Mihir Joshi

A service planning workspace showing printed work packages, a Gantt chart scheduling dashboard on a laptop, and task dependency annotations on a whiteboard in an industrial service office environment

Complex Field Service Scheduling: Why Planning Comes Before the Dispatch Console

Most field service scheduling problems in complex industrial environments aren’t tool problems rather planning problems. This article explains why standard dispatch automation doesn’t transfer to multi-technician complex jobs, where the planning layer breaks down, and what organizations need to design before a job reaches the dispatch console.

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AI in warranty - from manual processing to automation to agentic systems and autonomous warranty operations

AI in Warranty Operations: From Automation to Agents to Autonomous Operations

AI in warranty operations is moving through three distinct levels – automation, AI agents, and autonomous warranty. Most organizations are at Level 1 or early Level 2. This practitioner roadmap explains what each level actually delivers, what separates automation from genuine AI, and what integration and data prerequisites determine how far you can go.

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A pair of hands holding two parts packages side by side, an OEM branded box on the left and a plain independent aftermarket equivalent on the right — in a blurred automotive workshop environment

OEM Parts Pricing Strategy: When the Margin Lever Starts Working Against You

OEM spare parts are the highest-margin lever in aftersales, and OEMs have leaned on them heavily for decades. But data shows that pricing has crossed a tipping point. This article examines what’s driving defection, why OEMs keep pulling the same lever anyway, and what a sustainable aftermarket revenue model actually looks like.

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A service operations manager reviewing a contract management dashboard and asset hierarchy on dual monitors in an industrial office environment

Service Contract Management in Industrial Manufacturing: Managing Complexity at Scale

Service contract complexity is quietly undermining aftersales revenue in industrial manufacturing. Too many contract types, overlapping entitlements, and no asset-level visibility make upselling impossible and customer experience inconsistent. Drawing on two real implementation cases, this article outlines how to design and maintain service contracts that work at scale.

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Dealers Changing Role in OEM Aftersales

The Dealer’s Changing Role in OEM Aftersales: Partner, Channel, or Liability?

OEMs are quietly undermining the dealer networks they still depend on for last-mile service delivery. This article examines four friction points reshaping the OEM-dealer relationship in automotive and industrial manufacturing, and what people, process, and technology changes are needed to build a sustainable model.

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A service technician reviewing a warranty claims dashboard on a monitor in a manufacturing dealership environment

Warranty Management in Manufacturing: Why Outward Leakage Is Your Biggest Hidden Cost

Warranty management in manufacturing goes far beyond claims processing. Most organizations have digitized intake but still lose millions through dealer fraud, registration gaps, and ignored quality signals. This guide maps where warranty breaks down across the full lifecycle, and how AI in warranty management is helping close the gap.

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Iceberg diagram showing AI readiness gap with strategy above water and execution factors like data, process, and systems below

The AI Readiness Trap: Why AI Readiness Assessment Should Come Before Roadmap

Most organizations believe they’re ready for AI because the roadmap looks solid. But execution tells a different story. When ambition outpaces data, processes, and systems, initiatives stall. This gap between intent and capability is where AI efforts fail, and where the real work of readiness actually begins.

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AI prioritization framework showing progression from ideas to impact using business impact and data readiness model

Where Should You Start with AI in Aftersales? A Practical Prioritization Framework for Service Organizations

Most AI initiatives in aftersales fail not due to lack of ideas, but poor prioritization. This article introduces a practical framework to help service organizations decide where to start, based on business impact and data readiness, so they can sequence AI investments effectively and avoid costly, unscalable pilots.

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