This site uses cookies to improve your experience. To help us insure we adhere to various privacy regulations, please select your country/region of residence. If you do not select a country, we will assume you are from the United States. Select your Cookie Settings or view our Privacy Policy and Terms of Use.
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Used for the proper function of the website
Used for monitoring website traffic and interactions
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Strictly Necessary: Used for the proper function of the website
Performance/Analytics: Used for monitoring website traffic and interactions
Many global multinationals accelerated their investments in digitizing data during the pandemic. According to Colin Masson, a director of research at ARC Advisory Group, the opportunity to mine these vast quantities of data to achieve business value is “NOW.” Mr. Masson leads ARC’s research on industrial AI and data fabrics.
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.
The company aims to change this with the expansion of its data fabric portfolio. A supply chain data fabric can help companies augment their supply chain processes. Now companies are trying to collect data from multiple tiers of a supply chain in near real-time. Decisions need to be digitized.
Our daily lives are inundated with data. Supply chain teams face a similar dilemma – companies are overloaded with vast amounts of data, and the ability to sift through the noise and focus on relevant insights has become a critical capability. Why Context Matters Context transforms data into actionable insights.
This GEP-sponsored Supply Management Insider guide breaks down the data barriers to meeting sustainability goals. CPOs are now being measured on sustainability targets. How can they meet this new challenge?
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.
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. Most argue that when the UI is trained with the companys own data, the risk of hallucination is small.
Read more The post Reimagining Transportation Procurement: Leveraging Data and Technology for Smarter Freight Decisions appeared first on Talking Logistics with Adrian Gonzalez. Yet, we all know that waste and inefficiencies still.
As logistics networks become increasingly complex, the volume of real-time data generated by devices, equipment, vehicles, and facilities is growing rapidly. Edge computing processing data locally, near the source has emerged as a method to address these challenges by reducing latency and improving resiliency.
This GEP-sponsored report will show you how to leverage data for a collaborative supply chain that delivers results and how to future-proof supply chain management strategies. The C-suite is laser-focused on supply chain performance.
Our daily lives are inundated with data. Supply chain teams face a similar dilemma companies are overloaded with vast amounts of data, and the ability to sift through the noise and focus on relevant insights has become a critical capability. While the abundance of data is seen as an asset, the real question is: What do you do with it?
That’s where data analytics comes in. Modern supply chains thrive on real-time data, execution-focused applications, and dynamic decision-making. In this post, we’ll explore how data analytics can revolutionize your supply chain. Demand Forecasting: Analyze past data to predict future needs.
Forecasting has evolved into a sophisticated science, combining historical data, real-time market signals, and predictive analytics. Advanced Forecasting and AI Evolution With ongoing geopolitical disruptions and supply chain volatility, the need for responsive and sophisticated forecasting capabilities has never been more critical.
These sensors capture precise data on factors like location, speed, fuel usage, and driver behavior, transforming fleet management from reactive to data-driven decision-making. The IoT data allows managers to detect inefficiencies, predict maintenance needs, and even assess driver performance.
In response to these challenges, a leading heavy equipment manufacturer selected GEP to redesign its source-to-contract processes and implement a convergent data model to help manage procurement data across its multiple locations.
Edge Hardware: The battle for edge hardware also intensified in 2024, as companies sought to deploy AI capabilities closer to the source of data. These developments help enable real-time data processing, reduce the reliance on cloud connectivity, and democratize access to advanced AI technologies in industrial and robotic contexts.
Assessing Infrastructure and Technological Capabilities The first step in the readiness assessment is to evaluate the organization’s IT infrastructure and data management systems. Organizations must also evaluate the quality, integrity, and security of their data to ensure it is reliable enough for DPP purposes.
Data collection and verification remain areas of concern. Legacy procurement systems pose challenges, as they were not designed to capture and manage ESG-related data. Investments in digital supply chain platforms and data verification tools are increasingly important for managing complexity.
Understanding AI Agents At its core, an AI Agent is a reasoning engine capable of understanding context, planning workflows, connecting to external tools and data, and executing actions to achieve a defined goal. Integrate with External Tools and Data: AI Agents can augment their inherent language model capabilities with APIs and tools (e.g.,
Speaker: Andrew Skoog, Founder of MachinistX & President of Hexis Representatives
Join industry expert Andrew Skoog as he explores how manufacturers can leverage automation to enhance operations, streamline workflows, and make smarter, data-driven decisions—without losing the value of human insight! But how do you implement these tools with confidence and ensure they complement human expertise rather than override it?
Supply chain orchestration is about managing the movement of goods, data, and decisions across the entire supply networkstarting with suppliers and continuing through to the customer. Why Orchestration Matters The more connected a supply chain becomes, the more it depends on timely, accurate data and consistent communication across teams.
In this commentary we focus specifically on the importance of a broader end-to-end data management framework while overcoming the fragmentation of data that is locked in separate, unconnected software applications.
This proliferation has made the need for – and lack of – industry data standards all the more acute. And one of the main objectives of all this digitized data and integration in one source of truth is supply chain visibility , that will not only let logistics experts optimize operations, but also detect and react to disruptions.
Teams make recommendations based on latent data to their managers, but decisions are often bogged down in meetings and corporate politics. In this role, you would design data flows to enable self-service planning processes where the planner becomes the orchestrator to help business leaders make the decisions at the speed of business.
Speaker: Lee Andrews, Founder at LJA New Media & Tony Karrer, Founder and CTO at Aggregage
As a business executive, you’ll learn how to assess AI opportunities in your business, drive adoption across teams, and overcome internal resource constraints—without hiring a single data scientist.
Newage customers will benefit from streamlined customs declaration processes, eliminating the need for manual data entry duplication between freight operations and customs compliance. By eliminating the need for double data entry and minimizing delays, customers can save significant time and manpower costs.
The platform collects data and makes sure the master data is internally consistent. If a user makes changes to the plan, they log that data. You have to have a digital platform where you get all your relevant data.” And that data has “to be internally consistent. We are a platform. Bakkalbasi asks rhetorically.
Logility embeds AI directly into its solutions, helping businesses to go beyond basic data analysis, and enables those businesses to take actions they might not have anticipated. It makes real-time data and insights accessible across teams, creating a more collaborative and unified process. How do we prevent false information?
Pledges capabilities automate the collection and exchange of shipment data from logistics suppliers to facilitate accredited and traceable emissions calculations across all transport modes, including air, inland (e.g., truck, rail, barges), and sea.
From new pricing strategies and material substitutability to alternative suppliers and stockpiling, a new GEP-commissioned Economist Impact report reveals that enterprises are adopting a variety of approaches underpinned by data and technology.
Traditional supply chain planning, which relies on historical data and reactive adjustments, is no longer adequate for managing these challenges. AI as a Predictive Tool AI-driven supply chain planning integrates machine learning, real-time data analytics, and external risk monitoring to anticipate disruptions before they materialize.
The book goes beyond theoretical concepts and serves as a playbook for crafting data-driven go-to-market strategies. Company specializes in crafting GTM strategies that are grounded in data – backed insights and sophisticated mathematical models. Data-Driven Insights: Gain valuable insights into your marketing efforts.
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.
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. It turns out data fabrics are the necessary foundation on which to build advanced agentic AI solutions.
What’s Inside: How CPOs are driving strategic decision-making and technology adoption The top priorities and challenges for procurement in 2025 Why AI, sustainability, and data analytics are essential for success Read this essential report to chart your path forward and influence procurement tools and processes.
Data fabrics, knowledge graphs, a digital thread, and digital twin technologies are critical. John Galt’s Supply Chain Planning Platform John Galt is right to single out data fabrics as an increasingly important technology. This creates a unified view by stitching together data sources in real time.
The combination of SAP agent technologies and Databricks data fabric solution, sets the stage for end-to-end enterprise orchestration. Databricks offers a Data Intelligence Platform. Databricks type of solution is increasingly being called a data fabric or a data platform built on data fabric principles.
With an open API, integration of our data into your business platforms is easy, coupled with our ruggedized GPS asset tracking devices installed in less than 10 minutes, we can get you up and running quickly. Industry leading data security, low total cost of ownership and rapid ROI, BlackBerry Radar is Engineered for Intelligence.
Dynamic Pricing: Real-time data from decentralized oracles (such as Chainlink) can adjust contract terms based on market prices or demand fluctuations. How Smart Contracts Improve Logistics IoT-Enabled Tracking: Sensors on shipping containers continuously log real-time data (e.g., Solution: Layer-2 scaling solutions (e.g.,
Storytelling is more than just data visualization. Storytelling provides an organized approach for conveying data insights through visuals and narrative. Data-driven storytelling could be used to influence user actions, and ensure they understand what data matters the most.
Backup Data & Systems: Use a 3,2,1, strategy to back up data. Have 3 copies of your data, in 2 geographically dispersed locations, and 1 location off of your network. When that does happen, you’ll want to do anything to restore your data. Hackers know this and rely on your intense desperation to save your business.
For example, if an asset issue was detected, solving that issue could involve multiple applications used by multiple people, seeing different information, entering different data, bouncing emails and texts back and forth, and moving information from one place to another. We needed to model the data in a way that we can do simple searching.
Companies find it difficult to fully trust the data from suppliers, complicating efforts to ensure product authenticity, safety, and ethical sourcing. The specific origin data reinforces De Beers’ commitment to consumer confidence , transparency and ethical sourcing. ERP & SCM Systems (2000s2015): Centralized ERP suites (e.g.,
Supply chains need systemic change that must occur via communication, data sharing, and process modernization delivered through the use of orchestrated, interoperable AI agents and data fabrics across multiple enterprises. We have been seeing the need for significant modernization (i.e., transformation) dating back years now.
Organizational data literacy is regularly addressed, but it’s uncommon for product managers to consider users’ data literacy levels when building products. Product managers need to research and recognize their end users' data literacy when building an application with analytic features.
We organize all of the trending information in your field so you don't have to. Join 102,000+ users and stay up to date on the latest articles your peers are reading.
You know about us, now we want to get to know you!
Let's personalize your content
Let's get even more personalized
We recognize your account from another site in our network, please click 'Send Email' below to continue with verifying your account and setting a password.
Let's personalize your content