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Published 29 Apr 2026· via AWS Customer Stories· Customer story

KONE Unlocks New Efficiencies Using AWS IoT

KONE · Manufacturing · Finland

Headline result

−40%

cost

As reported by the source; not independently verified.

Summary

KONE, a global leader in elevator and escalator industry, built a robust and scalable IoT platform using AWS IoT Core and related services to connect and monitor a growing fleet of smart devices.

The challenge was to improve operational efficiency, reduce maintenance callouts, and support a large device base with highly available cloud architecture.

The solution involved migrating to AWS IoT Core, AWS IoT Device Management, AWS IoT TwinMaker, Amazon Simple Storage Service, and AWS Professional Services to enable near-real-time monitoring, provisioning, and predictive maintenance.

The impact included a 40% reduction in customer-reported issues and entrapment incidents, improved provisioning success near 100%, device fleet scale increasing from 100K to 500K, and foundation for AI and digital twin innovations.

The problem

  1. 01KONE needed a robust, scalable IoT platform to connect and monitor a large fleet of smart elevators and escalators across 60+ countries.
  2. 02Legacy IoT platform was limited in scalability, and a new solution was required to support near-real-time data ingestion and analytics for urban flow optimization.

What they built

  1. 01Rebuilt IoT infrastructure using AWS native IoT services and AWS Professional Services.
  2. 02Deployed AWS IoT Core for device connectivity, AWS IoT Device Management for device monitoring and management, and AWS IoT TwinMaker for digital twin capabilities.
  3. 03Data was stored in Amazon S3 enabling advanced analytics and AI workloads.

Reported results

  1. 01Reduced maintenance callouts by 40%, significantly lowering operational costs.
  2. 02Provisioning success rate achieved close to 100%, improving device activation and connectivity.
  3. 03Device fleet scale expanded fivefold from 100,000 to 500,000 devices without degrading service performance.
  4. 04Facilitated AI innovation and digital twin proofs of concepts to improve urban building management.
  • Expanded device fleet to support much larger scale
  • Reduced maintenance callouts and operational costs
  • Improved device provisioning success
  • Enabled AI and digital twin innovation

Source

AWS Customer Stories

Customer story · 29 Apr 2026

Read the original(opens in a new tab)

How this deployment compares

Computed from the library · data 10 Nov 2026

KONE has 5 deployment records in the library, from 2024 to 2026. It is one of 126 predictive maintenance deployments, 76 of them in Manufacturing. Its reported 40% reduction is within the typical range (median 33% across 14 figures).

KONE in the library

5 deployment records from KONE, 2024–2026

  1. May 2024Generative AI Transforming Industrial Manufacturing with AWS at Hannover Messe 2024AI platform
  2. Apr 2026This record · Predictive maintenance
  3. Apr 2026Kone uses Amazon Bedrock to build a Technician Assistant for field serviceStaff assistant
  4. May 2026KONE Uses Amazon Bedrock to Build a Secure Technician Troubleshooting AssistantKnowledge management
  5. Jun 2026KONE: Copilot Studio agent and Power Platform AI automation for contract processing and makersWorkflow automation

Too recent to judgeFollow-through is judged once a record is 1 year old; this one is 7 months old.

How KONE uses AI: company page

Same pattern, whole library

One of 126 predictive maintenance deployments

  • Manufacturing accounts for 76 of them (60%), more than any other industry.
  • 101 records of this pattern came before it; 8 have been published since.
  • Only 19 of the 126 put a number on the result (15%).
Provider mix
  • Microsoft81%
  • AWS16%
  • GCP4%
Most-used technology
  • Azure AI 44
  • Azure IoT 30
  • Azure ML 17
  • Power BI 16
  • AI 15
  • Azure 12

Shares describe this library, not the whole market.

Predictive maintenance analysis

Results against peers

40% reduction is within the typical range

Across 14 cost savings figures (Manufacturing), the median reduction is 33%; the middle half falls between 26% and 50%.

This recordMiddle half of peers

  • Adoption & scale8 figures, all industries · middle half 134%–1100%250%median increase
  • Time & speed35 figures, Manufacturing · middle half 27%–75%50%median reduction

Peer figures are as reported by their sources; percentage claims only.

How we checked this record

Evidence tier: supported

A credible source with a named customer, but not the organization's own account.

Named organization
KONE
Who published the source
The organization itself
Deployment status
Production (confidence 95%)
Quantified result
The source reports at least one figure
Summary checked against source
4 / 5 agreement

Last re-checked 13 Jun 2026.

This record is evidence for

Insight · industryManufacturing dossierOne of 391 Manufacturing deployments. See which patterns dominate there and what they report.Insight · patternPredictive maintenance126 deployments share this pattern. See where it is used and how it is built.Trend · momentumBuilding pattern6 new deployments in the last six months, against 7 in the six before.

Same pattern, other organizations

All predictive maintenance deployments
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    INVISTA transforms manufacturing operations with AWS data lake and machine learning

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  2. 20 Aug 2026
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    South East Water uses Amazon SageMaker AI to detect water-pressure anomalies and prioritize pipeline maintenance

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  3. 19 Jul 2026
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    DMRC increases predictive maintenance accuracy using Gemini in Vertex AI (plus BigQuery/Dataflow)

    Delhi Metro Rail Corporation · Predictive maintenance · GCP

    Delhi Metro Rail Corporation (DMRC) built a Data Center of Excellence on Google Cloud to move from reactive maintenance to predictive maintenance across its metro network. The solution uses Vertex AI and Gemini in Vertex AI to identify early fault patterns and interpret depot maintenance logs, while BigQuery and Dataflow unify data and support real-time operational analytics. DMRC monitors 300+ standardized KPIs and aims to reduce downtime for passengers while improving maintenance planning and decision-making.

    Production

  4. 9 Jun 2026
    Manufacturing

    Georgia-Pacific predicts converting line failure and optimizes production with AWS

    Georgia-Pacific · Predictive maintenance · AWS

    Georgia-Pacific, owned by Koch Industries, produces paper and tissue parent rolls at manufacturing facilities across North America. The company needed to reduce tears and breaks in converting lines, cut unplanned downtime, and predict equipment failure 60–90 days in advance. To address the challenge, Georgia-Pacific built an AWS-based advanced analytics solution centered on Amazon S3, Amazon EMR, and Amazon SageMaker. Real-time machine data was streamed into a central S3 data lake, transformed with EMR, and used to train ML models that recommend optimum machine speeds and detect risk of failure. The solution also helped Georgia-Pacific consolidate disparate production data and expert knowledge into a centralized analytics approach that more experienced operators and central support teams could use to improve production decisions.