Evaluating Use Cases
Evaluating and choosing the right projects for our level of digital readiness
A Comprehensive Guide for Manufacturers
Use this step-by-step book as your guide to a successful digital manufacturing journey.
Now that you understand what data readiness is about, what would you like to use your data for? What production-related improvements are you eager to make? Which uses cases, issues, or problems do you want to apply digital techniques to solve?
As you can see above, digital capabilities open up a universe of possibilities, once you have them in place… that last phrase being the key. A common misstep manufacturers make is pursuing projects they’re not yet ready for. Another problematic scenario is having various groups pursue diverse use cases, each using different data. For digital projects to scale — and for techniques like AI to be brought to bear — it takes a single source of data that digitally represents your full production process. This is the all-important advantage of building a unified data platform.
At the outset, align the digital goals you’d like to achieve with what you’re ready for. Assess your current capabilities, the elements you can build out in the near term, and the kind of use cases you can support across functional teams so that everyone moves forward in unison.
Exactly How Ready Are You?
From our experience with numerous manufacturers, we’ve identified 5 categories of readiness, with different use cases feasible for each.
- Connection Zone. At this stage, you have sensors on some production machines but not all the important ones, and are not yet capturing all relevant data. Some or all of your data streams are not networked, and there is no central repository. Cloud and data security strategies have yet to be defined.
- Visibility Zone. Here, most of your machines have sensors and are networked. IT leadership is on board and providing support and resources. You have operational people who understand how sensor data maps to production processes. Some collection processes remain to be automated, and data aggregation and harmonization capabilities need to be deployed. Change management expertise does not yet exist at the plant level.
- Efficiency Zone. Companies at this stage are capable of taking on projects that deliver significant operational and profitability impact. The technical connectivity is in place to capture machine downtime, defect, and batch/part serial numbers data, and information is flowing to a system of record. While plant leadership is committed to harnessing data to drive operational execution, analytics and application development resources are not in place at the plant level.
- Advanced Analytics Zone. Data scientists and engineers are available to extract and interpret insights using analytic tools, and IT staff has the knowhow to develop custom applications for the factory. Business process change resources are on hand but there is only limited support for multi-plant and supplier transformation.
- Transformation. At this most advanced stage, comprehensive technical excellence is present across the extended organization, including suppliers. Corporate, plant, and IT leadership are committed and aligned for transformation. Factory-level and corporate change management functions — including communications, business process, training, and analysts — are established and aligned.
Determine your Readiness Zone
Our Digital Readiness Index tool enables you to assess your stage of preparedness, facility by facility. Take the questionnaire, and the tool will use your responses to calculate your readiness zone.
Once you’ve ascertained what you’re ready for, take a look at these zone-specific suggested use cases.
Quick-Win Digital Projects for Each Zone
The first order of business is to get data flowing: connecting key sensors to the network and capturing all the data produced. At this level, plant personnel need to increase their understanding of what the data means, mapping out what each sensor describes about machine and product conditions at each step in the manufacturing process.
- Build offline data acumen
- Develop foundational data visibility
- Automate data capture
- Create a digital twin
At this level, it’s important to aggregate data into a global view of real-time production operations, so performance can be monitored and controlled to reduce scrap rates.
- Create a global operations view
- Access plant information
- Monitor OEE and KPIs
- Compare KPIs across the enterprise
- Benchmark plant, line, and machine performance
- Measure and trend quality and performance
- Monitor out-of-control processes and failures
- Automate collection of regulatory production data
- Set up alerting and notification of production challenges
- Monitor actual output vs. planned
- Improve machine performance
- Statistical process control
- Parameter relationships
Manufacturers at this readiness stage are able to move beyond visibility and apply analytics to improve efficiency and quality. You can increase profitability by addressing stubborn scrap and quality problems, and by optimizing processes to boost productivity.
- Track machine performance using the OEE metric
- Set up part traceability
- Perform high-level defect analysis
- Drive and measure continuous improvement efforts
- Analyze out-of-control processes and failures
- Measure and increase capacity utilization
- Analyze and reduce scrap
- Determine root causes
- Increase first-pass yield
Advanced Analytics Ready:
At this level you have the technical and organizational readiness to deploy projects with a high impact on operations and profitability. Apply predictive analysis to deliver advanced notification of impending downtime or defects, and use advanced statistical techniques (such as multivariate regression, factor analysis, decision trees, and clustering) to identify root causes of persistent problems. Potential benefits include increased production, reduced cycle times, and lower scrap rates.
- Deploy advanced statistical techniques to identify root causes
- Predict out-of-control processes and failures
- Implement track and trace
- Monitor and optimize energy use in production
- Perform predictive analysis
- Implement extensible analytics
Companies with these sophisticated capabilities are ready to utilize analytics learnings to develop transformational business models such as capacity-based pricing. Analysis can also be extended across supply chains, using third-party data to transform relationships with suppliers and customers.
- Establish cross-system analysis
- Perform machine/line level costing analysis
- Set up data-driven design to value
- Create supplier optimization programs
- Develop real-time capacity-based pricing
Choose and Vet Candidate Use Cases
Knowing your readiness zone doesn’t mean you’re limited to only those projects we suggest. However, the zone attributes help you understand the prerequisites to work on — understanding that’s critical for establishing budget and timeline expectations.
Choose one or more of these or similar use cases that line up with your readiness zone. Discuss them with your teams to see which inspire the greatest support and promise to deliver the highest value.
In the next section we’ll provide guidance in prioritizing your selected projects: determining and comparing the business value of each, and developing a roadmap for implementation.