PMMI Business Intelligence’s White Paper “Data Acquisition, Sharing, and Utilization” explores the obstacles and benefits of integrating digital connectivity and data utilization into manufacturing operations to enhance operational efficiency.
PMMI Business Intelligence: Data Acquisition, Sharing, and Utilization
PMMI Business Intelligence’s White Paper “Data Acquisition, Sharing, and Utilization” reveals the results of the Vision 2030 panel session at PACK EXPO International. It highlighted the significance of Overall Equipment Effectiveness (OEE) as a key metric for tracking efficiency and data utilization in manufacturing operations. OEE, which focuses on availability, performance, and quality, is often the starting point for companies integrating data capture into their processes. The panelists emphasized the importance of real-time data collection for predictive maintenance and efficiency improvements while acknowledging the challenges posed by legacy equipment and cost constraints.
How to Achieve Effective Data Utilization?
The collection of real-time data can guide predictive maintenance, as well as yield management and efficiency improvements. Quality data plays a crucial role in OEE, especially for end users; however, legacy equipment, cost constraints, and variable data sources often complicate scaling, making collaboration with OEMs essential for seamless data capture and interoperability. Panelists underscored the need for gradual implementation, ensuring projects secure management buy-in and focus on capturing quality data that genuinely influences performance. They also highlighted the importance of stakeholder support to ensure the most effective use of data, citing the provision of suitable modern PLCs to legacy machinery as an example. “Data is the new oil,” said one A data-centric mindset must flow from the top down to effect change and encourage the use of analytics. All stakeholders must understand the significance of the data they receive and record. Measuring output was seen as the most effective data capture process, as it provides information that can be extracted from most machines, even older models. It is also a necessary step before implementing data processes such as predictive or prescriptive maintenance.
One speaker recommended cultivating a culture of Statistical Process Control (SPC) at both the macro and micro level, explaining: “At the end of the day, operators are going to know what’s going on, but using technology, specifically SPC, at that micro level to engage leadership to say, ‘Hey, there’s something going on in this area. Go look at it.’ And then at the macro level, again, using SPC on top of a data set to say this is noise or this is actually something that we need to pay attention to.”
At the micro level, operators should be able to assess data and alert managers to any issues. Analyzing a data set using an SPC method allows tactical leadership and management to identify issues, enhancing maintenance and efficiency. Panelists stated that, where possible, dedicated teams should be created for data capture and analysis.
Scaling digital connectivity and data capture
A key topic among participants in the panel and roundtable sessions was how to enhance data capture processes across more extensive areas of their operations. A representative from a large FMCG (fast-moving consumer goods) multinational shared how they executed a 31-plant rollout for a new data capture process within a year. The company will now roll out 17 additional factories over the next eight months. By employing a gradual implementation process, where they refined their OEE platform in close collaboration with their SaaS (Software as a Service) provider, they leveraged the experience gained from the initial deployment to replicate the same process through parallel workstreams at various plants. The end user emphasized that in-person deployment leads to improved results, greater engagement with tools, and more effective user-developed applications for the OEE platform. Standardization was recognized as crucial for gathering data from diverse sources (including multiple vendors or OEMs in the end user’s situation) and facilitating solutions' scalability.
For CPGs, it was seen as imperative that they standardize software programs on different equipment, using systems such as SCADA (Supervisory Control and Data Acquisition) or MES (Manufacturing Execution Systems) to ensure that technology is interoperable. Some participants also flagged the need for OEMs to allow end users access to machine-generated data as a key issue. Following the first roundtable discussion on how to scale digital technologies, participants were asked to vote on which of the suggested solutions they would most likely implement when looking to scale digital technologies across multiple lines, plants, and regions. The most popular solutions were ‘translating data into understandable information’ (46%) and ‘creating standards and using data to improve [operations]’ (41%) - demonstrating a clear desire for data literacy and effective utilization. The third most popular solution was the ‘gradual rollout of technology’ (26%).
Integrating legacy equipment into data systems
A major barrier to achieving standardization discussed during the roundtable session was the difficulty of integrating legacy equipment into data connectivity networks and data capture processes. Challenges regarding non-standardization are more prevalent with older machines, particularly concerning compatibility with cybersecurity systems or platforms. However, it was suggested that legacy machines could be outfitted with devices that would connect them to digital connectivity networks. A key theme in the discussion was the need to simplify data capture while ensuring data accuracy. Recommendations included installing low-cost sensors and maintaining flexibility in data systems to accommodate diverse protocols from various equipment types. The importance of fostering a cultural shift by engaging employees and clearly demonstrating why data is being collected through actionable insights was emphasized.PMMI Business Intelligence: Data Acquisition, Sharing, and Utilization
Although integrating legacy machinery into digital networks was identified as an obstacle to effective data use, it was also noted that most packaging machinery is easier and cheaper to upgrade than commonly perceived. Participants voted on the results after presenting ideas from the roundtable discussion about the digital integration of legacy equipment into operations. The top three solutions were to implement only what’s necessary to get the job done (47%), upgrade existing automation (42%), and use low-cost sensors (28%).
The white paper highlights the critical need to embrace digital transformation and encourage collaboration across the packaging and processing industries to optimize operational performance. Companies can improve efficiency and minimize machine downtime by standardizing and scaling data processes. Success lies in implementing advanced technologies and fostering a company-wide culture that values data as a strategic asset and understands how it is captured and useds a strategic asset and understands how it is being captured and utilized.