Automotive manufacturers have spent billions of dollars on digital transformation initiatives over the last 20 years, but the ratio of warranty costs to revenue has not shown any significant improvement. Those Industry 4.0 investments went primarily into production operations and provided a ~15%-20% average improvement in overall equipment efficiency (OEE), but quality and warranty have not significantly benefited. One major reason for inefficient warranty claims is the time lag between receiving warranty claims data and initiating problem solving.

It’s important to understand the difference between express and implied warranties when considering warranty claims. The Magnuson-Moss Warranty Act of 1975 sets standards for consumer product warranties, protecting buyers from fraud and misrepresentations. An express warranty is a guarantee from a seller or manufacturer to a buyer that the purchased product will perform according to certain specifications, and these promises are typically documented in writing. An implied warranty, on the other hand, is a guarantee that the product functions as designed, even if not explicitly stated. The implied warranty ensures that a product is fit for its general purpose and functions as expected, unless specifically excluded. Warranty terms and conditions must be fully and clearly disclosed in writing to the buyer before they buy a product, ensuring legal enforceability and clarity.

For example, if a consumer buys a new car and the product fails due to a manufacturing defect within the warranty period, the buyer can file a warranty claim to have the issue repaired or the product replaced according to the terms set out in the written warranty. Express warranties are specific, documented promises made by manufacturers or sellers, and having these warranties in writing is crucial for legal protection if disputes arise.

Data Lags Add Weeks to Claims Processing

Lags in warranty claims resolution occur due to manual assessment processes. The warranty claims process starts when a customer files a claim under the warranty policy. Customer claims data requires transformation and normalization, and it takes time to collect plant quality data (like corrective action implementation dates) for validation. Manual assessment often requires gathering original purchase receipts, warranty agreements, serial numbers, and maintenance records. Repairs must be reported immediately, as delays can result in denied claims if the issue is deemed a result of neglect. Companies often deny claims if maintenance history cannot be proven according to manufacturer guidelines. Other causes include complex manual workflows, the need for manual data entry and file uploads, and disparate data systems that require integration. Here’s how the process works:

Businesses must check and validate claim details—often through an audit check—to ensure the product is within the warranty period and meets policy conditions.

After validation, businesses assess the issue to determine if it falls under the warranty’s coverage.

Once the assessment is complete, the business processes the claim, including documentation and communication with the customer.

It typically takes several weeks from receipt of initial customer claims until problem solving is initiated – time that could be spent solving the problem and preventing future claims.

Why Streamlining Warranty and Insurance Claims Matters

Streamlining warranty claims impacts multiple facets of the business, from supply chain to operations. A good warranty provides assurance to consumers that the goods they purchase are as advertised, offering a structured recourse should issues arise. But ignoring data lags and continuing with the status quo results in a number of negative consequences, including financial and brand burdens. The result of inefficient warranty claims can undermine consumer trust and satisfaction.

Financial Consequences of Repairs

Locked capital: When claim resolution is delayed and warranty costs exceed accruals, profits suffer and can impact stock price.

Higher labor costs: Delayed claims require more employee time to manage, which increases labor expenses.

Delayed reimbursements: For both manufacturers and their service providers, a slow claim process means delayed reimbursement for repairs and parts.

Brand Consequences

Customer satisfaction: When a warranty claim takes an extended amount of time, customers become dissatisfied and may question whether the manufacturer stands behind their products.

Public perception: Warranty data lags do not allow a manufacturer to get ahead of a significant problem, which could turn into a global recall of a product or component.

But streamlining warranty claims is easier said than done. There are significant data challenges that hinder the process. These include data lags, inconsistent data reporting, missing or incomplete data, unstructured text data, and data quality issues. There are additional challenges that auto manufacturers face in their warranty claims processes including vehicle technology complexities, evolving component reliability, usage, and environmental factors, supplier inconsistencies, and changing regulations.

Decision Intelligence Is the Solution

Streamlining warranty claims data analysis requires the integration of disparate quality and operations data, and AI-enabled decision intelligence. By automating data preparation and reporting, manufacturers can gain immediate analysis of customer warranty claims resulting in faster time to issue resolution. By leveraging real-time quality data, claims reserve predictions are more accurate. For example, reducing the time it takes to prepare and assess customer claims data and begin problem solving by four weeks equates to an 8% annual cost reduction. To put that number in perspective, in 2023 (the most recent year for which numbers are available), worldwide automakers made total warranty accruals of $65 billion. At 8% cost reduction, that equates to $5.2 billion in savings.

These two complementary technologies can help. They enable faster data integration, harmonization, and sharing across supplier networks.

InterSystems Supply Chain Orchestrator™ is an AI-enabled supply chain decision intelligence platform built to solve your supply chain problems. It unifies disparate data sources by providing a real-time connective tissue—with built-in predictive and prescriptive analytics—that’s complementary and non-disruptive to your existing infrastructure.

InterSystems Data Studio™ delivers unified and timely data, empowering supply chain practitioners to make better decisions faster. This low-code, self-service data gateway makes it quicker 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. It serves as the front-end data gateway to harmonize and onboard data to Supply Chain Orchestrator.

Business Value at a Glance

Unlock working capital: Long claim resolution cycles mean more claims and higher warranty reserves tying up capital.

Reduce labor costs: Delayed claims require more employee time to manage, which increases labor expenses.

Accelerate reimbursements: For both manufacturers and their service providers, a slow claim process means delayed reimbursement for repairs and parts.

Get ahead of product recalls: Warranty data lags prevent manufacturers from getting ahead of a significant problem, which could turn into a global recall of a product or component.

Final Thought

Warranty is a data problem disguised as a process problem. Decision intelligence speeds up warranty claims by eliminating manual reviews, bottlenecks, and enabling real-time, risk-based decisions across dealers, OEMs, and suppliers. In the end, it transforms warranty from a reactive cost center into a predictive quality and financial control function. Learn more about streamlining the warranty claims process here.

Chris Cunnane is the Global Product Marketing Manager for Supply Chain 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.

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Series connection: Part 1 described convergence at the enterprise level. This installment examines the warehouse as a practical proving ground, where orchestration must translate changing priorities into coordinated human and machine work. Part 3 extends the same logic beyond the facility by asking how visibility can trigger intervention.

Warehouse automation has traditionally been discussed in terms of equipment: conveyors, sortation systems, automated storage and retrieval systems, autonomous mobile robots, robotic arms, and automated packaging.

These technologies remain important. But as warehouses adopt more automation, the central problem is shifting.

The question is no longer simply, “What can we automate?”

It is becoming, “How do we coordinate all the people, robots, software, equipment, inventory, and orders already inside the building?”

That is the role of warehouse orchestration.

Automation Creates a Coordination Problem

A warehouse containing one major automation system can often be managed through a relatively stable set of interfaces and operating rules. A modern distribution center may contain several forms of automation, each with its own control logic, task queues, constraints, and performance characteristics.

An order might involve inventory stored in a conventional pick face, another item retrieved through an automated storage system, a mobile robot supporting travel, a worker using voice-directed instructions, and a packaging station with its own capacity limits.

Each component can function correctly while the overall process still performs poorly.

One zone may become overloaded while another is underutilized. Robots may wait for associates. Associates may wait for replenishment. Completed orders may accumulate upstream of packing. Transportation priorities may change after warehouse work has already been released.

Automation improves individual tasks. Orchestration improves the relationships among those tasks.

The Work Queue Is Becoming Dynamic

Traditional warehouse management systems generate work according to rules, waves, orders, inventory status, labor assignments, and shipping deadlines. An orchestration layer must continuously reconsider those assignments as conditions change.

Recent vendor developments reinforce this shift. In its January 2026 warehouse automation outlook, Locus Robotics argued that orchestration was moving from a technical platform feature to a performance strategy for managing distributed operations. Lucas Systems’ February 2026 warehouse agility study likewise focused on the cost of operations that cannot adapt quickly to changes in demand, labor, and resources. These companies occupy different parts of the warehouse technology stack, but both treat dynamic coordination as a core operating requirement rather than an optional layer.

These companies operate in different parts of the warehouse technology stack, but both reflect the same requirement: the next task cannot always be determined by a static sequence established at the beginning of a shift.

The best next action may depend on congestion, worker location, robot availability, order priority, trailer departure time, replenishment status, and downstream capacity. Those factors can change from minute to minute.

People Remain Part of the System

Warehouse orchestration is sometimes framed as a step toward fully autonomous fulfillment. That may occur in highly structured operations, but most warehouses will continue to depend on a mixed workforce of people and machines.

The objective is therefore not only machine orchestration. It is human-machine orchestration.

A worker may be more effective than a robot for irregular or exception-heavy tasks. A mobile robot may eliminate non-value-added walking while leaving item handling and judgment with the associate. Voice and vision technologies may guide the worker while software dynamically changes the sequence of assignments.

This is why workforce design remains important. Orchestration technology needs accurate information about skills, training levels, ergonomic constraints, shift patterns, and work rules, not merely machine availability.

The WMS Is Necessary but May Not Be Sufficient

The warehouse management system remains the transactional and operational foundation of most distribution centers. It maintains inventory accuracy, directs work, enforces process rules, and records execution.

Vendors such as Manhattan Associates, Blue Yonder, and Made4net have expanded warehouse and supply chain execution capabilities beyond basic transaction management. Manhattan Associates’ May 2026 WMS announcement emphasized cloud-native execution and embedded AI, while Blue Yonder’s 2026 warehouse commentary highlighted integrated, data-driven operations. Made4net used MODEX 2026 to present an AI-enabled WMS designed around real-time insight and more responsive distribution execution. The market direction is toward systems that coordinate warehouse decisions continuously, not simply record completed work.

The architectural question is whether orchestration should reside inside the WMS, in a broader supply chain execution platform, within an automation control layer, or across several coordinated applications.

The answer will vary by operation. What matters is that the orchestration logic has access to enough operational context to make useful decisions without undermining inventory control, safety, or process discipline.

Orchestration Changes the Investment Case

Automation projects have often been justified by evaluating the productivity or labor savings associated with a specific process. Orchestration requires a wider business case.

Its value may come from reducing idle time across several systems, increasing throughput without additional capital equipment, improving responsiveness to priority orders, balancing work among zones, and making existing automation more productive.

This can make orchestration particularly attractive in brownfield facilities. A company may not need to replace its installed automation to improve performance. It may need better coordination across the assets it already owns.

That does not eliminate the need for new equipment. It changes the sequence of the discussion. Before adding another machine, management should determine whether the current constraint is physical capacity or poor coordination.

The Next Warehouse Advantage

The warehouse of the future will not necessarily be the facility with the most robots.

It will be the facility that can continuously align orders, inventory, labor, machines, staging capacity, and transportation commitments as operating conditions change.

Automation performs the work. Orchestration determines how the work should flow.

As distribution centers become more technologically diverse, that coordinating intelligence will increasingly determine whether individual automation investments produce isolated improvements or measurable gains across the entire operation.

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Supply chain technology vendor Kinaxis has named Herb Yeh as Chief Financial Officer and Chief Strategy Officer, effective July 27, 2026. The newly created dual role consolidates the company’s financial operations and corporate strategy under a single executive officer as the enterprise software market sees an accelerated shift toward AI-driven planning architectures.

Leadership and Advisory Profile

Yeh brings more than 25 years of investment banking and corporate finance experience within the enterprise software and broader technology sectors. Prior to joining Kinaxis, he served as Senior Managing Director at Evercore, leading the firm’s strategic advisory practice for technology software.

His earlier background includes:

Global Co-Head of Technology Investment Banking and Vice Chairman at Citi

Senior investment banking leadership roles at Bank of America Merrill Lynch

Legal practice focused on corporate and securities law at Cleary Gottlieb Steen & Hamilton LLP

Executive Mandate and Operational Scope

In his dual capacity, Yeh will lead global finance, accounting, investor relations, corporate strategy, and corporate development. According to statement details from Kinaxis CEO Razat Gaurav, the mandate centers on strategic capital allocation, M&A execution, and supporting disciplined financial scaling alongside the expansion of the company’s Maestro supply chain orchestration platform.

The consolidated leadership structure aligns corporate finance directly with strategy development, a structure increasingly adopted by software vendors navigating shift-to-platform models, generative AI integrations, and shifting market demand across global supply networks.

“Kinaxis has built something genuinely differentiated: a platform that sits at the center of how enterprises make their most complex decisions,” said Yeh. “The supply chain planning market is being transformed by AI, and Kinaxis is exceptionally well positioned to lead that transformation. I look forward to partnering with Razat and the team to build on that foundation and deliver lasting value for customers and shareholders alike.”

Timing and Earnings Reporting

The leadership change takes effect ahead of the company’s second-quarter 2026 earnings report, scheduled for release after market close on Wednesday, August 5, 2026, followed by a conference call on August 6.

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