Helping customers define, design, and deliver solutions that deliver outcomes, putting the Industrial Internet to work.Download the 2019 Digital Transformation Playbook
You’re going to need more than imagination. You’re going to need ideas that move your business forward—ideas that work, and there’s plenty of inspiration if you just look around.
Start here, with real-world examples of digital transformation in action. GE Digital’s clients exemplify the spirit of digital transformation and operational excellence. They are imaginative, inspired, and constantly in motion.
GE Digital is proud of the results our clients are achieving with our software.
Tested and proven
Our software applications are used by more than 19,000 industrial customers around the world.
Our install base includes manufacturing, chemicals, steel, automotive, food & beverage, consumer packaged goods customers and beyond.
Data and Insights
Our software brings the right insights to the right team at the right time, from the field to the plant floor to the C-Suite.
Smarter decisions in real-time
From mobile solutions to executive dashboards, your team can make informed decisions every step of the way.
Outcomes that matter
Understand your options and deploy real-world solutions that move your business forward.
Plan for tomorrow
Understand some of the forces driving digital transformation today and the potential value that lies ahead.
SIG: Packaging systems and solutions for food and beverage
Food and beverage consumers increasingly seek safe, sustainable, affordable, and differentiated products. Producers face pressures including supply chain complexities and ever-shorter production cycles.
SIG understands what’s at stake. The company’s customers fill more than 10,000 unique products into SIG packaging across 65 countries worldwide. In any given year, SIG produces tens of billions of carton packs.
SIG is now deploying GE Digital’s Predix Asset Performance Management (Predix APM) across more than 400 customer factories worldwide—as part of an end-to-end platform that provides data-driven intelligence for better predicting, managing, and servicing the entire lifecycle of SIG filling lines. The move is helping SIG and its customers reimagine their supply chains, enhance quality control technologies, and evolve their portfolios.
Procter & Gamble: Global consumer packaged good manufacturer
Procter & Gamble (P&G) has been a long-time customer of GE Digital’s Plant Applications Manufacturing Execution System (MES). The company is now leveraging GE Digital’s Predix Manufacturing Data Cloud (MDC) capabilities, moving manufacturing data and running powerful analytics in the cloud.
Used in concert with a traditional MES, Predix MDC gives manufacturers operational analysis in the cloud and greater flexibility of deployment, helping reduce the size of on-premise systems and make them run more efficiently.
Procter & Gamble has already gained a detailed, data-supported view into its manufacturing processes, providing insights that drive efficiencies. The new offering is helping the company meet data compliance regulations and significantly increase the speed of its on-premise MES.
Polinter: Petrochemical company
Polinter was looking to streamline various data sources—to reduce costs, improve operational excellence, and remain competitive. The company recognized the need to adapt its quality management program into an asset performance management (APM) solution.
One critical part of merging Polinter’s quality management and APM programs together included incorporating a reliability process into existing quality management processes. Polinter developed four sub-processes: asset definition and classification, asset strategy management, asset performance evaluation, and recommendation management. These processes are driven by APM from GE Digital and support a continuous loop of improvement.
Developing a single APM program incorporating both quality and asset management helped Polinter drive multiple benefits—including improving the mechanical availability in two out of three of its plants.
These are just a handful of the opportunities, challenges and technological tools that currently exist in market and it’s enough to make a manufacturer’s head spin.
These aren’t just buzzwords, though. These concepts and technologies represent the possibility for real and new value for manufacturers. The potential is huge, and many industry leaders are seeing demonstrable value from them already as they connect machines to the IIoT.
Cost savings, improved productivity, reduced waste, optimized maintenance processes, new business models and improved customer service are all in the mix when it comes results. So how do you get from here to there? The path to unlocking new value typically includes three key steps:
This step allows you to develop real-time visibility into asset performance across devices.
With a thoroughly connected and digital-oriented enterprise, you can begin to capture more data and make it actionable.
Now you can use your data as a differentiator—leveraging relevant real-time insights to reduce delivery time and increase throughput, for example.
It’s about getting visibility into plant operations, boosting productivity, enabling flexibility, accelerating time to market, and meeting customers’ needs.
Take things to the next level with machine learning and AI, allowing you to enable value-based service differentiation and pricing.
One example: Leveraging advanced technology to optimize maintenance schedules and anticipate potential downtime before it happens.
As smart and IoT-enabled technologies become more prevalent throughout manufacturing sectors, the growth of data presents a litany of challenges among both information technology (IT) and operations technology (OT) professionals.
While IT professionals oversee information processing, hardware, and software installation and monitoring and data collection, OT professionals understand the factory floor, the sequences of events during production, the nuances of each machine, and the overall structure of the physical facility.
Mend the gap
As the Industrial Internet of Things becomes more commonplace, IT and OT are beginning to overlap. Many organizations understand the potential and challenges of big data, but there is an added layer of complexity when it comes to making data beneficial for both IT and OT professionals. Often, the challenge is as simple as IT and OT not speaking the same language—each often operating with a different set of guiding principles and goals. When the left hand doesn’t know what the right hand is doing, inefficiencies and confusion can emerge.
Software products, such as those developed by GE Digital, can help mend this gap by aggregating data to ensure that both IT and OT professionals have it available at their fingertips throughout the factory floor and within their network of facilities—with no need to wade through impossibly large data pools.
Today’s software and storage solutions can organize data, output automated reports, and create digital twins to support IT and OT needs alike. By standardizing data capture, analysis, and output, you can provide greater transparency and understanding among peer sets—making information more manageable and more easily available. And that’s just the beginning. As you deploy new solutions and progress along your digital transformation journey, the potential for true IT/OT convergence, streamlined business operations, and optimized production increases.
Artificial intelligence remains a hot topic when it comes to digital transformation—encompassing a broad set of technologies and applications, including machine learning.
Integral to how many leading industrial organizations operate today, machine learning allows computers to learn and improve without being explicitly programmed. It leverages data to intelligently automate processes and decisions, learning from past outcomes and optimizing activities through a “closed loop” approach—with feedback driving continuous self-learning.
Incorporating machine learning tools can be a game-changer—helping you efficiently automate repetitive events, free up human time, save costs, identify issues sooner, and improve operations overall. Start with these insights for leveraging machine learning more effectively.
Get your data ecosystem in order
Having clean, sufficient, trustable data is crucial. The better your data, the better the model you can build. You likely have tons of data archived or sitting idle as a “byproduct”—data you can use.
Remember people are still important
Data scientists, analysts, and others will guide your intelligent automation strategy, manage data, and support the model.
Build or buy your own tools
Time-to-value should drive your decision-making. (Do you have the resources and time to build your own capabilities?).
You can start small
For example, machine learning for just one component process. But see the bigger picture. Have an end-state vision of where things could go.
Find ways to show value quickly
Gain internal buy-in and to learn important lessons with quick wins.
Use machine learning to move the needle
Don't generate obvious observations or recommendations. Do your homework. Quantify the potential value. And when making the business case, bake in the impact of continuously improving results.
Powerful industrial time-series data collection for on-premise and cloud-based storage and analysis
Drive smarter operator decisions with model-based high performance HMI for faster response and development.
Reach manufacturing excellence through Industrial IoT insights and intelligence.
Consolidate and transform manufacturing data across plants for cloud storage, analysis, and analytics.
Optimizing the performance of assets to increase reliability and availability, minimize costs, and reduce operational risks.
Reduce waste, improve yields and increase revenue and margins by optimizing the performance and throughput of your lines, plants and enterprise