organizations that are struggling, the good news is that technology is offering up solutions that can help. Emerging AI capabilities such as natural language processing and natural language generation can automatically index and combine content across disparate platforms. These same technologies can also tag and organize information, automatically generating contextual metadata without human intervention and eliminating a major barrier to actually using the knowledge that an organizations people and networks create.

Why is that quote so interesting to me? Well I have always said that a KM strategy relies on good information management, and we are starting to understand that good information management practices with metadata, taxonomy and ontologies can really benefit the quality of outputs provided by AI systems.

We have a symbiotic relationship between information management and some elements of AI good practice in IM can improve AI by providing well-structured taxonomies and ontologies, at the same time elements of the AI toolkit such as NLP can help automatically create metadata. At the same time application of the AI toolkit to analytics capabilities helps us to derive value from the ever-expanding sea of big data. SoIM helps AI, AI in turn helps analyze big data.

Related Article: Using AI for Metadata Creation

Let's start with a reminder of what we mean by "big data."Wikipedia has a good definitionwhich includes this key statement: Big data usually includes data sets with sizes beyond the ability of commonly used software tools to capture, curate, manage, and process data within a tolerable elapsed time.

So we are talking about massive amounts of data, volumes so large commonly available tools (like Microsoft Excel) simple cannot handle them. Big data can be used as a source for business intelligence, but the two aren't the same. According to Wikipedia, BI uses applied maths tools and descriptive statistics, while big data uses mathematical analysis and optimization techniques, and inductive statistics. I cannot really pontificate further on this, as I am not a subject matter expert in big data. However one point we can all understand is the definition of big, got well, bigger in the last six years. One of the defining characteristics of big data is the volume of data it encompasses and the rate at which this volume expands is accelerating. Five or six years ago, we might have been talking about hundreds of gigabytes to terabytes. Now we are talking petabytes and upwards.

With so much data, analyzing it to uncover insights becomes problematical. You need data management to ensure data quality and to avoid being swamped by false signals. Data mining uncovers correlations and patterns.

From a technical perspective, a key element of the last five to six years is the ability to do in-memory analytics analyzing large data sets in fast system memory without swapping data back and forth from storage (hard drives). Products for data visualization have also advanced, and while data scientists can still use specialist tools, we've seen a move to allow non-specialists to create and manage their own dashboards. However in its 2020 Data and Analytics trends report, industry analyst Gartner predicts the demise of the pre-built dashboard as AI capabilities help analytics and business intelligence software vendors offer new user experiences beyond the now ubiquitous dashboard.

Which brings us nicely into the final element which sets 2021 apart from 2015, artificial intelligence.

The introduction of so-called artificial intelligence tools, such as natural language processing, machine learning, neural networks and deep learning have had a great impact on big data analysis. With so much data to analyze, even with the best in visualization technologies, it is difficult for human analysts to spot the most complex patterns and inter-relationships.

The application of AI capabilities to the analysis of enormous data sets will be key in moving forward with the creation of information which can then be combined with metadata, contextual information from other sources and tacit knowledge in order to create new insights for decision support and value generation for an organization.

So a lot has changed in the last five to six years with respect to how big data can be integrated into a KM strategy. Big data just keeps on getting bigger and that trend will never reverse. Good information management practices and tools can assist AI capabilities, that in turn will analyze the ever-growing data sets in our data warehouses and data lakes. Visualization technologies improved to help us find patterns, but that too will need an added layer of AI technologies to keep up. In the search for competitive advantage, things rarely get simpler. Dealing with the accelerating rate of data growth is certainly never going to be easy, but with improvements to tools and capabilities to help us generate knowledge and insights, it's up to us to do something with them!

Jed Cawthorne is Director, Security & Governance Solutions at NetDocuments. He is involved in product management and working with customers to make NetDocuments phenomenally successful products even more so.

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The Intersection of Big Data and KM: An Update for 2021 - CMSWire

Data Mining Software Market 2021 Will Reflect Significant Growth in Future with Size, Share, Growth, and Key Companies Analysis- SAS, IBM, Symbrium,…

DataIntelo has added a latest report on the Global Data Mining Software Market that covers the 360 scope of the market and various parameters that are speculated to proliferate the growth of the market during the forecast period, 2021-2028. The market research report provides in-depth analysis in a structured and concise manner, which in turn, is expected to help the esteemed reader to understand the market exhaustively.

Major Players Covered In This Report:

SASIBMSymbriumCoherisExpert SystemAptecoMegaputer IntelligenceMozendaGMDHUniversity of LjubljanaRapidMinerSalford SystemsLexalyticsSemantic Web CompanySaturamOptymyze

The research report confers information about latest and emerging market trends, key market drivers, restraints, and opportunities, supply & demand scenario, and potential future market developments that are estimated to change the future of the market. This report also serves the strategic market analysis, latest product developments, comprehensive analysis of regions, and competitive landscape of the market. Additionally, it discusses top-winning strategies that has helped industry players to expand their market share.

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9 Key Report Highlights

Historical, Current, and Future Market Size and CAGR

Future Product Development Prospects

In-depth Analysis on Product Offerings

Product Pricing Factors & Trends

Import/Export Product Consumption

Impact of COVID-19 Pandemic

Changing Market Dynamics

Market Growth in Terms of Revenue Generation

Promising Market Segments

Impact of COVID-19 Pandemic On Data Mining Software Market

The COVID-19 pandemic had persuaded state government bodies to impose stringent regulations on the opening of manufacturing facilities, corporate facilities, and public places. It had also imposed restrictions on travelling through all means. This led to the disruption in the global economy, which negatively impacted the businesses across the globe. However, the key players in the Data Mining Software market created strategies to sustain the pandemic. Moreover, some of them created lucrative opportunities, which helped them to leverage their market position.

The dedicated team at DataIntelo closely monitored the market from the beginning of the pandemic. They conducted several interviews with industry experts and key management of the top companies to understand the future of the market amidst the trying times. The market research report includes strategies, challenges & threats, and new market avenues that companies implemented, faced, and discovered respectively in the pandemic.

On What Basis the Market Is Segmented in The Report?

The global Data Mining Software market is fragmented on the basis of:



The drivers, restraints, and opportunities of the product segment are covered in the report. Product developments since 2017, products market share, CAGR, and profit margins are also included in this report. This segment confers information about the raw materials used for the manufacturing. Moreover, it includes potential product developments.


Large EnterprisesSmall and Medium-sized Enterprises (SMEs)

The market share of each application segment is included in this section. It provides information about the key drivers, restraints, and opportunities of the application segment. Furthermore, it confers details about the potential application of the products in the foreseeable future.


North America

Asia Pacific


Latin America

Middle East & Africa

Note: A country of choice can be included in the report. If more than one country needs to be added in the list, the research quote will vary accordingly.

The market research report provides in-depth analysis of the regional market growth to determine the potential worth of investment & opportunities in the coming years. This Data Mining Software report is prepared after considering the social and economic factors of the country, while it has also included government regulations that can impact the market growth in the country/region. Moreover, it has served information on import & export analysis, trade regulations, and opportunities of new entrants in domestic market.

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Provides In-Depth Actionable Insights to Make Crucial Decisions

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Below is the TOC of the report:

Executive Summary

Assumptions and Acronyms Used

Research Methodology

Data Mining Software Market Overview

Global Data Mining Software Market Analysis and Forecast by Type

Global Data Mining Software Market Analysis and Forecast by Application

Global Data Mining Software Market Analysis and Forecast by Sales Channel

Global Data Mining Software Market Analysis and Forecast by Region

North America Data Mining Software Market Analysis and Forecast

Latin America Data Mining Software Market Analysis and Forecast

Europe Data Mining Software Market Analysis and Forecast

Asia Pacific Data Mining Software Market Analysis and Forecast

Asia Pacific Data Mining Software Market Size and Volume Forecast by Application

Middle East & Africa Data Mining Software Market Analysis and Forecast

Competition Landscape

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Data Mining Software Market 2021 Will Reflect Significant Growth in Future with Size, Share, Growth, and Key Companies Analysis- SAS, IBM, Symbrium,...

Federal Judge Ruled Against Crain Over Right To Sue In Privacy Case 05/03/2021 – MediaPost Communications

Michigan publishers must be extra careful in complyingwith the states privacy law. Thats the import of a federal court decision involving Crain Communications.

Without ruling on the merits of the case, a U.S.District Court determined that an out-of-state resident had the standing to sue Crain under the Michigan Personal Privacy Protection Act (PPPA). And it dismissed a motion by Crain to dismiss the suitin late March.

Virginia subscriber Gary Lin filed a class action suit in 2019, alleging that Crainviolated the PPPA by selling his and other subscribers personal reading information to third parties without obtaining consent, JDSupra writes in an analysis.



Crain had earlier sought to squash the lawsuit on the groundthat Lin lacked standing to file suit as a non-state resident.

In January, the court concluded the PPPA does not impose a residency requirement for customers to haveprotections, thus allowing the Lin suit to go forward, JDSupra notes.

U.S. District Judge Victoria A. Roberts wrote: if the Michigan legislature intended to limit thestatute to Michigan residents, it could have done so explicitly,

Other state statutes, including the California Consumer Privacy Act (CCPA), the California Privacy RightsAct (CPRA), and the Virginia Consumer Data Protection Act (VCDPA) do not cover non-state residents, JDSupra explains.

The privacy rights created by these statutes extendonly to residents of California and Virginia, respectively, JDSupra writes. Moreover, unlike the PPPA, none of these statutes provide a private right of action for privacy-relatedviolations, it adds.

Lin had alleged that Crain violated his protected privacy interest by disclosing PRI to data-mining companies and third-party databasecooperatives.

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Federal Judge Ruled Against Crain Over Right To Sue In Privacy Case 05/03/2021 - MediaPost Communications

Georgia State Introduces Advanced Research Computing Technology & Innovation Core – Georgia State University News

ATLANTAGeorgia State University has introduced the Advanced Research Computing Technology & Innovation Core (ARCTIC) to support research that would not be possible with standard consumer-grade computing, including analysis, modeling, simulation and the prediction of complex phenomena.

Now a resource for investigators at Georgia State, ARCTIC will soon be made available to scientists around the world.

ARCTIC was developed using a $1.2 million grant from the National Science Foundation and offers researchers advanced cyber infrastructure along with training and support. The system includes high-performance computing clusters and data storage systems, research networking and cloud computing.

We dont just provide the hardware, said Suranga Edirisinghe, associate director of ARCTIC. We work hand in hand with investigators and guide them how to best use the resources.

ARCTIC is particularly aimed at investigators who are not traditionally served by high-performance computing, such as psychologists, biologists, neuroscientists or public policy researchers. The team also builds scientific gateways easily accessible Web portals to allow the public to access a projects findings.

These days, researchers have more and more data, but they may not have the technology to process that data. Were trying to fill the gap, said Edirisinghe. The goal is to reach out to people who dont traditionally use high-performing computing and bring them into the community, so theyre not constrained by resources. Thats also why the support piece is so important.

Anyone who has said, We can do this on my office computer with a small sample, but to really be representative, nail down the model and generalize what were finding, we need to be running this on millions of data points, thats who this is for, said Jessica Turner, professor of psychology and neuroscience and lead investigator on the grant.

Turner is among more than 300 Georgia State faculty who have begun using ARCTIC to conduct their research. She is testing whether an algorithm can predict depression in various racial groups by analyzing brain scans. Faculty can also use the resource in the classroom to teach students about data mining or big data analysis approaches.

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Georgia State Introduces Advanced Research Computing Technology & Innovation Core - Georgia State University News

Gyratory Crusher Market 2021 Analysis- Worldwide Opportunity, Key Insights and Emerging Growth Till 2027 KSU | The Sentinel Newspaper – KSU | The…

Gyratory Crusher Market recently Published Market Insights Reports with more than 100 industry informative desk and Figures spread through Pages and easy to understand detailed TOC on Gyratory Crusher Market.

Global Gyratory Crusher Market 2021: This Report provides highlighting opportunities, and supporting strategic and tactical decision-making. This report recognizes that in this rapidly-evolving and competitive environment, up-to-date Marketing information is essential to monitor performance and make critical decisions for growth and profitability. It provides information on trends and developments, and focuses on Markets and materials, capacities and on the changing structure of the Gyratory Crusher. The report also presents forecasts for Global Gyratory Crusher Market investments from 2021 till 2027.

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Leading key CompanysCovered for this Research are




Rackers Equipment Co.


Cooley Equipment Corp

P R Engineering Ltd

Modern Machinery


Excel Foundry & Machine

Market segment by Type, the product can be split intoOrdinary Gyratory Crusher

Hydraulic Gyratory Crusher

Market segment by Application, split intoHeavy Mining


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The research report on the global Gyratory Crusher Market is a comprehensive publication that aims to identify the financial outlook of the market. For the same reason, it offers a detailed understanding of the competitive landscape. It studies some of the leading players, their management styles, their research and development statuses, and their expansion strategies. The report also includes product portfolios and the list of products in the pipeline. It includes a thorough explanation of the cutting-edging technologies and investments being made to upgrade the existing ones.

Table of Content

1 Introduction of Global Gyratory Crusher Market

1.1 Overview of the Market

1.2 Scope of Report

1.3 Assumptions

2 Executive Summary

3 Research Methodology

3.1 Data Mining

3.2 Validation

3.3 Primary Interviews

3.4 List of Data Sources

4 Global Manufacturing & Construction Market Outlook

4.1 Overview

4.2 Market Dynamics

4.2.1 Drivers

4.2.2 Restraints

4.2.3 Opportunities

4.3 Porters Five Force Model

4.4 Value Chain Analysis

5 Global Gyratory Crusher Market, By Deployment Model

5.1 Overview

6 Global Gyratory Crusher Market, By Solution

6.1 Overview

7 Global Gyratory Crusher Market, By Vertical

7.1 Overview

8 Global Gyratory Crusher Market, By Geography

8.1 Overview

8.2 North America

8.2.1 U.S.

8.2.2 Canada

8.2.3 Mexico

8.3 Europe

8.3.1 Germany

8.3.2 U.K.

8.3.3 France

8.3.4 Rest of Europe

8.4 Asia Pacific

8.4.1 China

8.4.2 Japan

8.4.3 India

8.4.4 Rest of Asia Pacific

8.5 Rest of the World

8.5.1 Latin America

8.5.2 Middle East

9 Global Gyratory Crusher Market Competitive Landscape

9.1 Overview

9.2 Company Market Ranking

9.3 Key Development Strategies

10 Company Profiles

10.1.1 Overview

10.1.2 Financial Performance

10.1.3 Product Outlook

10.1.4 Key Developments

11 Appendix

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Gyratory Crusher Market 2021 Analysis- Worldwide Opportunity, Key Insights and Emerging Growth Till 2027 KSU | The Sentinel Newspaper - KSU | The...

Coal production from mining’s top ten set to increase | Sustainability – Mining Global – Mining News, Magazine and Website

Coal production from the top ten mining companies (Coal India, China Shenhua, Yanzhou Coal, Peabody, China National Coal, Glencore, Siberian Coal, PT Bumi, BHP and Arch Resources) fell from a collective 1,704Mt in 2019 to 1,633Mt in 2020, which is a 4.2% decline.

The most significant declines were observed from Arch Resources (28.6%), PT Bumi (24.9%), Glencore (23.9%), and Peabody (21.8%), according to GlobalData, a leading data and analytics company.

GlobalData expects production from the top ten companies to be between 1,683-1,740Mt in 2021, which is an increase of up to 6.6% compared with the collective output in 2020 (1,633Mt). Operating activities, backed by the rollout of vaccine and strict COVID-19 protocols on-site, returning to normal is expected to be a key production driver for companies in 2021.

Production from Arch Resources (formerly Arch Coal) declined primarily due to the sale of the Holden #22 Surface mine in December 2019, coupled with weak economic conditions during Q1 2020. In addition, the temporary suspension of the Viper mine in Q2 2020 further disrupted the companys coal production.

Vinneth Bajaj, Associate Project Manager at GlobalData, commented: Heavy rainfall amid the outbreak of COVID-19 impacted PT Bumis output in 2020, while Glencores coal output fell for the fourth consecutive quarter as the companys Colombian coal assets remained suspended as part of the COVID-19 preventive measures.

Scheduled production cuts across the Australian portfolio also impacted the companys overall output as did the placement of the Prodeco mine under care and maintenance and strikes at Cerrejon between August and December 2020.

Peabodys output dropped primarily due to the upgrade of the main line conveyor system at Shoal Creek, alongside pit sequencing work at Moorvale and a dragline outage at Coppabella. In addition, the company also suspended operations at the Wambo underground mine for around 59 days during the second half of 2020. The closure of mines including Kayenta and Cottage Grove (Q3 2019) and Wildcat Hills (Q2 2020) further impacted the companys output.

Bajaj continued: BHPs coking coal production was impacted by planned maintenance at the Saraji and Caval Ridge mines, environmental disruptions at La Nina, and lower yields at the South Walker Creek and Poitrel mines. Meanwhile, the thermal coal segment, from which the company is expected to exit in the near future, was affected by a 91-day strike at Cerrejon, which started on August 31, 2020.

In contrast, production from Coal India rose by 4% owing to a recovery in the offtake from Indias power sector, which was supported by a resumption in industrial and commercial activities as the countrys electricity demand started to recover from the COVID-19 setback, said Bajaj.

Output from China Shenhua, Yanzhou and China National Coal Group also increased, rising by 3.1%, 10.2% and 8.1%, respectively, in 2020, mainly owing to a quick recovery in China, particularly during the second half of 2020.

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Process Mining Software Market Projected to Show Strong Growth | Celonis, Software AG, Minit, QPR ProcessAnalyzer KSU | The Sentinel Newspaper – KSU…

A latest statistical data titled as Global Process Mining Software Market has been published by Infinity Business Insights. The report covers penetrative insights into distinctive market features such as recent trends which are comprehensively discussed in order to provide an in-depth analysis of the progress of the industries. Effective exploratory techniques such as qualitative and quantitative analysis are used in order to explore accurate data. Porters five analysis and SWOT analysis have been used to examine the strength, weaknesses, threats and opportunities.

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Top Key Players Included in This Report: Celonis, Software AG, Minit, QPR ProcessAnalyzer, Signavio, OpsOne, Datapolis, Disco, Fujitsu, Icaro, Kofax

SWOT and Porters Five analysis are also effectively discussed to analyse informative data such as cost, prices, revenue, and end-users. The research report has been evaluated on the basis of various attributes such as manufacturing base, products or services and raw material to understand the requirements of the businesses. The Market structure covers the value chain, player categories, product ranges, key players presence across products and end user segments of the market. The report also provides a snapshot of key competition, market trends with forecast years, anticipated growth rates and the principal factors driving and impacting growth market data and analytics are derived from a combination of primary and secondary sources.

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Key questions answered in Process Mining Software Market Report:

Furthermore, it also offers a holistic snapshot of the business sector. To understand the globalProcess Mining Software market clearly different verticals are examined. In addition, the market study is supported by significant economic facts with regards to pricing structures, profit margin, and market shares. To present the data accurately, the study also makes use of effective graphical presentation techniques such as tables, charts, graphs, and pictures. The report further also highlights recent trends, tools and technology platforms that are contribute to enhance the performance of the companies.

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Process Mining Software Market Projected to Show Strong Growth | Celonis, Software AG, Minit, QPR ProcessAnalyzer KSU | The Sentinel Newspaper - KSU...

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Global Data Base Management Systems Market Strategic recommendations, Trends, Segmentation, Use case Analysis, Competitive Intelligence, Global and Regional Forecast (to 2026)

Global Data Base Management Systems Market Report gives a complete knowledge of Data Base Management Systems Industry covering immensely significant boundaries including development scenario, size, share, challenges, cost structure, limit, growing technologies, mechanical developments, openings, key sellers and serious examination with top players, growth, size, share, and future forecast. This report is a mixture of qualitative and quantitative analysis of the Data Base Management Systems industry and provides data for making strategies to increase the growth of the Data Base Management Systems market and effectiveness.

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The report also focuses on the global major leading industry players of the Global Data Base Management Systems market providing information such as company profiles, product picture and specification, capacity, production, price, cost, revenue and contact information.

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Based on product, this report displays the production, revenue, price, market share and growth rate of each type, primarily split into:

Based on the end users/applications, this report focuses on the status and outlook for major applications/end users, consumption (sales), market share and growth rate for each application, including:

Geographically, the detailed analysis of consumption, revenue, market share and growth rate, historic and forecast of the following regions are:

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Data Base Management Systems Market landscape and the market scenario include:

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