No more excuses — new tech busts 8 myths about factory process optimization, allowing manufacturers to act.
Consider this: A large scale manufacturer with 160,000 employees and 16,000 suppliers serves more than 1,000 customers in a given year. It operates over 100 production facilities in 30 countries with more than 15,000 unique machines. These pieces of equipment were acquired from a plethora of vendors over a period of years, even decades, and use many different operating systems.
For an operation of this size, even a modest process improvement could have a significant impact on the bottom line. To make those gains, manufacturers need access to all of their data in order to gain insight into where improvement is possible. Implementing the enterprise-scale data automation solution that can make this happen may seem too complex and impossible to manage.
The technology exists to make factory process optimization possible. The opportunities are groundbreaking and put to rest the idea that data is too difficult to access or won’t generate actionable insights.
Innovative tech allows for innovative action.
New machine data technology can tackle this problem, such as what we have implemented in the ArchFX Platform. It extracts data from any machine type, new or legacy, unlocking and enabling real-time and predictive analytics. In one deployment, during the initial trial phase, data was extracted and analyzed in less than 24 hours from a factory machine that engineers had been working on without success for six months.
Extracting data is just the first step. To be successful, manufacturers must shift paradigms and do things differently than in the past. Historically, companies failed to find solutions because only a fraction of the available machine data was collected and made available globally. Arch Systems takes a different approach. In addition to calculating top-level KPIs, ArchFX collects all the rich machine data that a human SMT expert would need. It provides detailed descriptions of every operation performed and errors encountered — leaving humans to do the creative problem-solving that fuels innovation.
Accessing all machine data is too much work.
Outdated thinking: Accessing all machine data is too much work.
New paradigm: Technology exists to extract rich machine data from every piece of equipment on the line. To support human experts in doing more of what they want and less of what they don’t, Arch offers an advanced analytics module, Sessions Analysis.
Sessions Analysis extends and accelerates investigations into performance issues on SMT production lines. The combination of rich machine data, SMT domain expertise, and advanced algorithms empowers ArchFX to do this automatically for every line at once. Sessions Analysis eliminates the need for any manual data entry, including “standard cycle times” or manual annotations of “performance loss reasons.” The flexibility of the module allows for adaptations in a variety of other factory environments.
Arch solutions scale to any size operation and have been deployed at enterprise scale with dozens of sites across SMT and mechanicals production lines with over a thousand machines connected across hundreds of lines globally. In addition to feeding utilization analytics, the growing data lake provides a foundation for developing new insights. By combining in-house and customer data, scientists and operational experts can form hypotheses about the root causes of problems and opportunities for increased performance and retrospectively validate them using the historical data lake.
Quality is at 99 percent, so improvement is impossible.
New paradigm: The first part may be true, but the second surely isn’t. The tools and processes of industry 3.0 allowed 60 percent, 50 percent, even as low as 30 percent efficiency and asset utilization, and sometimes lower. That is lost money. With the power of data and Industry 4.0 tools like the ArchFX Platform, there is another horizon of efficiency to aim for. By leveraging the ability of advanced analytics to inspect millions of events per day across every machine, line, and site worldwide, manufacturers can continuously identify opportunities for improvement across all their key performance indicators.
New machines are a must for Industry 4.0.
Outdated thinking: New machines are a must for Industry 4.0.
New paradigm: Both new and legacy machines can provide rich data. Even many of the oldest legacy manufacturing machines have an attached database that can be tapped with a software database connector to get useful data. Customers are regularly surprised by the useful information pulled from unexpected machines.
It will take weeks or months to connect machines.
Outdated thinking: It will take weeks or months to connect machines.
New paradigm: In today’s technology landscape, it’s possible to rapidly instrument machines of any kind across a manufacturer’s entire global manufacturing infrastructure– all without any disruption to production. In many cases the process takes only minutes.
Machine software must be the latest version.
New paradigm: As long as the software provided by SMT equipment manufacturers functions, the machine will produce data. It’s true that newer software generally makes it easier to get usable data quickly, but useful data can be extracted from a variety of legacy interface versions—Arch Systems does it all the time.
Losses are due to lots of little problems, and it’s not cost-effective to fix any of them.
New paradigm: In reality, modern factory analytics excel at prioritizing problems by impact. They rapidly identify low-hanging “bad apples” that can be targeted for improvement. Seeing the breakdown enables operations to identify the most efficient path to improvement and effectiveness. Modern analytics enable manufacturers to precisely zero in on the easiest ways to make the biggest improvements.
By the time a company identifies problems, it is too late to intervene.
New paradigm: Manufacturers sometimes worry that they won’t be able to identify recurring problems that can be fixed proactively. In reality, the common problems on manufacturing lines by definition occur frequently, so manufacturers can focus on rapidly identifying them to improve performance as quickly as possible. Better still, machine learning makes predictive maintenance models possible.
Organizational boundaries will prevent fixes.
New paradigm: It is true that moving to Industry 4.0 requires a higher level of internal communication and interaction. But Industry 4.0 adoption brings global visibility into data and performance statistics and can help to surface these resource conflicts. Communication built on data can help correct for mismatched incentive structures, making it easier to collaborate on solutions. The latest Industry 4.0 manufacturing productivity solutions not only gather all the necessary machine data but also present the results of analysis in an understandable way for management to take action.
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