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[https://www.dhl.com/global-en/home/insights-and-innovation/insights/logistics-trend-radar.html] => https://www.dhl.com/global-en/home/insights-and-innovation/insights/logistics-trend-radar.html
[AI-driven big data analytics: the next big thing in supply chain management] => 人工智能驱动的大数据分析――供应链管理领域的下一个大事件
[Logistics experts are discovering the value of AI-driven, real-time analytics to achieve the visibility needed for more robust and resilient supply chains.] => 物流专家开始发现人工智能驱动的实时分析在实现更强大、更有韧性供应链所需的可视性方面的价值。
[There’s no end to the trend in big data analytics
We've been monitoring the trend of big data analytics in logistics and supply chains for years. The power of data-driven insights is transforming many industries, including logistics. So it was no surprise to see it again in the DHL Logistics Trend Radar 6.0 – the 2022 result of our ongoing trend research. We continue to see big data analytics as a high-impact, near-term trend.
Big data analytics doesn’t transform the supply chain physically, but it delivers greater visibility and a better foundation for sound decision-making toward strategic optimizations along supply chain segments. The result is substantially improved service levels, ranging from more efficient pallet storage in warehouses to better customer case handling. And the logistics industry already has a big head start here. Many industry leaders have begun harnessing big data to drive strategic decisions, and soon this trend will simply be standard business practice in logistics and supply chain management.
"We are seeing businesses transform logistics from a quiet, backend operation to a strategic asset and value driver," said Katja Busch, Chief Commercial Officer DHL and Head of DHL Customer Solutions and Innovation
Four types of big data analytics
To approach the daunting topic of AI-driven, real-time analytics of big data, with its potential to transform today’s supply chains, let’s break it down into its four main types: descriptive, diagnostic, predictive, and prescriptive.
Descriptive analytics is about understanding the status quo and describing what is happening, while diagnostic analytics asks why it happens. Predictive analytics forecasts what will likely happen in the future, while prescriptive analytics taps into historical and situational data to recommend strategic optimizations to be implemented.] =>
为了考察人工智能驱动和大数据实时分析这个宏大主题――它有可能改变如今供应链的面貌,我们将它分解成四个主要类型:描述型、诊断型、预测型和规范型。
描述型分析是为了了解现状和描述正在发生的情况,而诊断型分析是为了考察发生的原因。预测型分析是为了对未来可能发生的情况进行预测,而规范型分析是为了对历史数据和情境数据进行深入挖掘,从而提出需要实施的战略优化措施。
[Supply chain analytics in action
As the potential to harvest big data grows, so does the opportunity to harness it. But what does that look like in today's supply chains?
Let’s look at some real-world use cases for leveraging the power of big data analytics:
Inventory and asset optimization
Want to improve efficiency in warehouses and hubs? Big data analytics delivers the supply chain visibility needed to optimize how inventory is stored and moved through facilities and how assets are utilized and maintained.
Descriptive: Utilize sensor data to reveal inventory levels, available shelf space, and the location and status of warehouse assets.
Diagnostic: Show links between inventory types and the breakdown of assets like conveyors or vehicles or between external events and inventory levels.
Predictive: Fine-tune preventive maintenance intervals for assets and optimize inventory in anticipation of predictable seasonal fluctuations.
Prescriptive: Mine historical data to optimize space allocation by stock-keeping unit (SKU) and prevent unnecessarily high or critically low inventory levels.
Transport and delivery optimization
Have a particularly challenging supply chain segment? Big data analytics can help you achieve cost-effective, on-time performance while ensuring your goods arrive in good condition.
Descriptive: Use service-level data to obtain a real-time analysis of the status of transport vehicles or the condition of delivered goods.
Diagnostic: Determine why persistent delays occur. For example: Are schedules out of sync with traffic patterns?
Predictive: Examine various data sources to anticipate the risk of disruption along supply chain segments, even accounting for factors such as natural disasters and political violence.
Prescriptive: Analyze past data for insights into optimal scheduling and fleet sizes to make the most of your capacities and achieve your best on-time performance.
Supplier risk and due diligence assessment
Want to bypass the tedious work of evaluating current or potential supplier and vendor partnerships? Big data analytics is the key to optimizing risk-and-resilience due diligence.
Descriptive: Use data from sensors and other sources to evaluate supplier performance (timely delivery, quality, etc.) in real time.
Diagnostic: Apply this data to discern patterns and understand what makes some suppliers better than others.
Predictive: Support vendor selection by comparing supply chain criteria to automatically forecast each vendor’s likelihood of meeting your needs in the event of a disruption.
Prescriptive: Analyze vendor performance to grade and classify existing and potential partners and make purchasing decisions.
Customer management
Want to strengthen brand loyalty? Big data analytics can help you improve your customer experience and journey.
Descriptive: Group B2B and B2C customers by category using attributes like industry, age, region, order size, and needs – then display the data in an intuitive dashboard to better understand your customer base and who may be affected by supply chain changes.
Diagnostic: Demystify customer churn or preferences driven by price, convenience, or other variables.
Predictive: Generate customer-facing use cases, and use demand forecasting to alleviate supply chain bottlenecks or underutilization of facilities and fleets.
Prescriptive: Process historical data to determine price elasticity and ensure a better price point within customer expectations. Look at past orders to optimize the current workforce while protecting service levels. Mine consumer data to enable more personalized customer journeys and boost customer retention.
"Big data harbors a treasure trove for business insights into all parts of the supply chain for a portfolio of purposes, e.g., efficiency, resilience, and sustainability," said Klaus Dohrmann, VP Head of Innovation and Trend Research, DHL Customer Solutions & Innovation
The challenges of big data analytics
Most, if not all, logistics leaders today are harnessing big data analytics to drive strategic decisions. But even those who have taken the plunge admit there are many challenges. The three most-cited fear factors are “analysis,” “processing,” and “implementation of findings.”
We agree that working with big data analytics can be intimidating, which is why data analysts are essential members of our supply chain management teams. From the logistics perspective, we see the following three immediate challenges:
Identifying the target
Big data analytics needs data – so before embarking on a big data analytics journey, you must identify which data types are valuable to your organization, then build the appropriate data-collecting networks of sensors and other technologies.
Cleaning it up
Most data, especially from the internet, is unstructured and needs to be “cleaned” and filtered to achieve the quality required for meaningful analysis. Automating this process takes time, money, and talent.
Protecting your asset
Data is a valuable asset. Protecting it from bad actors requires a robust cybersecurity infrastructure.
What’s the final (big data) analysis?
Visibility is the key to building resilient supply chains. With today’s AI-driven analytics capabilities, big data is poised to improve supply chain performance and boost supply chain resilience with virtually no changes to existing supply chain infrastructure. Early adopters have continually integrated the latest advances for ever-better descriptive, diagnostic, predictive, and prescriptive insights. Sooner rather than later, these big data analytics techniques will be considered standard practice in supply chain management and expected by customers across all industry sectors.
This story was first published on DHL Delivered and was republished with permission.] =>
可视性是建立韧性供应链的关键。凭借当前的人工智能驱动分析能力,大数据能够在不对现有供应链基础设施进行变更的情况下改善供应链绩效并提高供应链韧性。早期采用者在不断引入最新技术进步,获得越来越好的描述型、诊断型、预测型和规范型洞察。这些大数据分析方法很快就会被视为供应链管理的标准做法,并且被各行各业的客户所翘首以待。
这篇文章最早在敦豪送达网站上发布,经允许后重新发布。
[Is big data analytics in supply chain management about to have another big breakthrough? Logistics experts are discovering the value of AI-driven, real-time analytics to achieve the visibility needed for more robust and resilient supply chains. And business leaders are discovering the value of state-of-the-art supply chain management to deal with disruption and achieve strategic goals.
Past, present, future
Knowing the past is critical to understanding the present and steering the future. What’s true in life is true in business, and data is the raw material that makes such insight possible. Supply chain professionals have grasped the importance of data for decades, as evidenced by the industry’s embrace of the internet of things (IoT) through sensors, smart tags, and such. The potential treasure trove of data generated annually from IoT devices, augmented by user-generated content, is predicted to balloon to some 181 trillion gigabytes by 2025.
Processing capacities have not kept up with this deluge of data, however. And the task grows even more daunting given the unstructured nature of much of this data, including audio and video files and social media feeds – vastly transcending anything that can be managed in spreadsheets.
Enter AI-driven big data analytics – the process of deploying smart tech such as automation and artificial intelligence (AI) to reveal patterns in the past, highlight real-time changes in the present, and reliably predict trends in the future.] => 供应链管理中的大数据会不会带来又一个重大突破?物流专家开始发现人工智能驱动的实时分析在实现更强大、更有韧性供应链所需的可视性方面的价值。商界领袖开始发现最先进的供应链管理在处理中断和实现战略目标方面的价值。
过去、现在和未来
了解过去对于洞察现在和预见未来至关重要。生活中的道理在商业领域同样适用,而数据正是让这种洞察成为可能的原材料。供应链专家认识到数据的重要性距今已有数十年之久,这一点可以从业界通过传感器、智能标签等手段采用物联网(IoT)看出。物联网设备每年生成的海量数据,在用户生成内容的加持下,有望在2025年激增到约181万亿千兆字节之多。
然而,处理能力还是跟不上数据量的大爆发。而且,考虑到其中很大一部分数据的无序性,这个任务变得更加令人生畏,包括音频和视频文件以及社交媒体推送内容――这大大超出了电子表格能够处理的极限。
让我们一起探索人工智能驱动大数据分析――部署自动化和人工智能(AI)等智能技术的过程,揭示过去的演化模式、了解现在重要的实时变化并且可靠地预测未来发展趋势。
[wysiwyg] => wysiwyg
[callout_box] => callout_box
[outbound_box] => outbound_box
[The Logistics Trend Radar 6.0 Edition] => 物流发展趋势雷达图6.0版
[A spectrum of upcoming logistics trends over the next decade.] => 未来十年内物流趋势图谱。
[Read more] => 了解更多
[Big data gets bigger, growth gets faster] => 大数据的数量越来越庞大、增长速度越来越快
[Types of data analytics] => 数据分析的类型
[Volume of data created, captured, copied, and consumed worldwide from 2010 to 2020, with forecasts for 2021 to 2025 (in gigabytes)
2010: 2 TRILLION
2020: 64 TRILLION
2025: 181 TRILLION
Source: Statista] => 2010年到2020年间全世界创建、收集、复制和使用的数据量以及2021年到2025年的预测(单位:千兆字节)
2010年:2万亿
2020年:64万亿
2025年:181万亿
资料来源:Statista全球统计数据库
[DESCRIPTIVE
What happened?
A careful analysis of big data in logistics can help you understand your supply chain performance.
DIAGNOSTIC
Why did it happen?
Big data unlocks the secrets of what went right or wrong – and why.
PREDICTIVE
What’s likely to happen next?
Your historical data holds valuable clues that can yield predictions about future performance.
PRESCRIPTIVE
What should we do about it?
AI can mine big data to generate “lessons learned” for strategies going forward.] => 描述型发生了什么情况?
对物流大数据的细致分析有助于了解自身供应链的表现。
诊断型为什么发生这种情况?
大数据可以揭开什么是正确的、什么是错误的以及为什么的秘密。
预测型接下来有可能发生什么情况?
历史数据包含了能够对未来表现进行预测的有价值线索。
规范型应当怎么处理这种情况?
人工智能可以挖掘大数据,获得可供未来制定战略时参考的“经验教训”。
[Logistics experts are discovering the value of AI-driven, real-time analytics to achieve the visibility needed for more robust and resilient supply chains. Is big data analytics in supply chain management about to have another big breakthrough? Logistics experts are discovering the value of AI-driven, real-time analytics to achieve the visibility needed for more robust and resilient supply chains. And business leaders are discovering the value of state-of-the-art supply chain management to deal with disruption and achieve strategic goals.
Past, present, future
Knowing the past is critical to understanding the present and steering the future. What’s true in life is true in business, and data is the raw material that makes such insight possible. Supply chain professionals have grasped the importance of data for decades, as evidenced by the industry’s embrace of the internet of things (IoT) through sensors, smart tags, and such. The potential treasure trove of data generated annually from IoT devices, augmented by user-generated content, is predicted to balloon to some 181 trillion gigabytes by 2025.
Processing capacities have not kept up with this deluge of data, however. And the task grows even more daunting given the unstructured nature of much of this data, including audio and video files and social media feeds – vastly transcending anything that can be managed in spreadsheets.
Enter AI-driven big data analytics – the process of deploying smart tech such as automation and artificial intelligence (AI) to reveal patterns in the past, highlight real-time changes in the present, and reliably predict trends in the future.
Big data gets bigger, growth gets fasterVolume of data created, captured, copied, and consumed worldwide from 2010 to 2020, with forecasts for 2021 to 2025 (in gigabytes)
2010: 2 TRILLION
2020: 64 TRILLION
2025: 181 TRILLION
Source: Statista
There’s no end to the trend in big data analytics
We’ve been monitoring the trend of big data analytics in logistics and supply chains for years. The power of data-driven insights is transforming many industries, including logistics. So it was no surprise to see it again in the DHL Logistics Trend Radar 6.0 – the 2022 result of our ongoing trend research. We continue to see big data analytics as a high-impact, near-term trend.
Big data analytics doesn’t transform the supply chain physically, but it delivers greater visibility and a better foundation for sound decision-making toward strategic optimizations along supply chain segments. The result is substantially improved service levels, ranging from more efficient pallet storage in warehouses to better customer case handling. And the logistics industry already has a big head start here. Many industry leaders have begun harnessing big data to drive strategic decisions, and soon this trend will simply be standard business practice in logistics and supply chain management.
“We are seeing businesses transform logistics from a quiet, backend operation to a strategic asset and value driver,” said Katja Busch, Chief Commercial Officer DHL and Head of DHL Customer Solutions and Innovation
Four types of big data analytics
To approach the daunting topic of AI-driven, real-time analytics of big data, with its potential to transform today’s supply chains, let’s break it down into its four main types: descriptive, diagnostic, predictive, and prescriptive.
Descriptive analytics is about understanding the status quo and describing what is happening, while diagnostic analytics asks why it happens. Predictive analytics forecasts what will likely happen in the future, while prescriptive analytics taps into historical and situational data to recommend strategic optimizations to be implemented.
Types of data analyticsDESCRIPTIVE
What happened?
A careful analysis of big data in logistics can help you understand your supply chain performance.
DIAGNOSTIC
Why did it happen?
Big data unlocks the secrets of what went right or wrong – and why.
PREDICTIVE
What’s likely to happen next?
Your historical data holds valuable clues that can yield predictions about future performance.
PRESCRIPTIVE
What should we do about it?
AI can mine big data to generate “lessons learned” for strategies going forward.
Supply chain analytics in action
As the potential to harvest big data grows, so does the opportunity to harness it. But what does that look like in today’s supply chains?
Let’s look at some real-world use cases for leveraging the power of big data analytics:
Inventory and asset optimization
Want to improve efficiency in warehouses and hubs? Big data analytics delivers the supply chain visibility needed to optimize how inventory is stored and moved through facilities and how assets are utilized and maintained.
Descriptive: Utilize sensor data to reveal inventory levels, available shelf space, and the location and status of warehouse assets.
Diagnostic: Show links between inventory types and the breakdown of assets like conveyors or vehicles or between external events and inventory levels.
Predictive: Fine-tune preventive maintenance intervals for assets and optimize inventory in anticipation of predictable seasonal fluctuations.
Prescriptive: Mine historical data to optimize space allocation by stock-keeping unit (SKU) and prevent unnecessarily high or critically low inventory levels.
Transport and delivery optimization
Have a particularly challenging supply chain segment? Big data analytics can help you achieve cost-effective, on-time performance while ensuring your goods arrive in good condition.
Descriptive: Use service-level data to obtain a real-time analysis of the status of transport vehicles or the condition of delivered goods.
Diagnostic: Determine why persistent delays occur. For example: Are schedules out of sync with traffic patterns?
Predictive: Examine various data sources to anticipate the risk of disruption along supply chain segments, even accounting for factors such as natural disasters and political violence.
Prescriptive: Analyze past data for insights into optimal scheduling and fleet sizes to make the most of your capacities and achieve your best on-time performance.
Supplier risk and due diligence assessment
Want to bypass the tedious work of evaluating current or potential supplier and vendor partnerships? Big data analytics is the key to optimizing risk-and-resilience due diligence.
Descriptive: Use data from sensors and other sources to evaluate supplier performance (timely delivery, quality, etc.) in real time.
Diagnostic: Apply this data to discern patterns and understand what makes some suppliers better than others.
Predictive: Support vendor selection by comparing supply chain criteria to automatically forecast each vendor’s likelihood of meeting your needs in the event of a disruption.
Prescriptive: Analyze vendor performance to grade and classify existing and potential partners and make purchasing decisions.
Customer management
Want to strengthen brand loyalty? Big data analytics can help you improve your customer experience and journey.
Descriptive: Group B2B and B2C customers by category using attributes like industry, age, region, order size, and needs – then display the data in an intuitive dashboard to better understand your customer base and who may be affected by supply chain changes.
Diagnostic: Demystify customer churn or preferences driven by price, convenience, or other variables.
Predictive: Generate customer-facing use cases, and use demand forecasting to alleviate supply chain bottlenecks or underutilization of facilities and fleets.
Prescriptive: Process historical data to determine price elasticity and ensure a better price point within customer expectations. Look at past orders to optimize the current workforce while protecting service levels. Mine consumer data to enable more personalized customer journeys and boost customer retention.
“Big data harbors a treasure trove for business insights into all parts of the supply chain for a portfolio of purposes, e.g., efficiency, resilience, and sustainability,” said Klaus Dohrmann, VP Head of Innovation and Trend Research, DHL Customer Solutions & Innovation
The challenges of big data analytics
Most, if not all, logistics leaders today are harnessing big data analytics to drive strategic decisions. But even those who have taken the plunge admit there are many challenges. The three most-cited fear factors are “analysis,” “processing,” and “implementation of findings.”
Source: Unsupervised
We agree that working with big data analytics can be intimidating, which is why data analysts are essential members of our supply chain management teams. From the logistics perspective, we see the following three immediate challenges:
Identifying the target
Big data analytics needs data – so before embarking on a big data analytics journey, you must identify which data types are valuable to your organization, then build the appropriate data-collecting networks of sensors and other technologies.
Cleaning it up
Most data, especially from the internet, is unstructured and needs to be “cleaned” and filtered to achieve the quality required for meaningful analysis. Automating this process takes time, money, and talent.
Protecting your asset
Data is a valuable asset. Protecting it from bad actors requires a robust cybersecurity infrastructure.
What’s the final (big data) analysis?
Visibility is the key to building resilient supply chains. With today’s AI-driven analytics capabilities, big data is poised to improve supply chain performance and boost supply chain resilience with virtually no changes to existing supply chain infrastructure. Early adopters have continually integrated the latest advances for ever-better descriptive, diagnostic, predictive, and prescriptive insights. Sooner rather than later, these big data analytics techniques will be considered standard practice in supply chain management and expected by customers across all industry sectors.
This story was first published on DHL Delivered and was republished with permission.
RELATED ARTICLESThe Logistics Trend Radar 6.0 EditionA spectrum of upcoming logistics trends over the next decade.] =>
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[big-data-analytics-supply-chain-management-02] => big-data-analytics-supply-chain-management-02
[big-data-analytics-supply-chain-management-05] => big-data-analytics-supply-chain-management-05
[LTR THUMBNAIL 300 x 248] => LTR THUMBNAIL 300 x 248
[big-data-analytics-supply-chain-management-key-image] => big-data-analytics-supply-chain-management-key-image
[big-data-analytics-supply-chain-management-single-column] => big-data-analytics-supply-chain-management-single-column
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[$value] => Is big data analytics in supply chain management about to have another big breakthrough? Logistics experts are discovering the value of AI-driven, real-time analytics to achieve the visibility needed for more robust and resilient supply chains. And business leaders are discovering the value of state-of-the-art supply chain management to deal with disruption and achieve strategic goals.
Past, present, future
Knowing the past is critical to understanding the present and steering the future. What’s true in life is true in business, and data is the raw material that makes such insight possible. Supply chain professionals have grasped the importance of data for decades, as evidenced by the industry’s embrace of the internet of things (IoT) through sensors, smart tags, and such. The potential treasure trove of data generated annually from IoT devices, augmented by user-generated content, is predicted to balloon to some 181 trillion gigabytes by 2025.
Processing capacities have not kept up with this deluge of data, however. And the task grows even more daunting given the unstructured nature of much of this data, including audio and video files and social media feeds – vastly transcending anything that can be managed in spreadsheets.
Enter AI-driven big data analytics – the process of deploying smart tech such as automation and artificial intelligence (AI) to reveal patterns in the past, highlight real-time changes in the present, and reliably predict trends in the future.
)
Array
(
[derick] => Array
(
[[]] =>
[https://www.dhl.com/global-en/home/insights-and-innovation/insights/logistics-trend-radar.html] => https://www.dhl.com/global-en/home/insights-and-innovation/insights/logistics-trend-radar.html
[AI-driven big data analytics: the next big thing in supply chain management] => 人工智能驱动的大数据分析――供应链管理领域的下一个大事件
[Logistics experts are discovering the value of AI-driven, real-time analytics to achieve the visibility needed for more robust and resilient supply chains.] => 物流专家开始发现人工智能驱动的实时分析在实现更强大、更有韧性供应链所需的可视性方面的价值。
[There’s no end to the trend in big data analytics
We've been monitoring the trend of big data analytics in logistics and supply chains for years. The power of data-driven insights is transforming many industries, including logistics. So it was no surprise to see it again in the DHL Logistics Trend Radar 6.0 – the 2022 result of our ongoing trend research. We continue to see big data analytics as a high-impact, near-term trend.
Big data analytics doesn’t transform the supply chain physically, but it delivers greater visibility and a better foundation for sound decision-making toward strategic optimizations along supply chain segments. The result is substantially improved service levels, ranging from more efficient pallet storage in warehouses to better customer case handling. And the logistics industry already has a big head start here. Many industry leaders have begun harnessing big data to drive strategic decisions, and soon this trend will simply be standard business practice in logistics and supply chain management.
"We are seeing businesses transform logistics from a quiet, backend operation to a strategic asset and value driver," said Katja Busch, Chief Commercial Officer DHL and Head of DHL Customer Solutions and Innovation
Four types of big data analytics
To approach the daunting topic of AI-driven, real-time analytics of big data, with its potential to transform today’s supply chains, let’s break it down into its four main types: descriptive, diagnostic, predictive, and prescriptive.
Descriptive analytics is about understanding the status quo and describing what is happening, while diagnostic analytics asks why it happens. Predictive analytics forecasts what will likely happen in the future, while prescriptive analytics taps into historical and situational data to recommend strategic optimizations to be implemented.] =>
为了考察人工智能驱动和大数据实时分析这个宏大主题――它有可能改变如今供应链的面貌,我们将它分解成四个主要类型:描述型、诊断型、预测型和规范型。
描述型分析是为了了解现状和描述正在发生的情况,而诊断型分析是为了考察发生的原因。预测型分析是为了对未来可能发生的情况进行预测,而规范型分析是为了对历史数据和情境数据进行深入挖掘,从而提出需要实施的战略优化措施。
[Supply chain analytics in action
As the potential to harvest big data grows, so does the opportunity to harness it. But what does that look like in today's supply chains?
Let’s look at some real-world use cases for leveraging the power of big data analytics:
Inventory and asset optimization
Want to improve efficiency in warehouses and hubs? Big data analytics delivers the supply chain visibility needed to optimize how inventory is stored and moved through facilities and how assets are utilized and maintained.
Descriptive: Utilize sensor data to reveal inventory levels, available shelf space, and the location and status of warehouse assets.
Diagnostic: Show links between inventory types and the breakdown of assets like conveyors or vehicles or between external events and inventory levels.
Predictive: Fine-tune preventive maintenance intervals for assets and optimize inventory in anticipation of predictable seasonal fluctuations.
Prescriptive: Mine historical data to optimize space allocation by stock-keeping unit (SKU) and prevent unnecessarily high or critically low inventory levels.
Transport and delivery optimization
Have a particularly challenging supply chain segment? Big data analytics can help you achieve cost-effective, on-time performance while ensuring your goods arrive in good condition.
Descriptive: Use service-level data to obtain a real-time analysis of the status of transport vehicles or the condition of delivered goods.
Diagnostic: Determine why persistent delays occur. For example: Are schedules out of sync with traffic patterns?
Predictive: Examine various data sources to anticipate the risk of disruption along supply chain segments, even accounting for factors such as natural disasters and political violence.
Prescriptive: Analyze past data for insights into optimal scheduling and fleet sizes to make the most of your capacities and achieve your best on-time performance.
Supplier risk and due diligence assessment
Want to bypass the tedious work of evaluating current or potential supplier and vendor partnerships? Big data analytics is the key to optimizing risk-and-resilience due diligence.
Descriptive: Use data from sensors and other sources to evaluate supplier performance (timely delivery, quality, etc.) in real time.
Diagnostic: Apply this data to discern patterns and understand what makes some suppliers better than others.
Predictive: Support vendor selection by comparing supply chain criteria to automatically forecast each vendor’s likelihood of meeting your needs in the event of a disruption.
Prescriptive: Analyze vendor performance to grade and classify existing and potential partners and make purchasing decisions.
Customer management
Want to strengthen brand loyalty? Big data analytics can help you improve your customer experience and journey.
Descriptive: Group B2B and B2C customers by category using attributes like industry, age, region, order size, and needs – then display the data in an intuitive dashboard to better understand your customer base and who may be affected by supply chain changes.
Diagnostic: Demystify customer churn or preferences driven by price, convenience, or other variables.
Predictive: Generate customer-facing use cases, and use demand forecasting to alleviate supply chain bottlenecks or underutilization of facilities and fleets.
Prescriptive: Process historical data to determine price elasticity and ensure a better price point within customer expectations. Look at past orders to optimize the current workforce while protecting service levels. Mine consumer data to enable more personalized customer journeys and boost customer retention.
"Big data harbors a treasure trove for business insights into all parts of the supply chain for a portfolio of purposes, e.g., efficiency, resilience, and sustainability," said Klaus Dohrmann, VP Head of Innovation and Trend Research, DHL Customer Solutions & Innovation
The challenges of big data analytics
Most, if not all, logistics leaders today are harnessing big data analytics to drive strategic decisions. But even those who have taken the plunge admit there are many challenges. The three most-cited fear factors are “analysis,” “processing,” and “implementation of findings.”
We agree that working with big data analytics can be intimidating, which is why data analysts are essential members of our supply chain management teams. From the logistics perspective, we see the following three immediate challenges:
Identifying the target
Big data analytics needs data – so before embarking on a big data analytics journey, you must identify which data types are valuable to your organization, then build the appropriate data-collecting networks of sensors and other technologies.
Cleaning it up
Most data, especially from the internet, is unstructured and needs to be “cleaned” and filtered to achieve the quality required for meaningful analysis. Automating this process takes time, money, and talent.
Protecting your asset
Data is a valuable asset. Protecting it from bad actors requires a robust cybersecurity infrastructure.
What’s the final (big data) analysis?
Visibility is the key to building resilient supply chains. With today’s AI-driven analytics capabilities, big data is poised to improve supply chain performance and boost supply chain resilience with virtually no changes to existing supply chain infrastructure. Early adopters have continually integrated the latest advances for ever-better descriptive, diagnostic, predictive, and prescriptive insights. Sooner rather than later, these big data analytics techniques will be considered standard practice in supply chain management and expected by customers across all industry sectors.
This story was first published on DHL Delivered and was republished with permission.] =>
可视性是建立韧性供应链的关键。凭借当前的人工智能驱动分析能力,大数据能够在不对现有供应链基础设施进行变更的情况下改善供应链绩效并提高供应链韧性。早期采用者在不断引入最新技术进步,获得越来越好的描述型、诊断型、预测型和规范型洞察。这些大数据分析方法很快就会被视为供应链管理的标准做法,并且被各行各业的客户所翘首以待。
这篇文章最早在敦豪送达网站上发布,经允许后重新发布。
[Is big data analytics in supply chain management about to have another big breakthrough? Logistics experts are discovering the value of AI-driven, real-time analytics to achieve the visibility needed for more robust and resilient supply chains. And business leaders are discovering the value of state-of-the-art supply chain management to deal with disruption and achieve strategic goals.
Past, present, future
Knowing the past is critical to understanding the present and steering the future. What’s true in life is true in business, and data is the raw material that makes such insight possible. Supply chain professionals have grasped the importance of data for decades, as evidenced by the industry’s embrace of the internet of things (IoT) through sensors, smart tags, and such. The potential treasure trove of data generated annually from IoT devices, augmented by user-generated content, is predicted to balloon to some 181 trillion gigabytes by 2025.
Processing capacities have not kept up with this deluge of data, however. And the task grows even more daunting given the unstructured nature of much of this data, including audio and video files and social media feeds – vastly transcending anything that can be managed in spreadsheets.
Enter AI-driven big data analytics – the process of deploying smart tech such as automation and artificial intelligence (AI) to reveal patterns in the past, highlight real-time changes in the present, and reliably predict trends in the future.] => 供应链管理中的大数据会不会带来又一个重大突破?物流专家开始发现人工智能驱动的实时分析在实现更强大、更有韧性供应链所需的可视性方面的价值。商界领袖开始发现最先进的供应链管理在处理中断和实现战略目标方面的价值。
过去、现在和未来
了解过去对于洞察现在和预见未来至关重要。生活中的道理在商业领域同样适用,而数据正是让这种洞察成为可能的原材料。供应链专家认识到数据的重要性距今已有数十年之久,这一点可以从业界通过传感器、智能标签等手段采用物联网(IoT)看出。物联网设备每年生成的海量数据,在用户生成内容的加持下,有望在2025年激增到约181万亿千兆字节之多。
然而,处理能力还是跟不上数据量的大爆发。而且,考虑到其中很大一部分数据的无序性,这个任务变得更加令人生畏,包括音频和视频文件以及社交媒体推送内容――这大大超出了电子表格能够处理的极限。
让我们一起探索人工智能驱动大数据分析――部署自动化和人工智能(AI)等智能技术的过程,揭示过去的演化模式、了解现在重要的实时变化并且可靠地预测未来发展趋势。
[wysiwyg] => wysiwyg
[callout_box] => callout_box
[outbound_box] => outbound_box
[The Logistics Trend Radar 6.0 Edition] => 物流发展趋势雷达图6.0版
[A spectrum of upcoming logistics trends over the next decade.] => 未来十年内物流趋势图谱。
[Read more] => 了解更多
[Big data gets bigger, growth gets faster] => 大数据的数量越来越庞大、增长速度越来越快
[Types of data analytics] => 数据分析的类型
[Volume of data created, captured, copied, and consumed worldwide from 2010 to 2020, with forecasts for 2021 to 2025 (in gigabytes)
2010: 2 TRILLION
2020: 64 TRILLION
2025: 181 TRILLION
Source: Statista] => 2010年到2020年间全世界创建、收集、复制和使用的数据量以及2021年到2025年的预测(单位:千兆字节)
2010年:2万亿
2020年:64万亿
2025年:181万亿
资料来源:Statista全球统计数据库
[DESCRIPTIVE
What happened?
A careful analysis of big data in logistics can help you understand your supply chain performance.
DIAGNOSTIC
Why did it happen?
Big data unlocks the secrets of what went right or wrong – and why.
PREDICTIVE
What’s likely to happen next?
Your historical data holds valuable clues that can yield predictions about future performance.
PRESCRIPTIVE
What should we do about it?
AI can mine big data to generate “lessons learned” for strategies going forward.] => 描述型发生了什么情况?
对物流大数据的细致分析有助于了解自身供应链的表现。
诊断型为什么发生这种情况?
大数据可以揭开什么是正确的、什么是错误的以及为什么的秘密。
预测型接下来有可能发生什么情况?
历史数据包含了能够对未来表现进行预测的有价值线索。
规范型应当怎么处理这种情况?
人工智能可以挖掘大数据,获得可供未来制定战略时参考的“经验教训”。
[Logistics experts are discovering the value of AI-driven, real-time analytics to achieve the visibility needed for more robust and resilient supply chains. Is big data analytics in supply chain management about to have another big breakthrough? Logistics experts are discovering the value of AI-driven, real-time analytics to achieve the visibility needed for more robust and resilient supply chains. And business leaders are discovering the value of state-of-the-art supply chain management to deal with disruption and achieve strategic goals.
Past, present, future
Knowing the past is critical to understanding the present and steering the future. What’s true in life is true in business, and data is the raw material that makes such insight possible. Supply chain professionals have grasped the importance of data for decades, as evidenced by the industry’s embrace of the internet of things (IoT) through sensors, smart tags, and such. The potential treasure trove of data generated annually from IoT devices, augmented by user-generated content, is predicted to balloon to some 181 trillion gigabytes by 2025.
Processing capacities have not kept up with this deluge of data, however. And the task grows even more daunting given the unstructured nature of much of this data, including audio and video files and social media feeds – vastly transcending anything that can be managed in spreadsheets.
Enter AI-driven big data analytics – the process of deploying smart tech such as automation and artificial intelligence (AI) to reveal patterns in the past, highlight real-time changes in the present, and reliably predict trends in the future.
Big data gets bigger, growth gets fasterVolume of data created, captured, copied, and consumed worldwide from 2010 to 2020, with forecasts for 2021 to 2025 (in gigabytes)
2010: 2 TRILLION
2020: 64 TRILLION
2025: 181 TRILLION
Source: Statista
There’s no end to the trend in big data analytics
We’ve been monitoring the trend of big data analytics in logistics and supply chains for years. The power of data-driven insights is transforming many industries, including logistics. So it was no surprise to see it again in the DHL Logistics Trend Radar 6.0 – the 2022 result of our ongoing trend research. We continue to see big data analytics as a high-impact, near-term trend.
Big data analytics doesn’t transform the supply chain physically, but it delivers greater visibility and a better foundation for sound decision-making toward strategic optimizations along supply chain segments. The result is substantially improved service levels, ranging from more efficient pallet storage in warehouses to better customer case handling. And the logistics industry already has a big head start here. Many industry leaders have begun harnessing big data to drive strategic decisions, and soon this trend will simply be standard business practice in logistics and supply chain management.
“We are seeing businesses transform logistics from a quiet, backend operation to a strategic asset and value driver,” said Katja Busch, Chief Commercial Officer DHL and Head of DHL Customer Solutions and Innovation
Four types of big data analytics
To approach the daunting topic of AI-driven, real-time analytics of big data, with its potential to transform today’s supply chains, let’s break it down into its four main types: descriptive, diagnostic, predictive, and prescriptive.
Descriptive analytics is about understanding the status quo and describing what is happening, while diagnostic analytics asks why it happens. Predictive analytics forecasts what will likely happen in the future, while prescriptive analytics taps into historical and situational data to recommend strategic optimizations to be implemented.
Types of data analyticsDESCRIPTIVE
What happened?
A careful analysis of big data in logistics can help you understand your supply chain performance.
DIAGNOSTIC
Why did it happen?
Big data unlocks the secrets of what went right or wrong – and why.
PREDICTIVE
What’s likely to happen next?
Your historical data holds valuable clues that can yield predictions about future performance.
PRESCRIPTIVE
What should we do about it?
AI can mine big data to generate “lessons learned” for strategies going forward.
Supply chain analytics in action
As the potential to harvest big data grows, so does the opportunity to harness it. But what does that look like in today’s supply chains?
Let’s look at some real-world use cases for leveraging the power of big data analytics:
Inventory and asset optimization
Want to improve efficiency in warehouses and hubs? Big data analytics delivers the supply chain visibility needed to optimize how inventory is stored and moved through facilities and how assets are utilized and maintained.
Descriptive: Utilize sensor data to reveal inventory levels, available shelf space, and the location and status of warehouse assets.
Diagnostic: Show links between inventory types and the breakdown of assets like conveyors or vehicles or between external events and inventory levels.
Predictive: Fine-tune preventive maintenance intervals for assets and optimize inventory in anticipation of predictable seasonal fluctuations.
Prescriptive: Mine historical data to optimize space allocation by stock-keeping unit (SKU) and prevent unnecessarily high or critically low inventory levels.
Transport and delivery optimization
Have a particularly challenging supply chain segment? Big data analytics can help you achieve cost-effective, on-time performance while ensuring your goods arrive in good condition.
Descriptive: Use service-level data to obtain a real-time analysis of the status of transport vehicles or the condition of delivered goods.
Diagnostic: Determine why persistent delays occur. For example: Are schedules out of sync with traffic patterns?
Predictive: Examine various data sources to anticipate the risk of disruption along supply chain segments, even accounting for factors such as natural disasters and political violence.
Prescriptive: Analyze past data for insights into optimal scheduling and fleet sizes to make the most of your capacities and achieve your best on-time performance.
Supplier risk and due diligence assessment
Want to bypass the tedious work of evaluating current or potential supplier and vendor partnerships? Big data analytics is the key to optimizing risk-and-resilience due diligence.
Descriptive: Use data from sensors and other sources to evaluate supplier performance (timely delivery, quality, etc.) in real time.
Diagnostic: Apply this data to discern patterns and understand what makes some suppliers better than others.
Predictive: Support vendor selection by comparing supply chain criteria to automatically forecast each vendor’s likelihood of meeting your needs in the event of a disruption.
Prescriptive: Analyze vendor performance to grade and classify existing and potential partners and make purchasing decisions.
Customer management
Want to strengthen brand loyalty? Big data analytics can help you improve your customer experience and journey.
Descriptive: Group B2B and B2C customers by category using attributes like industry, age, region, order size, and needs – then display the data in an intuitive dashboard to better understand your customer base and who may be affected by supply chain changes.
Diagnostic: Demystify customer churn or preferences driven by price, convenience, or other variables.
Predictive: Generate customer-facing use cases, and use demand forecasting to alleviate supply chain bottlenecks or underutilization of facilities and fleets.
Prescriptive: Process historical data to determine price elasticity and ensure a better price point within customer expectations. Look at past orders to optimize the current workforce while protecting service levels. Mine consumer data to enable more personalized customer journeys and boost customer retention.
“Big data harbors a treasure trove for business insights into all parts of the supply chain for a portfolio of purposes, e.g., efficiency, resilience, and sustainability,” said Klaus Dohrmann, VP Head of Innovation and Trend Research, DHL Customer Solutions & Innovation
The challenges of big data analytics
Most, if not all, logistics leaders today are harnessing big data analytics to drive strategic decisions. But even those who have taken the plunge admit there are many challenges. The three most-cited fear factors are “analysis,” “processing,” and “implementation of findings.”
Source: Unsupervised
We agree that working with big data analytics can be intimidating, which is why data analysts are essential members of our supply chain management teams. From the logistics perspective, we see the following three immediate challenges:
Identifying the target
Big data analytics needs data – so before embarking on a big data analytics journey, you must identify which data types are valuable to your organization, then build the appropriate data-collecting networks of sensors and other technologies.
Cleaning it up
Most data, especially from the internet, is unstructured and needs to be “cleaned” and filtered to achieve the quality required for meaningful analysis. Automating this process takes time, money, and talent.
Protecting your asset
Data is a valuable asset. Protecting it from bad actors requires a robust cybersecurity infrastructure.
What’s the final (big data) analysis?
Visibility is the key to building resilient supply chains. With today’s AI-driven analytics capabilities, big data is poised to improve supply chain performance and boost supply chain resilience with virtually no changes to existing supply chain infrastructure. Early adopters have continually integrated the latest advances for ever-better descriptive, diagnostic, predictive, and prescriptive insights. Sooner rather than later, these big data analytics techniques will be considered standard practice in supply chain management and expected by customers across all industry sectors.
This story was first published on DHL Delivered and was republished with permission.
RELATED ARTICLESThe Logistics Trend Radar 6.0 EditionA spectrum of upcoming logistics trends over the next decade.] =>
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[$value] => There’s no end to the trend in big data analytics
We've been monitoring the trend of big data analytics in logistics and supply chains for years. The power of data-driven insights is transforming many industries, including logistics. So it was no surprise to see it again in the DHL Logistics Trend Radar 6.0 – the 2022 result of our ongoing trend research. We continue to see big data analytics as a high-impact, near-term trend.
Big data analytics doesn’t transform the supply chain physically, but it delivers greater visibility and a better foundation for sound decision-making toward strategic optimizations along supply chain segments. The result is substantially improved service levels, ranging from more efficient pallet storage in warehouses to better customer case handling. And the logistics industry already has a big head start here. Many industry leaders have begun harnessing big data to drive strategic decisions, and soon this trend will simply be standard business practice in logistics and supply chain management.
"We are seeing businesses transform logistics from a quiet, backend operation to a strategic asset and value driver," said Katja Busch, Chief Commercial Officer DHL and Head of DHL Customer Solutions and Innovation
Four types of big data analytics
To approach the daunting topic of AI-driven, real-time analytics of big data, with its potential to transform today’s supply chains, let’s break it down into its four main types: descriptive, diagnostic, predictive, and prescriptive.
Descriptive analytics is about understanding the status quo and describing what is happening, while diagnostic analytics asks why it happens. Predictive analytics forecasts what will likely happen in the future, while prescriptive analytics taps into historical and situational data to recommend strategic optimizations to be implemented.
)
Array
(
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[[]] =>
[https://www.dhl.com/global-en/home/insights-and-innovation/insights/logistics-trend-radar.html] => https://www.dhl.com/global-en/home/insights-and-innovation/insights/logistics-trend-radar.html
[AI-driven big data analytics: the next big thing in supply chain management] => 人工智能驱动的大数据分析――供应链管理领域的下一个大事件
[Logistics experts are discovering the value of AI-driven, real-time analytics to achieve the visibility needed for more robust and resilient supply chains.] => 物流专家开始发现人工智能驱动的实时分析在实现更强大、更有韧性供应链所需的可视性方面的价值。
[There’s no end to the trend in big data analytics
We've been monitoring the trend of big data analytics in logistics and supply chains for years. The power of data-driven insights is transforming many industries, including logistics. So it was no surprise to see it again in the DHL Logistics Trend Radar 6.0 – the 2022 result of our ongoing trend research. We continue to see big data analytics as a high-impact, near-term trend.
Big data analytics doesn’t transform the supply chain physically, but it delivers greater visibility and a better foundation for sound decision-making toward strategic optimizations along supply chain segments. The result is substantially improved service levels, ranging from more efficient pallet storage in warehouses to better customer case handling. And the logistics industry already has a big head start here. Many industry leaders have begun harnessing big data to drive strategic decisions, and soon this trend will simply be standard business practice in logistics and supply chain management.
"We are seeing businesses transform logistics from a quiet, backend operation to a strategic asset and value driver," said Katja Busch, Chief Commercial Officer DHL and Head of DHL Customer Solutions and Innovation
Four types of big data analytics
To approach the daunting topic of AI-driven, real-time analytics of big data, with its potential to transform today’s supply chains, let’s break it down into its four main types: descriptive, diagnostic, predictive, and prescriptive.
Descriptive analytics is about understanding the status quo and describing what is happening, while diagnostic analytics asks why it happens. Predictive analytics forecasts what will likely happen in the future, while prescriptive analytics taps into historical and situational data to recommend strategic optimizations to be implemented.] =>
为了考察人工智能驱动和大数据实时分析这个宏大主题――它有可能改变如今供应链的面貌,我们将它分解成四个主要类型:描述型、诊断型、预测型和规范型。
描述型分析是为了了解现状和描述正在发生的情况,而诊断型分析是为了考察发生的原因。预测型分析是为了对未来可能发生的情况进行预测,而规范型分析是为了对历史数据和情境数据进行深入挖掘,从而提出需要实施的战略优化措施。
[Supply chain analytics in action
As the potential to harvest big data grows, so does the opportunity to harness it. But what does that look like in today's supply chains?
Let’s look at some real-world use cases for leveraging the power of big data analytics:
Inventory and asset optimization
Want to improve efficiency in warehouses and hubs? Big data analytics delivers the supply chain visibility needed to optimize how inventory is stored and moved through facilities and how assets are utilized and maintained.
Descriptive: Utilize sensor data to reveal inventory levels, available shelf space, and the location and status of warehouse assets.
Diagnostic: Show links between inventory types and the breakdown of assets like conveyors or vehicles or between external events and inventory levels.
Predictive: Fine-tune preventive maintenance intervals for assets and optimize inventory in anticipation of predictable seasonal fluctuations.
Prescriptive: Mine historical data to optimize space allocation by stock-keeping unit (SKU) and prevent unnecessarily high or critically low inventory levels.
Transport and delivery optimization
Have a particularly challenging supply chain segment? Big data analytics can help you achieve cost-effective, on-time performance while ensuring your goods arrive in good condition.
Descriptive: Use service-level data to obtain a real-time analysis of the status of transport vehicles or the condition of delivered goods.
Diagnostic: Determine why persistent delays occur. For example: Are schedules out of sync with traffic patterns?
Predictive: Examine various data sources to anticipate the risk of disruption along supply chain segments, even accounting for factors such as natural disasters and political violence.
Prescriptive: Analyze past data for insights into optimal scheduling and fleet sizes to make the most of your capacities and achieve your best on-time performance.
Supplier risk and due diligence assessment
Want to bypass the tedious work of evaluating current or potential supplier and vendor partnerships? Big data analytics is the key to optimizing risk-and-resilience due diligence.
Descriptive: Use data from sensors and other sources to evaluate supplier performance (timely delivery, quality, etc.) in real time.
Diagnostic: Apply this data to discern patterns and understand what makes some suppliers better than others.
Predictive: Support vendor selection by comparing supply chain criteria to automatically forecast each vendor’s likelihood of meeting your needs in the event of a disruption.
Prescriptive: Analyze vendor performance to grade and classify existing and potential partners and make purchasing decisions.
Customer management
Want to strengthen brand loyalty? Big data analytics can help you improve your customer experience and journey.
Descriptive: Group B2B and B2C customers by category using attributes like industry, age, region, order size, and needs – then display the data in an intuitive dashboard to better understand your customer base and who may be affected by supply chain changes.
Diagnostic: Demystify customer churn or preferences driven by price, convenience, or other variables.
Predictive: Generate customer-facing use cases, and use demand forecasting to alleviate supply chain bottlenecks or underutilization of facilities and fleets.
Prescriptive: Process historical data to determine price elasticity and ensure a better price point within customer expectations. Look at past orders to optimize the current workforce while protecting service levels. Mine consumer data to enable more personalized customer journeys and boost customer retention.
"Big data harbors a treasure trove for business insights into all parts of the supply chain for a portfolio of purposes, e.g., efficiency, resilience, and sustainability," said Klaus Dohrmann, VP Head of Innovation and Trend Research, DHL Customer Solutions & Innovation
The challenges of big data analytics
Most, if not all, logistics leaders today are harnessing big data analytics to drive strategic decisions. But even those who have taken the plunge admit there are many challenges. The three most-cited fear factors are “analysis,” “processing,” and “implementation of findings.”
We agree that working with big data analytics can be intimidating, which is why data analysts are essential members of our supply chain management teams. From the logistics perspective, we see the following three immediate challenges:
Identifying the target
Big data analytics needs data – so before embarking on a big data analytics journey, you must identify which data types are valuable to your organization, then build the appropriate data-collecting networks of sensors and other technologies.
Cleaning it up
Most data, especially from the internet, is unstructured and needs to be “cleaned” and filtered to achieve the quality required for meaningful analysis. Automating this process takes time, money, and talent.
Protecting your asset
Data is a valuable asset. Protecting it from bad actors requires a robust cybersecurity infrastructure.
What’s the final (big data) analysis?
Visibility is the key to building resilient supply chains. With today’s AI-driven analytics capabilities, big data is poised to improve supply chain performance and boost supply chain resilience with virtually no changes to existing supply chain infrastructure. Early adopters have continually integrated the latest advances for ever-better descriptive, diagnostic, predictive, and prescriptive insights. Sooner rather than later, these big data analytics techniques will be considered standard practice in supply chain management and expected by customers across all industry sectors.
This story was first published on DHL Delivered and was republished with permission.] =>
可视性是建立韧性供应链的关键。凭借当前的人工智能驱动分析能力,大数据能够在不对现有供应链基础设施进行变更的情况下改善供应链绩效并提高供应链韧性。早期采用者在不断引入最新技术进步,获得越来越好的描述型、诊断型、预测型和规范型洞察。这些大数据分析方法很快就会被视为供应链管理的标准做法,并且被各行各业的客户所翘首以待。
这篇文章最早在敦豪送达网站上发布,经允许后重新发布。
[Is big data analytics in supply chain management about to have another big breakthrough? Logistics experts are discovering the value of AI-driven, real-time analytics to achieve the visibility needed for more robust and resilient supply chains. And business leaders are discovering the value of state-of-the-art supply chain management to deal with disruption and achieve strategic goals.
Past, present, future
Knowing the past is critical to understanding the present and steering the future. What’s true in life is true in business, and data is the raw material that makes such insight possible. Supply chain professionals have grasped the importance of data for decades, as evidenced by the industry’s embrace of the internet of things (IoT) through sensors, smart tags, and such. The potential treasure trove of data generated annually from IoT devices, augmented by user-generated content, is predicted to balloon to some 181 trillion gigabytes by 2025.
Processing capacities have not kept up with this deluge of data, however. And the task grows even more daunting given the unstructured nature of much of this data, including audio and video files and social media feeds – vastly transcending anything that can be managed in spreadsheets.
Enter AI-driven big data analytics – the process of deploying smart tech such as automation and artificial intelligence (AI) to reveal patterns in the past, highlight real-time changes in the present, and reliably predict trends in the future.] => 供应链管理中的大数据会不会带来又一个重大突破?物流专家开始发现人工智能驱动的实时分析在实现更强大、更有韧性供应链所需的可视性方面的价值。商界领袖开始发现最先进的供应链管理在处理中断和实现战略目标方面的价值。
过去、现在和未来
了解过去对于洞察现在和预见未来至关重要。生活中的道理在商业领域同样适用,而数据正是让这种洞察成为可能的原材料。供应链专家认识到数据的重要性距今已有数十年之久,这一点可以从业界通过传感器、智能标签等手段采用物联网(IoT)看出。物联网设备每年生成的海量数据,在用户生成内容的加持下,有望在2025年激增到约181万亿千兆字节之多。
然而,处理能力还是跟不上数据量的大爆发。而且,考虑到其中很大一部分数据的无序性,这个任务变得更加令人生畏,包括音频和视频文件以及社交媒体推送内容――这大大超出了电子表格能够处理的极限。
让我们一起探索人工智能驱动大数据分析――部署自动化和人工智能(AI)等智能技术的过程,揭示过去的演化模式、了解现在重要的实时变化并且可靠地预测未来发展趋势。
[wysiwyg] => wysiwyg
[callout_box] => callout_box
[outbound_box] => outbound_box
[The Logistics Trend Radar 6.0 Edition] => 物流发展趋势雷达图6.0版
[A spectrum of upcoming logistics trends over the next decade.] => 未来十年内物流趋势图谱。
[Read more] => 了解更多
[Big data gets bigger, growth gets faster] => 大数据的数量越来越庞大、增长速度越来越快
[Types of data analytics] => 数据分析的类型
[Volume of data created, captured, copied, and consumed worldwide from 2010 to 2020, with forecasts for 2021 to 2025 (in gigabytes)
2010: 2 TRILLION
2020: 64 TRILLION
2025: 181 TRILLION
Source: Statista] => 2010年到2020年间全世界创建、收集、复制和使用的数据量以及2021年到2025年的预测(单位:千兆字节)
2010年:2万亿
2020年:64万亿
2025年:181万亿
资料来源:Statista全球统计数据库
[DESCRIPTIVE
What happened?
A careful analysis of big data in logistics can help you understand your supply chain performance.
DIAGNOSTIC
Why did it happen?
Big data unlocks the secrets of what went right or wrong – and why.
PREDICTIVE
What’s likely to happen next?
Your historical data holds valuable clues that can yield predictions about future performance.
PRESCRIPTIVE
What should we do about it?
AI can mine big data to generate “lessons learned” for strategies going forward.] => 描述型发生了什么情况?
对物流大数据的细致分析有助于了解自身供应链的表现。
诊断型为什么发生这种情况?
大数据可以揭开什么是正确的、什么是错误的以及为什么的秘密。
预测型接下来有可能发生什么情况?
历史数据包含了能够对未来表现进行预测的有价值线索。
规范型应当怎么处理这种情况?
人工智能可以挖掘大数据,获得可供未来制定战略时参考的“经验教训”。
[Logistics experts are discovering the value of AI-driven, real-time analytics to achieve the visibility needed for more robust and resilient supply chains. Is big data analytics in supply chain management about to have another big breakthrough? Logistics experts are discovering the value of AI-driven, real-time analytics to achieve the visibility needed for more robust and resilient supply chains. And business leaders are discovering the value of state-of-the-art supply chain management to deal with disruption and achieve strategic goals.
Past, present, future
Knowing the past is critical to understanding the present and steering the future. What’s true in life is true in business, and data is the raw material that makes such insight possible. Supply chain professionals have grasped the importance of data for decades, as evidenced by the industry’s embrace of the internet of things (IoT) through sensors, smart tags, and such. The potential treasure trove of data generated annually from IoT devices, augmented by user-generated content, is predicted to balloon to some 181 trillion gigabytes by 2025.
Processing capacities have not kept up with this deluge of data, however. And the task grows even more daunting given the unstructured nature of much of this data, including audio and video files and social media feeds – vastly transcending anything that can be managed in spreadsheets.
Enter AI-driven big data analytics – the process of deploying smart tech such as automation and artificial intelligence (AI) to reveal patterns in the past, highlight real-time changes in the present, and reliably predict trends in the future.
Big data gets bigger, growth gets fasterVolume of data created, captured, copied, and consumed worldwide from 2010 to 2020, with forecasts for 2021 to 2025 (in gigabytes)
2010: 2 TRILLION
2020: 64 TRILLION
2025: 181 TRILLION
Source: Statista
There’s no end to the trend in big data analytics
We’ve been monitoring the trend of big data analytics in logistics and supply chains for years. The power of data-driven insights is transforming many industries, including logistics. So it was no surprise to see it again in the DHL Logistics Trend Radar 6.0 – the 2022 result of our ongoing trend research. We continue to see big data analytics as a high-impact, near-term trend.
Big data analytics doesn’t transform the supply chain physically, but it delivers greater visibility and a better foundation for sound decision-making toward strategic optimizations along supply chain segments. The result is substantially improved service levels, ranging from more efficient pallet storage in warehouses to better customer case handling. And the logistics industry already has a big head start here. Many industry leaders have begun harnessing big data to drive strategic decisions, and soon this trend will simply be standard business practice in logistics and supply chain management.
“We are seeing businesses transform logistics from a quiet, backend operation to a strategic asset and value driver,” said Katja Busch, Chief Commercial Officer DHL and Head of DHL Customer Solutions and Innovation
Four types of big data analytics
To approach the daunting topic of AI-driven, real-time analytics of big data, with its potential to transform today’s supply chains, let’s break it down into its four main types: descriptive, diagnostic, predictive, and prescriptive.
Descriptive analytics is about understanding the status quo and describing what is happening, while diagnostic analytics asks why it happens. Predictive analytics forecasts what will likely happen in the future, while prescriptive analytics taps into historical and situational data to recommend strategic optimizations to be implemented.
Types of data analyticsDESCRIPTIVE
What happened?
A careful analysis of big data in logistics can help you understand your supply chain performance.
DIAGNOSTIC
Why did it happen?
Big data unlocks the secrets of what went right or wrong – and why.
PREDICTIVE
What’s likely to happen next?
Your historical data holds valuable clues that can yield predictions about future performance.
PRESCRIPTIVE
What should we do about it?
AI can mine big data to generate “lessons learned” for strategies going forward.
Supply chain analytics in action
As the potential to harvest big data grows, so does the opportunity to harness it. But what does that look like in today’s supply chains?
Let’s look at some real-world use cases for leveraging the power of big data analytics:
Inventory and asset optimization
Want to improve efficiency in warehouses and hubs? Big data analytics delivers the supply chain visibility needed to optimize how inventory is stored and moved through facilities and how assets are utilized and maintained.
Descriptive: Utilize sensor data to reveal inventory levels, available shelf space, and the location and status of warehouse assets.
Diagnostic: Show links between inventory types and the breakdown of assets like conveyors or vehicles or between external events and inventory levels.
Predictive: Fine-tune preventive maintenance intervals for assets and optimize inventory in anticipation of predictable seasonal fluctuations.
Prescriptive: Mine historical data to optimize space allocation by stock-keeping unit (SKU) and prevent unnecessarily high or critically low inventory levels.
Transport and delivery optimization
Have a particularly challenging supply chain segment? Big data analytics can help you achieve cost-effective, on-time performance while ensuring your goods arrive in good condition.
Descriptive: Use service-level data to obtain a real-time analysis of the status of transport vehicles or the condition of delivered goods.
Diagnostic: Determine why persistent delays occur. For example: Are schedules out of sync with traffic patterns?
Predictive: Examine various data sources to anticipate the risk of disruption along supply chain segments, even accounting for factors such as natural disasters and political violence.
Prescriptive: Analyze past data for insights into optimal scheduling and fleet sizes to make the most of your capacities and achieve your best on-time performance.
Supplier risk and due diligence assessment
Want to bypass the tedious work of evaluating current or potential supplier and vendor partnerships? Big data analytics is the key to optimizing risk-and-resilience due diligence.
Descriptive: Use data from sensors and other sources to evaluate supplier performance (timely delivery, quality, etc.) in real time.
Diagnostic: Apply this data to discern patterns and understand what makes some suppliers better than others.
Predictive: Support vendor selection by comparing supply chain criteria to automatically forecast each vendor’s likelihood of meeting your needs in the event of a disruption.
Prescriptive: Analyze vendor performance to grade and classify existing and potential partners and make purchasing decisions.
Customer management
Want to strengthen brand loyalty? Big data analytics can help you improve your customer experience and journey.
Descriptive: Group B2B and B2C customers by category using attributes like industry, age, region, order size, and needs – then display the data in an intuitive dashboard to better understand your customer base and who may be affected by supply chain changes.
Diagnostic: Demystify customer churn or preferences driven by price, convenience, or other variables.
Predictive: Generate customer-facing use cases, and use demand forecasting to alleviate supply chain bottlenecks or underutilization of facilities and fleets.
Prescriptive: Process historical data to determine price elasticity and ensure a better price point within customer expectations. Look at past orders to optimize the current workforce while protecting service levels. Mine consumer data to enable more personalized customer journeys and boost customer retention.
“Big data harbors a treasure trove for business insights into all parts of the supply chain for a portfolio of purposes, e.g., efficiency, resilience, and sustainability,” said Klaus Dohrmann, VP Head of Innovation and Trend Research, DHL Customer Solutions & Innovation
The challenges of big data analytics
Most, if not all, logistics leaders today are harnessing big data analytics to drive strategic decisions. But even those who have taken the plunge admit there are many challenges. The three most-cited fear factors are “analysis,” “processing,” and “implementation of findings.”
Source: Unsupervised
We agree that working with big data analytics can be intimidating, which is why data analysts are essential members of our supply chain management teams. From the logistics perspective, we see the following three immediate challenges:
Identifying the target
Big data analytics needs data – so before embarking on a big data analytics journey, you must identify which data types are valuable to your organization, then build the appropriate data-collecting networks of sensors and other technologies.
Cleaning it up
Most data, especially from the internet, is unstructured and needs to be “cleaned” and filtered to achieve the quality required for meaningful analysis. Automating this process takes time, money, and talent.
Protecting your asset
Data is a valuable asset. Protecting it from bad actors requires a robust cybersecurity infrastructure.
What’s the final (big data) analysis?
Visibility is the key to building resilient supply chains. With today’s AI-driven analytics capabilities, big data is poised to improve supply chain performance and boost supply chain resilience with virtually no changes to existing supply chain infrastructure. Early adopters have continually integrated the latest advances for ever-better descriptive, diagnostic, predictive, and prescriptive insights. Sooner rather than later, these big data analytics techniques will be considered standard practice in supply chain management and expected by customers across all industry sectors.
This story was first published on DHL Delivered and was republished with permission.
RELATED ARTICLESThe Logistics Trend Radar 6.0 EditionA spectrum of upcoming logistics trends over the next decade.] =>
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[$value] => Supply chain analytics in action
As the potential to harvest big data grows, so does the opportunity to harness it. But what does that look like in today's supply chains?
Let’s look at some real-world use cases for leveraging the power of big data analytics:
Inventory and asset optimization
Want to improve efficiency in warehouses and hubs? Big data analytics delivers the supply chain visibility needed to optimize how inventory is stored and moved through facilities and how assets are utilized and maintained.
Descriptive: Utilize sensor data to reveal inventory levels, available shelf space, and the location and status of warehouse assets.
Diagnostic: Show links between inventory types and the breakdown of assets like conveyors or vehicles or between external events and inventory levels.
Predictive: Fine-tune preventive maintenance intervals for assets and optimize inventory in anticipation of predictable seasonal fluctuations.
Prescriptive: Mine historical data to optimize space allocation by stock-keeping unit (SKU) and prevent unnecessarily high or critically low inventory levels.
Transport and delivery optimization
Have a particularly challenging supply chain segment? Big data analytics can help you achieve cost-effective, on-time performance while ensuring your goods arrive in good condition.
Descriptive: Use service-level data to obtain a real-time analysis of the status of transport vehicles or the condition of delivered goods.
Diagnostic: Determine why persistent delays occur. For example: Are schedules out of sync with traffic patterns?
Predictive: Examine various data sources to anticipate the risk of disruption along supply chain segments, even accounting for factors such as natural disasters and political violence.
Prescriptive: Analyze past data for insights into optimal scheduling and fleet sizes to make the most of your capacities and achieve your best on-time performance.
Supplier risk and due diligence assessment
Want to bypass the tedious work of evaluating current or potential supplier and vendor partnerships? Big data analytics is the key to optimizing risk-and-resilience due diligence.
Descriptive: Use data from sensors and other sources to evaluate supplier performance (timely delivery, quality, etc.) in real time.
Diagnostic: Apply this data to discern patterns and understand what makes some suppliers better than others.
Predictive: Support vendor selection by comparing supply chain criteria to automatically forecast each vendor’s likelihood of meeting your needs in the event of a disruption.
Prescriptive: Analyze vendor performance to grade and classify existing and potential partners and make purchasing decisions.
Customer management
Want to strengthen brand loyalty? Big data analytics can help you improve your customer experience and journey.
Descriptive: Group B2B and B2C customers by category using attributes like industry, age, region, order size, and needs – then display the data in an intuitive dashboard to better understand your customer base and who may be affected by supply chain changes.
Diagnostic: Demystify customer churn or preferences driven by price, convenience, or other variables.
Predictive: Generate customer-facing use cases, and use demand forecasting to alleviate supply chain bottlenecks or underutilization of facilities and fleets.
Prescriptive: Process historical data to determine price elasticity and ensure a better price point within customer expectations. Look at past orders to optimize the current workforce while protecting service levels. Mine consumer data to enable more personalized customer journeys and boost customer retention.
"Big data harbors a treasure trove for business insights into all parts of the supply chain for a portfolio of purposes, e.g., efficiency, resilience, and sustainability," said Klaus Dohrmann, VP Head of Innovation and Trend Research, DHL Customer Solutions & Innovation
The challenges of big data analytics
Most, if not all, logistics leaders today are harnessing big data analytics to drive strategic decisions. But even those who have taken the plunge admit there are many challenges. The three most-cited fear factors are “analysis,” “processing,” and “implementation of findings.”
[caption id="attachment_33913" align="aligncenter" width="1024"] Source: Unsupervised[/caption]
We agree that working with big data analytics can be intimidating, which is why data analysts are essential members of our supply chain management teams. From the logistics perspective, we see the following three immediate challenges:
Identifying the target
Big data analytics needs data – so before embarking on a big data analytics journey, you must identify which data types are valuable to your organization, then build the appropriate data-collecting networks of sensors and other technologies.
Cleaning it up
Most data, especially from the internet, is unstructured and needs to be “cleaned” and filtered to achieve the quality required for meaningful analysis. Automating this process takes time, money, and talent.
Protecting your asset
Data is a valuable asset. Protecting it from bad actors requires a robust cybersecurity infrastructure.
What’s the final (big data) analysis?
Visibility is the key to building resilient supply chains. With today’s AI-driven analytics capabilities, big data is poised to improve supply chain performance and boost supply chain resilience with virtually no changes to existing supply chain infrastructure. Early adopters have continually integrated the latest advances for ever-better descriptive, diagnostic, predictive, and prescriptive insights. Sooner rather than later, these big data analytics techniques will be considered standard practice in supply chain management and expected by customers across all industry sectors.
This story was first published on DHL Delivered and was republished with permission.
)