STEP 01 - 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:
STEP 02 - 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.
STEP 03 - 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.
STEP 04 - 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.
STEP 05 - 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.