Smart Manufacturing Blog

Implementing lean in manufacturing is a tough but rewarding endeavor. Companies often report at least some turnover during the process. Is this normal? What (if anything) should managers and lean coaches do as part of the change management process to prevent losing team members?

If your manufacturing company has decided to invest in shop floor scheduling, it's time to determine what solution is the best production scheduling software for the organization. By this point, the production planner and operations management have probably built a business case for production scheduling software. You’ve engaged corporate executives, quantified the financial impact, and articulated production scheduling software advantages. Now it’s time to figure out which manufacturing scheduling software will align with manufacturing processes, integrate with existing manufacturing and business technology, and empower production planners to solve both common and complex problems.

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.

Perhaps the hardest part to get started with continuous improvement is figuring out how and where to start. Change is hard, especially where teams are concerned, and the thought of stumbling at the outset is scary. One continuous improvement (CI) launch in particular comes to mind when I think about the ups and downs of this kind of initiative. Nearly two decades ago when we were first formally launching CI, our team had many conversations that seemed to go in circles. We also received lots of advice — mostly about which tool to embrace first, such as “start with 5S, it’s the foundation.” Why was there so much confusion around something that was to bring clarity to the business? The biggest thing I’ve learned since those early projects is that companies often make things harder than they need to be. But that’s not all I’ve discovered along the way.

Until recently, nearly every company had an expectation that the manufacturing ERP system should be sufficient to run the business. However, despite the name: enterprise resource planning (ERP), most ERP systems rarely cover all processes enterprise-wide. The reality is that software vendors build them to focus on core operations. The planning capability is typically limited to high-level budgeting while providing placeholders for business users to populate estimates from external sources. In this context, perhaps a more fitting name would be financial resource and document tracking (FRDT) system. To become genuinely enterprise-wide and provide robust planning capabilities, most systems require additional modules, customization, special reports, and bolt-on sub-systems. This approach might initially seem odd. However, it’s quite reasonable once you consider the rationale for designing them this way.

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.

If you’re an operations leader or a production planner in manufacturing, you might know how hard it is to get top management approval for production scheduling software. Where technology funding is concerned, senior executives will often question why the ERP system isn’t enough to keep the daily manufacturing schedule on track. However, the ERP system isn’t usually equipped to handle events that happen in real time on the shop floor. When everyday problems disrupt production orders, the people in charge of production scheduling and operations management can join forces to decide if production scheduling software is right for the manufacturing company. Building a business case for production scheduling software helps you quantify the financial impact, articulate how it impacts business goals, and create a powerful tool for engaging corporate executives.

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.

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.

Manufacturers must be nimble; it is essential they have a methodology to protect and sustain manufacturing performance during turbulent times. Companies hungry to achieve operational excellence are continually assessing their performance versus their plan, questioning why they are not hitting KPIs.

Implementing lean in manufacturing is a tough but rewarding endeavor. Companies often report at least some turnover during the process. Is this normal? What (if anything) should managers and lean coaches do as part of the change management process to prevent losing team members?

If your manufacturing company has decided to invest in shop floor scheduling, it's time to determine what solution is the best production scheduling software for the organization. By this point, the production planner and operations management have probably built a business case for production scheduling software. You’ve engaged corporate executives, quantified the financial impact, and articulated production scheduling software advantages. Now it’s time to figure out which manufacturing scheduling software will align with manufacturing processes, integrate with existing manufacturing and business technology, and empower production planners to solve both common and complex problems.

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.

Perhaps the hardest part to get started with continuous improvement is figuring out how and where to start. Change is hard, especially where teams are concerned, and the thought of stumbling at the outset is scary. One continuous improvement (CI) launch in particular comes to mind when I think about the ups and downs of this kind of initiative. Nearly two decades ago when we were first formally launching CI, our team had many conversations that seemed to go in circles. We also received lots of advice — mostly about which tool to embrace first, such as “start with 5S, it’s the foundation.” Why was there so much confusion around something that was to bring clarity to the business? The biggest thing I’ve learned since those early projects is that companies often make things harder than they need to be. But that’s not all I’ve discovered along the way.

Until recently, nearly every company had an expectation that the manufacturing ERP system should be sufficient to run the business. However, despite the name: enterprise resource planning (ERP), most ERP systems rarely cover all processes enterprise-wide. The reality is that software vendors build them to focus on core operations. The planning capability is typically limited to high-level budgeting while providing placeholders for business users to populate estimates from external sources. In this context, perhaps a more fitting name would be financial resource and document tracking (FRDT) system. To become genuinely enterprise-wide and provide robust planning capabilities, most systems require additional modules, customization, special reports, and bolt-on sub-systems. This approach might initially seem odd. However, it’s quite reasonable once you consider the rationale for designing them this way.

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.

If you’re an operations leader or a production planner in manufacturing, you might know how hard it is to get top management approval for production scheduling software. Where technology funding is concerned, senior executives will often question why the ERP system isn’t enough to keep the daily manufacturing schedule on track. However, the ERP system isn’t usually equipped to handle events that happen in real time on the shop floor. When everyday problems disrupt production orders, the people in charge of production scheduling and operations management can join forces to decide if production scheduling software is right for the manufacturing company. Building a business case for production scheduling software helps you quantify the financial impact, articulate how it impacts business goals, and create a powerful tool for engaging corporate executives.

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.

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.

Manufacturers must be nimble; it is essential they have a methodology to protect and sustain manufacturing performance during turbulent times. Companies hungry to achieve operational excellence are continually assessing their performance versus their plan, questioning why they are not hitting KPIs.