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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.
As customers increasingly demand rapid and reliable delivery, optimizing this final leg of transportation becomes essential for businesses aiming to enhance customer satisfaction and operational efficiency. Data-driven approaches, such as predictive analytics, facilitate real-time adjustments in delivery operations.
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.
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.
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.
Traditional supply chain planning, which relies on historical data and reactive adjustments, is no longer adequate for managing these challenges. They integrate AI into demand forecasting, inventory optimization, and logistics operations to improve efficiency, reduce costs, and mitigate risks.
Datacenter Hardware: The demand for powerful computing to train ever larger and more accurate AI models is insatiable. AWS , Google , and Microsoft are also investing heavily in custom AI chips to reduce their dependence on NVIDIA and optimize performance and cost. Google is also reportedly working on its own Arm-based chips.
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.
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
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.
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. Blue Yonder, for example, has created a microservice for transportation optimization.
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.
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.
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 technologies have emerged as game-changers for third-party logistics (3PL) and supply chain experts: large language models (LLMs), freight optimization platforms and no-code automation. These AI-driven models can understand and generate human-like text based on the input provided. The answer lies in data.
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.
Before a potential customer buys an autonomous mobile robot solution, Locus Robotics often uses different types of simulation to determine the type of robots needed and the number needed to optimize productivity at a warehouse. DES allows the modeling of complex warehouse operations at various levels of detail.
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.
Optimization is a ubiquitous term in the supply chain and logistics industry. We all talk about how we need to optimize our operations. In practice, however, relatively few companies are using optimization technology, particularly in transportation. Why is transportation optimization key today? Types of optimization.
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.
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.
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?
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.
The first product of this partnership is TacticalOps, a Planning & Optimization solution for Food Manufacturers. I spoke with Luis Pinto, Partner at UniSoma, to understand the need for new planning and optimization solutions in the global food supply chain. I’ve seen the attitude towards optimization evolving yes.
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.
Optimize Inventory Management Inventory often represents one of the largest expenses in a supply chain. Solution: Use data-driven forecasting to predict demand as accurately as possible. Example: Retail giant Zara uses real-time data from its stores to adjust inventory dynamically.
Inventory Control Techniques that use Stock Optimization Best Practices. So we thought we’d focus on the lesser known topic of ‘stock optimization’ – this is an inventory control technique that’s becoming more popular with inventory managers to improve the efficiency of their supply chain. What is stock optimization?
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.
If you have been through this process at least once, you already have a good idea of what supply chain design is about: optimization. When most people hear the word “optimization,” they immediately think about minimizing costs. But optimization is much more than that! First comes the data and how well we understand it.
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.
The theory is that as more and more devices throughout the supply chain and manufacturing process become part of the ‘Internet of Things,’ they will produce an incredibly rich data stream that will send signals in real-time to trigger a wide variety of events. Digital Twin Model Builders. PlanIQ includes Amazon Forecast.
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?
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).
12,000 SAP customers and partners attended the event, and another 15,000 watched remotely. The RISE with SAP offering includes an AI-powered cloud ERP that’s managed and optimized by SAP. When SAP refers to AI, it refers to generative AI based on large language models or AI based on machine learning.
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.
Amazon, Walmart, and other leading enterprises win by ensuring that their product is close to the customer, optimized for the best shipping time, and held in the correct quantity. To scale, an optimized distribution network is required to meet customer expectations and business growth targets.
Agility can also reflect a company’s ability to effectively deal with unexpected constraints caused by strikes, earthquakes, political strife, and a variety of other events. ARC defines supply chain planning (SCP) products as including supply planning, demand planning/inventory optimization, and network planning.
In recent months, I ‘ve been active in several events in the region and I’ve noticed a changing trend. Companies are increasingly eager to hear about optimization and advanced analytics. There are several areas where companies are eager to apply optimization. Their CEO wanted to adopt optimization really early on.
It provides an early warning system that helps business stakeholders sense and optimize their responses. These are: End-to-end visibility: integrate with live data and gain visibility into detailed flow data of product availability and customer demand, as well as KPIs. How does this work in practice?
Additionally, software vendors continuously invest in tuning the performance of their algorithms and models. For impactful scenario planning, planners must spend time on analysis rather than collating data and manually creating scenarios.
This approach was suitable for a time where disruptions were rare, supply and demand variability were limited, and the supply chain was optimized to lower costs and low complexity. Event-driven IBP – technological capabilities to monitor internal and external events (Supply Chain Control Tower) in real time.
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.
We can say things have changed, and the pandemic is not just an anomaly event after all. According to data from a recent research survey, the following were on top of the supply chain headaches not addressed by their current systems: Supply shortages due to supplier’s inability to meet expected performance targets.
Supply chain planning involves interaction with different types of information based on internal and external data sources. These data sources are often spread across multiple platforms and come in various formats. Planners spend their precious time collecting and synthesizing the data to drive insights.
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