Representative End User Clients
Representative Automation Clients
Representative Software Clients
Industrial Analytics is the use of advanced data analysis techniques to extract insights and gain a deeper understanding of industrial processes, systems, and operations. It involves collecting and analyzing large volumes of data generated within industrial settings with goals such as optimizing efficiency, improving productivity, and making data driven decisions. Industrial analytics leverages various tools and methodologies to uncover patterns, trends, and anomalies in the data, and provide actionable insights for process improvement and optimization in industrial environments. Real-time analytics has become increasingly important because it enables manufacturers to respond swiftly to changing circumstances and emerging opportunities. Artificial intelligence technologies answer questions, discover insights, and provide recommendations. These systems hypothesize and formulate answers based on available evidence and can be trained through the ingestion of vast amounts of content.
Global Disruption on Many Fronts is Driving Adoption of Industrial Analytics and AI
Whether it is political unrest, climate change, pandemics, or global macroeconomic uncertainty, companies are concerned about their operational resilience, agility, and efficiencies. The biggest costs and pain points that manufacturers face today are supply chain disruptions, materials shortages, labor shortages, order backlogs, rising prices, and shipping and logistics. Almost every company across the globe has felt the impact of market and business disruptions, Industrial Analytics are finding their way as part of a larger company-wide digital transformation strategy.
SAS AI and Advanced Analytics Solutions for Manufacturers
SAS IoT Solutions: SAS Analytics for IoT is an IoT analytics solution with embedded AI and streaming capabilities. As a comprehensive cloud native solution, it’s designed to analyze and derive insights from IoT data for users across the enterprise It helps organizations make fast and accurate decisions at the edge and in the cloud, predict future trends, and optimize processes.
Features include:
Streaming Analytics
Edge Stream Processing
Machine Learning Integration
Edge Analytics
A more complete description can be found on the SAS ESP webpage www.sas.com/esp and SAS Analytics for IoT product page www.sas.com/analytics-iot
SAS Event Stream Processing, SAS’ streaming analytics capability, is a component of SAS Analytics for IoT. It is specifically designed for analyzing and acting on streaming data from various sources, including IoT devices. It allows organizations to identify and respond to critical events in real-time. Features include complex event processing, rule-based processing and scalability.
SAS Viya: SAS Viya is an open, cloud native and cloud agnostic AI and analytics platform that helps companies innovate faster while boosting productivity with high performance analytics and seamless movements across the analytics lifecycle. It serves as the foundation for various SAS solutions providing collaboration among all user types, faster model development and deployment for massive scale (no recoding necessary) and flexibility for analytics processes.
Georgia Pacific’s Analytics Journey with SAS
Georgia Pacific, a leading pulp and paper company has evolved into a diversified manufacturing giant. With a history dating back to 1927, the company has become a leading producer and marketer of a wide array of products, including tissue, packaging, paper, building materials, and chemicals. Georgia-Pacific operates 140 manufacturing facilities across various industry segments, emphasizing innovation, sustainability, and customer satisfaction. Georgia-Pacific’s embrace of technology, exemplified by its use of SAS Analytics, underscores its dedication to enhance operational efficiencies, optimize processes and make informed, data-driven decisions while staying at the forefront of innovation.
In terms of challenges, Georgia-Pacific is no different than any other industrial manufacturer. Up-time of critical assets, quality of products, energy demands, worker safety, shortages, supply-chain issues are always concerns. The company began its industrial analytics journey in 2018. One of the key factors to their success today is the incremental approach and realistic expectations over time.
According to Roshan Shah, Vice President of Collaboration and Support Center Operations:
“Analytics and AI is not an app you download. It is a process where you crawl first and then you walk before you can run. Everyone has a unique set of assets and business opportunity to make applying analytics possible.”
Georgia-Pacific realized there are a lot of vendors out there who claim their pre-packaged solutions simply integrate into any manufacturing environment, but it isn’t really the case, particularly for complex manufacturing environments. In reality, these solutions have to be modified and trained to fit. SAS and Georgia-Pacific worked together to identify a problem statement or a business process outcome and then took incremental steps to get there. Getting 50% of the way there is a good start.
“Maybe version 1 is not perfect, but it chips away at a larger problem. I would argue that analytics is a journey similar to quality, it doesn’t happen overnight.”
Patience paid off for Georgia-Pacific. By taking bite size pieces over time and never losing sight of the business problem at hand, they were able to deploy continuous learning processes and develop even more mature models. Georgia-Pacific has achieved impressive quantifiable results:
30% reduction in downtime
10% Improvement in OEE
Went from 0 models to over 16,000 real-time models in less than two years
Adoption and rollout of computer vision for worker safety
Future Perspectives
In the fast-paced world of manufacturing, Georgia-Pacific has demonstrated that staying ahead requires more than just producing high quality products. By harnessing the capabilities of SAS AI and real time analytics, the company has not only achieved operational efficiencies but has also positioned itself as a leader in leveraging data for a sustainable competitive advantage. As industrial manufacturers navigate the digital landscape, Georgia-Pacific and SAS stand as a testament to the profound impact that advanced analytics are having today and the role they play in shaping the future of manufacturing. The integration of AI and advanced analytics in operations and other areas of the manufacturing value chain is playing a growing and pivotal role in driving innovation, improving processes, and maintaining a competitive edge in the market.