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What is hopit Manufacturing?

Hopit Manufacturing provides cutting-edge software solutions designed to help small and medium-sized manufacturing businesses track Overall Equipment Effectiveness (OEE), monitor downtime, and manage production processes in real-time. This innovative software offers instant visibility into the operational status of machinery, current production orders, and essential metrics such as availability, performance, quality, and OEE. It automatically logs instances of downtime, allowing users to add specific reason codes which aid in identifying ongoing losses and potential areas for improvement. Furthermore, hopit Edge facilitates the seamless collection of machine and production data from diverse sources, including PLCs, CNC controllers, and various industrial devices, leveraging protocols like Siemens SINUMERIK, Siemens S7, Beckhoff ADS/TwinCAT, OPC UA, Modbus, and MQTT. By establishing a reliable data foundation, the platform enhances various functions such as reporting, analytics, and AI-driven applications, ultimately driving improved manufacturing efficiency and informed decision-making. Moreover, this comprehensive system empowers manufacturers to take a proactive approach in resolving issues and optimizing their processes, leading to increased productivity and operational excellence. As a result, Hopit Manufacturing stands out as a vital partner for manufacturers seeking to thrive in a competitive landscape.

What is ABB OEE Software?

A cutting-edge software solution aimed at unlocking hidden potential and motivating production teams to enhance equipment utilization, minimize downtime, and improve product quality. ABB’s OEE software enables clients to calculate and analyze Overall Equipment Effectiveness metrics in real-time by utilizing data sourced from their manufacturing operations. It cultivates a company culture where awareness of OEE is prevalent, fostering teamwork towards common production efficiency goals. This tool plays a crucial role in identifying areas that are primed for improvement, while focusing on actions that lead to operational enhancements. It effectively identifies and mitigates production bottlenecks, allowing for rapid interventions that can resolve issues before they escalate into larger problems. Furthermore, it aggregates historical data for thorough analysis and benchmarking and automates the assessment and prioritization of factors that lead to capacity loss, thus ensuring ongoing operational progress. This holistic methodology not only boosts immediate performance but also lays the groundwork for long-term sustainable growth, ensuring that organizations remain competitive in a dynamic market. Ultimately, the software serves as a catalyst for continuous improvement and innovation in production processes.

Media

Media

Integrations Supported

AWS IoT Core
Azure IoT Hub
HiveMQ
Thingsboard

Integrations Supported

HPE OneView
Mainsail
MyEtherWallet (MEW)
Rayven

API Availability

Has API

API Availability

Pricing Information

Pricing not provided
Free Trial Offered?

Pricing Information

Pricing not provided

Supported Platforms

SaaS
Windows
On-Prem
Linux

Supported Platforms

SaaS

Customer Service / Support

Standard Support
Web-Based Support

Customer Service / Support

Standard Support
Web-Based Support

Training Options

Documentation Hub
Online Training
On-Site Training

Training Options

Documentation Hub
On-Site Training

Company Facts

Organization Name

HEAP Engineering GmbH

Date Founded

2019

Company Location

Austria

Company Website

hopit.at/en/manufacturing.html

Company Facts

Organization Name

ABB

Date Founded

1988

Company Location

Switzerland

Company Website

new.abb.com/industrial-software/operational-excellence/mom-software-process-production-intelligence/overall-equipment-efficiency-oee

Categories and Features

OEE

Downtime Tracking
Historical Reporting
Performance Metrics
Real Time Reporting
Root Cause Analysis
Trend Analysis

Categories and Features

OEE

Benchmarking
Historical Reporting
Performance Metrics
Quality Control
Real Time Reporting
Root Cause Analysis

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