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Steve Tracey, Executive Director, Penn State University
Supply chains are becoming incredibly complex due to global networks of interdependent organizations, rising consumer expectations, and volatile business environments. Complex supply chains bring a wealth of data created by people, systems, and machines from a variety of sources, including new types of data such as social media and IoT. The potential value of this data is enormous for supply chain management (SCM), but simply having rich data does not realize the potential for it.
To make data truly valuable, companies need the ability to analyze large amounts of data and convert them into actionable insights. In the search for an engine for Big Data analysis, companies will find answers in artificial intelligence (AI). With its algorithmic advancements and powerful computing capabilities, today’s AI systems can process large amounts of data in profound depth, in an extremely short time, and with autonomous learning capability. AI capabilities are well suited to SCM and the key processes of plan, buy, make, and flow.
AI in Supply Chain Planning
Supply chain planning is a data-driven and analytical intensive process. Companies invest in expensive enterprise resource planning (ERP) systems, but they typically do not come fully-equipped with appropriate algorithmic technologies and capabilities. Thus, some of the actual planning efforts are left to spreadsheet analyses to develop needed insight. Companies are turning to AI-powered analytics to perceive patterns of demand for products/services, across geographic and socioeconomic segments, while simultaneously considering factors like economic cycles, political developments, and weather conditions. More accurate forecasts are achieved at varying hierarchies (e.g. product, store, warehouse levels) and timeframes (e.g. daily, weekly, monthly). Planning for other processes like raw material sourcing, inventory management, and production are also improved as a result.
AI in Sourcing
Sourcing intelligence and opportunities for improvement can be facilitated through spend analysis. Prior to the AI revolution, commonly used tools for spend analysis included on-line analytical processing, data warehouses, and spreadsheets. While these tools certainly can be helpful, they typically lack classification capabilities and robust mathematical functions, and so are more focused on static instead of dynamic reporting. In contrast, AI-powered spend analytics automate the entire process and optimize the value of spend-related data through comprehensive dashboards and reporting capabilities. Companies can interact and view data in multiple ways as they continually delve into data exploration, thus enabling informed decision making.
Additionally, AI is making inroads in supplier risk monitoring, using readily available data (e.g. credit scores, legal filings, government data, customer reviews). Existing approaches are limited in their need to manually update relevant information, whereas with AI-powered data cleansing and classifying technologies, supplier data can be uploaded and analyzed in real time. Also, there is a growing popularity of embedding AI into predictive supplier risk monitoring systems that provide companies with multidimensional, dynamic risk scoring, real-time dashboards, and alerts and recommended responses when conditions change.
AI in Production and Distribution
C. John Langley Jr., Director of Development & Kusumal Ruamsook, Research Associate, Center for Supply Chain Research, Smeal College of Business, Penn State UniversityWhile industrial robots are not new, it was not until recently that the new generation of more sophisticated, AI-powered robots emerged. In supply chains, the integration of intelligent robotics is becoming prominent in manufacturing and warehousing operations. Historically, assembly-line robots were designed for a single task, required hours to reprogram, and configured with a clear division of labor between humans and robots. Today, intelligent robots are able to learn from and work collaboratively with humans, and to significantly extend their capabilities.
Currently, the uses of intelligent robots in warehouses are accelerated by the challenges of ecommerce order fulfillment. Traditional operations that largely rely on manual picking systems have become ineffective amidst changing warehouse demand profile, from full case handling for store replenishment to single SKU items for unique online orders. Recent development of autonomous mobile robots features the embedded intelligence and application software that are the key differentiating characteristics of these systems, rendering picking process more productive, accurate, and efficient.
AI in Logistics
For companies that operate fleets and logistics facilities, the use of AI and predictive analytics can produce significant efficiencies. An example is the application of these tools to predict when maintenance may be needed for various types of assets. Whereas the scheduling of maintenance activities was traditionally done in advance, or when failures occurred, predictive maintenance helps by forecasting when asset failures are likely to occur. Benefits include prevention of failures before they occur, reduction of loss of asset downtime to remedy the failure, and decrease in overall cost reduction.
Meanwhile, more intelligent robotics are becoming increasingly effective in facilitating autonomous vehicle applications. One example is that of line-haul transportation that frequently involves long journeys overnight to support drivers’ health and safety. On the delivery end, and impacted significantly by the growth of ecommerce, intelligent robotics are increasingly benefiting the efficiency and effectiveness of last-mile deliveries.
The use of AI also helps to create great benefits in freight billing and payment processes that commonly rely on manual-entry accounting systems. AI-based systems create automation for these repetitive tasks, offering capabilities to identify shippers and consignees, read forms and bills of lading submitted in non-traditional formats, while learning to look for specific data as it scans bills. Results are more efficient, accurate, and transparent processes. Software firms are working to expand these AI-enabled capabilities to other logistics processes such as carrier selection and freight tendering that still largely involve manual approaches like emails, phones, and faxes.
Revolutionary AI represents a tremendous opportunity for companies to harness the value of data to better manage ever more complex supply chains. To date, emerging AI impact themes are more continuous and concurrent data analysis, more interactive data exploration, and more intelligent automation. Thanks to these AI-enabled improvements, supply chain managers are now able to adapt strategies with far more proficiency than before. In the next three to five years, we expect that AI will become more integrated in broader aspects of supply chain management, bringing repetitive tasks automation and intelligence to supply chain systems to the next level.
Global Nanorobotics Systems market 2019 research provides a basic overview of the industry including definitions, classifications, applications and industry chain structure. The report also analyzes international markets including development trends, competitive landscape, business opportunities, investment plans and expert opinions. The report then estimates, market development trends of the Nanorobotics Systems industry till forecast to 2026.
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Major Players in Nanorobotics Systems market are:
Nanorobotics Systems Market 2019 This market report Provides historical data along with future forecast and detailed analysis and also expected opportunities for Nanorobotics Systems on a global and regional level. The report also explains information about the market size, share, company growth, regional demands, trends, and technical analysis.
Nanorobotics Systems Market Segmentation by Types:
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Most widely used downstream fields of Nanorobotics Systems market covered in this report are:
Most widely used downstream fields of Nanorobotics Systems market covered in this r
Major Regions play vital role in Nanorobotics Systems market are:
Middle East & Africa
What the Global Nanorobotics Systems Market Report Contains:
Price of Report: $ 2960 (SUL)
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Some Major Points from Table of Content (TOC)
1 Nanorobotics Systems Introduction and Market Overview
1.1 Objectives of the Study
1.2 Definition of Nanorobotics Systems
1.3 Nanorobotics Systems Market Scope and Market Size Estimation
1.3.1 Market Concentration Ratio and Market Maturity Analysis
1.3.2 Global Nanorobotics Systems Value ($) and Growth Rate from 2013-2026
1.4 Market Segmentation
1.4.1 Types of Nanorobotics Systems
1.4.2 Applications of Nanorobotics Systems
1.4.3 Research Regions
184.108.40.206 North America Nanorobotics Systems Production Value ($) and Growth Rate (2013-2018)
220.127.116.11 Europe Nanorobotics Systems Production Value ($) and Growth Rate (2013-2018)
18.104.22.168 China Nanorobotics Systems Production Value ($) and Growth Rate (2013-2018)
22.214.171.124 Japan Nanorobotics Systems Production Value ($) and Growth Rate (2013-2018)
126.96.36.199 Middle East & Africa Nanorobotics Systems Production Value ($) and Growth Rate (2013-2018)
188.8.131.52 India Nanorobotics Systems Production Value ($) and Growth Rate (2013-2018)
184.108.40.206 South America Nanorobotics Systems Production Value ($) and Growth Rate (2013-2018)
1.5 Market Dynamics
220.127.116.11 Emerging Countries of Nanorobotics Systems
18.104.22.168 Growing Market of Nanorobotics Systems
1.6 Industry News and Policies by Regions
1.6.1 Industry News
1.6.2 Industry Policies
2 Industry Chain Analysis
2.1 Upstream Raw Material Suppliers of Nanorobotics Systems Analysis
2.2 Major Players of Nanorobotics Systems
2.2.1 Major Players Manufacturing Base and Market Share of Nanorobotics Systems in 2017
2.2.2 Major Players Product Types in 2017
2.3 Nanorobotics Systems Manufacturing Cost Structure Analysis
2.3.1 Production Process Analysis
2.3.2 Manufacturing Cost Structure of Nanorobotics Systems
2.3.3 Raw Material Cost of Nanorobotics Systems
2.3.4 Labor Cost of Nanorobotics Systems
2.4 Market Channel Analysis of Nanorobotics Systems
2.5 Major Downstream Buyers of Nanorobotics Systems Analysis
3 Global Nanorobotics Systems Market, by Type
3.1 Global Nanorobotics Systems Value ($) and Market Share by Type (2013-2018)
3.2 Global Nanorobotics Systems Production and Market Share by Type (2013-2018)
3.3 Global Nanorobotics Systems Value ($) and Growth Rate by Type (2013-2018)
3.4 Global Nanorobotics Systems Price Analysis by Type (2013-2018)
4 Nanorobotics Systems Market, by Application
4.1 Global Nanorobotics Systems Consumption and Market Share by Application (2013-2018)
4.2 Downstream Buyers by Application
4.3 Global Nanorobotics Systems Consumption and Growth Rate by Application (2013-2018)
5 Global Nanorobotics Systems Production, Value ($) by Region (2013-2018)
5.1 Global Nanorobotics Systems Value ($) and Market Share by Region (2013-2018)
5.2 Global Nanorobotics Systems Production and Market Share by Region (2013-2018)
5.3 Global Nanorobotics Systems Production, Value ($), Price and Gross Margin (2013-2018)
5.4 North America Nanorobotics Systems Production, Value ($), Price and Gross Margin (2013-2018)
5.5 Europe Nanorobotics Systems Production, Value ($), Price and Gross Margin (2013-2018)
5.6 China Nanorobotics Systems Production, Value ($), Price and Gross Margin (2013-2018)
5.7 Japan Nanorobotics Systems Production, Value ($), Price and Gross Margin (2013-2018)
5.8 Middle East & Africa Nanorobotics Systems Production, Value ($), Price and Gross Margin (2013-2018)
5.9 India Nanorobotics Systems Production, Value ($), Price and Gross Margin (2013-2018)
5.10 South America Nanorobotics Systems Production, Value ($), Price and Gross Margin (2013-2018)
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NEW YORK, NY / ACCESSWIRE / May 10, 2019 / The Season 8 of Game of Thrones has sparked a global obsession. This is a magnificent and well-made American drama, and one of the features is that you never know what will happen to the next character.
In order to better speculate on the development of the plot, students at the Technical University of Munich have developed an algorithm to predict the death possibility of the important characters in Game of Thrones and put these predictions on a website.
In fact, just before the airing of Season 6 in 2016, this survival rate algorithm had predicted that Jon Snow would be resurrected. Guy Yachdav, a mentor at the Technical University of Munich, said, “Although the data of this prediction algorithm are from science fiction stories, this artificial intelligence (AI) technology can also be applied in the real world.”
With the further integrated and deepening AI + entertainment and a large-scale investment on the policy and funding level, the intelligent auxiliary technology for image production has also provided help in multiple entertainment segments.
1. Video recognition and production: 10,000 frames per second
Video recognition will become one of the basic application technologies of production enterprises. According to the Video Technology Forecast for 2019-2020: Video AI report released by Forrester, 90% of Chinese video platforms are using professional video recognition technologies to structurize data for video, generating financial performance in fields of video content originality, video marketing, commercial use of video structuralization, video big data, and robotic process, and facilitating industrial upgrading through automated image processing auxiliary technology and production technology.
In practical applications, for example, a variety show might be short of hands during production, and at the same time, it has to face a myriad of complex video contents and change of actors, so under the circumstances, missed editing, wrong editing, and temporary replacements, etc will be inevitable. By contrast, video recognition can structurize the stock video, identify the contents and correlations between key frames, make predictions, and regenerate a complete video through a machine, so as to improve the broadcasting efficiency, reduce the rates of mistakenly aired or missing footage, and provide data for business prediction.
Fan Shuo, the technical lead of Moviebook, believes that the AI system based on image analysis and production can quickly analyze image information in real-time transmission, detect and analyze key pixel information, thus helping image production and operation enterprises better formulate a comprehensive scheme and improve efficiency. Even image production enterprises in different places can access the system platform, indicating that the newly developed AI system can be deployed efficiently even in remote rural areas.
At present, Moviebook’s AI image production system has been adopted by most entertainment enterprises in China. Meanwhile, an survey shows that most video companies set an important goal in 2019, namely, by using this innovative AI technology, they will improve video production efficiency, video monetization level, and video data application efficiency on a yearly basis, so as to reduce the cost of one-time investment. Moreover AI-aided diagnosis by doctors in the imaging department will meet rigid demands of the market.
2. Intelligent business: content is the best carrier of marketing
Intelligent image production technology generates intra-frame correlation and prediction through in-depth learning. It is capable of human-like creative thinking and reasoning, and is superior to human in memory, computing speed and accuracy.
In recent years, Moviebook’s AI image production system has been deeply applied in many popular variety shows and dramas. These hit dramas choose technical means different from traditional ones. By adopting Moviebook’s intelligent image production technology, brand owners’ advertisements will be made part of the original videos as “virtual advertising props” and be viewed when videos are been watched.
This is the achievement of AI technology. Through AI, an increase in commercial value of the ads in videos can be achieved without much cost and time in the initial stage. Relying on Moviebook’s intelligent image production technology, the video platform can achieve intelligent commercialization.
3. Information carrier fusion: seamless conversion of graphics, text, sound and image
In China, the fusion of different information carriers, such as graphics, text, sound and image, is becoming an important infrastructure for the media industry.
In reality, converting images, text, audio, and video into each other is traditionally completed through manual operation, usually taking one week and costing 263 USD on average, and the content distortion rate is as high as 52%. On the contrary, information medium fusion and interchange will be become quicker, cheaper and more effective with the aid of AI. For example, a reporter can generate video and audio that match with the content he/she has written through AI with one click.
Although some media outlets are still skeptical about this, most experts predict that the competition in “AI + entertainment” is becoming increasingly fierce. With the rise of AI, ordinary front-end editors will soon equip themselves with new tools to improve efficiency and save time when creating good content. For example, by adopting Moviebook’s information visualization program during the NPC & CPPCC, www.gmw.cn has made the content production more interesting.
4. Video production robots: the “standard equipment” of future entertainment industry
Video production robots are also slowly penetrating our entertainment life and attracting more investment.
In Asia, venture capitalists are still optimistic about the breadth and depth of the application of technology differentiation in the field of AI. For example, Moviebook, a leader in intelligent image production technologies and entertainment industry applications, raised 1.56 billion RMB yuan in total in the latest round of financing, making it an important holding target for most Asian investors.
Video production robots have been widely used in the entertainment industry. For example, in the short video field, one-click video editing robots are mainly used to assist users in video clipping. When users set a script or scene picture, the robot will automatically generate a short video collection.
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