The perfect factory – one that produces only high-quality products, with lines running as fast and efficiently as possible 100% of the time – does not exist. All factories experience downtime and quality issues that result in rework and scrap every now and then, with many running at only 60% overall equipment effectiveness (OEE). That’s the nature of the manufacturing industry—there will always be challenges to overcome and issues to resolve.
For decades, the traditional way of approaching common production challenges such as availability, performance, and quality loss has been through manual analysis, which is both time- and resource-intensive. Fortunately, there’s a different approach available today. Software-defined platforms make continuous production improvements easier to identify and faster to implement, advancing manufacturers’ digital transformation efforts and bringing them several steps closer to the “perfect” factory.
Traditional Methods For Solving Production Challenges
With most production steps executed by a combination of people and machines – along with the reliance on third-party vendors to supply quality components – there are many opportunities along the production cycle for something to go wrong. Typically, assembly line challenges center around three different categories.
Availability Loss: There’s unplanned downtime on the assembly line
Availability loss is fairly straightforward. This occurs when there’s not enough of the required material available, either because of scheduling errors or supply chain delays, for example, which causes a station to stop and wait for more material (i.e., a material stall). Availability loss also happens when there’s equipment failure that causes downtime.
Traditionally, to reduce availability loss, engineering teams will manually review manufacturing execution system (MES) data to better understand how the material replenishment schedule can be improved to ensure there will always be enough materials. Or, for equipment failure issues, they’ll review MES and enterprise resource planning (ERP) data to set a better maintenance schedule so they can avoid downtime with preventive, proactive maintenance on their equipment.
Performance Loss: Assembly lines are not running as efficiently as possible
Performance loss occurs when there are small stops along the assembly line that lead to suboptimal performance.
Operators themselves can become a bottleneck. Say an operator is working at a station that should have a cycle time of 30 seconds, but they’re taking 32 or 35 seconds to complete the step; that time adds up quickly and can significantly impact performance in the long run. One way to minimize operator-related performance loss is by manually collecting data from sensors and stopwatches to review where and why delays are occurring. From there, the team can revise work instructions to streamline the station and make it less prone to slowdowns. Or, in some cases, they might discover certain operators are better suited for some stations than others based on skill set and experience, so they need to rework the schedule to better match up operators and stations.
Cycle times have a direct impact on performance loss as well. Typically, when a line is getting up and running, the manufacturer will start with safer, more conservative cycle times as they work out any issues. Later, as product demand ramps up, they’ll look for ways to increase the speed across all lines. The engineering team will review MES data and run various experiments to understand where and how to increase cycle time without impacting quality. Then, as cycle times improve, they might learn that certain assembly lines have become underutilized and can be consolidated to improve OEE.
Quality Loss: Assembly lines are producing under 100%
Quality loss occurs when there are issues along the assembly line that impact the quality of the end product. This typically results in either defects (i.e., when a product doesn’t pass its quality assurance [QA] tests) or returns (i.e., when the end customer returns the faulty product). Test time during the QA process could also impact quality loss by introducing too many redundant, unnecessary tests that don’t have an impact on yield.
To reduce quality loss, operations teams typically review failure modes to determine what caused the defect, which could be a component from a supplier or a certain station that’s having issues, and then make changes to fix the root cause. They might also collect and review quality management system (QMS) data to improve yield by refining tooling and processes, benchmarking certain suppliers, and/or removing redundant tests.
How a Software-first Approach Enables Continuous Improvement
Traditional methods for understanding where and why availability, performance, and quality loss are occurring are slow and tedious. They require extensive manual data collection and analysis, and until solutions are identified, issues will continue to impact OEE and overall production performance.
A platform-based approach that leverages flexible, intelligent, software-defined automation simplifies and streamlines everything. By creating a truly connected factory, users anywhere on the network gain access to the data they need to quickly identify and solve challenges. The data itself is served up within an automated analysis, with algorithms running in the background to enhance continuous improvement efforts.
The data that’s analyzed is also vast, so each unique team and role has the information they need to make decisions quickly. For example, platform-based approaches gather and monitor product data such as design files, components, consumables, serial numbers, product status, and return data as well as equipment data such as configuration, readings and measurements, events, settings, standard logs, and equipment status. Process data, including flow, tools, inspection results, settings used, and people involved, is also captured. The data is condensed into a view that makes the decision-making process smarter, better, and faster.
Today’s manufacturers don’t have time to manually collect and analyze data for all-too-common assembly line issues — not when other more prominent challenges, such as ongoing supply chain disruptions, labor shortages, and reshoring efforts, need to be prioritized. It’s time to embrace modern, intelligent solutions that are designed to streamline production and enhance OEE by providing the insights and information needed for continuous improvements. This ultimately allows teams to focus on bigger problems and better opportunities that will get them closer to the dream of the perfect factory.
Watch this video to learn more about how software can help improve OEE.
About Bright Machines
Headquartered in San Francisco, Bright Machines is a technology company pioneering an innovative approach to intelligent, software-defined manufacturing automation. It leverages a full-stack approach to fundamentally change the flexibility, scalability, and economics of production. Bright Machines operates R&D and Field Operation centers in San Francisco, Tel Aviv and Guadalajara, with additional support in North America, Asia and Europe. Bright Machines is reimagining how products can be designed and produced to address the realities of today and the future ahead. Rethink everything you ever knew about manufacturing. Visit www.brightmachines.com.
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