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Talk to any Data Scientist and they will tell you that the most frustrating projects are where they are asked to find patterns in data without any indication of what to solve. It is like asking, how long is a piece of string?
We want to continue helping businesses to take advantage of their data. So, using our roundtable process, T-DAB designed an introductory assessment that you can apply to your business and data projects. Try working through the 10 questions covering business, technology, data and skills and see if you’re a Data Zero or Data Hero. Then work with the team to build an action plan and maximise your data opportunity.
Sign-up to our blog to receive the latest updates on T-DAB.AI including the write up of our round-table discussions.
In many ways it goes back to the now tried and tested Lean tools such as the Lean business improvement cycle of Plan – Do – Check – Act (PDCA), which is still highly relevant to solving business problems. It is data that ‘oils’ the improvement process. We’ll be covering these steps in more detail in another post – sign-up to our newsletter below to receive the next update.
So why in today’s world are we making it appear so complex with jargon and terms that appear to mask common sense. This is one of the reasons that Si2 wanted to work with T-DAB and why we delivered a series of round tables at the IDS last year. We wanted to help manufacturers cut through the hype and understand how to move from Business Problem to Data Solution.
Talk to any Data Scientist and they will tell you that the most frustrating projects are where they are asked to find patterns in data without any indication of what to solve. It is like asking, how long is a piece of string?
We want to continue helping businesses to take advantage of their data. So, using our roundtable process, T-DAB designed an introductory assessment that you can apply to your business and data projects. Try working through the 10 questions covering business, technology, data and skills and see if you’re a Data Zero or Data Hero. Then work with the team to build an action plan and maximise your data opportunity.
Sign-up to our blog to receive the latest updates on T-DAB.AI including the write up of our round-table discussions.
In many ways it goes back to the now tried and tested Lean tools such as the Lean business improvement cycle of Plan – Do – Check – Act (PDCA), which is still highly relevant to solving business problems. It is data that ‘oils’ the improvement process. We’ll be covering these steps in more detail in another post – sign-up to our newsletter below to receive the next update.
So why in today’s world are we making it appear so complex with jargon and terms that appear to mask common sense. This is one of the reasons that Si2 wanted to work with T-DAB and why we delivered a series of round tables at the IDS last year. We wanted to help manufacturers cut through the hype and understand how to move from Business Problem to Data Solution.
Talk to any Data Scientist and they will tell you that the most frustrating projects are where they are asked to find patterns in data without any indication of what to solve. It is like asking, how long is a piece of string?
We want to continue helping businesses to take advantage of their data. So, using our roundtable process, T-DAB designed an introductory assessment that you can apply to your business and data projects. Try working through the 10 questions covering business, technology, data and skills and see if you’re a Data Zero or Data Hero. Then work with the team to build an action plan and maximise your data opportunity.
Sign-up to our blog to receive the latest updates on T-DAB.AI including the write up of our round-table discussions.
As their understanding of the plants operation deepened, they started to be able to identify problems and advise their sister plants in the downstream operations. They also began to identify and solve problems in their upstream supply chain. For example, they were able to identify that one supplier’s material handling equipment was dropping the aluminium coil in the same place, and damaging it, such that the manufacturing process was influenced. The result was that their relationship with external suppliers and internal customers became much closer and more inter-dependent. Technology and data allows a far greater understanding of the value stream, which is a well-articulated second principle of Lean.
More profoundly, their culture moved from ‘How do I stop that fault from happening’ to ‘How do we understand what happened, how do we get the process under control, how do we eliminate the problem’. Their teams managing the line went from being ‘machine minders’ to ‘process optimisers’. The key to their success has been to present data in a very easy to use and visual format that their teams can easily action and improve.
And now that a data driven problem solving culture is in place, the Agfa team are going back to the machine learning analytics technologies that they first started to investigate in 1994. The difference now is that their level of maturity in understanding data is at a completely different level.
In many ways it goes back to the now tried and tested Lean tools such as the Lean business improvement cycle of Plan – Do – Check – Act (PDCA), which is still highly relevant to solving business problems. It is data that ‘oils’ the improvement process. We’ll be covering these steps in more detail in another post – sign-up to our newsletter below to receive the next update.
So why in today’s world are we making it appear so complex with jargon and terms that appear to mask common sense. This is one of the reasons that Si2 wanted to work with T-DAB and why we delivered a series of round tables at the IDS last year. We wanted to help manufacturers cut through the hype and understand how to move from Business Problem to Data Solution.
Talk to any Data Scientist and they will tell you that the most frustrating projects are where they are asked to find patterns in data without any indication of what to solve. It is like asking, how long is a piece of string?
We want to continue helping businesses to take advantage of their data. So, using our roundtable process, T-DAB designed an introductory assessment that you can apply to your business and data projects. Try working through the 10 questions covering business, technology, data and skills and see if you’re a Data Zero or Data Hero. Then work with the team to build an action plan and maximise your data opportunity.
Sign-up to our blog to receive the latest updates on T-DAB.AI including the write up of our round-table discussions.
The Turning Point
As their understanding of the plants operation deepened, they started to be able to identify problems and advise their sister plants in the downstream operations. They also began to identify and solve problems in their upstream supply chain. For example, they were able to identify that one supplier’s material handling equipment was dropping the aluminium coil in the same place, and damaging it, such that the manufacturing process was influenced. The result was that their relationship with external suppliers and internal customers became much closer and more inter-dependent. Technology and data allows a far greater understanding of the value stream, which is a well-articulated second principle of Lean.
More profoundly, their culture moved from ‘How do I stop that fault from happening’ to ‘How do we understand what happened, how do we get the process under control, how do we eliminate the problem’. Their teams managing the line went from being ‘machine minders’ to ‘process optimisers’. The key to their success has been to present data in a very easy to use and visual format that their teams can easily action and improve.
And now that a data driven problem solving culture is in place, the Agfa team are going back to the machine learning analytics technologies that they first started to investigate in 1994. The difference now is that their level of maturity in understanding data is at a completely different level.
In many ways it goes back to the now tried and tested Lean tools such as the Lean business improvement cycle of Plan – Do – Check – Act (PDCA), which is still highly relevant to solving business problems. It is data that ‘oils’ the improvement process. We’ll be covering these steps in more detail in another post – sign-up to our newsletter below to receive the next update.
So why in today’s world are we making it appear so complex with jargon and terms that appear to mask common sense. This is one of the reasons that Si2 wanted to work with T-DAB and why we delivered a series of round tables at the IDS last year. We wanted to help manufacturers cut through the hype and understand how to move from Business Problem to Data Solution.
Talk to any Data Scientist and they will tell you that the most frustrating projects are where they are asked to find patterns in data without any indication of what to solve. It is like asking, how long is a piece of string?
We want to continue helping businesses to take advantage of their data. So, using our roundtable process, T-DAB designed an introductory assessment that you can apply to your business and data projects. Try working through the 10 questions covering business, technology, data and skills and see if you’re a Data Zero or Data Hero. Then work with the team to build an action plan and maximise your data opportunity.
Sign-up to our blog to receive the latest updates on T-DAB.AI including the write up of our round-table discussions.
Agfa’s data story started in 2001 when they faced a major corporate re-organisation. The Leeds plant would no longer sell the lithographic plates which lie at the heart of modern printing presses. They would now be selling a semi-finished product that would be finished elsewhere in the supply chain. Up to then, their main method of validating quality was to do destructive testing of the finished product. This approach was no longer possible, and they needed to find new ways to manage in-process quality.
First, they reviewed their individual processes, creating control plans as required. However, faced with a very complex manufacturing process with many interdependent control parameters, this approach was not good enough. They knew their plant operations had over 8000 digital inputs but had never really used this resource. Now that their survival depended on becoming a benchmark for ’lean’ production, their first instinct was to find a way to view the interactions going on in their plant.
Using some basic visualisation tools that already existed in the Distributed Control System, they began to visualise how the material flowed through the plant and how key performance variables such as energy, flow and acidity varied along the manufacturing process. To their amazement they started to see patterns in the plant operating characteristics, which not only allowed them to manage their process, but to also identify extremely obscure problems that had always existed, but the route cause never identified.
The Turning Point
As their understanding of the plants operation deepened, they started to be able to identify problems and advise their sister plants in the downstream operations. They also began to identify and solve problems in their upstream supply chain. For example, they were able to identify that one supplier’s material handling equipment was dropping the aluminium coil in the same place, and damaging it, such that the manufacturing process was influenced. The result was that their relationship with external suppliers and internal customers became much closer and more inter-dependent. Technology and data allows a far greater understanding of the value stream, which is a well-articulated second principle of Lean.
More profoundly, their culture moved from ‘How do I stop that fault from happening’ to ‘How do we understand what happened, how do we get the process under control, how do we eliminate the problem’. Their teams managing the line went from being ‘machine minders’ to ‘process optimisers’. The key to their success has been to present data in a very easy to use and visual format that their teams can easily action and improve.
And now that a data driven problem solving culture is in place, the Agfa team are going back to the machine learning analytics technologies that they first started to investigate in 1994. The difference now is that their level of maturity in understanding data is at a completely different level.
In many ways it goes back to the now tried and tested Lean tools such as the Lean business improvement cycle of Plan – Do – Check – Act (PDCA), which is still highly relevant to solving business problems. It is data that ‘oils’ the improvement process. We’ll be covering these steps in more detail in another post – sign-up to our newsletter below to receive the next update.
So why in today’s world are we making it appear so complex with jargon and terms that appear to mask common sense. This is one of the reasons that Si2 wanted to work with T-DAB and why we delivered a series of round tables at the IDS last year. We wanted to help manufacturers cut through the hype and understand how to move from Business Problem to Data Solution.
Talk to any Data Scientist and they will tell you that the most frustrating projects are where they are asked to find patterns in data without any indication of what to solve. It is like asking, how long is a piece of string?
We want to continue helping businesses to take advantage of their data. So, using our roundtable process, T-DAB designed an introductory assessment that you can apply to your business and data projects. Try working through the 10 questions covering business, technology, data and skills and see if you’re a Data Zero or Data Hero. Then work with the team to build an action plan and maximise your data opportunity.
Sign-up to our blog to receive the latest updates on T-DAB.AI including the write up of our round-table discussions.
Agfa’s Story
Agfa’s data story started in 2001 when they faced a major corporate re-organisation. The Leeds plant would no longer sell the lithographic plates which lie at the heart of modern printing presses. They would now be selling a semi-finished product that would be finished elsewhere in the supply chain. Up to then, their main method of validating quality was to do destructive testing of the finished product. This approach was no longer possible, and they needed to find new ways to manage in-process quality.
First, they reviewed their individual processes, creating control plans as required. However, faced with a very complex manufacturing process with many interdependent control parameters, this approach was not good enough. They knew their plant operations had over 8000 digital inputs but had never really used this resource. Now that their survival depended on becoming a benchmark for ’lean’ production, their first instinct was to find a way to view the interactions going on in their plant.
Using some basic visualisation tools that already existed in the Distributed Control System, they began to visualise how the material flowed through the plant and how key performance variables such as energy, flow and acidity varied along the manufacturing process. To their amazement they started to see patterns in the plant operating characteristics, which not only allowed them to manage their process, but to also identify extremely obscure problems that had always existed, but the route cause never identified.
The Turning Point
As their understanding of the plants operation deepened, they started to be able to identify problems and advise their sister plants in the downstream operations. They also began to identify and solve problems in their upstream supply chain. For example, they were able to identify that one supplier’s material handling equipment was dropping the aluminium coil in the same place, and damaging it, such that the manufacturing process was influenced. The result was that their relationship with external suppliers and internal customers became much closer and more inter-dependent. Technology and data allows a far greater understanding of the value stream, which is a well-articulated second principle of Lean.
More profoundly, their culture moved from ‘How do I stop that fault from happening’ to ‘How do we understand what happened, how do we get the process under control, how do we eliminate the problem’. Their teams managing the line went from being ‘machine minders’ to ‘process optimisers’. The key to their success has been to present data in a very easy to use and visual format that their teams can easily action and improve.
And now that a data driven problem solving culture is in place, the Agfa team are going back to the machine learning analytics technologies that they first started to investigate in 1994. The difference now is that their level of maturity in understanding data is at a completely different level.
In many ways it goes back to the now tried and tested Lean tools such as the Lean business improvement cycle of Plan – Do – Check – Act (PDCA), which is still highly relevant to solving business problems. It is data that ‘oils’ the improvement process. We’ll be covering these steps in more detail in another post – sign-up to our newsletter below to receive the next update.
So why in today’s world are we making it appear so complex with jargon and terms that appear to mask common sense. This is one of the reasons that Si2 wanted to work with T-DAB and why we delivered a series of round tables at the IDS last year. We wanted to help manufacturers cut through the hype and understand how to move from Business Problem to Data Solution.
Talk to any Data Scientist and they will tell you that the most frustrating projects are where they are asked to find patterns in data without any indication of what to solve. It is like asking, how long is a piece of string?
We want to continue helping businesses to take advantage of their data. So, using our roundtable process, T-DAB designed an introductory assessment that you can apply to your business and data projects. Try working through the 10 questions covering business, technology, data and skills and see if you’re a Data Zero or Data Hero. Then work with the team to build an action plan and maximise your data opportunity.
Sign-up to our blog to receive the latest updates on T-DAB.AI including the write up of our round-table discussions.
Agfa Graphic’s plant in Leeds has developed into a best in class manufacturing facility based on the principal of using data to eliminate waste from its manufacturing process. But it was not always like that.
At Industrial Data Summit in 2018, we met with Graham Cooper, Site Manager at Agfa Graphics Leeds Production facility, and his Production Manager, Charles Meldrum. We discussed what Industry 4.0 or the digitisation of manufacturing really meant. Their answers were illuminating and humbling and we captured their story in The The Manufacturer.
Agfa’s Story
Agfa’s data story started in 2001 when they faced a major corporate re-organisation. The Leeds plant would no longer sell the lithographic plates which lie at the heart of modern printing presses. They would now be selling a semi-finished product that would be finished elsewhere in the supply chain. Up to then, their main method of validating quality was to do destructive testing of the finished product. This approach was no longer possible, and they needed to find new ways to manage in-process quality.
First, they reviewed their individual processes, creating control plans as required. However, faced with a very complex manufacturing process with many interdependent control parameters, this approach was not good enough. They knew their plant operations had over 8000 digital inputs but had never really used this resource. Now that their survival depended on becoming a benchmark for ’lean’ production, their first instinct was to find a way to view the interactions going on in their plant.
Using some basic visualisation tools that already existed in the Distributed Control System, they began to visualise how the material flowed through the plant and how key performance variables such as energy, flow and acidity varied along the manufacturing process. To their amazement they started to see patterns in the plant operating characteristics, which not only allowed them to manage their process, but to also identify extremely obscure problems that had always existed, but the route cause never identified.
The Turning Point
As their understanding of the plants operation deepened, they started to be able to identify problems and advise their sister plants in the downstream operations. They also began to identify and solve problems in their upstream supply chain. For example, they were able to identify that one supplier’s material handling equipment was dropping the aluminium coil in the same place, and damaging it, such that the manufacturing process was influenced. The result was that their relationship with external suppliers and internal customers became much closer and more inter-dependent. Technology and data allows a far greater understanding of the value stream, which is a well-articulated second principle of Lean.
More profoundly, their culture moved from ‘How do I stop that fault from happening’ to ‘How do we understand what happened, how do we get the process under control, how do we eliminate the problem’. Their teams managing the line went from being ‘machine minders’ to ‘process optimisers’. The key to their success has been to present data in a very easy to use and visual format that their teams can easily action and improve.
And now that a data driven problem solving culture is in place, the Agfa team are going back to the machine learning analytics technologies that they first started to investigate in 1994. The difference now is that their level of maturity in understanding data is at a completely different level.
In many ways it goes back to the now tried and tested Lean tools such as the Lean business improvement cycle of Plan – Do – Check – Act (PDCA), which is still highly relevant to solving business problems. It is data that ‘oils’ the improvement process. We’ll be covering these steps in more detail in another post – sign-up to our newsletter below to receive the next update.
So why in today’s world are we making it appear so complex with jargon and terms that appear to mask common sense. This is one of the reasons that Si2 wanted to work with T-DAB and why we delivered a series of round tables at the IDS last year. We wanted to help manufacturers cut through the hype and understand how to move from Business Problem to Data Solution.
Talk to any Data Scientist and they will tell you that the most frustrating projects are where they are asked to find patterns in data without any indication of what to solve. It is like asking, how long is a piece of string?
We want to continue helping businesses to take advantage of their data. So, using our roundtable process, T-DAB designed an introductory assessment that you can apply to your business and data projects. Try working through the 10 questions covering business, technology, data and skills and see if you’re a Data Zero or Data Hero. Then work with the team to build an action plan and maximise your data opportunity.
Sign-up to our blog to receive the latest updates on T-DAB.AI including the write up of our round-table discussions.
Agfa Graphic’s plant in Leeds has developed into a best in class manufacturing facility based on the principal of using data to eliminate waste from its manufacturing process. But it was not always like that.
At Industrial Data Summit in 2018, we met with Graham Cooper, Site Manager at Agfa Graphics Leeds Production facility, and his Production Manager, Charles Meldrum. We discussed what Industry 4.0 or the digitisation of manufacturing really meant. Their answers were illuminating and humbling and we captured their story in The The Manufacturer.
Agfa’s Story
Agfa’s data story started in 2001 when they faced a major corporate re-organisation. The Leeds plant would no longer sell the lithographic plates which lie at the heart of modern printing presses. They would now be selling a semi-finished product that would be finished elsewhere in the supply chain. Up to then, their main method of validating quality was to do destructive testing of the finished product. This approach was no longer possible, and they needed to find new ways to manage in-process quality.
First, they reviewed their individual processes, creating control plans as required. However, faced with a very complex manufacturing process with many interdependent control parameters, this approach was not good enough. They knew their plant operations had over 8000 digital inputs but had never really used this resource. Now that their survival depended on becoming a benchmark for ’lean’ production, their first instinct was to find a way to view the interactions going on in their plant.
Using some basic visualisation tools that already existed in the Distributed Control System, they began to visualise how the material flowed through the plant and how key performance variables such as energy, flow and acidity varied along the manufacturing process. To their amazement they started to see patterns in the plant operating characteristics, which not only allowed them to manage their process, but to also identify extremely obscure problems that had always existed, but the route cause never identified.
The Turning Point
As their understanding of the plants operation deepened, they started to be able to identify problems and advise their sister plants in the downstream operations. They also began to identify and solve problems in their upstream supply chain. For example, they were able to identify that one supplier’s material handling equipment was dropping the aluminium coil in the same place, and damaging it, such that the manufacturing process was influenced. The result was that their relationship with external suppliers and internal customers became much closer and more inter-dependent. Technology and data allows a far greater understanding of the value stream, which is a well-articulated second principle of Lean.
More profoundly, their culture moved from ‘How do I stop that fault from happening’ to ‘How do we understand what happened, how do we get the process under control, how do we eliminate the problem’. Their teams managing the line went from being ‘machine minders’ to ‘process optimisers’. The key to their success has been to present data in a very easy to use and visual format that their teams can easily action and improve.
And now that a data driven problem solving culture is in place, the Agfa team are going back to the machine learning analytics technologies that they first started to investigate in 1994. The difference now is that their level of maturity in understanding data is at a completely different level.
In many ways it goes back to the now tried and tested Lean tools such as the Lean business improvement cycle of Plan – Do – Check – Act (PDCA), which is still highly relevant to solving business problems. It is data that ‘oils’ the improvement process. We’ll be covering these steps in more detail in another post – sign-up to our newsletter below to receive the next update.
So why in today’s world are we making it appear so complex with jargon and terms that appear to mask common sense. This is one of the reasons that Si2 wanted to work with T-DAB and why we delivered a series of round tables at the IDS last year. We wanted to help manufacturers cut through the hype and understand how to move from Business Problem to Data Solution.
Talk to any Data Scientist and they will tell you that the most frustrating projects are where they are asked to find patterns in data without any indication of what to solve. It is like asking, how long is a piece of string?
We want to continue helping businesses to take advantage of their data. So, using our roundtable process, T-DAB designed an introductory assessment that you can apply to your business and data projects. Try working through the 10 questions covering business, technology, data and skills and see if you’re a Data Zero or Data Hero. Then work with the team to build an action plan and maximise your data opportunity.
Sign-up to our blog to receive the latest updates on T-DAB.AI including the write up of our round-table discussions.
Agfa Graphic’s plant in Leeds has developed into a best in class manufacturing facility based on the principal of using data to eliminate waste from its manufacturing process. But it was not always like that.
At Industrial Data Summit in 2018, we met with Graham Cooper, Site Manager at Agfa Graphics Leeds Production facility, and his Production Manager, Charles Meldrum. We discussed what Industry 4.0 or the digitisation of manufacturing really meant. Their answers were illuminating and humbling and we captured their story in The The Manufacturer.
Agfa’s Story
Agfa’s data story started in 2001 when they faced a major corporate re-organisation. The Leeds plant would no longer sell the lithographic plates which lie at the heart of modern printing presses. They would now be selling a semi-finished product that would be finished elsewhere in the supply chain. Up to then, their main method of validating quality was to do destructive testing of the finished product. This approach was no longer possible, and they needed to find new ways to manage in-process quality.
First, they reviewed their individual processes, creating control plans as required. However, faced with a very complex manufacturing process with many interdependent control parameters, this approach was not good enough. They knew their plant operations had over 8000 digital inputs but had never really used this resource. Now that their survival depended on becoming a benchmark for ’lean’ production, their first instinct was to find a way to view the interactions going on in their plant.
Using some basic visualisation tools that already existed in the Distributed Control System, they began to visualise how the material flowed through the plant and how key performance variables such as energy, flow and acidity varied along the manufacturing process. To their amazement they started to see patterns in the plant operating characteristics, which not only allowed them to manage their process, but to also identify extremely obscure problems that had always existed, but the route cause never identified.
The Turning Point
As their understanding of the plants operation deepened, they started to be able to identify problems and advise their sister plants in the downstream operations. They also began to identify and solve problems in their upstream supply chain. For example, they were able to identify that one supplier’s material handling equipment was dropping the aluminium coil in the same place, and damaging it, such that the manufacturing process was influenced. The result was that their relationship with external suppliers and internal customers became much closer and more inter-dependent. Technology and data allows a far greater understanding of the value stream, which is a well-articulated second principle of Lean.
More profoundly, their culture moved from ‘How do I stop that fault from happening’ to ‘How do we understand what happened, how do we get the process under control, how do we eliminate the problem’. Their teams managing the line went from being ‘machine minders’ to ‘process optimisers’. The key to their success has been to present data in a very easy to use and visual format that their teams can easily action and improve.
And now that a data driven problem solving culture is in place, the Agfa team are going back to the machine learning analytics technologies that they first started to investigate in 1994. The difference now is that their level of maturity in understanding data is at a completely different level.
In many ways it goes back to the now tried and tested Lean tools such as the Lean business improvement cycle of Plan – Do – Check – Act (PDCA), which is still highly relevant to solving business problems. It is data that ‘oils’ the improvement process. We’ll be covering these steps in more detail in another post – sign-up to our newsletter below to receive the next update.
So why in today’s world are we making it appear so complex with jargon and terms that appear to mask common sense. This is one of the reasons that Si2 wanted to work with T-DAB and why we delivered a series of round tables at the IDS last year. We wanted to help manufacturers cut through the hype and understand how to move from Business Problem to Data Solution.
Talk to any Data Scientist and they will tell you that the most frustrating projects are where they are asked to find patterns in data without any indication of what to solve. It is like asking, how long is a piece of string?
We want to continue helping businesses to take advantage of their data. So, using our roundtable process, T-DAB designed an introductory assessment that you can apply to your business and data projects. Try working through the 10 questions covering business, technology, data and skills and see if you’re a Data Zero or Data Hero. Then work with the team to build an action plan and maximise your data opportunity.
Sign-up to our blog to receive the latest updates on T-DAB.AI including the write up of our round-table discussions.
