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10 Ways A Data Gateway Improves Time to Value Across Your End-to-End Supply Chain
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1 an agoon
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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. A data gateway is essentially a connective tissue across your supply chain, providing unified access to supply chain data from various sources, including enterprise systems, data feeds, data warehouses, data lakes, data marts, and business entities.
Here are 10 ways a supply chain data gateway can improve your performance across the end-to-end supply chain.
1.Enables You to Identify Inefficiencies and Make Better and Informed Decisions
A unified view of your data accelerates informed decision making and provides you with a comprehensive understanding of your supply chain. For example, with a data gateway, a supply planner gains accelerated access to customer orders, inventory levels, and transportation schedules, all in one place, to increase the user experience of making the right choice to identify inefficiencies and make better, more informed decisions.
2. Reduces Implementation Times
Enterprises and supply chain software providers strive to reduce application implementation times. A data gateway can serve as a front-end for a range of supply chain software applications, speeding and simplifying data ingestion, integration, and staging processes, significantly reducing application implementation times, lowering operational costs, and accelerating time to value.
3. Provides the Right Data for the Right Users
Making it easier to provide the right data for the right consuming users and applications at the right time and in the proper format reduces dependency on IT resources. This can be achieved through low-code and self-service access, making formerly siloed data accessible to business users and data stewards, faster and with less overhead, eliminating reliance on developers.
4. Allows for Growth
Long-term growth and relevance for your organization depends on your ability to adapt to changing business needs and data requirements. As an organization grows, and its data requirements expand, a supply chain data gateway’s performance should not suffer when demand increases. Instead, a high level of performance is expected even when dealing with a significantly large volume of users, data, and requests.
5. Automates Data Operations
Managing data operations can require a lot of human capital and operational costs. With a data gateway you can automate data operations, reducing the need for manual intervention and improving overall efficiency. This includes automated data processing, transformation, and management tasks, which help streamline data operations, reduce errors, and lower operational costs.
6. Provides Flexibility to Connect with a Wide Range of Data Sources
Flexibility is crucial for organizations that need to connect with a wide range of data sources and applications. With a data gateway you have the flexibility to support open data access and enable seamless integration with other systems and applications. It should be easy to connect to new data sources as the need arises, such as ESG or SNEW (social, news, events, weather) data.
A data gateway gives you the flexibility to support supply chain data unification and exchange with an extensible canonical supply chain data model, ensuring that data is stored and managed in a consistent and structured manner, and allowing for easy integration and growth. It also feeds downstream applications including BI, reporting, and supply chain applications, with the right data sets, in the formats the applications expect, and at the right time the data is needed.
7. Improves Supply Chain Visibility and Efficiency
Identifying bottlenecks, optimizing inventory levels, and improving overall efficiency are goals for all supply chain practitioners. Achieving these goals requires visibility into the entire supply chain. This visibility, a comprehensive view of data across the entire supply chain, is made faster and easier with a data gateway. A manufacturing company, for example, can monitor real-time data from its suppliers, production lines, and distribution centers. By analyzing this data, the company can identify areas for improvement and implement changes to improve operational efficiency.
8. Accelerates Decision-Making and Strategic Planning
The ability to access and analyze timely, accurate, and consistent data is essential for effective decision-making and strategic planning. A data gateway provides users with real-time data to make accelerated, informed decisions, based on data from the entire supply chain. This enables companies to react faster to disruptions and exceptions and know that you are making the most informed decision possible.
9. Ensures High Security and Reliability
A cloud-based approach allows an organization to focus on core business activities by reducing the need for in-house IT management. With a data gateway that is fully managed and hosted in major cloud providers, organizations can be ensured high security and reliability so you can focus on making sense of the data.
10. Facilitates Sustainability Reporting and Environmental Compliance Goals
ESG (environmental, social, and governance) reporting and compliance are growing in importance and yet many organizations are struggling to collect and connect data from some of these new sources. A data gateway provides a unified and harmonized view of supply chain data, which is essential for generating accurate and reliable ESG reports. By integrating data from various sources, including IoT devices and third-party systems, organizations can monitor and manage their environmental impact more effectively. In manufacturing, companies can track and report on carbon emissions, water usage, and waste generation, reducing their environmental footprint and improving sustainability performance.
Final Thought
Quick and easy access to live and historical data is critical for supply chain practitioners, data analysts, stewards, and engineers in any industry. Here are just a few examples of industries that can benefit from a supply chain data gateway:
Fast Moving Consumer Goods and Consumer Packaged Goods (FMCG and CPG): In FMCG and CPG, the ability to make rapid, data-driven decisions is crucial for staying competitive in a fast-paced market. Companies can optimize their supply chain operations by using a data gateway that provides a unified and harmonized view of data. For instance, a logistics manager can monitor real-time data on inventory levels, customer orders, and transportation schedules to make better informed decisions and reduce lead times and costs while improving customer satisfaction.
Healthcare: In healthcare, a data gateway can improve supply chain visibility and inventory optimization by providing a unified and harmonized connective tissue of data. This provides a data foundation to optimize medical and supply fulfillment to limit procedure cancellations along with real-time data analytics.
Third-Party Logistics (3PL): In the 3PL sector, a data gateway can significantly enhance decision making by providing a unified and harmonized view of data. By integrating data from different sources, logistics managers can make more informed decisions about when and how to fulfill orders. Additionally, the real-time data access and analytics capabilities of a data gateway can help in identifying and addressing issues as they arise, such as delays in transportation or shortages in inventory.
Application and Solution Providers: For application and solution providers, a data gateway can reduce customer implementation times and lower operational costs. By providing a low-code, self-service data gateway front-end, software providers accelerate time to revenue and improve customer satisfaction.
Wholesale Distribution: In wholesale distribution, a data gateway can help optimize inventory levels and improve supply chain visibility. By providing a unified and harmonized view of data, distributors can gain a comprehensive understanding of their operations, from supplier relationships to customer demand. This can help in identifying inefficiencies and implementing changes to improve operations and customer satisfaction.
Automotive: Automotive manufacturers face a myriad of challenges, but having access to anticipated supplier disruptions to ensure parts availability is one of the most notable challenges. With a data gateway, you gain visibility across their suppliers, enabling them to provide accurate data for actionable insights through a prescriptive control tower to drive a resilient, agile, and intelligent supply chain.
Manufacturing: A smart factory relies on IT-OT integration. With a data gateway, you can easily combine data from OT systems and real time signals from the shop floor with enterprise IT and analytics systems to enable manufacturers to improve quality, efficiency, respond faster to events, and predict and avoid problems before they occur.
Public Sector: Government agencies are engaged with supply chains from multiple perspectives. They monitor food, drug, and public safety, transportation, materials and other sectors for real-time visibility and decision support. They provide supply chain logistics for agencies as they deal with thousands of suppliers and need real-time insights to drive efficiency. And they support maintenance, repair, and operations (MRO) for agencies that need to track and maintain assets and infrastructure across multiple sectors of the economy. Access to real-time, unified data makes all of these processes more efficient and compliant.
If it sounds impossible to achieve all the benefits outlined above through one solution, I assure you, it is not. A data gateway makes it faster and simpler to integrate, harmonize, and normalize disparate data and deliver it to the right consuming users and applications at the right time and in the proper format to accelerate time to value.
Learn more at InterSystems.com/DataGateway.
Mark Holmes
Head of Supply Chain Market Strategy
InterSystems
Mark Holmes is Head of Global Supply Chain Market Strategy at InterSystems, a creative data technology provider. He brings more than 25 years of experience in consulting, manufacturing operations, and software development from such organizations as Dow Chemical, GS1 (Brussels), Aspen Technology, and CGI. He specializes in working with manufacturers and retailers/CPG to solve their most difficult supply chain issues through digital transformation with a modern data fabric architecture. Breaking down data silos and leveraging artificial intelligence and machine learning to drive actionable insights throughout an organization’s global supply chain, Mark has delivered value to companies like Tyson Foods, Ferrero Roche, TJX Companies, Hard Rock Café, and Albertsons.
Mark joined InterSystems in 2021 to broaden InterSystems global market in supply chain. Holmes has been a board member for the Association for Supply Chain Management and is APICS certificated in Transportation, Logistics and Distribution (CTLD) from the same organization. He earned a BS degree in business administration from Indiana University in Bloomington, Indiana, and an MBA from Bentley University in Waltham, Massachusetts.
The post 10 Ways A Data Gateway Improves Time to Value Across Your End-to-End Supply Chain appeared first on Logistics Viewpoints.
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Walmart AI Pricing Patents Signal Shift Toward Real-Time Retail Execution
Published
3 jours agoon
20 mars 2026By
Walmart’s new patents and digital shelf rollout point to a more tightly integrated model linking demand forecasting, pricing, and store-level execution.
Walmart has secured two patents related to automated pricing and demand forecasting, drawing attention to how large retailers are evolving their pricing and execution capabilities.
One patent, System and Method for Dynamically Updating Prices on an E-Commerce Platform, covers a system that can dynamically update online prices based on changing market conditions. A second, Walmart Pricing and Demand Forecasting Patent Classification, relates to demand forecasting technology designed to estimate what customers will buy and recommend pricing accordingly. At the same time, Walmart is expanding digital shelf labels across its U.S. stores, replacing paper labels with centrally managed electronic displays.
Individually, none of these elements are new. Retailers have long used forecasting models, pricing tools, and store execution processes. What is notable is the combination.
Walmart now has three capabilities aligned:
Demand forecasting tied to predictive models
Price recommendation based on that demand
Store-level infrastructure capable of rapid execution
That combination reduces the operational friction historically associated with pricing in physical retail.
Pricing Moves Closer to Execution
Traditional store pricing changes required coordination across multiple steps: analysis, approval, printing, distribution, and manual shelf updates. That process introduced delay and inconsistency.
Digital shelf labels materially change that constraint. Prices can be updated centrally and executed across stores with significantly less manual intervention.
This does not change the underlying logic of pricing decisions. Retailers have always adjusted prices based on demand, competition, and margin targets. What changes is the speed and consistency of execution.
As a result, pricing moves closer to real-time operational control.
Implications for Supply Chain Operations
Pricing is not an isolated commercial function. It directly influences demand patterns, inventory flow, replenishment timing, and markdown activity.
When pricing becomes faster and more responsive, those linkages tighten.
Three implications are clear:
1. Increased Execution Speed
Retailers can align pricing decisions more quickly with current demand conditions, reducing lag between signal and action.
2. Stronger Dependence on Forecast Accuracy
When pricing recommendations are driven by predictive models, the quality of demand sensing becomes more consequential. Forecast errors can propagate more quickly into sales and inventory outcomes.
3. Closer Coupling of Merchandising and Supply Chain
Pricing decisions influence demand. Demand impacts inventory, replenishment, and store execution. Faster pricing cycles compress the distance between these functions.
Centralization and Control
Walmart has positioned its digital shelf label rollout as an efficiency and accuracy initiative. Centralized price management improves consistency between systems and store execution while reducing labor tied to manual updates.
That positioning aligns with the operational realities of large-scale retail. At Walmart’s footprint, even small improvements in execution efficiency translate into material cost and accuracy gains.
At the same time, the shift toward algorithm-supported pricing introduces standard enterprise control requirements. Organizations need clear governance around how pricing recommendations are generated, reviewed, and executed, particularly as systems become more automated.
A Broader Technology Pattern
Walmart’s patents are best understood as part of a broader shift in supply chain and retail technology.
AI and advanced analytics are moving closer to operational decision points. Forecasting models are no longer confined to planning environments; they are increasingly connected to systems that can act.
In this case, that connection spans:
Demand sensing
Price recommendation
Store-level execution
The result is a more tightly integrated operating model in which commercial decisions and supply chain execution are linked through software.
What This Signals
The significance of Walmart’s move is not tied to public debate over surge pricing scenarios. The underlying development is structural.
Retailers now have the ability to connect demand forecasting, pricing logic, and execution infrastructure into a faster decision loop.
For supply chain leaders, that represents a clear direction:
Execution is becoming more digital, more centralized, and more tightly coupled to predictive models.
The companies that benefit will be those that can align forecasting, pricing, and operational execution within a controlled, coordinated system.
The post Walmart AI Pricing Patents Signal Shift Toward Real-Time Retail Execution appeared first on Logistics Viewpoints.
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Supply Chain and Logistics News March 16th-19th 2026
Published
3 jours agoon
20 mars 2026By
This week’s installment of Supply Chain and Logistics news includes stories about record increases in oil prices, Rivian’s autonomous taxis, and much more. Firstly, the Trump administration has issued a 60-day waiver of the Jones Act, a century-old regulation that requires goods moved between US ports to be transported by US-built vessels, etc. Additionally, this week Uber & Rivian announced a partnership for Rivian to build 50,000 autonomous robotaxis by 2031 with over a billion dollars in investment from Uber. Schneider Electric and EcoVadis announced a partnership to target emissions in the health care sector. Lastly, DHL announces 10 warehousing sites to be used for data center manufacturing capacity, and Mind Robotics raises 100 million in series A funding.
Your Biggest Stories in Supply Chain and Logistics here:
Trump Administration Issues Pause on Century-old Maritime Law to Ease Oil Prices
The Trump administration has issued a 60-day waiver of the Jones Act. This century-old regulation typically requires goods moved between US ports to be carried on vessels that are US-built, US-owned, and US-crewed. However, with oil prices surging toward $100 a barrel due to escalating conflict in the Middle East, the suspension aims to ease logistics for vital commodities like oil, natural gas, and fertilizer. While the move is intended to lower costs at the pump and support farmers during the spring planting season, it has sparked a debate between those seeking immediate economic relief and domestic maritime unions concerned about the long-term impact on American shipping and labor.
Uber and Rivian Partner to Deploy up to 50,000 Fully Autonomous Robotaxis
Uber and Rivian have announced a massive strategic partnership that signals a major shift in the future of autonomous logistics and urban mobility. Under the terms of the deal, Uber is set to invest up to $1.25 billion in Rivian through 2031, a move specifically tied to the achievement of key autonomous performance milestones. The primary focus of this collaboration is the deployment of a specialized fleet of fully autonomous R2 robotaxis, with an initial order of 10,000 vehicles and an option to scale up to 50,000 units. From a supply chain perspective, this represents a significant commitment to vertical integration; Rivian is managing the end-to-end production of the vehicle, the compute stack, and the sensor suite, including its in-house RAP1 AI chips, while Uber provides the scaled platform for deployment. Commercial operations are slated to begin in San Francisco and Miami in 2028, eventually expanding to 25 cities globally by 2031.
Schneider Electric and EcoVadis Announce Partnership to Decarbonize Global Healthcare Supply Chains
Schneider Electric, a major player in the digital transformation of energy management and automation, and EcoVadis, a provider of business sustainability ratings, have announced a strategic partnership aimed at accelerating decarbonization within the healthcare industry. “Energize” is a collective initiative to engage pharmaceutical industry suppliers in climate action. The collaboration focuses on addressing Scope 3 emissions, those generated within a company’s value chain, which often represent the largest portion of a healthcare organization’s carbon footprint. By combining Schneider Electric’s expertise in energy procurement and sustainability consulting with EcoVadis’s supplier monitoring and rating platform, the partnership provides a structured pathway for pharmaceutical and medical device companies to transition their global suppliers toward renewable energy.
Mind Robotics, a Rivian spin-off, raises $500 million in Series A Funding
RJ Scaringe, CEO of Rivian, is positioning his new $2 billion spin-off, Mind Robotics, as a technological solution to the chronic shortage of manufacturing labor in the Western world. By developing a “foundation model” that acts as an industrial brain alongside specialized mechatronic bodies, the company aims to move beyond the rigid, fixed-motion plans of traditional robotics toward systems capable of human-like reasoning and adaptation. Scaringe emphasizes that while these machines must perform with human-level dexterity, they don’t necessarily need to be humanoid in form; instead, the focus is on creating a data-driven “flywheel” within Rivian’s own facilities to lower production costs and help domestic manufacturing remain globally competitive.
DHL is significantly scaling its data center logistics (DCL) footprint in North America, announcing the addition of 10 dedicated sites totaling over seven million square feet of warehousing capacity. This expansion is a direct response to the explosive demand for AI-driven infrastructure and the specific needs of hyperscale and colocation data center operators. By offering specialized services like rack pre-configuration, white-glove handling of sensitive IT hardware, and warehouse-to-site transportation, DHL is positioning itself as an end-to-end partner in a sector where 85% of operators express a preference for a single logistics provider. This move not only addresses the logistical complexities of moving high-value components like GPUs and cooling systems across global borders but also underscores the critical role of integrated supply chains in maintaining the build speed of the digital backbone.
Song of the Week:
The post Supply Chain and Logistics News March 16th-19th 2026 appeared first on Logistics Viewpoints.
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How to Capitalize Quickly to Address Hyperconnected Industrial Demand
Published
4 jours agoon
19 mars 2026By
This first in a blog series offers a review of discussion that occurred during ARC Advisory Group’s 2026 Industry Leadership Forum. Specifically, it details a keynote conversation held with senior executives from Rolls-Royce, BTX Precision, and MxD.
The New Fabric of Demand: Modernizing Collaboration and Transparency for Real-Time Production
Industrial leaders have been talking about tearing down workflow and data silos for decades. Yet here we are again. For most, the reality is that most operations and supply chains today typically don’t indicate much progress. A few leaders have figured out how to use digital tools to scale and build pathways forward, a whopping 12.9% according to our latest data (yes, that’s sarcasm). However, even as they struggle to coordinate, orchestrate, and innovate across their operations and enterprise, much less tightly collaborate outside their four walls. In a digital world, this continued capability gap, the inability to closely link market signals to responsive production and external supply chains, is very quickly becoming a liability.
Recently, at the 30th Annual ARC Industry Leadership Forum in Orlando, I had the privilege of leading a keynote discussion entitled The New Fabric of Demand: Modernizing Collaboration and Transparency for Real-Time Production. As part of that, I moderated an excellent conversation that included Global Commodity Executive Greg Davidson of Rolls-Royce, CEO Berardino Baratta of MxD, and CRO Jamie Goettler of BTX Precision.
In this four-part series, we will explore that conversation fully, digging into how the “fabric of market demand” has fundamentally changed, and why structural modernization, both human and technological, is no longer just an option. It is an industrial imperative that will increasingly determine who wins in disrupted markets.
Why Legacy Workflow Will Actually Get Modernized
If we examine the present through the lens of the past, the fundamental laws of supply and demand haven’t really changed. What has changed is the hyperconnectivity of the world and our compressed time to both reward and volatility.
The hard truth is that legacy linear workflows simply do not work in hyperconnected, digitally-driven environments, which are non-linear by nature. As our industrial environments become more digital, they naturally open up countless new ways for how things can get done and how risk can enter the organization. As a result, disruption has shifted from a rare event to a fairly continuous and pervasive reality. In this new reality, responsiveness differentiates you from the competition, and lag time kills.
To survive and thrive in non-linear environments, tighter, integrated ecosystems are required, where silos are actively torn down or redesigned so that barriers to value can be continuously identified and quickly eliminated. At the core, this concept is unfolding around data access, contextualization, and sharing. It provides the urgency behind the need for building industrial data fabrics.
This rewiring certainly extends beyond operations and enterprise processes, enabling the entirety of the supply chain to be judged on its collective responsiveness to the market, all the way down to the individual company level. In this scenario, data can quickly point out laggards who limit value. As the orchestrators of these supply chains identify these limitations on value, they quickly break off and discard the connection and move on without these weak links.
Pillars of the New Fabric of Demand
To achieve necessary level of operational and supply chain responsiveness, the roles of every entity within an ecosystem must be rethought. In the subsequent three blogs of this series, we will take a deep dive into the three distinct pillars that make up this modern architecture, but I’ll begin by laying them out here:
The Market Signal is the catalyst of the entire ecosystem. It dictates the “what” and the “when,” defining what value, success and risk look like in real-time. In blog 2, I’ll explore how to move from reactive assumptions to proactively capturing the market signals that actually matter.
The Demand Architect is moving beyond traditional order-taking. The Demand Architect designs and orchestrates the ecosystem, aligning external partners as true extensions of the enterprise. In blog 3, I’ll discuss the structural agility required to lead this response, rather than just manage a process.
The Agile Partner is the engine of execution. The Agile Partner links supply chain dynamics directly to the shop floor, differentiating themselves through their responsiveness to the market signal. In the final blog in the series, I’ll tackle how data transparency and trust become technical requirements, not just buzzwords, without exposing mission-critical IP.
Building the Modern Industrial Enterprise
Legacy workflows cannot survive in a non-linear world. Industrial organizations must re-architect operations and ecosystems for real-time responsiveness and secure, transparent collaboration. To do so, they will need to:
Improve the measurement of responsiveness: Efficiency and margin-squeezing are important, but they aren’t game-changers. Your competitive edge now relies on how quickly you can adapt to market signals.
Embrace transparency over secrecy: Modern collaboration requires providing a contextualized “lens” into production status without compromising proprietary IP or cybersecurity. Industrial data fabrics are key.
As always, view technology as a tool, not an outcome: Industrial data fabrics are needed to break silos and AI to manage complexity and improve accuracy and speed of decisions. However, the age-old adage remains true. Just because you can apply AI to something doesn’t mean you should. It must be grounded in measurable Value on Investment (VOI), not just return.
The New Fabric of Demand Blog Series
This is the first in a series of four on The New Fabric of Demand: Modernizing Collaboration and Transparency for Real-Time Production. Over the coming days, I’ll publish a perspective from each of the three pillars of the new fabric of demand:
Pillar 1: The Market Signal
Pillar 2: The Demand Architect
Pillar 3: The Agile Partner
By Mike Guilfoyle, Vice President.
For more than two decades, Michael has assisted organizations, including numerous Fortune 500 companies, in identifying and capitalizing on growth opportunities and market disruption presented by the effects of digital economies, energy transition, and industrial sustainability on the energy, manufacturing, and technology industries.
The post How to Capitalize Quickly to Address Hyperconnected Industrial Demand appeared first on Logistics Viewpoints.
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