Digital transformation advisors, supply chain experts, smart manufacturing engineers, data and applications architects, and manufacturing business consultants showing manufacturers how systems should work and how to get the ROI you expect.
Over the years, our smart manufacturing consultants and systems engineers have narrowed down the top three reasons why manufacturing companies say they turn off OEE software:
Part one of the Planning, Scheduling, and OEE Mighty Trio series provides a brief summary of what OEE is and explains how one of the OEE data components – efficiency – can have a meaningful impact on planning, scheduling, and optimization. This edition explores using unplanned downtime data from OEE, and OEE efficiency data by SKU for planning, scheduling, and optimization.
This article is part two of a two-part series. Continue reading, or check out part one now.
Is your company using overall equipment effectiveness (OEE) data for better planning, scheduling, and optimization? Production schedulers are typically masters at applying available information in creative ways. However, some production planners are unsure how best to put OEE data to work in their production scheduling software to improve and optimize the manufacturing production schedule. If your company has an OEE system to record unplanned downtime, micro stops, and other reasons for capacity loss, you have access to a rich source of efficiency data. Every machine, line, and work center using the OEE system can benefit even more from your schedule optimization efforts if you apply the OEE data for a better production schedule. The key is understanding what the OEE data means and how to use it for even better planning, scheduling, and optimization.
This article is part one of a two-part series. Continue reading, or check out part two now
Data, particularly machine data, is a foundational component of every smart factory or OEE initiative. It fuels analytics, triggers actions ahead of problems or shutdowns, and provides insight for continuous improvement. Many companies start the smart factory journey with an automated OEE system because it’s a natural progression with a set of metrics they’re already familiar with. Despite advances in automated data collection and production analytics, a lot of manufacturing executives are still at odds about exactly which machine data is required to calculate OEE. Unfortunately, a lot of projects get sidetracked early on as stakeholders try to utilize every piece of machine-generated data from the multitude available. A more pragmatic approach is to apply machine data based on priorities around business goals. To that end, capturing the machine data required for OEE is a critical early activity for most smart factory initiatives.
If your manufacturing business has ever survived a crisis — supply chain disruption, market demand fluctuation, distribution problems, or perhaps even a natural or economic catastrophe — it had an uncommon opportunity to shine through the adversity. More importantly it gives you a chance to learn from the outcomes generated by the organization’s responses to the disaster. We don’t know any that are eager to flush it all away — not just the performance gains, but the opportunity to do even more with the unexpected wisdom.
Recently, the Toward Zero team examined some effects of organization culture on manufacturing execution system (MES) decisions. In response to our article, “MES Tug of War: The Battle Continues,” Farukh Naqvi succinctly lays out ISA’s ongoing work to develop a framework and methodology for “solving conflict among the three diverse stakeholders” of an MES effort. While the ISA-95 framework delivers perspective on system integration and the thousands of actions and data points throughout a manufacturing enterprise, it also inadvertently reveals the need for enterprise-wide cross-functional collaboration.
An overwhelming number of manufacturing execution system (MES) deployments fall short of the finish line; in their wake, they leave partially implemented and, therefore, ineffective solutions. A typical outcome: stalled digital transformation and a budgetary freeze for additional IIoT projects. It’s not an uncommon scenario for MES projects, yet most manufacturing companies seem unaware that many MES deployments fail to hit expectations.
We see it constantly: “corporate” insists on one brand of MES, while the manufacturing plant wants something different. The execs running the show regionally or globally don’t trust plant management’s choice, while the plant believes corporate’s pick won’t work for them ― because “their situation and production environment is different.” This kind of battle isn’t limited to one or just a few industrial sectors ― indeed, automotive manufacturers, life sciences companies, food and beverage manufacturers, and consumer products manufacturers (also known as CPG companies) around the world have all seen and are at risk when the “MES tug of war” ensues.
Over the past several years, many large manufacturing companies, at some level, have invested in a digital manufacturing initiative. With a typical budget of $500K to $5M, these projects range from a strategy for smart manufacturing to implementing a manufacturing execution system (MES). Successful MES projects have delivered significant value and ROI. When considering a smart manufacturing project today, it's important to know what designs have proven effective and more importantly, what hasn't worked.
Over the years, our smart manufacturing consultants and systems engineers have narrowed down the top three reasons why manufacturing companies say they turn off OEE software:
Part one of the Planning, Scheduling, and OEE Mighty Trio series provides a brief summary of what OEE is and explains how one of the OEE data components – efficiency – can have a meaningful impact on planning, scheduling, and optimization. This edition explores using unplanned downtime data from OEE, and OEE efficiency data by SKU for planning, scheduling, and optimization.
This article is part two of a two-part series. Continue reading, or check out part one now.
Is your company using overall equipment effectiveness (OEE) data for better planning, scheduling, and optimization? Production schedulers are typically masters at applying available information in creative ways. However, some production planners are unsure how best to put OEE data to work in their production scheduling software to improve and optimize the manufacturing production schedule. If your company has an OEE system to record unplanned downtime, micro stops, and other reasons for capacity loss, you have access to a rich source of efficiency data. Every machine, line, and work center using the OEE system can benefit even more from your schedule optimization efforts if you apply the OEE data for a better production schedule. The key is understanding what the OEE data means and how to use it for even better planning, scheduling, and optimization.
This article is part one of a two-part series. Continue reading, or check out part two now
Data, particularly machine data, is a foundational component of every smart factory or OEE initiative. It fuels analytics, triggers actions ahead of problems or shutdowns, and provides insight for continuous improvement. Many companies start the smart factory journey with an automated OEE system because it’s a natural progression with a set of metrics they’re already familiar with. Despite advances in automated data collection and production analytics, a lot of manufacturing executives are still at odds about exactly which machine data is required to calculate OEE. Unfortunately, a lot of projects get sidetracked early on as stakeholders try to utilize every piece of machine-generated data from the multitude available. A more pragmatic approach is to apply machine data based on priorities around business goals. To that end, capturing the machine data required for OEE is a critical early activity for most smart factory initiatives.
If your manufacturing business has ever survived a crisis — supply chain disruption, market demand fluctuation, distribution problems, or perhaps even a natural or economic catastrophe — it had an uncommon opportunity to shine through the adversity. More importantly it gives you a chance to learn from the outcomes generated by the organization’s responses to the disaster. We don’t know any that are eager to flush it all away — not just the performance gains, but the opportunity to do even more with the unexpected wisdom.
Recently, the Toward Zero team examined some effects of organization culture on manufacturing execution system (MES) decisions. In response to our article, “MES Tug of War: The Battle Continues,” Farukh Naqvi succinctly lays out ISA’s ongoing work to develop a framework and methodology for “solving conflict among the three diverse stakeholders” of an MES effort. While the ISA-95 framework delivers perspective on system integration and the thousands of actions and data points throughout a manufacturing enterprise, it also inadvertently reveals the need for enterprise-wide cross-functional collaboration.
An overwhelming number of manufacturing execution system (MES) deployments fall short of the finish line; in their wake, they leave partially implemented and, therefore, ineffective solutions. A typical outcome: stalled digital transformation and a budgetary freeze for additional IIoT projects. It’s not an uncommon scenario for MES projects, yet most manufacturing companies seem unaware that many MES deployments fail to hit expectations.
We see it constantly: “corporate” insists on one brand of MES, while the manufacturing plant wants something different. The execs running the show regionally or globally don’t trust plant management’s choice, while the plant believes corporate’s pick won’t work for them ― because “their situation and production environment is different.” This kind of battle isn’t limited to one or just a few industrial sectors ― indeed, automotive manufacturers, life sciences companies, food and beverage manufacturers, and consumer products manufacturers (also known as CPG companies) around the world have all seen and are at risk when the “MES tug of war” ensues.
Over the past several years, many large manufacturing companies, at some level, have invested in a digital manufacturing initiative. With a typical budget of $500K to $5M, these projects range from a strategy for smart manufacturing to implementing a manufacturing execution system (MES). Successful MES projects have delivered significant value and ROI. When considering a smart manufacturing project today, it's important to know what designs have proven effective and more importantly, what hasn't worked.