Manufacturers have traditionally relied on descriptive and diagnostic analytics to get insights into inefficiencies in their operations. But these reactive technologies focus on what went wrong after it happened. Go from reactive to proactive with ProcessMiner’s AI predictive analytics. With autonomous process control capabilities, ProcessMiner automates corrective action and prevents process inefficiencies before they happen. Increase yield, improve product quality, minimize human errors, and reduce waste.




THE OLD WAY OF RUNNING PROCESSES Traditional technologies use descriptive and diagnostic analytics with a reactive focus on what happened and why it happened.


THE PROCESSMINER DIFFERENCE Powered by 2,000+ prediction models and proprietary algorithms, our AI analyzes your historical and real-time data and closes the loop on data-driven proactive corrections, preventing process inefficiencies.

ProcessMiner brings together all your process data to give you a single view of your plant. Detect inefficiencies undercutting product quality. Find flaws increasing scrap and waste. See the most efficient ways to use raw materials. And highlight the deviations that undermine your operating efficiency. All with ProcessMiner's closed-loop control of your machines with continuous, intelligent process automation.  

Process Data Connectivity Continuous access to machine data is provided through ongoing connectivity to the historian and production IIoT devices, which store, log, and unify your data. Data Fusion OT is mapped and enhanced, giving you a holistic view of operations. Advanced Analytics/Machine Learning Dashboard tracks your main KPIs. Predictions become more accurate overtime via adaptive machine learning capabilities. Continous Optimizization Modeling prediction prescriptions provide accurate predictions on production process outcomes and delivers continuous automated improvements. Autonomous Control IIoT devices and controllers provide recommendations based on your data, giving you autonomous control of your machines.

Closed Loop


Clean, relevant, and actionable data is key to process optimization. Our platform monitors all your historical and real-time data to make sure your server is powered with trustworthy data you can act on. Data analysis uncovers gaps, faulty sensors, and needed adjustments to improve your processes. Connected to sensors across your plant, ProcessMiner reviews plant data 24 hours a day, 7 days a week.


With the modeling complete, ProcessMiner’s machine-learning algorithms use the aggregated data to spot inefficiencies and problems before they occur. Paired with AI, our platform then forecasts what will happen during the production run, equipping you with recommendations to improve operational efficiency and quality.


ProcessMiner provides easy-to-read suggestions on improving processes and performance measures. Simply log into your dashboard for an overview of your unique performance metrics. Recommendations are listed according to their potential impact and are aimed at helping you improve product quality, increase yield and throughput, and reduce waste and energy.

Closed Loop

With each observation, recommendation, and adjustment, ProcessMiner’s machine learning and AI gain more information regarding your processes. It uses these insights to perfect future predictions and forecasts and optimize processes over time so you can improve quality, minimize the use of raw materials, reduce defects and errors, and cut costs. 


Advanced machine learning models adjust to the constantly changing data our sensors identify in your manufacturing processes. Our self-learning, self-adapting AI was built to improve over time. The longer ProcessMiner is used, the more it improves.


With good, clean data behind it, our software applies advanced data science and machine learning techniques to your manufacturing operations, giving you new insights into how to improve your processes and drive ROI. From Microsoft Azure to Litmus, our clients use AI-delivered insights to improve product quality, cut down on waste, hit their sustainability goals, and save up to 500k per year per production line on average. 

Our goal is to deliver low-cost implementation in eight weeks or less.


Our manufacturing data experts do their due diligence to understand how to best tailor ProcessMiner to your unique operational needs and current performance. Together, we will define the scope and KPIs, covering the metrics that matter most to you and your business.


We review your current and historical processes and production data to ensure they're reliable, relevant, and of high quality. After reviewing the data and making any necessary changes to help ensure quality standards and reliability, we move to implementation.


You can only improve what you measure. Our team digs into the results to understand the impact of our recommendations. Once we secure the consensus of your team, you sign off and we deploy to production.