Adding Value to OEE for Manufacturing with ThingWorx Digital Performance Management
Historically, overall equipment effectiveness (OEE) has been a trusted reporting mechanism for visibility into line, plant, and enterprise-wide performance trends. Now, manufacturers are building on the value of OEE with ThingWorx Digital Performance Management (DPM), a best-in-class, closed-loop performance management system that sets and prioritizes efficiency improvements, tracking and validating improvement outcomes across production.
DPM allows you to take OEE initiatives further.
Source: Transforming Operational Excellence white paper
Applications of OEE for Manufacturing
As a common standard for gauging manufacturing productivity, OEE shows your use of available uptime, scrap and energy waste, and process efficiencies related to downtime and changeovers. But even if you follow OEE best practices, you only get a limited view of improvements. For example, rudimentary asset-level OEE measures machine performance—but that doesn’t show the full picture of factory- or plant-level performance. And conventional enterprise-level OEE often misses specific asset-level constraints, meaning you could be focusing resources on the wrong problems.
Depending on your OEE maturity level, business impact is moderate at best—and the challenges of this approach can outweigh the benefits.
Challenges of OEE for Manufacturing
Learn the challenges OEE presents, then click to reveal how their outcomes impact your business.
Challenge
Outcome
Different calculations for different environments add complexity
Misalignment between frontline workers can cause inconsistent decision-making
Percentage-based reporting doesn’t provide a sufficiently accurate view of performance
Percentage-based reporting obscures the relative importance of issues, limiting your ability to improve performance in a meaningful way
Different OEE techniques and methodologies introduce multiple data sources and standards of measurement
Inconsistent data and standards mean siloed corrective actions with no comparative, macro-level insights into performance improvements
Sources:
• https://www.ptc.com/en/blogs/iiot/confronting-complexity-in-manufacturing
• Transforming Operational Excellence white paper
These challenges and their outcomes lead to siloed problem-solving, creating significant roadblocks to efficiency. Click on your OEE numbers below to identify your bottleneck.
[Include a dynamic diagram illustrating OEE data set example from slide 13 of objection-handling deck]
See a representation of DPM in action on your plant floor— your bottleneck has already been identified.
[Include a simple, direct comparison of OEE and DPM on the same production line, building on the first diagram from the same slide of objection-handling deck]
PTC Customer Builds on OEE Initiatives with DPM
A leading electronics manufacturer identified an opportunity to improve its manual data collection process, get more time loss insights, and increase throughput. Building on their OEE foundation, this manufacturer did an initial deployment of DPM with a one- to two-week install and ramp up period and a 14-day duration.
As a result of this initial deployment, the manufacturer identified 29 hours of downtime over the 14-day period. With goals to build on this value, the manufacturer plans to implement DPM across 10 sites in two years and scale to 100% of its sites from there.
Source: PTC customer results
Maximize Manufacturing Productivity with DPM
DPM is a digital solution that removes the limitations of OEE for manufacturing. It empowers your enterprise to problem-solve more accurately, quantifies productivity more comprehensively, identifies specific improvement actions, and continuously measures those improvements. Watch the video below for an overview of how DPM works:
With DPM’s best-in-class, closed-loop approach, your organization will be on track to maximize manufacturing productivity. Learn more about DPM to get started today.
More accurate problem identification and prioritization
More continuous impact validation
More specific improvement actions
More precise root-cause analysis
Gather and standardize all sources of loss for sure-fire prioritization
Use real-time visibility and analytics to go beyond first-glance losses
Get specific recommendations to put more production hours back into your day
Validate corrective actions through closed-loop traceability
Explore DPM Resources
4
3
2
1
Source: Transforming Operational Excellence white paper
ADD DPM
OEE
Problem-centric model doesn’t reveal how improvement actions impact overall performance
Provides closed-loop traceability of improvement actions, allowing you to quantify the financial impact of continuous improvement
prev
Defines improvement actions solely on a single point in time
Provides continuous, prescriptive recommendations for improvement actions through AI
prev
Next
Analyzes plant performance with non-standardized percentages, which are often calculated differently across the organization; measures availability, performance, and quality without accurately capturing 100% of losses
Uses the hour as a common unit of productivity, measuring all the reasons for lost time, in real-time (or at a time you select), at a higher level of granularity
prev
Next
Identifies problems but doesn’t prioritize bottleneck areas
Next
Identifies bottlenecks across the organization over time so you always know when, where, and how to solve the right problem
Why DPM?
While OEE provides a framework to measure performance, DPM amplifies what that framework can do for your business. DPM unlocks the true value of OEE initiatives through actionable insights, putting hours back into your schedule to reduce costs, increase throughput, and enable continuous financial improvements. Compare approaches to learn how DPM adds value to OEE for manufacturing.
Below, click to see the benefits of each feature:
90
120
110
95
Try again. Spending money here will not help your throughput.
90
87
Machine 1:
Try again. Spending money here will not help your throughput.
120
120
Machine 2:
Yes, this is your bottleneck. Spending money here will help your throughput.
95
80
Machine 4:
Try again. Spending money here will not help your throughput.
110
100
Machine 3:
90
95
120
110
Bottleneck is machine 4. Spending money here will not help your throughput.
90
87
Machine 1:
Bottleneck is machine 4. Spending money here will not help your throughput.
120
120
Machine 2:
Bottleneck is machine 4. Spending money here will not help your throughput.
110
100
Machine 3:
This is your bottleneck. Spending money here will help your throughput.
95
80
Machine 4:
Identified 29 hours of downtime
Plans to implement dpm across ten sites in 2 years
Plans to scale 100% of sites
More accurate problem identification and prioritization