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How to Optimize Fulfillment with Unified Data
Published
10 mois agoon
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Order fulfillment is the complete process from when an order is placed until the shipment is delivered. Accurately fulfilling thousands of orders for millions of items is extremely challenging. Many large organizations have multiple systems for order, warehouse, or transportation management that are barely integrated – frequently not at all. However, large organizations are often equipped to handle fulfillment in-house, leveraging their extensive resources and capabilities. An organization with tens of thousands of different products may have to move them across many modes of transportation, IT systems, and third-party logistics partners, all adding to complexity, as well as loss of visibility and control.
Sudden and significant changes in demand, especially in consumer markets, stack up more challenges, requiring order revision and reallocation. If your systems are disjointed and you lack the ability to analyze masses of data in real time, you will struggle to deliver on-time, in-full and your reputation and revenue will be negatively impacted.
Optimizing fulfillment requires a series of steps to get a shipment from its source to the end customer. These steps include sourcing and receiving inventory, storing inventory, order processing, picking and packing an order, shipping the order, and returns management. Standard sizes and categorizations play a crucial role in determining the costs associated with shipping products that meet standard criteria in fulfillment centers. The fulfillment process is further complicated by ongoing shifts in customer expectations and demands and geo-political and weather disruptions.
Introduction to OTIF Fulfillment
The key measurement of fulfillment is on-time in-full (OTIF) fulfillment, which is calculated as a percentage of orders that are delivered on the requested delivery date and in the quantity requested by the customer. The formula for OTIF is:
Measuring a supply chain against OTIF metrics is a key strategy that helps decision makers attach a tangible value to the success of their fulfillment and allows them to determine key strategies. Factors like planning tools, inventory management, demand patterns, and innovations in technology contribute to the success or failure of fulfillment optimization. Establishing standard benchmarks for services and innovations in fulfillment centers is crucial in this context. Fulfillment costs can significantly impact profit margins, making it crucial for businesses to understand these financial implications and how they influence consumer spend.
The question then becomes “what is a good OTIF score to shoot for?” Fulfillment success, and the associated OTIF score, will vary by industry, region, and other assorted factors, but generally speaking, an OTIF score is considered good if it falls between 80% and 90%. Many companies aim for 95% or higher, which can be a daunting task. For suppliers, the penalties associated with missing OTIF goals can be significant. For example, Walmart’s OTIF program mandates that suppliers should meet the 90% on-time and 95% in-full goals to avoid penalties. Walmart fines suppliers 3% of the cost of goods sold (COGS) for orders that fail to meet on-time and in-full delivery requirements.
A good fulfillment strategy can help businesses boost customer satisfaction (CSAT), reduce inefficiencies, and increase sales. By setting clear expectations and standards for fulfillment operations, including OTIF rates, shipping times, and inventory levels, businesses can ensure that they meet customer demands and maintain high levels of satisfaction. Regularly monitoring and analyzing fulfillment operations can help identify areas for improvement and implement strategies to optimize these processes.
Effective fulfillment requires a well-designed system, efficient logistics, and a reliable supplier network to ensure timely and accurate delivery of products. Companies have two options to consider for fulfillment operations: in-house fulfillment or outsourcing fulfillment to a third-party logistics (3PL) provider. While outsourcing to a 3PL is a common strategy, new technologies and approaches now exist to achieve higher OTIF rates in house.
Warehouse Fulfillment Complexities and Inefficiencies
InterSystems surveyed 450 senior supply chain practitioners to examine key supply chain technology challenges, trends, and decision-making strategies across five key use cases: fulfillment optimization; demand sensing and forecasting; supply chain orchestration; production planning optimization; and environmental, social, and governance (ESG). These respondents came from 13 countries and 12 industries, representing decision-makers across project management, fleet management, sales & marketing, HR, and finance.
This blog is Part 1 in our Optimizing Supply Chain Performance with Unified Data series, with a focus on optimizing fulfillment. Effective inventory management strategies are crucial for businesses looking to expand their operations and improve delivery efficiency, particularly when scaling to multiple warehouse locations. Looking to the future, businesses should prepare for trends such as the growth of micro fulfillment centers and the need for adaptive strategies to stay competitive in the evolving landscape.
Ability to Meet Fulfillment Goals
According to the survey, only a mere 1% of respondents achieve 80% or higher for their OTIF metrics, with the average percentage of OTIF being a mediocre 62.21%. The ability to meet fulfillment goals is impeded by several issues. When asked to name their top three challenges for fulfillment optimization, respondents cited the high volumes and complexities of SKUs (59%), inadequacies of existing planning tools (51%), and volatile demand (42%). Considering that the majority of respondents are using manual processes, legacy systems, or multiple solutions from different vendors to integrate and prepare disparate data, this makes sense.
SKU complexity generally refers to the challenges and inefficiencies associated with keeping a large number of SKUs within a store, warehouse, or factory. This includes picking the correct items from inventory, packing them appropriately, and ensuring their timely delivery to customers. Managing too many SKUs leads to higher inventory carrying costs and general inefficiencies. On top of that, the lifecycle of a SKU is getting shorter, especially as more businesses turn to e-commerce for direct-to-consumer selling. A SKU is designed, received, and pushed to the market, but often it is not available six months later, making replenishment nearly non-existent. Without re-stocks, optimizing fulfillment from the right location is more important than ever. Strategies for managing excess inventory and preventing overstocking are crucial to maintaining efficient operations.
Inadequacy of Planning Tools
The second challenge identified by respondents was the inadequacy of planning tools. This can lead to fulfillment failure from the standpoint of missed deadlines, increased costs, or poor customer satisfaction. Timely information is critical, as data older than a few days can lead to costly supply chain disruptions. Perhaps not surprisingly, the industries that reported they would see the biggest improvement in fulfillment rates if able to ingest real-time data and provide actionable insights to business users were automotive and aeronautics (55%), FMCG (44%), and manufacturing/CPG (43%).
Demand Volatility
The final challenge associated with optimized fulfillment is demand volatility. Demand volatility is the sudden and unpredictable variation in customer demand for products or services over a specific time. The root causes are not always easy to identify, but they can be attributed to changing customer expectations and demands, changing promotions, or a shift in market dynamics such as external weather events, geo-political instability, and shipping disruptions like the Francis Scott Key Bridge collapse or the blockage of the Suez Canal. These changes make it harder for companies to forecast demand in both the near and long term and can lead to further supply chain disruptions. Effective returns management is also crucial in handling the unpredictable nature of demand, ensuring that returned products are inspected, restocked, or disposed of efficiently. Tracking how much inventory is held and assessing inventory age are essential to making informed decisions about restocking and mitigating risks such as stockouts and overstocking.
Fulfillment Strategies
Respondents were asked to identify the data technology innovations they would most want to implement to achieve fulfillment optimization. The top response was the use of artificial intelligence (AI) and machine learning (ML) (46%), which outpaced predictive and prescriptive analytics (37%), the use of a decision intelligence platform within supply chain (37%), real-time harmonized and normalized data from multiple sources internal and external (37%), and streamlined integration of different solutions (37%).
These technologies can be directly integrated with existing systems, allowing businesses to automate workflows and reduce errors in managing inventory and order fulfillment.
AI and ML impact every stage of the order fulfillment process, with a specific emphasis on forecasting, inventory management, order processing and picking, and last mile deliveries. For improved OTIF, AI and ML help companies make smarter decisions faster, improve turnaround times, and simplify manual processes in the warehouse. The real desire for survey respondents is to improve upon current systems and processes to make better sense of their data, enabling optimized fulfillment processes. Inventory management systems can ensure businesses are notified when stock levels are low, allowing timely replenishment and minimizing the risk of stockouts.
Actionable insights drive significant efficiencies in every area, increasing automation and significantly boosting productivity. Supply Chain Orchestrator provides the infrastructure needed to optimize raw materials handling from point-of-supply to end consumption. Organizations can integrate transportation, warehouse management systems, and advanced robotics. Packaging plays a crucial role in the fulfillment process, ensuring items are carefully packaged for safe transport.
By increasing automation through Supply Chain Orchestrator, organizations accelerate decision-making, offer self-service access to analytics, and remove human errors. Organizations are ready to implement AI and ML-driven prediction and productivity gains. They achieve rapid adaptation to any changes in demand, logistics disruptions, or business priorities, leading to increased CSAT and higher revenue. An efficient fulfillment system is essential in managing order delivery and inventory, contributing to better operational efficiency.
Order Accuracy and Efficiency
Order accuracy and efficiency are critical aspects of fulfillment operations, as they directly impact a business’s ability to fulfill orders on time and in full. Effective order picking and shipping processes are essential for improving order accuracy and efficiency, reducing fulfillment costs, and enhancing the overall customer experience.
By implementing efficient logistics and shipping strategies to ship orders, businesses can reduce shipping times, improve their OTIF rates, and increase CSAT. Regular monitoring and analysis of picking and shipping processes are vital for identifying areas for improvement and implementing strategies to optimize fulfillment operations.
Technology plays a significant role in improving order accuracy and efficiency. Automated packaging and shipping systems can help businesses streamline their operations, reduce errors, and lower fulfillment costs. By leveraging these technologies, businesses can ensure that their customers receive their orders accurately and on time, leading to higher levels of satisfaction and loyalty. But technology plays an even bigger role in data unification and management, especially when it comes to integrating new technology with existing applications.
Final Thought on Fulfillment and Repeat Purchases
These survey findings confirm that most organizations lack the necessary capabilities to optimize highly complex supply chains with interwoven dependencies. To be truly agile and competitive, organizations must be capable of extracting critical insights in near real-time. But as things stand, this remains a significant challenge when so many businesses lack end-to-end visibility, or rely on manual data analysis and ad hoc assemblages of different solutions.
In the face of constant change, disruption, and opportunity, organizations need a streamlined source of standardized, clean, meaningful, and reliable data that is available to business users. Maintaining proper stock levels is crucial to ensure product availability and prevent issues like stockouts or overstocking. An intelligent data platform eliminates the significant data challenges that organizations encounter on their path to optimized fulfillment and repeat purchases.
Read the full report here.
Chris Cunnane is the Supply Chain Product Marketing Manager at InterSystems. In this role, he is responsible for developing and executing marketing strategy and content for the InterSystems supply chain technology suite. Chris has 20+ years of supply chain expertise, leading the supply chain practice at ARC Advisory Group, as well as holding various sales, marketing, and operations roles in the wholesale, retail, and automotive parts markets. He holds a BA in Communications from Stonehill College and an MA in Global Marketing Communications from Emerson College.
The post How to Optimize Fulfillment with Unified Data appeared first on Logistics Viewpoints.
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Walmart AI Pricing Patents Signal Shift Toward Real-Time Retail Execution
Published
2 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
2 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.
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How to Capitalize Quickly to Address Hyperconnected Industrial Demand
Published
2 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.
Walmart AI Pricing Patents Signal Shift Toward Real-Time Retail Execution
Supply Chain and Logistics News March 16th-19th 2026
How to Capitalize Quickly to Address Hyperconnected Industrial Demand
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