HCM: Harnessing AI to Reduce Machinery Downtime

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Eiji Fukunishi, VP and Executive Officer and President of the Mining Business Unit at Hitachi Construction Machinery
Hitachi Construction Machinery has invested US$3m in Canadian tech firm Rithmik Solutions in a bid to reshape strategy and increase productivity

Hitachi Construction Machinery has committed US$3m to Rithmik Solutions, a Canadian technology company, in a move to enhance the use of artificial intelligence (AI) in large-scale earthmoving.

The investment strengthens Hitachi's digital strategy for heavy equipment and supports more productive, efficient and sustainable site operations.

Rithmik Solutions, led by CEO Ross Barichievy, develops AI tools that analyse machine data in real time. These tools are designed to detect abnormalities in heavy equipment before failures occur.

Masafumi Senzaki, President and Executive Officer at Hitachi Construction Machinery

AI-driven equipment health monitoring

Rithmik’s specialism is analysing large-scale operational data to improve the health monitoring of heavy machinery.

In a year-long demonstration, the company gathered data from 40 dump trucks and six ultra-large hydraulic excavators. The test evaluated how Rithmik’s models compare real-time operational conditions against design-based benchmarks.

This comparison allows Rithmik to precisely identify machine anomalies. Early identification enables site teams to intervene before damage escalates, helping to reduce fuel use and unplanned downtime. Rithmik Solutions reports that this "contributed to improved productivity and reduced environmental impact". 

For Hitachi Construction Machinery, this AI-based analysis is a key step in the digitalisation of machinery. Instead of relying on static design values, the AI sets reference points based on live operating data. This innovative approach uses real site conditions to define what is optimal for each individual machine.

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Integrating AI with digital platforms

The investment aligns with Hitachi’s digital platform, LANDCROS Connect Insight, launched in April 2025.

LANDCROS collects near real-time machine data from work sites and uses it to support operational decisions. By integrating Rithmik’s AI tools, Hitachi aims to offer more intelligent and tailored solutions.

This strategic investment also strengthens Hitachi’s ambitions to expand its digital solutions globally. Hitachi plans to "provide more flexible solutions to our customers as well as contribute to increased productivity and reduced environmental impact at mining sites" through the partnership.

Eiji Fukunishi, Vice President and Executive Officer at Hitachi Construction Machinery, says: "This investment marks an important step towards Hitachi Construction Machinery's goal of building open digital platforms with various partners."

He adds that integrating Rithmik's AI will support a wider range of applications.

Mining operations rely heavily on heavy equipment such as excavators, trucks and loaders. Picture: Getty Images

Shaping digital-first construction

Major projects rely heavily on equipment like excavators and trucks, which generate vast volumes of sensor data. Analysing this information allows operators to predict equipment failure, understand inefficiencies and manage energy use more effectively.

With tools like LANDCROS Connect Insight and Rithmik's AI models, Hitachi is giving construction firms real-time insight into equipment conditions.

For Rithmik, the partnership opens the door to international scale. 

Ross states: "We’re honoured to collaborate with a company that shares our dedication to innovation, operational excellence and collaboration.

Ross Barichievy, CEO at Rithmik Solutions

"By leveraging HCM’s global presence with Rithmik’s expertise in mining analytics, we aim to deliver exceptional performance, reliability and sustainability to customers worldwide. This partnership is a key development in the advancement of smart mining."

By applying machine learning directly to operating data, Hitachi and Rithmik are working to move the sector away from standardised assumptions and towards condition-based optimisation that reflects real-life use on site.

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