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Shippers, brokers, carriers, news organizations and industry analysts rely on DAT for trends and data insights based on a database of $150 billion in annual market transactions. Real-time Market Insights: DAT provides real-time data on spot market rates, capacity availability, and lane-specific trends, enabling informed decision-making.
Companies use risk management software , like the Interos solution, to monitor and analyze supplier risk events in real time. These are big data platforms that monitor news sources and assorted databases from governments, financial institutions, ESG NGOs, and other sources to detect when an adverse event has occurred or may be about to occur.
Global supply chains have been tested repeatedly by a series of disruptive events, including the COVID-19 pandemic, U.S.-China In response, many organizations have shifted toward decentralized and regionalized supply chain models, distributing production and sourcing across multiple regions.
Supply chain practitioners seeking the best way to speed decision intelligence, unify supply chain data, and increase operational efficiency can benefit from a supply chain data gateway. Here are 10 ways a supply chain data gateway can improve your performance across the end-to-end supply chain.
Speaker: Dr. Ken Fordyce, Solutions Director, Supply Chain and Advanced Analytics at Arkieva
Risk management is defined as anticipating and responding to an event not yet part of the plan of record that requires a significant adjustment in your demand-supply network (DSN). Creating a successful plan demands a thoughtful combination of data science and computational models to anticipate structural weak points.
Supply chain practitioners seeking the best way to speed decision intelligence, unify supply chain data, and increase operational efficiency can benefit from a supply chain data gateway. Here are 10 ways a supply chain data gateway can improve your performance across the end-to-end supply chain.
Curtis is hosting the industry’s first live LTL Mastermind Event, November 9 th and 10 th in High Point, North Carolina. About Understand LTL The Understand LTL education brand is a new initiative that is focused on simplifying the LTL industry and helping people to build the mental models in their mind to think about LTL clearly.
Thad is CEO at Austin-based Talroo , a data-driven job and hiring event advertising platform that helps businesses reach the candidates they need to build their essential workforce. Talroo is a data-driven job and hiring event advertising platform that helps businesses reach the candidates they need to build their essential workforce.
The following Google Cloud solutions were discussed: Supply chain twin is a digital representation of a company’s supply chain with end-to-end visibility, alert-driven event management, analytics, and collaboration across teams. Manufacturing Data Engine and Connect. Document.ai Document.ai Learn More About Google Cloud Supply Chain.
Recent disruptions have exposed significant vulnerabilities in traditional models, driven by geopolitical instability, fluctuating demand, and operational inefficiencies. A data-driven, technology-enabled approach is required to build resilience and efficiency. Resilience is now taking precedence.
Traditional supply chain planning, which relies on historical data and reactive adjustments, is no longer adequate for managing these challenges. Organizations examine past sales trends, apply seasonal adjustments, and make forecasts based on historical models.
Datacenter Hardware: The demand for powerful computing to train ever larger and more accurate AI models is insatiable. AWS has custom AI chips Trainium and Inferentia , for training and running large AI models. The battle here is to develop hardware that can handle this massive computational load efficiently and cost-effectively.
For example, signs that a company is moving in the right direction, talent-wise, might include: The deployment of staff in new roles, absent the traditional supply-chain-centric titles and instead, hybridizing across data-science and logistics skill sets.
Three months into 2025, we have seen a barrage of on-again, off-again tariffs that have supply chain and logistics teams reeling, as they must rethink everything from next weeks shipping route to their foundational network models. The Ukraine-Russia conflict is ongoing. Tensions flare in the Middle East without warning. billion to $23.07
Enter the industrial data scientist, a new breed of data analyst with access to more industrial data than ever before and the advanced technology to translate that information into actionable intelligence. However, leveraging AI requires data science capability, which adds additional complexity to an already complex environment.
Executives at Blue Yonder refer to this as a “cliff event.” To avoid a cliff event, Blue Yonder has proceeded by turning its supply chain applications into applications that are part traditional software code and part microservices. Microservices also open new sales opportunities.
Dr. Versace has spoken at numerous events, including a keynote at Mobile World Congress Drone Summit, TEDx, NASA, the Pentagon, MIT Tech Review’s Future Compute and IMA Sensing Days. His work has been featured in Forbes, CNN, Fortune, MSNBC, the Associated Press, TechCrunch, VentureBeat, and more.
The quarter was particularly impressive given, as you know, we were, a victim of a cyber ransomware event. Blue Yonder and Agentic AI Blue Yonder announced they were working with Snowflake, a company providing an enterprise data fabric solution, to transform access to disparate data for supply chain management in March of 2022.
With extensive connectivity across multi-tier supplier networks, stringent authentication, encryption, and zero-trust models are crucial for secure data sharing and mitigating vulnerabilities. In our digital era, cyber threats like ransomware from organized, state-sponsored groups pose significant risks to global supply chains.
Instead of relying solely on a single, monolithic AI model (based on a massive large language model), a company can orchestrate a team of specialized agents, each leveraging the best AI or mathematical technique for its specific task. One event could create so much churn, Mr. Al Syed explained. Data does not move.
Increasing supply chain data visibility is a priority for logistics organizations looking to improve resilience. Supply chain recovery hinges on incorporating robust data analytics and other data-driven tools into business operations to increase efficiency, reduce costs and proactively manage risk.
Smart factories use IoT-enabled technologies like sensors and smart machines to generate data, often in real-time, to improve information about production processes and help decision-making. Together MOM and MES provide the intelligent systems to collect, deliver and analyze production data to empower industry strategy and smart factories.
Data is the lifeblood of AI in the supply chain. Without sufficient data, AI models can’t uncover meaningful patterns, make accurate predictions, or provide valuable insights for informed decision-making in complex and dynamic environments. At the same time, feeding your AI models too much data can also be a problem.
Table of Contents [Open] [Close] Significance of Last-Mile Delivery Optimization Implementing Innovative Strategies The Role of Data Analytics Sustainability: A Necessary Focus 1. Data-driven approaches, such as predictive analytics, facilitate real-time adjustments in delivery operations. Electric and Alternative Fuel Vehicles 2.
CONA is a strategic partner that provides its bottlers with a common set of processes, data standards, and technology platforms. While they are separate and independently-owned organizations, they agreed with The Coca-Cola Company to come on to a common data platform with common data standards. Specific products?
Digital twins are emerging as digital transformation accelerators for supply chain and logistics organizations seeking enterprise-level visibility, real-time scenario modeling, and operational agility under disruption. These are not static dashboards or simple visualizationstheyre living, data-rich models of real-world operations.
Data for data’s sake lacks value, especially in the view of the supply chain. And across the market, submitted data becomes rapidly outdated. And in some industries, outdated data can have disastrous consequences. For instance, take the value added by more accurate data in the health industry.
One of the key approaches to simulating warehouse operations is based on employing discrete event simulation (DES) techniques and tools. DES allows the modeling of complex warehouse operations at various levels of detail. Typically, modeling is done by highly trained engineers with an industrial engineering background.
During COVID, this more agile and resilient model allowed the firm to grow their market share. The platform will look at all the potential alternatives and the cost of those alternatives, and it will make a recommendation for a supply chain person to go in and look at the event. We can now have really good data-driven conversations.
She has led programs ranging from acquisitions to technology deployment with a strong focus on lean manufacturing and data management. Socio-political events like trade wars and political upheavals around the world. Companies will need to implement solutions that give this data in real-time or in the shortest time possible.
The resilience of your supply chain is determined by its structure and operations, whether we’re dealing with major immediate events like a pandemic or gradual systemic changes to your business environment over time. Effective modelling can have a significant impact on your supply chains resilience.
Again and again, digitization and data were at the heart of panel and networking conversations. Even headline speakers were professing “data got sexy” and data is now a core strategy for companies looking to succeed. Supply Chain Analytics Maturity Model (Source: Hackett Group).
For example, signs that a company is moving in the right direction, talent-wise, might include: The deployment of staff in new roles, absent the traditional supply-chain-centric titles and instead, hybridizing across data-science and logistics skill sets. Bringing Production Closer to the Customer. Investing in Supply Chain?
PAXAFE predicts adverse events through the supply chain to de-risk B2B shipments and enable intelligent cargo insurance. CONTXT enables shippers, 3PLs / carriers and insurance providers the ability to intercept at-risk shipments, automate claim diagnosis and root cause analysis, and improve loss ratios via improved underwriting.
Whether it’s the seasonal spikes or sudden increases due to events, being able to predict and adjust to these fluctuations is key. Grasping Demand Dynamics In food and beverage shipping, demand can vary significantly based on factors like seasons and events.
Event-driven IBP – technological capabilities to monitor internal and external events (Supply Chain Control Tower) in real time. Contextualize and quantify event impact and be able to trigger re-plan in an integrated supply chain planning solution to create an executable and feasible plan.
Foundational Model This is where the training/learning takes place, where you’re teaching the AI how to look at things and look at input. Large Language Model (LLM) This model is trained on vast amounts of text, can interpret what you’re asking of it, and can put a response in words that you can understand.
Situation Companies are increasingly confronted with complex planning scenarios due to predictable events such as mergers and acquisitions, category expansions, supplier changes, and distribution evolution, as well as disruptive events including demand volatility, material shortages, capacity constraints, and logistical surprises.
Organizations must take the following steps to bring departments together to create truly resilient and sustainable supply chains: Leverage external data to sense market shifts Look to external causal factors and forecasting models to identify market shifts. By identifying these gaps, you can create sourcing events to close them.
Machine learning (ML) techniques can be applied to provide more accurate transit information and estimated arrival times (ETAs) by analyzing the historical shipment data in your transportation management systems. The model learns continuously and can adapt to changing conditions in the network.
For it to be an optimal solution, a mathematical model needs to be used. That model can then be used to analyze every new situation that arises. The model will help a company find a solution that is best for their relocated employees as a whole. First comes the data and how well we understand it.
The supply chain planning market got started when supply chain models were put into in-memory databases in the early 1990s. Data is stored just like you might sketch ideas on a whiteboard. Those insights are driven from data connections across the vast amounts of data these companies have access to. This is much faster.
So, going into 2025, I would like to focus on current congestion data, global trends and what U.S. Weather events will continue to impact in 2025. years on planning and operating through a hub model. exporters can expect in the new year in regard to cargo fluidity.
Led some supply chain planning supplier to create new digital twins – new supply chain models – that model this supply chain much deeper than it had been previously modeled. A more granular model means better planning – planning that more fully reflects the constraints that exist in these supply chains.
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