How Digital Tech is Moving the Needle on Manufacturing Productivity
Advanced technologies like AI, the Industrial Internet of Things (IIoT) and data analytics are routinely hyped for driving transformative efficiencies and productivity gains. Yet the reality is most manufacturers are relatively early in the process of developing their digital agenda.
One factor contributing to this is how manufacturing productivity, the metric associated with how quickly a company can turn out goods, has been stymied by rising customer demand for bespoke products.
“It’s easy to get better when you’re doing the same thing all the time, but that can change as soon as variability hits,” explained Keith Chambers, vice president for manufacturing solutions at Aveva. “It takes more time to deal with new variations, products, formulations or customer requirements. It has a huge impact.”
To address this and other production related challenges, manufacturers are implementing an expansive digital technology agenda. Despite a turbulent business climate, 98% of global manufacturers surveyed for Deloitte’s Digital Maturity Index survey have started a digital transformation journey compared to 78% in 2019. Respondents said technology investments accounted for 30% of their 2024 operating budgets, with cloud, generative AI and 5G connectivity topping the list of digital technologies successfully delivering ROI.
Even with ramped-up digitalization efforts, manufacturers’ maturity in this area remains all over the map. Some have made great strides connecting plant floor assets and harnessing data and advanced analytics to fuel overall equipment effectiveness (OEE) and predictive maintenance applications that result in productivity gains. Others are experimenting with cutting-edge technologies like generative AI, augmented and virtual reality, as well as digital twins to deliver new experiences designed to help operators work smarter and faster.
Outside of these leading-edge examples, however, far too many manufacturers are still heavily rooted in manual processes, with productivity hamstrung by brownfield production operations, legacy infrastructure, and siloed data and automation systems.
“Lots of things show great promise, but we’re not yet seeing a leapfrog in efficiency and productivity gains today,” noted Tim Shope, vice president of digital transformation solutions at Endress+Hauser, a manufacturer of process instrumentation and automation gear. “There is still of work to be done.”
With productivity targets squarely in manufacturers’ sights, industrial players are coalescing around a broad spectrum of digital transformation technologies. Following are four technology categories, often working in concert, that have been proven to deliver productivity gains.
IIoT-connectivity and cloud platforms
Manufacturers are deploying a growing constellation of smart machine assets and IIoT connectivity to access machine data reflecting availability, energy usage and performance levels, among other metrics. In the cloud, this data is analyzed and contextualized for data-driven insights that affect actions taken at the asset, plant or multi-plant level.
One example of this is Endress+Hauser’s Netilion cloud-based IIoT ecosystem, which connects the physical and digital worlds to collect and store what was previously islands of interoperable process instrumentation and asset health data. With the Netilion platform, these former data silos are opened and contextualized to deliver a holistic view of asset performance and health, including metrics gauging productivity, sustainability and energy consumption, according to Tim Shope, vice president of digital transformation solutions at Endress+Hauser.
Netilion’s digital service portfolio includes: Netilion Health, for access to multi-brand asset health monitoring and diagnostic data used for predictive maintenance and remote diagnostics; Netilion Value, for access to temperature, volume flow and other measurement metrics to manage operational quality and compliance; and Netilion Analytics, which provides transparency into technical information on every device deployed in pursuit of operational efficiency.
In one pilot example, Endress+Hauser developed a remote diagnostic system using the Netilion platform, digital services and API connectivity to automatically detect device issues and create work orders in a CMMS (computerized maintenance management system). As each work order is approved, the platform securely transfers documentation illustrating the proper corrective action to a technician in the field on a mobile device. It also establishes a secure channel that allows the operator to complete the work on the intended asset. Once the maintenance task is complete, the system is closed, restricting outside access to the equipment.
For industrial companies, the ability to orchestrate corrective diagnostic and maintenance operations remotely is crucial for closing the skills gap. “It’s difficult today to find an instrument technician that wants to put on their boots and jeans and go into a facility every day,” Shope said. “The best and brightest want to fix those issues [remotely] on an iPad.”
Advanced data analytics
For manufacturers, the hurdle isn’t having enough data — it’s contextualizing the data so that it’s useful. “We have all the data out there from devices, but the adoption challenges have been connectivity to devices and building context around the data,” said Todd Montpas, business manager of Industry Solutions at Rockwell Automation.
Historically, this process has been time consuming, requiring manual analysis of large data volumes that often lack context and are missing key relationships. That’s where Rockwell’s Factory Talk Analytics platform comes into play. By accessing data from Rockwell intelligent devices and production automation systems, enterprises can monitor and track asset performance KPIs (key performance indicators) such as downtime, waste, production counts and OEE in a quest to improve asset utilization and overall system efficiency.
FactoryTalk Analytics LogixAI pushes the power of analytics even further with machine learning functionality that helps companies stay ahead of product quality and process issues, once again, minimizing risk of downtime. Unlike other AI/ML-driven analytics offerings that require data science expertise, LogixAI is accessible out of the box to operators and technicians who understand plant floor processes and dynamics, Montpas said.
In one scenario, a consumer packaged goods manufacturer used LogixAI to improve liquid filling machine performance. Using a soft sensor at the edge of the machine, FactoryTalk Analytics LogixAI used a model trained on process variables like pressure, temperature and buffer tank levels to make real-time predictions of dose weight based on current operating conditions to improve control of fill levels. The implementation helped reduce filling errors, minimize costly giveaways and increase plant performance.
Emerson Discrete Automation’s Pro.Lean module, part of its Movicon.NExT SCADA framework, uses equipment data and advanced analytics to elevate OEE (overall equipment effectiveness) and downtime analysis. Through OEE and real-time KPIs (key performance indicators), Pro.Lean provides a holistic view of availability, performance and quality, helping manufacturers drill down into the real capacity of production lines and machinery and address inefficiencies.
"Customers are implementing OEE in one way or another, but looking at it as separate entities doesn’t provide the full picture,” noted Silvia Gonzalez, director of software for Emerson Discrete Automation. Greif, an industrial packaging provider, deployed Movicon.NExT and Pro.Lean to calculate OEE and maximize production across 115 plants. Productivity analysis revealed causes of machine/production line downtimes, enabling Greif to regulate and optimize production efficiency. In addition, dashboards were designed to display real-time data related to work shifts, schedules, OEE, downtime and other KPIs. Between Pro.Lean’s flexibility of deployment and holistic visibility, Greif was able to reach its productivity targets ahead of schedule with a competitive cost for the project, Gonzalez said.
Artificial intelligence
As manufacturers get more adept at data management strategies, especially when it comes to creating interoperability and connectivity between data silos, AI and its subset of machine learning (ML) tech is playing a more prominent role. Deloitte’s 2025 Manufacturing Outlook found that more than 40% of manufacturers plan to increase investment in AI/ML over the next three years, and a 2024 survey of manufacturers by the Manufacturing Leadership Council found that 78% of respondents indicate their AI initiatives are part of the company’s overall digital transformation strategy. Companies are making investments to improve production capabilities and drive better decision making despite expected hurdles surrounding data and AI skills.
As part of Rockwell Automation’s FactoryTalk Analytics platform, AI/ML capabilities are now powering the GuardianAI module, which provides continuous condition-based monitoring capabilities for predictive maintenance. The software alerts maintenance teams to problems related to asset hardware, providing insights into the probable cause of failure and helping to avoid costly downtime.
VisionAI is another AI-driven addition to the FactoryTalk Analytics suite. By taking a no-code approach to vision inspection, non-AI experts can configure cameras, capture images, and train and deploy models to detect anomalies, simplifying quality inspection. (Learn more about recent updates to VisionAI that enable closed-loop quality control.)
Use of generative AI (genAI) tech like ChatGPT and digital assistants is still very nascent in industry, although there is plenty of enthusiasm for the technology to address persistent talent challenges and boost manufacturing productivity. Respondents to the Manufacturing Leadership Council survey said they were experimenting with genAI tools for knowledge management applications to help identify process improvements and to aid in preventative and predictive plant floor maintenance.
Aveva is hoping to capitalize on the interest with Industrial AI Assist, a tool built in collaboration with Microsoft and running on Microsoft Azure OpenAI Service, which provides a virtual expert to help extract insights from scattered data sets to answer complex questions.
“Companies might not have an expert on a particular piece of equipment or process in their plant,” Chambers said. “Industrial AI Assist helps in knowledge sharing between teams and subject matter experts.”
Digital twins
BMW Motoren GmbH, the engine plant and diesel competence center for BMW, turned to Siemens Digital Industries Software’s Tecnomatix to design and simulate production lines comprising machine tools and robot cells, as well as material handling and transport systems. The software helped them rearrange factory floor layouts to optimize tool and machine utilization and accelerate overall production cycles. The plant simulation capabilities, combined with a digital twin of manufacturing processes, also aided in energy analysis to identify potential load imbalances and to optimize utilization of individual machines to achieve energy usage savings. With this approach, BMW was able to achieve 1.66 million kilowatt hours of electricity savings on the shop floor.
But before enterprises can really capitalize on any of these digital technologies, it’s imperative they properly understand the business problems and define their digital transformation goals.
“You must understand who is affected by the problems and bring in the right stakeholders based on the challenges you have,” said Emerson Discrete Automation’s Gonzales. “If you don’t define digital transformation properly at the beginning, your chances of success are slim.”
