Insights

Field service organizations track dozens of KPIs, yet still struggle to improve uptime, customer trust, and service profitability. This article explains how manufacturing and automotive companies should design, structure, and govern field service KPIs as a connected system that drives decisions, manages trade-offs, and scales with service maturity.

After-sales service is now a strategic growth lever for manufacturing and automotive organizations. This article presents an end-to-end view of after-sales service, covering operating models, KPIs, execution challenges, and the practical role of digital and AI, grounded in real-world service transformation experience.

Proactive Resolution Rate (PRR) is an emerging KPI that measures how effectively manufacturing and automotive service organizations prevent failures before customer impact. As AI, predictive analytics, and condition monitoring scale, PRR connects proactive insights to real outcomes, shifting service excellence from fixing failures faster to preventing them altogether.

Dealer technicians often lose valuable time searching for information instead of fixing equipment. This case study shows how a material handling equipment manufacturer improved uptime by 15% by reducing cognitive load at the point of service, using a search-driven technician self-service approach rather than overengineering AI.

Most service transformation programs don’t fail, they quietly stall. Early momentum, strong leadership attention, and rapid implementation create the illusion of success. But as priorities shift, budgets tighten, and ownership blurs, outcomes plateau. This article explores why service transformations lose momentum after year two, and what leaders must do differently.

In the AI age, service leadership is being redefined by a paradoxical mandate: lower costs, shrinking workforces, and rising customer expectations. Move beyond the hype of “predictive” service to master Data Orchestration and Force Multiplication. Discover the strategic framework to transform your service department from a cost center into a high-performance value engine.

2026 marks the end of AI experimentation and the rise of Agentic Execution. As assets become software-defined and margins shift to uptime, service leaders must master Industrial FinOps and ‘teleporting expertise’ to protect Customer Lifetime Value. Discover the five pillars of the 2026 service blueprint.

2025 was a reality-check year for aftersales and field service. Demand stayed resilient, but workforce shortages, asset complexity, AI scaling limits, and regulatory pressure reshaped execution. Leading organizations shifted from experimentation to fundamentals, anchoring AI to KPIs, prioritizing productivity over headcount, and treating service as an end-to-end system under real economic constraints across manufacturing and…

Mean Time to Repair (MTTR) measures how quickly service organizations restore failed assets to operation. In manufacturing and automotive after-sales, MTTR reflects diagnostic readiness, parts availability, process discipline, and system integration. Used correctly, it highlights structural bottlenecks; used in isolation, it can drive superficial optimization and missed root causes.