Increasing efficiency on the production line with Big Data & Analytics
wood-processing machines and systems manufacturer
The world’s leading provider of solutions in the woodworking sector, with thousands of employees and presence in over one hundred countries.
- Data_ Analytics and AI
2017 to present
In the interest of optimizing its machinery, software solutions, and operations, as well as creating new business models, the company decided to enhance its data analysis processes. Due to lack of in-house expertise and their structure based on data silos, which kept information scattered across various formats and locations, they sought a trusted tech partner with proven experience in data engineering.
We started by securely ingesting data from their machines to Azure to ensure uniformity and facilitate processing. We then developed a set of predictive algorithms that enabled data gathering and forecasting. Lastly, together with our partner's team, we developed several user-friendly Power BI reports & analytics and hybrid apps that provide them and their customers with real-time data and facilitate decision making.
With cleansed and validated data and a large part of the production workflow digitized, the company could monitor processes through a real-time data streaming system, leading to less machine downtime and fewer product quality issues. Additionally, production time predictions rose in accuracy from 20% to 80% thanks to the machine learning model we implemented, enabling better data-driven decision-making and planning.
The first step in our collaboration was to learn as much as possible about the company's business objective and how best to help them reach those goals through digital solutions. Together with our partner, we identified areas for improvement and designed a comprehensive solution to address their needs.
The first challenge we tackled was ensuring data quality and uniformity, as well as quick collection and storage in a centralized data lake. For this task, we used Azure. Once we had a better overview of the data and validation procedure in place, we developed predictive algorithms using machine learning to help the partner’s team perform their tasks and gather actionable insights. Then that information could be used to optimize operations and create a positive impact.
With a more dependable stream of data coming from the production line, we created custom reports and analytics in Power BI to help the company monitor its system’s performance. We took great care to make the reports user-friendly and intuitive, so users could more easily spot areas of concern and correct them as needed.
Lastly, we developed a suite of easy-to-use apps that relay useful factory metrics via a web or mobile interface. With this software, the company could gather real-time insights and analyze their impact. Due to more reliable insights, business decisions became easier and with more easily forecasted results.
Successfully integrated data from several relevant sources into a single data lake;
Cleansed and validated a substantial portion of the data;
Developed interactive solutions to provide data visualization and empower data-driven decision-making;
Generated, gathered and cleaned data for developing and deploying one machine learning model in production within 6 months;
Implemented a real-time data streaming system with 99,9% uptime within less than a year;
Reduced machine downtime;
Increased production efficiency and prediction;
Digitized a part of the end user’s production workflow;
Reduced the number of product quality issues.
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WHAT HAPPENS NEXT?
After you submit a contact form on accesa.eu, one of our representatives will review the information and get back to you in 1-2 business days.
We will then assign a Technical Presales expert to have a deep dive and assess your requirements and objectives.
The Presales expert will work with a bid team and a Software Architect to prepare a high level project estimation and the Sales expert will provide you with a commercial offer.
We will get back to you within 1 to 2 business days. We will also provide a proposed project allocation and start date after a minimum of 15 days from the deep dive session.