by Jennifer Hart Yim | May 13, 2015 | Blog, Leadership, Strategy, Supply Chain
This article is part of a series of articles written by MBA students and graduates from the University of New Hampshire Peter T. Paul College of Business and Economics.
Corey Ducharme is Green Belt Certified in Six Sigma. He has a BA in Business and is currently in the MBA program at the University of New Hampshire. He has consulted at major corporations including Johnson & Johnson, Universal Studios, Sony Pictures, Oklahoma Oil & Gas, and Suncor as a management consultant at D&A Management. He can be reached via e-mail.
We all use Six Sigma problem solving whether we know it or not
How do humans tend to solve problems? Consciously or unconsciously humans use a four-step method that is defined as:
Table 1: Traditional Problem Solving Process
In the above example, the root cause is identified – exercise and eating less equals losing weight – and verified. The conventional wisdom is proven true and there is little need to consider a more robust or analytical method. Most humans solve their day-to-day issues in this manner whether consciously or unconsciously. My root-cause analysis in this case (I exercise and eat less) is verified by the fact that I lost ten pounds and sustainable until my goal is achieved. (Losing 20 pounds).
Six Sigma Problem Solving 
What if the root-cause lies outside of conventional wisdom or is difficult to determine?
These needle-in-a-haystack problems – due to limited business resources – cause businesses to lose revenue and lead to process failures, poor quality, and poor customer service. These types of problems are at the heart of Six Sigma Problem Solving and are a way to find the needle-in-the-haystack.
The Six Sigma methodology is based on the DMAIC process and using our weight loss example we begin to see the similarities between the two methods.
- Define Phase: What is the problem and set the end goal.
- Measure Phase: What is the current state?
- Analyze Phase: 1. Develop cause-and-effect analysis of problem. What are the real causes and prove cause and effect links.
- Improve Phase: Action
- Control Phase: 1. Verify improvement and 2. Sustainability
What differs between the phases of conventional and Six Sigma problem solving begins in the Analyze Phase. Six Sigma methodology demands the proof of cause be determined before a clear course of action is taken. The proof of cause must be data-driven as root-cause analysis is at the core of Six Sigma problem solving. It is also the reason that the Analyze phase is divided into the development of cause-effects and proof of cause-effect links.
The second difference is in the fifth phase. In the Traditional problem-solving methodology, verification comes in the form of losing weight. I can prove it by weighing myself. In Six Sigma problem solving, a two-step process is needed. Verification is essential (Did I improve?) and if so, a plan to sustain our gains is created. This is not necessary in a Traditional methodology because our cause-effect is proven (exercise/eat less = weight loss); however, in the Six Sigma methodology our cause-effect must be tested and verified.
The Linguistics of Six Sigma: Y=f(X)
To speak in the language of Six Sigma, we need to change ‘problem’ with ‘Y’ and ‘cause’ with ‘X’. The Y is the output and the X(s) are any inputs that are involved in producing the output. In other words, the Y = 100% customer satisfaction and X(s) are the variables that affect the level of customer satisfaction. For more, click the following link: Y=f(X).
Using Six Sigma linguistics and the DMAIC process, we can combine the Traditional problem solving steps in Table 1 and we see that our four-step process has become a more robust seven-step process. We can now use DMAIC to ask ourselves the most essential question:
If our root-cause analysis is discovered and proven true, then can the problem be solved or reduced by controlling or removing the cause(s)?
Table 2: DMAIC Problem Solving
Real World Example: Boeing
Boeing’s Six Sigma team in Everett, WA discovered that root cause analysis is often like finding a needle-in-a-haystack, especially for the maker of the world’s largest commercial twin-engine airplane with millions of components. (To read the entire story, click the following link: Problem-solving approach helps team pinpoint solution). Boeing discovered that recirculating air fans were being rejected during production, costing Boeing money in waste, removal, testing, and cost of replacing the component.
Boeing assembled a cross-functional ‘detective squad’ that included employees from Engineering, Quality, Manufacturing, Supply Management, Procurement, and their supplier Hamilton Sundstrand who began the problem-solving techniques of the DMAIC process by examining data from the fans and beginning root-cause analysis.
This analysis determined that Foreign Object Debris (FOD) damaged the fans and the test tools. Job done, right? Not according to Kent Kuiper, Six Sigma expert at Boeing, the team had to dig deeper. “For example,” he said, “when we found that FOD was a problem and determined the source for it, removing the FOD and replacing the fan wasn’t going to get us where we needed to go. We had to figure out a way to keep the FOD from happening again.”
Further inspection led the team to discover failures in ductwork caps and plastic sheeting – two items ironically intended to prevent FOD damage and two electrical issues. One failure of improperly modified test equipment and the second issue related to crimping procedures in manufacturing process that improved connections in the fans. The results? Although the FOD in the fans was the main X, the results were clear. “After 18 fan failures in two years, we went four and a half months without a rejection,” said Max Limb, a supplier field service representative. “We haven’t completely eliminated the rejections, but we’re close.”
“Our team has become well-versed in the concept of Six Sigma,” said Valerie Feiberti, Supply Management and Procurement Director of the Lean Promotion Team. “We feel very strongly that it provides a way to correct production-related problems and proactively design-in quality.”
Summary
The DMAIC methodology is essentially a series of common sense questions to determine root-cause analysis, identification of X(s), elimination of problems, and maintaining of gains. The DMAIC process asks the following questions:
- Define: What is the Y that is performing poorly?
- Measure: What is Y’s current performance?
- Analyze: What are the X(s)? Are they real?
- Improve: How can X(s) be controlled/eliminated?
- Control: How can X(s) be controlled to sustain gains in Y?
Six Sigma problem solving is the data-driven representation of the conscious or unconscious thinking we use to solve problems in our lives and can be used to solve may needle-in-a-haystack problems that vex businesses.
by Jennifer Hart Yim | May 12, 2015 | Big Data, Blog, Data/Analytics, Strategy
This article is part of a series of articles written by MBA students and graduates from the University of New Hampshire Peter T. Paul College of Business and Economics.
Josh Hutchins received his B.S. in Business Administration from the University of New Hampshire in 2005. He is currently pursuing his MBA at the Peter T. Paul School at the University of New Hampshire; on course to graduate in May 2016.
“Working with data is something I do every day as a financial analyst. I enjoy crunching the data and experimenting with various data analytic techniques. I’ve found that my love for playing with the data and thinking in unconventional ways has led me to be efficient and successful at work. The way data is being used is revolutionizing the way we do business. I’m glad I can be part of this wave of the future.”
The responsibility of big data
Data is coming into businesses at incredible speeds, in large quantities, and in all types of different formats. In a world full of big data it’s not just having the data – it’s what you do with it that matters. Big data analysis is becoming a very powerful tool used by companies of all sizes. Companies are analyzing and using the data in order to create sustainable business models and gain a competitive advantage over their competition. However, as one company would come to learn – with big data comes big responsibility.
Solid Gold Bomb T-Shirt Company
In 2011, Solid Gold Bomb, an Amazon Marketplace merchant based out of Australia, thought of an ingenious way to create fresh slogans for t-shirts. The main concept behind Solid Gold Bomb’s operation was that by utilizing a computer programming script, they could create clever t-shirt slogans that no one had thought of previously. The company created various t-shirt slogans that played off of the popular British WWII era phrase Keep Calm and Carry On. Under the systematic script method, Solid Gold Bombs was able to create literally millions of t-shirt offerings without the need to have them on hand in inventory. With the substantial increase in product offerings, the chances of customers stumbling upon a Solid Gold Bomb shirt increased dramatically. By utilizing this new on-demand approach to t-shirt printing, Solid Gold Bomb was able to reduce expenses, while simultaneously increasing potential revenue by offering exponential products at little additional marginal cost.
Use of ‘Big Data’
The computer script relied on the following to operate: a large pool of words, associated rule learning, and an algorithm.
Large Pool of Words – Solid Gold Bomb gathered a list of approximately 202,000 words that could be found in the dictionary. Of these words, they whittled it down to approximately 1,100 of the most popular words. Some of the words were too long to be included on a t-shirt, so the list was further culled. They settled on 700 different verbs and corresponding pronouns. These words would be used by the computer script to generate t-shirt slogans.
Associated rule learning – Associated rule learning is the degree to which two variables in a given list relate to each other. The first step in associated rule learning is to identify and isolate the most frequent variables. The second step is forming rules based on different constraints on the variables – assigning an “interestingness” factor. In the case of Solid Gold Bomb, the associated rule learning assigned an interestingness factor to verb-pronoun combinations.
Algorithm – The algorithm designed by Solid Gold Bomb was very simple. Each shirt would begin with “Keep calm and”. The algorithm script would then search through the word pool and pull back the most highly associated verb and pronoun combinations. The words would then be put into the typical format of the Keep Calm and Carry On. An image of each individual combination would be mocked up and posted to their Amazon merchant account. The process would continuously loop, creating millions of combinations.
The Big Data Blues
With one innocent mistake, Solid Gold Bomb fell apart in the blink of an eye. Amazon started getting complaints about offensive slogans on Solid Gold Bomb’s t-shirts. Images of t-shirts with phrases such as “Keep Calm and Rape Her” and “Keep Calm and Hit Her” were being sold on their Amazon merchant account. Their typical weekend orders for around 800 shirts were reduced to just 3 – few enough to count on one hand. Amazon ended up pulling their entire line of clothing, essentially putting Solid Gold Bomb out of business.
What went wrong?
While Solid Gold Bomb had a good handle on how to use data that they had gathered and how to use it to their advantage, they neglected to consider the potential hidden consequences of unintentional misuse of the data. When culling 202,000 words down to 700 useable words, words such as “rape” should have been eliminated from the useable word pool. From a high level perspective, the human mind is incapable of naming all the potential combinations of the 700 useable words without the assistance of a computer program. However, the end user needs to be aware that the computer program logic will create every potential combination based on the word pool.
Moral of the Story
Big data by itself is not beneficial to a company. The real value is in the analytics that are applied to the data. The results of the analytics can be utilized in numerous ways – to make more informed decisions, create new revenue streams, and create competitive advantages, to name a few. When a company makes the decision to utilize big data analytics, each process needs to be mapped out to have an intimate understanding of how the data will be used. In the case of Solid Gold Bomb, they failed to have this intimate understanding of how the data would be used throughout the process. As a result, they paid the ultimate price; they were not able to sustain themselves through this debacle. The morale of the story: With big data comes big responsibility. Know your data and know the potential uses of the data better. Don’t be afraid to think outside the box, but know the potential consequences.
For information about another big data faux pas, learn how Target predicted that a 16 year old girl was pregnant before her father knew.
by Jennifer Hart Yim | May 12, 2015 | Big Data, Blog, Data/Analytics, Strategy
This article is part of a series of articles written by MBA students and graduates from the University of New Hampshire Peter T. Paul College of Business and Economics.
Josh Hutchins received his B.S. in Business Administration from the University of New Hampshire in 2005. He is currently pursuing his MBA at the Peter T. Paul School at the University of New Hampshire; on course to graduate in May 2016.
“Working with data is something I do every day as a financial analyst. I enjoy crunching the data and experimenting with various data analytic techniques. I’ve found that my love for playing with the data and thinking in unconventional ways has led me to be efficient and successful at work. The way data is being used is revolutionizing the way we do business. I’m glad I can be part of this wave of the future.”
The responsibility of big data
Data is coming into businesses at incredible speeds, in large quantities, and in all types of different formats. In a world full of big data it’s not just having the data – it’s what you do with it that matters. Big data analysis is becoming a very powerful tool used by companies of all sizes. Companies are analyzing and using the data in order to create sustainable business models and gain a competitive advantage over their competition. However, as one company would come to learn – with big data comes big responsibility.
Solid Gold Bomb T-Shirt Company
In 2011, Solid Gold Bomb, an Amazon Marketplace merchant based out of Australia, thought of an ingenious way to create fresh slogans for t-shirts. The main concept behind Solid Gold Bomb’s operation was that by utilizing a computer programming script, they could create clever t-shirt slogans that no one had thought of previously. The company created various t-shirt slogans that played off of the popular British WWII era phrase Keep Calm and Carry On. Under the systematic script method, Solid Gold Bombs was able to create literally millions of t-shirt offerings without the need to have them on hand in inventory. With the substantial increase in product offerings, the chances of customers stumbling upon a Solid Gold Bomb shirt increased dramatically. By utilizing this new on-demand approach to t-shirt printing, Solid Gold Bomb was able to reduce expenses, while simultaneously increasing potential revenue by offering exponential products at little additional marginal cost.
Use of ‘Big Data’
The computer script relied on the following to operate: a large pool of words, associated rule learning, and an algorithm.
Large Pool of Words – Solid Gold Bomb gathered a list of approximately 202,000 words that could be found in the dictionary. Of these words, they whittled it down to approximately 1,100 of the most popular words. Some of the words were too long to be included on a t-shirt, so the list was further culled. They settled on 700 different verbs and corresponding pronouns. These words would be used by the computer script to generate t-shirt slogans.
Associated rule learning – Associated rule learning is the degree to which two variables in a given list relate to each other. The first step in associated rule learning is to identify and isolate the most frequent variables. The second step is forming rules based on different constraints on the variables – assigning an “interestingness” factor. In the case of Solid Gold Bomb, the associated rule learning assigned an interestingness factor to verb-pronoun combinations.
Algorithm – The algorithm designed by Solid Gold Bomb was very simple. Each shirt would begin with “Keep calm and”. The algorithm script would then search through the word pool and pull back the most highly associated verb and pronoun combinations. The words would then be put into the typical format of the Keep Calm and Carry On. An image of each individual combination would be mocked up and posted to their Amazon merchant account. The process would continuously loop, creating millions of combinations.
The Big Data Blues
With one innocent mistake, Solid Gold Bomb fell apart in the blink of an eye. Amazon started getting complaints about offensive slogans on Solid Gold Bomb’s t-shirts. Images of t-shirts with phrases such as “Keep Calm and Rape Her” and “Keep Calm and Hit Her” were being sold on their Amazon merchant account. Their typical weekend orders for around 800 shirts were reduced to just 3 – few enough to count on one hand. Amazon ended up pulling their entire line of clothing, essentially putting Solid Gold Bomb out of business.
What went wrong?
While Solid Gold Bomb had a good handle on how to use data that they had gathered and how to use it to their advantage, they neglected to consider the potential hidden consequences of unintentional misuse of the data. When culling 202,000 words down to 700 useable words, words such as “rape” should have been eliminated from the useable word pool. From a high level perspective, the human mind is incapable of naming all the potential combinations of the 700 useable words without the assistance of a computer program. However, the end user needs to be aware that the computer program logic will create every potential combination based on the word pool.
Moral of the Story
Big data by itself is not beneficial to a company. The real value is in the analytics that are applied to the data. The results of the analytics can be utilized in numerous ways – to make more informed decisions, create new revenue streams, and create competitive advantages, to name a few. When a company makes the decision to utilize big data analytics, each process needs to be mapped out to have an intimate understanding of how the data will be used. In the case of Solid Gold Bomb, they failed to have this intimate understanding of how the data would be used throughout the process. As a result, they paid the ultimate price; they were not able to sustain themselves through this debacle. The morale of the story: With big data comes big responsibility. Know your data and know the potential uses of the data better. Don’t be afraid to think outside the box, but know the potential consequences.
For information about another big data faux pas, learn how Target predicted that a 16 year old girl was pregnant before her father knew.
by Jennifer Hart Yim | Dec 11, 2014 | Blog, Supply Chain
Note: This is a guest post written by Barbara Jorgensen, managing editor, Electronics Purchasing Strategies.
Barb has more than 20 years’ experience as a journalist, working for leading electronics industry publications such as Electronic Business, Electronic Buyers’ News and EDN. As a freelance writer, Barb wrote and managed an award-winning custom publication for Sager Electronics; was a leading contributor to Avnet Global Perspectives magazine; was a regular columnist for the National Electronics Distributors Association monthly newsletter and wrote for industry associations such as IPC. Barb was also a featured blogger on the B2B Website Allbusiness.com and helped launch Electronics Sourcing North America, a start-up magazine serving purchasing professionals in the Americas.
Prior to her freelance career, Barb was a senior editor at Electronic Business, the pre-eminent management magazine for the electronics industry, featuring world-class manufacturing companies such as Dell, Hewlett-Packard, Cisco and Flextronics International. Before joining EB for the second time, Barb spent 6 years with Electronic Buyers’ News as managing editor, distribution, winning several awards for coverage of the distribution beat. A graduate of the University of Binghamton, Barb began her journalism career with the Gannett newspaper chain. She has worked for a number of local newspapers in the Greater Boston area and trade journal publishers Reed Business Information and UBM.
Barb can be reached at [email protected].
Why supply and demand remain unbalanced, even in the connected world
With the advent of the internet and social media, it would seem that the supply chain has more opportunity than ever to collect and disseminate information. In the electronics industry, component makers, distributors and OEMs communicate in traditional ways: EDI, Excel, the internet, extranets, MRP/ERP systems and good old-fashioned e-mail; along with cloud-based platforms, Twitter, Facebook and other social media. It’s impossible to NOT be connected.
Yet, component suppliers and contract manufacturers say that that OEMs’ ability to forecast is worse than it has ever been. OEMs still can’t predict their customers’ demand. Component suppliers—many of which have a minimum of 16-week lead times for production – often end up with too much product. Distributors pick up the slack, but as soon as inventory starts to build in the channel alarm bells go off. With so many opportunities for communication, how is this possible?
There are a couple of industry dynamics that could explain this. First, it’s been at least a decade since the electronics industry has seen any kind of significant shortage. Spot shortages cropped up following the Japan tsunami and Thailand floods of 2011, but nothing that could be termed industry-wide. Buyers have become accustomed to getting what they want when they want it. Moreover, the internet has made inventory and pricing information available to anyone with a search engine. Components appear to be available 24/7, 365 days a year. The urgency to forecast has diminished.
Then there is lean, just-in-time and build-to-order. All of these practices have effectively shortened the time between order and fulfillment. In practice, OEMs are working with an actual order – not a forecast – and the correct number of components is stored nearby. Lean has diminished the levels of inventory in the pipeline, so as long as everything is flowing as planned, there shouldn’t be any surprises.
Finally, the supply chain has figured out that it has to be more responsive and nimble regarding last-minute changes. In order to respond to JIT and BTO, inventories have to be maintained closer to manufacturing sites. Instead of single mega-hubs, suppliers and distributors have warehousing in key regions of the globe, and utilize third-party logistics when necessary. The ability to respond within 24 hours is a reality in most parts of the world.
So why are supply and demand in a state of perpetual imbalance? It’s not a dearth of data. Partners don’t necessarily trust the information they receive. Distributors routinely compare customer forecasts to historic orders to see if something is out of whack. Certain types of information are still withheld from partners: OEMs don’t share their preferred-pricing agreements with EMS. Online inventory is treated with a grain of salt: depending on how often data is refreshed, parts may or may not available at the price at which they are listed. Social media seems to be best used during disasters and for taking the pulse of market—what is trending and what is not.
Not sharing certain types of information is considered strategic by companies in the supply chain; and double-checking forecasts is a responsible business practice. However, these practices mean the supply chain may never be transparent. Information may be more available than ever, but visibility of data is an entirely different matter. Yet, even lack of information is no longer a problem in the supply chain, but full visibility remains elusive.
by Jennifer Hart Yim | Dec 11, 2014 | Blog, Supply Chain
Note: This is a guest post written by Barbara Jorgensen, managing editor, Electronics Purchasing Strategies.
Barb has more than 20 years’ experience as a journalist, working for leading electronics industry publications such as Electronic Business, Electronic Buyers’ News and EDN. As a freelance writer, Barb wrote and managed an award-winning custom publication for Sager Electronics; was a leading contributor to Avnet Global Perspectives magazine; was a regular columnist for the National Electronics Distributors Association monthly newsletter and wrote for industry associations such as IPC. Barb was also a featured blogger on the B2B Website Allbusiness.com and helped launch Electronics Sourcing North America, a start-up magazine serving purchasing professionals in the Americas.
Prior to her freelance career, Barb was a senior editor at Electronic Business, the pre-eminent management magazine for the electronics industry, featuring world-class manufacturing companies such as Dell, Hewlett-Packard, Cisco and Flextronics International. Before joining EB for the second time, Barb spent 6 years with Electronic Buyers’ News as managing editor, distribution, winning several awards for coverage of the distribution beat. A graduate of the University of Binghamton, Barb began her journalism career with the Gannett newspaper chain. She has worked for a number of local newspapers in the Greater Boston area and trade journal publishers Reed Business Information and UBM.
Barb can be reached at [email protected].
Why supply and demand remain unbalanced, even in the connected world
With the advent of the internet and social media, it would seem that the supply chain has more opportunity than ever to collect and disseminate information. In the electronics industry, component makers, distributors and OEMs communicate in traditional ways: EDI, Excel, the internet, extranets, MRP/ERP systems and good old-fashioned e-mail; along with cloud-based platforms, Twitter, Facebook and other social media. It’s impossible to NOT be connected.
Yet, component suppliers and contract manufacturers say that that OEMs’ ability to forecast is worse than it has ever been. OEMs still can’t predict their customers’ demand. Component suppliers—many of which have a minimum of 16-week lead times for production – often end up with too much product. Distributors pick up the slack, but as soon as inventory starts to build in the channel alarm bells go off. With so many opportunities for communication, how is this possible?
There are a couple of industry dynamics that could explain this. First, it’s been at least a decade since the electronics industry has seen any kind of significant shortage. Spot shortages cropped up following the Japan tsunami and Thailand floods of 2011, but nothing that could be termed industry-wide. Buyers have become accustomed to getting what they want when they want it. Moreover, the internet has made inventory and pricing information available to anyone with a search engine. Components appear to be available 24/7, 365 days a year. The urgency to forecast has diminished.
Then there is lean, just-in-time and build-to-order. All of these practices have effectively shortened the time between order and fulfillment. In practice, OEMs are working with an actual order – not a forecast – and the correct number of components is stored nearby. Lean has diminished the levels of inventory in the pipeline, so as long as everything is flowing as planned, there shouldn’t be any surprises.
Finally, the supply chain has figured out that it has to be more responsive and nimble regarding last-minute changes. In order to respond to JIT and BTO, inventories have to be maintained closer to manufacturing sites. Instead of single mega-hubs, suppliers and distributors have warehousing in key regions of the globe, and utilize third-party logistics when necessary. The ability to respond within 24 hours is a reality in most parts of the world.
So why are supply and demand in a state of perpetual imbalance? It’s not a dearth of data. Partners don’t necessarily trust the information they receive. Distributors routinely compare customer forecasts to historic orders to see if something is out of whack. Certain types of information are still withheld from partners: OEMs don’t share their preferred-pricing agreements with EMS. Online inventory is treated with a grain of salt: depending on how often data is refreshed, parts may or may not available at the price at which they are listed. Social media seems to be best used during disasters and for taking the pulse of market—what is trending and what is not.
Not sharing certain types of information is considered strategic by companies in the supply chain; and double-checking forecasts is a responsible business practice. However, these practices mean the supply chain may never be transparent. Information may be more available than ever, but visibility of data is an entirely different matter. Yet, even lack of information is no longer a problem in the supply chain, but full visibility remains elusive.
