Connect with us

Non classé

Solving Supply Chain Challenges with Data-Driven Intelligence – Practical Steps to Unlock the Value of Supply Chain Data

Published

on

Solving Supply Chain Challenges With Data Driven Intelligence – Practical Steps To Unlock The Value Of Supply Chain Data

At InterSystems READY 2025, a recurring message resonated across sessions: the most significant barriers in supply chains today are not futuristic, nor are they rooted in the complexity of AI models. Instead, they lie in the foundational issues of fragmented, inconsistent, and unreliable data.

The session “Solving Supply Chain Challenges with Data, Driven Intelligence” focused on the practical steps organizations must take to unlock the value of supply chain data. The discussion was led by Mark Holmes – Head of Supply Chain Market Strategy, Ming Zhou – Head of Supply Chain Product Strategy and Emily Cohen – Senior Solution Developer. Together, they mapped out the realities of supply chain data challenges and presented approaches that are less about grand visions and more about achievable steps: reconcile the data, automate repetitive work, and then apply intelligence in a way that improves day, to, day performance.

Why Supply Chain Data Remains a Bottleneck

Supply chains have become increasingly digitized, but digitization has not solved the core issue of data fragmentation. Procurement teams often operate with supplier records scattered across multiple ERPs. Logistics departments rely on siloed warehouse management systems. Planning teams pull reports from disconnected forecasting applications.

Mark Holmes pointed out that this patchwork of systems leads to duplicated supplier records, mismatched product identifiers, and time lost reconciling basic facts. These are not rare occurrences but daily realities. The consequence is predictable: planning decisions are made on flawed inputs, delays cascade through the network, and advanced analytics projects fail before they begin.

Ming Zhou added that while many organizations rush toward predictive AI, the truth is that most forecasting models fail because they are built on weak data foundations. Without consistency, even the best model produces unreliable outputs.

Emily Cohen emphasized that this is where organizations need to focus first, not on sophisticated models, but on establishing a baseline of clean, validated, and governed data.

Data Fabric Studio: A Practical Toolset

The centerpiece of the discussion was InterSystems Data Fabric Studio, a platform designed to connect disparate data sources, Snowflake, Kafka, AWS S3, and ERP databases, and transform them into unified, reliable datasets.

Unlike traditional ETL (Extract, Transform, and Load) projects that require months of coding and testing, Data Fabric Studio employs recipes, configurable workflows that clean, reconcile, and standardize data. These recipes automate repeatable processes, ensuring that once supplier records are aligned or product codes are standardized, the consistency holds over time and applied to add data sets across data sources.

Mark Holmes explained that this approach eliminates the cycle of one, off data projects that fall apart as soon as new data flows in. Instead, organizations can lock in data quality improvements and free staff from repetitive, manual reconciliation.

Case Study: Supplier Data Across ERPs

One example shared by Holmes and Cohen involved supplier records managed across two ERP systems. The inconsistencies were predictable but damaging:

One supplier might appear under multiple names.
Different identifiers were used across systems, complicating invoice matching.
Purchase orders could not be reconciled without manual intervention.

By applying Data Fabric Studio, the team:

Mapped suppliers to a single source of truth using identifiers such as DUNS numbers.
Standardized supplier names and records across systems.
Built lookup tables to automatically reconcile discrepancies in the future.
Scheduled daily refreshes so data quality stayed intact.

The result was a cleaner supplier database, faster onboarding, and fewer invoice disputes. What stands out in this example is not the sophistication of the solution but its practicality. The gains came from structured data reconciliation, not from exotic algorithms.

Forecasting Through Structured Snapshots

Zhou shifted the focus to forecasting. His point was simple: forecasts are only as good as the data used to build them. Too often, planners must run ad hoc queries across inconsistent systems, leading to variable inputs and unstable forecasts.

The recommended practice is to create structured data snapshots, capturing consistent baselines such as:

Open purchase orders every Monday morning.
Inventory by location at shift change.
Fulfillment cycle times at the close of each reporting period.

These snapshots provide planners with stable, repeatable inputs. While this may sound basic, the effect is significant: forecasting accuracy improves because the inputs are reliable, and planners spend less time chasing down missing data.

Zhou was clear that this is not advanced predictive AI. Instead, it is the groundwork that enables predictive AI to succeed. Without clean, consistent snapshots, AI models are destined to fail.

AI, Ready Data: From Vector Search to RAG

Cohen emphasized that AI does not fail because of weak models, it fails because of bad data. Large language models, predictive algorithms, and advanced optimization engines all require structured, validated, and governed data. Without it, the insights generated are misleading at best and damaging at worst.

To address this, Data Fabric Studio incorporates tools for vector search and retrieval, augmented generation (RAG). These enable:

Semantic search across suppliers, contracts, or parts databases, allowing staff to locate the right information even when queries are imprecise.
Feeding current and validated data into language models so that natural language queries return fact, based answers.
Allowing non, technical staff to use natural language interfaces that generate SQL queries or summarize trends.

Prescriptive Insights: Non, Traditional Data as Signals

Holmes expanded the conversation by drawing an analogy from the healthcare sector. In a study presented earlier this week, researchers found that analyzing patients’ shopping habits, specifically purchases of over, the, counter medication, could reveal early indicators of ovarian cancer before any clinical diagnosis was made.

This insight is directly applicable to supply chain management: valuable signals may not always be derived from conventional dashboards. Anomalies in supplier invoices, discrepancies in delivery documentation, or shifts in employee communications could help identify emerging risks before they are detected through traditional metrics. Organizations that systematically integrate these non, traditional data sources into their analytics framework are better positioned to identify disruptions at an earlier stage.

A central theme involves prescriptive insights enabled by AI, ready data. For example, to prevent procedure cancellations, such as a heart surgery being postponed due to a missing valve kit component, the application of advanced, AI, driven prescriptive analytics is critical. As demonstrated by Ming in his presentation, predictive tools identified which surgeries were at risk of delay or cancellation due to unavailable inventory. By leveraging AI, enabled insights, the team proactively sourced the missing components from another warehouse, ensuring surgical schedules remained intact. This outcome underscores the importance of not only preparing data for AI but also implementing advanced supply chain optimization through intelligent prescriptive solutions.

Modular Deployment: Start Small, Scale Gradually

A recurring point from Zhou was the importance of modularity. Data Fabric Studio does not require wholesale system replacement. Organizations can begin with a single use case, supplier data reconciliation, for example, and expand gradually to include forecasting snapshots, vector search, or natural language assistants.

This modular approach minimizes risk and allows organizations to demonstrate value incrementally. It also makes it easier to integrate with existing ERP, warehouse management, and planning systems rather than replacing them outright.

Scalability and Infrastructure

Finally, the speakers emphasized scalability. InterSystems IRIS, the engine behind Data Fabric Studio, has already been proven in healthcare environments, where it supports hundreds of millions of real, time transactions.

For supply chains, this track record matters. As data becomes central to operations, the infrastructure must scale without becoming a bottleneck. Inconsistent or unreliable infrastructure undermines even the best data practices.

Key Takeaways

From the READY 2025 session, the roadmap outlined by Holmes, Zhou, and Cohen is clear:

Reconcile and harmonize data across systems. Clean data is the foundation of everything that follows.
Automate repetitive processes. Recipes in Data Fabric Studio reduce manual reconciliation and enforce consistency.
Use structured snapshots for forecasting. Reliable baselines are essential for both planners and predictive AI.
Introduce AI gradually. Take care of data first, and then apply the right AI technology one use case at a time, and grow from there.
Ensure infrastructure scalability. Proven engines like InterSystems IRIS reduce risk as volumes grow.

A Disciplined Order of Operations

The session leaders were clear: digital transformation in supply chains is not about chasing the latest technology. It is about establishing discipline in the order of operations:

Get the data right.
Automate manual tasks.
Scale the infrastructure.
Apply AI only when the groundwork is complete.

This sequence ensures that AI enhances decision, making rather than amplifying bad data.

Intersystems READY 2025 event, and especially the session “Solving Supply Chain Challenges with Data, Driven Intelligence” underscored that the most effective supply chain strategies are practical, not speculative. By focusing first on unifying and governing data, organizations can lay the foundation for automation, forecasting, and AI applications that deliver real value.

The lesson is straightforward but often overlooked: data comes first, intelligence comes later. Supply chains that adopt this discipline will not only resolve today’s data bottlenecks but also position themselves to adapt to the demands of tomorrow’s networks.

The post Solving Supply Chain Challenges with Data-Driven Intelligence – Practical Steps to Unlock the Value of Supply Chain Data appeared first on Logistics Viewpoints.

Continue Reading

Non classé

Walmart AI Pricing Patents Signal Shift Toward Real-Time Retail Execution

Published

on

By

Walmart Ai Pricing Patents Signal Shift Toward Real Time Retail Execution

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.

Continue Reading

Non classé

Supply Chain and Logistics News March 16th-19th 2026

Published

on

By

Supply Chain And Logistics News March 16th 19th 2026

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 Expands North American Logistics Infrastructure Amid Growing Global Demand for Data Center Logistics Services

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.

Continue Reading

Non classé

How to Capitalize Quickly to Address Hyperconnected Industrial Demand

Published

on

By

How To Capitalize Quickly To Address Hyperconnected Industrial Demand

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.

Continue Reading

Trending