October | 2011 | Michel Baudin's Blog
GDP. Gross Domestic Product (GDP), for example, is in the news everyday. If you pollute and spend money to clean up your toxic waste, you contribute more to the GDP than if you produce cleanly. Because of this kind of absurdity, GDP as a metric has been criticized by many economists, including Joseph Stiglitz. In 2009, he even convinced French president Nicolas Sarkozy to seek alternatives. Yet, two years later, the same president is pushing to include in the country’s constitution a “Golden Rule” that caps budget deficits at a percentage of the same flawed GDP!
Fiber calories are included in nutrition information, but only in come countries. In the US, it is legal to not put in fiber calories because they are not digestible. Therefore, they do not “count” as such. however, if you, like most people, tend to underestimate cals sightly, there is nothing wring with including them to create a “buffer zone.”
Do we behave the same way in the manufacturing world? Yes. For example, many companies measure productivity in terms of Sales/Employee. There is an easy way to boost this metric: outsource all production, close all plants and become a trading company. It is not easy to find metrics for quality, cost, delivery, safety and morale that are meaningful and cannot be gamed, but it can be done. For overall company productivity, for example, you can use Value added/Employee, where
Value added = Sales – (Materials + Energy + Outsourced Services)
This is what Peter Drucker called Contributed Value. Value added/Employee is not a perfect metric, but at least it does not provide a perverse incentive to outsource, and the US census bureau publishes statistics on value added and employment by industry, that are helpful for benchmarking.
A better value for the metric always means better business performance for the company. One of the most difficult characteristics to guarantee is that a better value of a metric always translates to better business performance for the company. Equipment efficiency measures are notorious for failing in this area, because maximizing them often leads to overproduction and WIP accumulation.
 Posted in Metrics	 Tagged industrial engineering, Management	10	Oct
I think the fundamental mistake is the assumption that a buffet is like an assembly line, providing sequential access to dishes. This means that you cannot get to the Alo Gobi until the person in front of you is done with the Tandoori. The ideal buffet would instead provide random access, meaning that each customer would have immediate access to all dishes at all times. While it may not be feasible, you can get much closer to it than with the linear buffet. The following picture shows an alternative organization of a buffet in circular islands that is non-sequential. Figure 2. A buffet island at the Holiday Inn in Visalia, CA
 Posted in Technology	 Tagged Buffet, industrial engineering, Lean manufacturing, Restaurant	3	Oct
 Posted in Technology	 Tagged Data mining, Lean manufacturing, Manufactuting	0	Oct
 Posted in History	 Tagged History of technology, industrial engineering, Lean manufacturing, Manufacturing engineering	1	Oct
Prof. Mei-chen Lo, of National University and Kainan University in Taiwan, worked with Operations Managers in two semiconductor companies to establish a list of 16 dimensions of data quality. Most are not parameters that can be measured, and should be considered instead as questions to be asked about a company’s data. I learned it from her at an IE conference in Kitakyushu in 2009, and found it useful by itself as a checklist for a thorough assessment of a current state. Her research is about methods for ranking the importance of these criteria.
They are grouped in four main categories:
Accuracy. Accuracy is the most obvious issue, and is measurable. If the inventory data says that slot 2-3-2 contains two bins of screws, then can we be confident that, if we walk to aisle 2, column 3, level 2 in the warehouse, we will actually find two bins of screws.
Fact or judgement. That slot 2-3-2 contains two bins of screws is a statement of fact. Its accuracy is in principle independent of the observer. On the other hand, “Operator X does not get along with teammates” is a judgement made by a supervisor and cannot carry the same weight as a statement of fact.
Believability of the content. Data can unbelievable becauase it is valid news of extraordinary results, or because it is inaccurate. In either case, it warrants special attention.
Relevance. Companies often collect data because they can, rather than because it is relevant. It is the corporate equivalent of looking for keys at night under the street light rather than next to the car. In the semiconductor industry, where this list of criteria was established, measurements are routinely taken after each step of the wafer process and plotted in control charts. This data is relatively easy to collect but of little relevance to the control and improvement of the wafer process as a whole. Most of the relevant data cannot be captured until the circuits can be tested at the end of the process.
Value added. Some of the data produced in a plant has a direct economic value. Aerospace or defense goods, for example, are delivered with documentation containing a record of their production process, and this data is part of the product. More generally, the data generated by commercial transactions, such as orders, invoices, shipping notices, or receipts, is at the heart of the company’s business activity. This is to be contrasted with data that is generated satisfy internal needs, such as, for example, the number of employees trained in transaction processing on the ERP system.
Completeness. Measurements must be accompanied by all the data characterizing where, when, how and by whom they were collected and in what units they are expressed.
Interpretability. What inferences can you draw directly from the data? If the demand for an item has been rising 5%/month for the past 18 months, it is no stretch to infer that this trend will continue next month. On the other hand, if you are told that a machine has an Overall Equipment Effectiveness (OEE) of 35%, what can you deduce from it? The OEE is the product of three ratios: availability, yield, and actual over nominal speed. The 35% figure may tell you that there is a problem, but not where it is.
Ease of understanding. Management accounting exists for the purpose of supporting decision making by operations managers. Yet the reports provided to managers are often in a language they don’t understand. This does not have to be, and financial officers like Orrie Fiume have modified the vocabulary used in these reports to make them easier for actual managers to understand. The understandability of technical data can also be impaired when engineers use cryptics instead of plain language.
Consistency. Consistency problems often arise as a result of mergers and acquisition, when the different data models of the companies involved need to be mashed together.
Convenience of access. Data that an end-user can retrieve directly through a graphic interface is conveniently accessible; data in paper folders on library shelves is not. Neither are databases in which each new query requires the development of a custom report by a specially trained programmer.
Usability. High-usability data, for example, comes in the form of lists of property names and values can easily be tabulated into spreadsheets or database tables, and, from that point on, selected, filtered and summarized in a variety of informative ways. Low-usability data often comes in the form of a string of characters, that first needs to be separated, with character 1 to 5 being one field, 6 to 12 another, etc., and the meaning of each of these substrings needs to be retrieved from a correspondence table, to find that ’00at3′ means “lime green.”
Security. Manufacturing data contain some of the company’s intellectual property, which must be protected not only from theft but from inadvertent alterations by unqualified employees. But effective security must also be provided efficiently, so that qualified, authorized employees are not slowed down by security procedures when accessing data.
Prof. Mei-Chen Lo’s research on this topic was published in The assessment of the information quality with the aid of multiple criteria analysis
European Journal of Operational Research, Volume 195, Issue 3, 16 June 2009, Pages 850-856
 Posted in Management	 Tagged Data mining, Information systems, Information technology, IT, Lean manufacturing, Manufacturing, Quality	1	Oct
On LinkedIn Lean Business Process group, Ralph Bartelmann asked the following:
Is it really the matter to squeeze the last cent out of production ? In many environments production costs represent a minor part of the over all product cost. Following Pareto reasoning it seems more reasonable to work on other parts of the value stream like supplier developpement, product design etc. What is your opinion and experience ? What are the real challenges ?
It’s not about what production costs but about what it does for the business. Improving production is about making is faster, better, safer, less tedious,… and cheaper. It needs to be faster to make you more responsive, better so that production does not introduce defects that harm your reputation, safer and less tedious so that you can retain your work force and grow its skills.
If you improve on all these fronts, guess what? Your costs go down, and not only in production but in other parts of the value stream too, because they are not independent of production. For example, there is no point in trying to develop just-in-sequence suppliers unless you practice leveled-sequencing (a.k.a. Heijunka) in your assembly line.
A manufacturing company ignoring production is an army ignoring combat on the grounds that more money is spent moving soldiers and keeping them supplied.
 Posted in Management	 Tagged industrial engineering, Lean manufacturing, Manufacturing engineering	0	Oct
 Posted in Polls	 Tagged Data mining, Information systems, Information technology, IT, Lean manufacturing	3	Oct
Kaizen without prerequisites
Question from Pedro Burgos: You have an assembly line where ‘Standardized Work’ is not utilized. The operators have ‘work instructions’ and follow them. They do not miss steps. But there are differences between operators in how the work is performed in terms of sequence. All of them perform under takt time, some more than others. Due to ‘wastes’ of motion. You are asked to do a Kaizen to reduce inventory quantities and space. Inventory quantities on the production floor varies, some parts have 3 days of inventory, some have 2 days, some have 1 day (16 hours). The goal is a ‘kanban’ of 2 hours of inventory in the assembly line. Do you think is possible to do this Kaizen? (Discussion in the LinkedIn Global Lean & Six Sigma Network)
Your question reminds me of the old story about asking a farmer in the countryside of Ireland how to get to Dublin, and getting the answer that “you don’t start from here.” In fact, regardless of its current state, a production line can always be improved.
Looking further at the way you describe the challenge, however, several things disturb me. First, while Kaizen, meaning improvement, is always possible, “a Kaizen” seems to designate a Kaizen event, a project template which is not always appropriate. Second, your description suggests opportunities for productivity and quality improvement, but neither is stated as an objective.
A top-down mandate to use a particular method to pursue two goals like inventory and space reduction does not strike me as a recipe for success. Instead, someone needs to do the following:
1. Take a close look at this assembly line.
2. Identify improvement opportunities in all dimensions of its performance.
3. Select one or two to pursue in priority based on improvement potential, technical feasibility, and human feasibility.
4. Determine the project management approach based on content, rather than the other way around.
This approach is much more likely to achieve the desired inventory and space reductions, among other improvements, than just running a Kaizen event to pursue these objectives directly and exclusively.
 Posted in Management	 Tagged industrial engineering, Kaizen, Lean manufacturing, Manufacturing engineering	0	Oct
 Posted in Events	 Tagged Data mining, Lean manufacturing, Manufacturing	0	Oct
 Posted in Management	 Tagged Continuous improvement, industrial engineering, Kaizen, Lean manufacturing, Manufacturing engineering	0	Post navigation
Turning Success into Mediocrity | Bill Waddell wp.me/p1UTIj-214 18 hours ago
Lean for Managing versus Managing for Lean wp.me/p1UTIj-1Zx 1 day ago
October | 2011 | Michel Baudin's Blog
GDP. Gross Domestic Product (GDP), for example, is in the news everyday. If you pollute and spend money to clean up your toxic waste, you contribute more to the GDP than if you produce cleanly. Because of this kind of absurdity, GDP as a metric has been criticized by many economists, including Joseph Stiglitz. In 2009, he even convinced French president Nicolas Sarkozy to seek alternatives. Yet, two years later, the same president is pushing to include in the country’s constitution a “Golden Rule” that caps budget deficits at a percentage of the same flawed GDP!
Fiber calories are included in nutrition information, but only in come countries. In the US, it is legal to not put in fiber calories because they are not digestible. Therefore, they do not “count” as such. however, if you, like most people, tend to underestimate cals sightly, there is nothing wring with including them to create a “buffer zone.”
Do we behave the same way in the manufacturing world? Yes. For example, many companies measure productivity in terms of Sales/Employee. There is an easy way to boost this metric: outsource all production, close all plants and become a trading company. It is not easy to find metrics for quality, cost, delivery, safety and morale that are meaningful and cannot be gamed, but it can be done. For overall company productivity, for example, you can use Value added/Employee, where
Value added = Sales – (Materials + Energy + Outsourced Services)
This is what Peter Drucker called Contributed Value. Value added/Employee is not a perfect metric, but at least it does not provide a perverse incentive to outsource, and the US census bureau publishes statistics on value added and employment by industry, that are helpful for benchmarking.
A better value for the metric always means better business performance for the company. One of the most difficult characteristics to guarantee is that a better value of a metric always translates to better business performance for the company. Equipment efficiency measures are notorious for failing in this area, because maximizing them often leads to overproduction and WIP accumulation.
 Posted in Metrics	 Tagged industrial engineering, Management	10	Oct
I think the fundamental mistake is the assumption that a buffet is like an assembly line, providing sequential access to dishes. This means that you cannot get to the Alo Gobi until the person in front of you is done with the Tandoori. The ideal buffet would instead provide random access, meaning that each customer would have immediate access to all dishes at all times. While it may not be feasible, you can get much closer to it than with the linear buffet. The following picture shows an alternative organization of a buffet in circular islands that is non-sequential. Figure 2. A buffet island at the Holiday Inn in Visalia, CA
 Posted in Technology	 Tagged Buffet, industrial engineering, Lean manufacturing, Restaurant	3	Oct
 Posted in Technology	 Tagged Data mining, Lean manufacturing, Manufactuting	0	Oct
 Posted in History	 Tagged History of technology, industrial engineering, Lean manufacturing, Manufacturing engineering	1	Oct
Prof. Mei-chen Lo, of National University and Kainan University in Taiwan, worked with Operations Managers in two semiconductor companies to establish a list of 16 dimensions of data quality. Most are not parameters that can be measured, and should be considered instead as questions to be asked about a company’s data. I learned it from her at an IE conference in Kitakyushu in 2009, and found it useful by itself as a checklist for a thorough assessment of a current state. Her research is about methods for ranking the importance of these criteria.
They are grouped in four main categories:
Accuracy. Accuracy is the most obvious issue, and is measurable. If the inventory data says that slot 2-3-2 contains two bins of screws, then can we be confident that, if we walk to aisle 2, column 3, level 2 in the warehouse, we will actually find two bins of screws.
Fact or judgement. That slot 2-3-2 contains two bins of screws is a statement of fact. Its accuracy is in principle independent of the observer. On the other hand, “Operator X does not get along with teammates” is a judgement made by a supervisor and cannot carry the same weight as a statement of fact.
Believability of the content. Data can unbelievable becauase it is valid news of extraordinary results, or because it is inaccurate. In either case, it warrants special attention.
Relevance. Companies often collect data because they can, rather than because it is relevant. It is the corporate equivalent of looking for keys at night under the street light rather than next to the car. In the semiconductor industry, where this list of criteria was established, measurements are routinely taken after each step of the wafer process and plotted in control charts. This data is relatively easy to collect but of little relevance to the control and improvement of the wafer process as a whole. Most of the relevant data cannot be captured until the circuits can be tested at the end of the process.
Value added. Some of the data produced in a plant has a direct economic value. Aerospace or defense goods, for example, are delivered with documentation containing a record of their production process, and this data is part of the product. More generally, the data generated by commercial transactions, such as orders, invoices, shipping notices, or receipts, is at the heart of the company’s business activity. This is to be contrasted with data that is generated satisfy internal needs, such as, for example, the number of employees trained in transaction processing on the ERP system.
Completeness. Measurements must be accompanied by all the data characterizing where, when, how and by whom they were collected and in what units they are expressed.
Interpretability. What inferences can you draw directly from the data? If the demand for an item has been rising 5%/month for the past 18 months, it is no stretch to infer that this trend will continue next month. On the other hand, if you are told that a machine has an Overall Equipment Effectiveness (OEE) of 35%, what can you deduce from it? The OEE is the product of three ratios: availability, yield, and actual over nominal speed. The 35% figure may tell you that there is a problem, but not where it is.
Eas
e of understanding. Management accounting exists for the purpose of supporting decision making by operations managers. Yet the reports provided to managers are often in a language they don’t understand. This does not have to be, and financial officers like Orrie Fiume have modified the vocabulary used in these reports to make them easier for actual managers to understand. The understandability of technical data can also be impaired when engineers use cryptics instead of plain language.
Consistency. Consistency problems often arise as a result of mergers and acquisition, when the different data models of the companies involved need to be mashed together.
Convenience of access. Data that an end-user can retrieve directly through a graphic interface is conveniently accessible; data in paper folders on library shelves is not. Neither are databases in which each new query requires the development of a custom report by a specially trained programmer.
Usability. High-usability data, for example, comes in the form of lists of property names and values can easily be tabulated into spreadsheets or database tables, and, from that point on, selected, filtered and summarized in a variety of informative ways. Low-usability data often comes in the form of a string of characters, that first needs to be separated, with character 1 to 5 being one field, 6 to 12 another, etc., and the meaning of each of these substrings needs to be retrieved from a correspondence table, to find that ’00at3′ means “lime green.”
Security. Manufacturing data contain some of the company’s intellectual property, which must be protected not only from theft but from inadvertent alterations by unqualified employees. But effective security must also be provided efficiently, so that qualified, authorized employees are not slowed down by security procedures when accessing data.
Prof. Mei-Chen Lo’s research on this topic was published in The assessment of the information quality with the aid of multiple criteria analysis
European Journal of Operational Research, Volume 195, Issue 3, 16 June 2009, Pages 850-856
 Posted in Management	 Tagged Data mining, Information systems, Information technology, IT, Lean manufacturing, Manufacturing, Quality	1	Oct
On LinkedIn Lean Business Process group, Ralph Bartelmann asked the following:
Is it really the matter to squeeze the last cent out of production ? In many environments production costs represent a minor part of the over all product cost. Following Pareto reasoning it seems more reasonable to work on other parts of the value stream like supplier developpement, product design etc. What is your opinion and experience ? What are the real challenges ?
It’s not about what production costs but about what it does for the business. Improving production is about making is faster, better, safer, less tedious,… and cheaper. It needs to be faster to make you more responsive, better so that production does not introduce defects that harm your reputation, safer and less tedious so that you can retain your work force and grow its skills.
If you improve on all these fronts, guess what? Your costs go down, and not only in production but in other parts of the value stream too, because they are not independent of production. For example, there is no point in trying to develop just-in-sequence suppliers unless you practice leveled-sequencing (a.k.a. Heijunka) in your assembly line.
A manufacturing company ignoring production is an army ignoring combat on the grounds that more money is spent moving soldiers and keeping them supplied.
 Posted in Management	 Tagged industrial engineering, Lean manufacturing, Manufacturing engineering	0	Oct
 Posted in Polls	 Tagged Data mining, Information systems, Information technology, IT, Lean manufacturing	3	Oct
Kaizen without prerequisites
Question from Pedro Burgos: You have an assembly line where ‘Standardized Work’ is not utilized. The operators have ‘work instructions’ and follow them. They do not miss steps. But there are differences between operators in how the work is performed in terms of sequence. All of them perform under takt time, some more than others. Due to ‘wastes’ of motion. You are asked to do a Kaizen to reduce inventory quantities and space. Inventory quantities on the production floor varies, some parts have 3 days of inventory, some have 2 days, some have 1 day (16 hours). The goal is a ‘kanban’ of 2 hours of inventory in the assembly line. Do you think is possible to do this Kaizen? (Discussion in the LinkedIn Global Lean & Six Sigma Network)
Your question reminds me of the old story about asking a farmer in the countryside of Ireland how to get to Dublin, and getting the answer that “you don’t start from here.” In fact, regardless of its current state, a production line can always be improved.
Looking further at the way you describe the challenge, however, several things disturb me. First, while Kaizen, meaning improvement, is always possible, “a Kaizen” seems to designate a Kaizen event, a project template which is not always appropriate. Second, your description suggests opportunities for productivity and quality improvement, but neither is stated as an objective.
A top-down mandate to use a particular method to pursue two goals like inventory and space reduction does not strike me as a recipe for success. Instead, someone needs to do the following:
1. Take a close look at this assembly line.
2. Identify improvement opportunities in all dimensions of its performance.
3. Select one or two to pursue in priority based on improvement potential, technical feasibility, and human feasibility.
4. Determine the project management approach based on content, rather than the other way around.
This approach is much more likely to achieve the desired inventory and space reductions, among other improvements, than just running a Kaizen event to pursue these objectives directly and exclusively.
 Posted in Management	 Tagged industrial engineering, Kaizen, Lean manufacturing, Manufacturing engineering	0	Oct
 Posted in Events	 Tagged Data mining, Lean manufacturing, Manufacturing	0	Oct
 Posted in Management	 Tagged Continuous improvement, industrial engineering, Kaizen, Lean manufacturing, Manufacturing engineering	0	Post navigation
Turning Success into Mediocrity | Bill Waddell wp.me/p1UTIj-214 18 hours ago
Lean for Managing versus Managing for Lean wp.me/p1UTIj-1Zx 1 day ago