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The Challenges of DRIP Syndrome in Manufacturing
In today's increasingly digital manufacturing landscape, the abundance of data is both a boon and a bane for businesses. The phenomenon of being Data-Rich but Information-Poor — commonly referred to as DRIP — is a significant hurdle that manufacturers must overcome to achieve the benefits promised by digital transformation. While manufacturing plants typically generate vast amounts of data every day during the production process, the ability to transform this data into meaningful insights often remains elusive. This disconnect between data collection and data utilization hinders decision-making and results in lost opportunities to improve operational efficiency.
DRIP in the manufacturing sector stems from several key issues. In most cases, it's the result of data being siloed within various departments and systems, leading to a lack of holistic visibility for the enterprise. In addition, manufacturers oftentimes find themselves overwhelmed with the volume of data generated from different sources — from machine sensors to operator inputs. This data often remains underutilized due to the lack of integration and contextualization, making it challenging for manufacturers to extract actionable insights that drive meaningful change and improvement across their operations.
Tackling DRIP and turning data into insights is not just about collecting more data, but rather it's making better use of the data that’s already being collected. Manufacturers must shift their focus towards effectively integrating, contextualizing, and analyzing it in order enhance decision-making, improve process efficiencies, and ultimately strengthen their competitive advantage.
The Need for Data Contextualization in Digital Transformation
In addressing these challenges, the DIKW (Data, Information, Knowledge, Wisdom) hierarchy emerges as a helpful framework for understanding the need for contextualization. This hierarchy illustrates the process of transforming raw data into actionable insights, a fundamental pillar of digital transformation. For example, consider the process of monitoring a machine on the shop floor.
Data: 100 units/hour — This represents the raw output data from a machine, indicating production speed.
Information: 100 units/hour on Machine X — Adding context to the raw data by associating it with a specific machine, it becomes information.
Knowledge: 100 units/hour on Machine X with a target of 120 units/hour — With additional context, in this case from scheduling, we now understand that Machine X is underperforming, which gives us knowledge about the machine's efficiency.
Wisdom: Machine X needs to be optimized to meet the 120 units/hour target — By understanding the implications of knowledge, we realize that maintenance or some other intervention could help Machine X achieve its production target.
As the example illustrates, sensors on a machine alone can only collect data on its performance. But, when integrated with contextual information like production schedules from an MES or maintenance history provided by your operators, you can start to proactively address potential breakdowns or bottlenecks. This predictive capability is a step change from traditional reactive approaches, allowing manufacturers to preemptively address issues before they escalate into costly downtime.
The concept of interconnected systems to achieve data contextualization goes far beyond just machine monitoring. By breaking down silos and integrating data across various business systems — from your ERP to SCADA and quality systems — manufacturers are able to gain a comprehensive, real-time view of their operations. Being able to go all the way from raw data to deep insights that enable predictive decision-making and strategic planning at scale is foundational to digital transformation. And the key to achieving this level of informed decision-making is contextualization.
Best Practices for Making Data Actionable
Drawing from the real-world lessons shared in our recent webinar, "Contextualizing Data in Manufacturing: A Roadmap to Success," here are three best practices you can implement to effectively contextualize data and leverage it to improve your operations.
1. Contextualize at the Source
Rather than trying to piece data together from different sources after the fact, the true value of data is unlocked when it is coupled with relevant context at the moment it is recorded. For example, data captured from a machine should be associated with identifiers like the machine ID, operational status, and time stamps. By integrating this data with work order or scheduling data from an MES, for example, you can gain an even more holistic view of how your operations are running. DataOps solutions and frontline operations platforms can play an instrumental part in this process by facilitating the aggregation of data from diverse sources to add layers of contextual information.
2. Empower Operators to Add Context
Your production data should not only be contextualized with data from your other business systems, but also from your operators on the shop floor. Traditionally, they would capture data manually and record it on a whiteboard or clipboard, and then periodically enter it into a spreadsheet for analysis — opening up many opportunities for human error to be introduced. With digital tools, this data collection can be automated, eliminating errors and freeing up your operators from doing menial data-entry tasks. These operators are intimately familiar with the production process, and by providing digital interfaces for them to capture data and add context in real-time on the shop floor, you can unlock a whole new spectrum of insights into your operations.
3. Break the ‘Document’ Mindset
Historically, manufacturers have taken the approach of handling and viewing data as static, isolated, and often paper-based records or documents. This mindset is characterized by reliance on manual processes for data entry and analysis, and a tendency to consolidate data into fixed formats like PDFs or printed reports, which are periodically reviewed and archived.
This approach has a number of downsides. Documents typically act as static snapshots of data, lacking real-time relevance and often becoming outdated quickly in a fast-paced manufacturing setting. The process of manually creating, updating, and managing such documents is time-consuming and prone to errors. Critically, documents often reside in specific locations or systems, making it difficult to share and access information across the enterprise or enable for real-time decision-making.
By contextualizing data right at its source and leveraging your operators’ expertise, manufacturers can overcome the challenges of DRIP syndrome. This strategy paves the way for data-driven decision-making and strategic planning, allowing you to realize the exponential improvements promised by digital transformation.
The Path to Data-Driven Manufacturing
Transforming manufacturing data into actionable insights that provide real value is pivotal for modern manufacturers grappling with the challenges of being data-rich but information-poor. By integrating data across systems, empowering operators to add real-time context on the shop floor, and moving beyond the static ‘document’ mindset, manufacturers can address problems proactively and achieve new levels of productivity.
These strategies represent not just incremental improvements; they are essential for maintaining a competitive edge in the rapidly evolving manufacturing landscape. The future of the industry hinges on this transformation, where data becomes an invaluable asset for achieving continuous improvement and innovation.
Contextualizing Data in Manufacturing
Check out our on-demand webinar for further insights and real-world examples on how to turn your production data into real value for your business.