Toward Zero Blog for Digital Transformation Best Practices

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:

1. Not enough data - Some OEE systems take up too much operator time to collect data and enter it into the system. Data is often spotty or inaccurate, making the OEE software useless.
   

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

As companies continue to prepare for digital transformation, the debate around which data protocol is better for smart manufacturing systems rages on. The need to capture data from HAAS machines and other manufacturing equipment is a critical component because much of the data required for smart manufacturing originates in your company’s machines, robots, processing equipment, and inspection equipment. In some cases, the data resides in a computer on board the equipment; in many cases, the data sits in a proprietary controller. These data sources use a wide variety of protocols. Many standards organizations have attempted to consolidate communication protocols. A few leaders have emerged, but that perhaps has made decisions about how to capture machine data for smart manufacturing even more complex.

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.

Are manufacturers aware they have hidden production capacity?  Companies that find and free up hidden production capacity can avoid costly equipment purchases, open the door for additional sales, or reduce the time and cost of existing production.

I’ve been a part of organizations that were struggling to satisfy customer demand — we simply could not produce enough product, or at least the right product at the right time. Determining how to resolve the issue would often put the operations engineering and production planning teams at odds. The solution most obvious to engineering was to continue running products in basically the same lot sizes, but at higher speeds. This approach required expensive new equipment. Besides the equipment purchase, there were other significant costs: inventory builds, downtime for installation, debug after installation, and operator training.

Manufacturers serious about improving OEE need to invest in automated data collection. Though the intention of manual data collection is good, this approach doesn’t provide real-time insight or the level of accuracy and detail that an automated data collection can produce. Perhaps more importantly, automated data collection helps you shift employees’ attention to high-value work like running machines and solving problems that hinder operational performance.

Manufacturing companies are still eager to use OEE.  Some industry analysts say that it's “dead,” but not everyone in manufacturing agrees.  Yes, it’s high-level, and as a standalone metric it’s not particularly actionable.  However it’s also a powerful measurement that nearly anyone in a company can quickly digest and use as a starting point to uncover why things aren’t going the way they’re supposed to.  That’s not to say that OEE hasn’t been the subject of significant debate — and even angst — since it first made an appearance in Seiichi Nakajima's 1982 book TPM Tenkai. (later published as Introduction to TPM: Total Productive Maintenance, also by Seiichi Nakajima).

Not everyone in manufacturing roles has a lot of experience with Lean or OEE.  In fact, here we are nearly 40 years after OEE was first described in Introduction to TPM: Total Productive Maintenance , and people are still asking how to calculate OEE.  Everyone has a story about how they learned about OEE and Lean, including me.  What’s fascinating is that despite all that’s been written on these topics, manufacturing companies still struggle to capture significant value from Lean tools and OEE calculations.

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.

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:

1. Not enough data - Some OEE systems take up too much operator time to collect data and enter it into the system. Data is often spotty or inaccurate, making the OEE software useless.
   

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

As companies continue to prepare for digital transformation, the debate around which data protocol is better for smart manufacturing systems rages on. The need to capture data from HAAS machines and other manufacturing equipment is a critical component because much of the data required for smart manufacturing originates in your company’s machines, robots, processing equipment, and inspection equipment. In some cases, the data resides in a computer on board the equipment; in many cases, the data sits in a proprietary controller. These data sources use a wide variety of protocols. Many standards organizations have attempted to consolidate communication protocols. A few leaders have emerged, but that perhaps has made decisions about how to capture machine data for smart manufacturing even more complex.

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.

Are manufacturers aware they have hidden production capacity?  Companies that find and free up hidden production capacity can avoid costly equipment purchases, open the door for additional sales, or reduce the time and cost of existing production.

I’ve been a part of organizations that were struggling to satisfy customer demand — we simply could not produce enough product, or at least the right product at the right time. Determining how to resolve the issue would often put the operations engineering and production planning teams at odds. The solution most obvious to engineering was to continue running products in basically the same lot sizes, but at higher speeds. This approach required expensive new equipment. Besides the equipment purchase, there were other significant costs: inventory builds, downtime for installation, debug after installation, and operator training.

Manufacturers serious about improving OEE need to invest in automated data collection. Though the intention of manual data collection is good, this approach doesn’t provide real-time insight or the level of accuracy and detail that an automated data collection can produce. Perhaps more importantly, automated data collection helps you shift employees’ attention to high-value work like running machines and solving problems that hinder operational performance.

Manufacturing companies are still eager to use OEE.  Some industry analysts say that it's “dead,” but not everyone in manufacturing agrees.  Yes, it’s high-level, and as a standalone metric it’s not particularly actionable.  However it’s also a powerful measurement that nearly anyone in a company can quickly digest and use as a starting point to uncover why things aren’t going the way they’re supposed to.  That’s not to say that OEE hasn’t been the subject of significant debate — and even angst — since it first made an appearance in Seiichi Nakajima's 1982 book TPM Tenkai. (later published as Introduction to TPM: Total Productive Maintenance, also by Seiichi Nakajima).

Not everyone in manufacturing roles has a lot of experience with Lean or OEE.  In fact, here we are nearly 40 years after OEE was first described in Introduction to TPM: Total Productive Maintenance , and people are still asking how to calculate OEE.  Everyone has a story about how they learned about OEE and Lean, including me.  What’s fascinating is that despite all that’s been written on these topics, manufacturing companies still struggle to capture significant value from Lean tools and OEE calculations.

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.