Predictive analytics
to increase quality,
uptime and yield

We use machine learning in our analytics solutions to solve your biggest problems.

Prevent downtime
before it happens

Predict the probability of asset failure and optimize maintenance and repair schedules to minimize disruptions and extend the life of assets. Predictive analytics detects asset anomalies and helps prevent problems before they happen. 

Improve quality
and reduce costs

Accelerate your time to market by predicting and preventing quality issues. With predictive quality analytics, you will quickly identify root causes for the issues, allowing you to weed them out by making right changes to specs. 

Increase yield and
optimize throughput

Ensure quality consistency, reduce waste and increase throughput by tracking quality metrics across the shop floor. Our solution predicts and prevents production waste by identifying areas of loss and prescribing focused actions that reduce product defects and inefficiencies. 

5 key steps to drive performance with data analytics



a problem



data sources



and monitor production performance



the root-causes of bottlenecks



your people to act on the insights


Identify a problem to solve

The key thing to a successful Smart Factory implementation is correct problem identification. When the problem is well defined and understood, it’s possible to start looking for an optimal solution. Equally important is to make sure from the start what you will do with the results and how solutions can be implemented in practice. What decisions will be driven by the insights? What actions will be taken? Together with our experts, goals, KPIs and actions are set to ensure customer success.

The main challenges customers are trying to solve:

  • Lack of visibility
  • Unplanned downtime
  • Unstable product and process quality


Connect and combine
your data sources

Predictive analytics, like any other analytics process, relies on data. To ensure accurate analytics, you need to extract enough accurate data of your systems. That’s why it’s so important to connect all your data sources, as you cannot make good decisions based on a fraction of data.

Elisa Smart Factory connects to any data source (individual machines, PLC, MES and ERP system etc) and location. Elisa Smart Factory provides an option for a traditional data warehouse approach. With Elisa Smart Factory, it is possible to leave parts of data in the source systems and do queries, produce aggregates and return results to user applications in real time.


  • More agile and cost efficient than traditional data warehouse
  • Supports rapid development iterations (weeks instead of months)
  • Easy to add new data to the solution in each iteration


Visualize and monitor
your production performance

Are you planning today’s production goals based on yesterday’s throughput? You are not alone, many companies are trying to make decisions unaware of the problems that will show up today until tomorrow.

Luckily there is another way. Elisa Smart Factory empowers you to make decisions based on real-time insights, combining machine and sensor data with data from systems like MES and ERP. This eliminates time delays between the systems, so if, for example, a rush order is placed in the ERP, the shop floor is immediately aware of it and can start planning daily production accordingly. Furthermore, data regarding material availability, inventories, and current machine condition is visually available at all times, further enhancing the visibility and understanding of the big picture. This makes it easy to identify bottlenecks in production and reduce unplanned stops.

By using Elisa Smart Factory, shop floor supervisors can change decision making from being reactive to proactive, as they have timely, accurate, and relevant information ready at their fingertips, requiring no IT knowledge or extra work to extract.


  • Empowers proactive decision making
  • Always real-time insights
  • Provides a big picture understanding of your current situation


Analyze the root causes
of your bottlenecks

We start with the problem to solve: what do you want to know about the future based on the past? Challenges we typically solve relate to quality improvement, waste reduction, as well as equipment optimization. Regardless of the industry vertical or challenges, the common characteristic for all the organizations is the need to find insights more quickly and make better and decisions faster.

Business improvement begins with data analysis. Elisa Smart Factory goes beyond traditional rule-based systems by using AI / machine learning to identify anomalies and patterns proactively. Its algorithms analyze all relevant information including sensor data, SCADA data, MES and ERP data, plus structured and unstructured data. It identifies irregular or unusual patterns across the data sources based on statistical understanding of their performance. It is good for catching process-wide issues and identifying local failures before they cause damage.


  • Reduce unplanned downtime with optimized maintenance scheduling
  • Boost overall Equipment Effectiveness by optimizing the production conditions of each machine and process
  • Maximize throughput by identifying process bottlenecks, and testing alternative setups without affecting production
  • Reduce defects and improve product quality with accurate predictive quality and optimized machine performance


Empower your people
to act on the insights

A connected factory is ultimately useful when it drives changes. By using predictive analytics, anomalies can be quickly detected and fixed and maintenance schedules can be optimized based on need. This reduces unplanned downtime, lowers maintenance costs and creates cost savings.

Elisa Smart Factory enables everyone in an organization to see and understand data, with offerings for every user type – even without any technical skills. Management, supervisors and shop-floor staff can quickly interact with and collaborate on insights, drill-down into details to find their own answers.

There is no limit to the kinds of data-driven improvements that become possible. Actions may range from sending a simple command to a machine, to tweaking operational parameters, to performing an action on another software system, to implementing company-wide operational improvement programs. Other types of changes may include optimizing process to reduce waste and bottlenecks, replacing equipment and adjusting staffing. Once you start collecting data automatically and gaining visibility, finding operational opportunities and making changes is a natural next step.


  • Empower employees to engage and make better decisions
  • An easy-to-understand visualization with relevant views for different roles
  • Speeding up and improving the quality of decision-making processes
  • Digitizing your production will provide you with a valuable base for benefiting from the future technologies and

Customer Case Study

Discover how we helped a global pharmaceutical manufacturer detect anomalies and predict yield

Find out the root causes of your production bottlenecks