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Ten success factors for implementing OEE systems and data-driven production

We have accumulated many success factors after guiding hundreds of factories around the world to implement data-driven production into their operations.

The following ten success factors are compiled together with customers whom our experts have guided.

1. Don't start with a technology focus, such as databases and machine signals

It's easy for anyone to start focusing on technology when discussing factory digitalization, industry 4, and technical solutions. Avoid this pitfall. Look at the technology as an enabler Focus on your organization's requirements and what information is needed to support your business processes.

Let the information needs guide the requirements on the technical solution. This approach increases the likelihood that your factory will achieve the required behavioral change to reach your strategic targets.

2. Make information available at the right time and in the right way

It is paramount to create relevant reports and visualizations, supporting your current business processes. For each role in the organization, answer the question of what information requirements there are at different times (morning meetings, shift changes, ongoing deviations, weekends, and more). For example, set-up up alarm escalation, shift reports, daily, weekly, and monthly reports.

Distribute the report to the right people in the right way. For example, share some reports using e-mail, display other reporting data on TV-screens throughout the factory, while some reports get printed automatically to be used on whiteboards as a decision basis in daily meetings.

Carefully consider the time resolution for different reports and recipients. A production manager might want detailed information for an ongoing shift. In contrast, production and factory management might require overview data for a month.

3. Tailor the reports to the recipient

One data-driven production basic lesson is to provide relevant information at the right time. This method ensures that the responsible role can perform the right activities in the right way.

Configuring reports and data visualization with this in mind can create a better understanding of the current situation or problem.

If operators are not familiar with the OEE (Overall Equipment Efficiency) concept or other composite metrics used, then avoid using these terms when presenting information to operators. Instead, communicate information in a way that is relevant and understandable to drive desired behaviors leading to increased efficiency. For example

  • actual status compared to plan (both time difference and approved amount),
  • average time between stop and average stop times,
  • deviations from your standardized working methods.

A practical detail to keep in mind is to visualize target and actual results such as produced volume and net volume in a unit of measure that is natural for the operators. Not necessarily in the same unit the product is produced (kg, ton, amount, and more), but probably the unit in which operators produce (for example, cartons or pallets).

4. Eye on the prize - Find your factory's OEE culture

OEE is a powerful tool for increasing value-adding activities and improve resource efficiency. However, only striving to improve the OEE value can lead to sub-optimizing if the OEE metric gets regarded as more important than real improvements.

Therefore, when OEE is used to identify losses, it's paramount to take all losses and deviations into account. This way, OEE will be an objective metric. In a healthy OEE culture, the value itself does not have an intrinsic value. Which OEE metric is considered a good one always needs to relate to the capacity you need to serve your customers at the right cost. OEE is an "enabler" to create more capacity or reduce cost, whichever is required.

5. Use a precise OEE model with accountability for different roles

All mature production organizations have clearly defined roles and responsibilities. By using a precise OEE model, you can identify specific losses in detail. Well-known models such as "6 Big Losses" or "16 Major Losses" can be a great starting point to provide a precise understanding of how much each loss limits production, and which role is responsible.

6. Apply an easy-to-use model

If you do not have a precise and well-thought-out structure for losses, there is a risk that follow-up can be hard to understand for both production management and operators.

A key lesson for many factories is to keep loss reasons and station codes apart. Let loss reason codes describe WHY a loss occurred. The station code shows WHERE the loss occurred. This approach makes it easier for everyone to understand. Otherwise, there is a significant risk that you will get a vague understanding of losses, redundant data, and overall resulting in data harder to manage and understand over time.

By splitting loss reason codes and station codes, operators will have a simpler user interface with few choices. At the same time, analysis and identification of improvements will have a more accurate description of the underlying loss root causes.

  • WHERE do we have the most disruptions, and WHY do we have the most disruptions right there?
  • WHY do we have the most common disorders disruptions, and WHERE do they occur?

7. Documentation creates scalability

Avoid creating a solution relying on a specific person. Document the solution and working methods:

  • Organization and roles
  • Processes and reports
  • System set-up and configuration

8. Develop an implementation plan for quality assurance and training

When introducing data-driven production methods, the initial period is critical. Ensure that the technical installation works as expected before the solution is rolled-out throughout the factory. Conduct regular tests to ensure data quality from both machines and operators are correct and that visualizations and reporting work as expected. A major success factor in our experience is to let quality assurance take the time it needs.

It's critical to have an onboarding plan when introducing data-driven production to your organization — for example, answering the questions of how users should be informed about and trained in the new digital tools?

9. Organize management and support

A system for data-driven production needs a well-thought-out organization for administration and support. For example, to manage operations, support, and problems that might occur for the users.

Set up a model that goes through what mandates different roles have. The cohesive link between the users and the supplier is a system owner in your factory. It is vital to define the tasks for this role. Define how much time is allocated to this role since this person usually has other jobs too.

Successfully working with data-driven production might need tasks and responsibilities defined for other roles too. Clearly describe the job, time allocation, and what training is need for the role.

Quick checklist for starting with data-driven production

  • Ensure data quality (bad data kills the system)
    • Times and quantities 
    • Losses without specified reason 
    • Scheduling
    • Discard and rework
    • Manual reporting
  • Create and maintain forms and checklists
  • Provide the organization with visualizations and facts for recurring meetings
  • Adding new articles
  • Change optimal cycle times after improvements
  • Perform analysis and produce facts in specific cases
  • Carry out updates on the product and spread knowledge about features and capabilities
  • Provide first-line support. You may also need to set up processes for handling various support cases (software, server operation, and network, OPC, and machine signals).

10. Network and exchange experiences with colleagues

Inspiration from exchanging experiences with other users of data-driven production systems is often much appreciated and is an excellent source for learning how others have excelled.

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