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Scraitec

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Software Status:Active

About Scraitec

Scraitec by Resolto Informatik by Resolto Informatik is an advanced Internet of Things platform focused on condition monitoring, predictive maintenance, and industrial analytics.

Scraitec Details

Vendor
Resolto Informatik
Year Launched
2003
Location
Resolto Informatik GmbH Schillerstraße 16, 32052 Herford
Deployment
cloud, web browser
Training Options
Countries Served
All Countries
Languages
English, Spanish, French, German, Italian, Portuguese, Dutch, Russian, Japanese, Chinese, Korean
Users
Administrator, Developer, Data Analyst, End User
Industries Served
Healthcare, Education, Manufacturing, Retail, Transportation, Agriculture, Energy, Construction, Hospitality, Government, Automotive.
Tags
IoT, internet of things, connectivity, data management, automation, smart devices, real-time monitoring, analytics, remote control, sensor integration, device management

Scraitec's In-App Market Place

Does Scraitec have an in-app market place?

Yes

How many Mini-Apps in the marketplace?

0

Mini Apps

Pricing Options

Free trial
Free version
Request a quote
Promo Offer

Accepted Payment Currencies

USD ($), EUR (€), GBP (£), JPY (¥), AUD ($), CAD ($), CHF (CHF), CNY (¥), SEK (kr), NZD ($), KRW (₩), RUB (₽)

Pros & Cons

  • Empowers OEMs to generate new revenue streams through innovative AI-based service business models.
  • Leverages existing manufacturing data, requiring no massive overhaul of current data collection infrastructure.
  • Secures consistent product standards by proactively detecting factors that lead to quality loss.
  • Minimizes operational expenses by optimizing energy consumption and avoiding expensive peak load bottlenecks.
  • Prevents costly unplanned downtime by identifying equipment wear and potential failures before they happen.
  • Reliance on a cloud-based SaaS model may raise data sovereignty concerns for some sensitive industrial sectors.
  • The predictive nature of the software requires a period of baseline data collection to define "normal."
  • Initial integration with older legacy manufacturing plants could present technical compatibility challenges.
  • Implementation may require significant technical expertise to align domain knowledge with machine learning models.
  • Effectiveness depends heavily on the quality and consistency of the existing data being collected.

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