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Squeezing More Value Out of Manufacturing Data

Manufacturing Intelligence (MI) projects are initiated to improve manufacturing operations by turning data into actionable information that drives business results.

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How can manufacturers extract maximum value out of their manufacturing data?

Technological advances in automation, instrumentation and networking over the past several decades has resulted in a tremendous amount of diagnostic data being available for consumption. A typical manufacturing system easily can contain millions of data points. Sorting through this data to determine how to best use it creates unique challenges, including:

• Data Overload: With the massive amount of data available, finding what you need, when you need it, becomes a frustrating exercise that limits effective decision-making.
• Untimely Data: Looking at yesterday’s data is not always conducive to improving today’s operations.
• Lack of User-Based Data: A manufacturing enterprise contains various users who want to extract value from this data, including production, engineering, plant management, quality control, purchasing, corporate management, etc. However, each user requires a unique view of the data.
• Lack of Context-Based Data: It’s beneficial to see a data metric, such as a Key Performance Indicator (KPI), trended along with other variables that might affect it, but often that is not part of the KPI calculation.
• Inaccurate Data: Reporting inaccurate data typically leads to lack of user confidence in the data. Bad data is worse than no data. Several potential causes for this exist, including manual data collection, poor configuration, mislabeling of data points and not understanding what makes up each data point.

Manufacturing Intelligence addresses challenges

A well-thought-out plan to address the above challenges is essential for delivering expected business results. The plan should focus on how to convert the data into useful information. But who determines what information is useful? Every manufacturer needs to determine what information is required to enable better decision-making. This approach focuses the effort on the users’ needs, not the endless data points available. A typical plan might consist of the following steps:
1. Identify users and requirements for each user role.
2. Identify how each user will use the requested data to improve operations.
3. Identify KPIs required and standardize for apples-to-apples comparisons.
4. Identify data sources (PLCs, HMIs, Historians, ERP, CRM, WMS, LIM, etc.) to access.
5. Identify data presentation interfaces, such as dashboards, Web pages, HMIs and smartphones that best suit each user.
6. Select software solutions to collect, integrate and present the information.
7. Identify gaps in infrastructure and technology for achieving project goals.
8. Identify and execute pilots to test any areas of concern and provide users with a test drive.
9. Roll out across all manufacturing sites.

User Requirements. Representatives from each user group (i.e. operators, maintenance, quality, safety, plant management, corporate, IT, etc.) should be interviewed to determine what data can enable them to make better decisions in their day-to-day functions.

How Data Will Be Used. During the discovery period to determine user requirements, the focus is on how each information requirement will translate to improving the business. This ensures that everyone keeps the goal in mind at all times. Once manufacturers understand and document each user requirement and improvement opportunity, they have a framework for what success means for their projects.

Standardized KPIs. KPIs provide a common benchmark for the plant’s metrics and allow meaningful comparison of data. For example, having a different cost-per-case metric for each manufacturing system or plant causes confusion and doesn’t allow effective comparison. Getting everyone to speak a common language allows for better understanding and provides the framework for improvement.

Data Sources. Context-based information might require data to be pulled from various sources to provide comparative analysis. For instance, energy usage data is meaningful when compared to production information such as production counts and products run. In addition, a cost-per-case metric, for example, might require data labor costs from the ERP system, production counts from the historian, parts costs from asset management and energy costs from an external source.

Data Presentation. Technology now allows the same information to be presented easily in a variety of interfaces, including Web pages, smartphones and large LCD displays. Manufacturers should evaluate selected technologies for each data requirement and user group based on how each consumes information and reacts to it.

Software Solutions. MI software such as FactoryTalk(R) VantagePoint from Rockwell Automation provides functionality that allows manufacturers to bring live data from multiple disparate sources to integrate and present data in meaningful ways. The software provides a common interface to view and analyze the data without having to access different software interfaces separately for KPIs, such as cost per case that requires data from multiple systems.

In addition, FactoryTalk VantagePoint integration into Microsoft SharePoint allows for even greater access to business intelligence data and leverages SharePoint’s core capabilities of collaboration, information sharing, content management, search, blogs, wikis and security.

Gap Analysis. Once the future state is identified, the present state should be evaluated to determine what infrastructure and technology gaps need to be addressed to access the desired data and present it as planned.

Proof-of-Concept. Prior to rolling out a full-scale deployment, test any areas of concern that could affect functionality delivered by the MI project. A pilot test typically is a small-scale implementation for this purpose. In addition, a pilot provides manufacturers with a system test drive so they can obtain better feedback before rolling out the complete system.

Rollout. Once pilots are complete and lessons learned are incorporated, a rollout strategy can be developed to deliver project requirements to all identified users. A continuous improvement plan should be part of the rollout to ensure the system continues to be optimized based on user feedback and as needs change. Ideally, the manufacturer assigns a champion to lead this effort.

MI is a valuable tool for driving operational improvements. Understanding best practices, avoiding status-quo pitfalls, obtaining alignment from all stakeholders early in the project, and committing to a disciplined plan for executing these projects will help ensure success and deliver the promised results to the business.

By Richard Phillips, Manufacturing Intelligence Product Manager, Polytron, Inc., Duluth, Georgia, is a Rockwell Automation Solution Provider and a system integrator with areas of expertise that include process systems; power, automation and information; manufacturing intelligence; technology transfer and training; machine automation; food and beverage manufacturing; and water wastewater treatment.

For more information on Manufacturing Intelligence solutions, visit

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