I thought for a moment and went browsing and here’s what I can up with:

• Believe NAPM’s list has been obsoleted by other codes such as the Harmonized Tariff Schedule http://www.usitc.gov/tata/hts/bychapter/
• UN Standard Products and Services Codes http://www.unspsc.org/
• Depending on how granular you need to go you could also use SIC codes to build commodity groups. http://www.sec.gov/info/edgar/siccodes.htm
• HazMat US DOT/UN Codes might be what you need http://bhs.econ.census.gov/bhs/cfs/Hazmat%20Code%20List.pdf
• Pharma has NDC’s http://www.fda.gov/drugs/informationondrugs/ucm142438.htm

Have any better ideas, lets us know!

The post Material Classification Logic appeared first on Lean Sigma Supply Chain.

We know what you’re thinking, “Lean Math?!” Now, that’s a subject that evokes passion in the heart of every lean practitioner…right?

But, the truth is effective lean transformations require some level of math, whether it’s the often deceptively simple takt time calculation, sizing kanbans, calculating process capability, or anything in between. It’s hard to get away from math. There is no such thing as math-free lean and certainly not math-free six sigma!

Lean Math^TM is not intended to be some purely academic study and it does not pretend to be part of the heart and soul of lean principles. Rather, it’s a tool and a construct for thinking. Here we want to integrate lean math theories and examples with experimentation and application.

Some background. Within the next year, the Society of Manufacturing Engineers will be publishing a book, tentatively entitled, Lean Math. Mark Hamel, author of the Shingo Award-winning book, Kaizen Event Fieldbook and founder of the Gemba Tales^TM blog, and Michael O’Connor, Ph.D. (a.k.a. Dr. Mike) are co-authoring this work. They are also getting a ton (!) of help from Larry Loucka, friend, colleague, and fellow-blogger at Lean Sigma Supply Chain.

No surprise, we’re the three folks who are launching this blog. The formal launch date is February 14^th – because we LOVE math! Ok, love may be a bit strong. We really LIKE math.

Here are some of our first blog posts:

• Time
• Cycle Time
• Square Root Law
• Min/Max Cut Theorem
• Coefficient of Variation

Ultimately, we hope that you will join our fledgling Lean Math^TM community and that it lives up to our blog tag line, “Figuring to improve.”

Best regards,

Mark Hamel, Larry Loucka, Michael O’Connor, Ph.D.

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It’s never enough just to tell people about some new insight. Rather, you have to get them to experience it in a way that evokes its power and possibility. Instead of pouring knowledge into people’s heads, you need to help them grind a new set of lenses so they can see the world in a new way.

The post New set of eyeglasses appeared first on Lean Sigma Supply Chain.

Here’s an example of a couple Leaders Standard Work Observation Cards we’ve deployed.

References:

http://www.xqa.com.ar/visualmanagement/

http://www.targetprocess.com/blog/2009/05/lean-and-kanban-software-development.html

http://en.wikipedia.org/wiki/Kanban_%28development%29

http://blogs.seapine.com/2011/07/the-kanban-board-and-gemba-walk-two-powerful-mechanisms-for-agile-communication-and-change/

The post Kamishibai appeared first on Lean Sigma Supply Chain.

X2 = (X1) * √ (n2/n1)

n1 = number of existing facilities
n2 = number of future facilities
X1 = existing inventory
X2 = future inventory

Here’s an example:

Current inventory is 4000 units, 2 facilities grow to 8.  Using the square root law the future inventory = (4000) * √ (8/2) = 8000 units.

The post Square Root Law – inventory in multiple locations appeared first on Lean Sigma Supply Chain.

Which of the two do you want your office or shop floor to be like, Disney or the carnival?  If Disney is your destination, how do you get from here to there?

1. Information is the output of an office.  Information can take many forms: email, databases, presentations,decisions.
2. Wastes of Defects, Storage, Motion, Overprocessing, Waiting, Overproduction all occur in much the same ways as on a factory floor.  For example motion can be seen in walking, reaching, searching, questioning, interrupting.  Each of these activities cause delays and stress.  But we adapt and accept and live with the abnormal.  We just get used to it.
3. Time is the inventory of the office.  Time piles up in our in-boxes and databases.  Time happens when work stops.  We run out of information, need a signature, find a mistake and then set that work aside and pick up some other job, file, task.  We keep busy.  But the thing we were working on sits and waits, the clock ticking away.

Making the abnormal viable, finding where the time is piling up isn’t easy, but that’s the mission of the visual office; making the piles of time visible, and then once we can see the inventory of time we just might get uncomfortable and creative and go do something to reduce the inventory, and speed up the flow.

Here are a couple references …

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Simulations can be used for:

• process design
• testing new ideas
• debugging designs
• testing understanding
• gaining commitment
• testing alternatives
• communicating and training

Sometimes the simulation is role playing theater, other times the ‘game’ has logic and is reproducible, with known inputs and expected outputs.  The photo here is of a recent workshop where we studied how the warehouse dock floor would look after changing the pick waving rules and packaging.  Here outbound goods will be switching from trailer loose stack to returnable shipping containers.

Would we need more floor space?  Do we have enough pickers and loaders?  How do we pick and load multiple deliveries nose-to-tail?

While computer modeling is certainly a consideration the use of table-top simulation has many benefits:

• Many problems are difficult or expensive to test in real life
• Many people process information visually
• A number of alternatives can be quickly tested as the team uncovers issues and finds solutions
• Simulation costs are very low; you don’t need expensive software or extensive training

Here’s the process we used to build our ‘war game’:

1. Decide what we wanted to test; i.e. the output – in this case floor loading and labor resources
2. Gather the input – shipping orders for a typical busy day, number of pickers by zone, number of packing loaders, shift schedules, picking and loading rates, floor space and equipment dimensions (carts, containers, trailers, etc)
3. Determine the constraints, rules; e.g. number of loaders per trailer, length of breaks
4. Document assumptions; e.g any trailer can be at any dock door, break and lunches can be staggered, etc.
5. Be creative and design the game pieces (entities) and determine their quantities; in this exercise carts, containers, bins, trailers
6. Scale physically (1inch=5feet), scale time (1 day of 10 hours took an hour of game time)
7. Collect metrics, such as; line per hour, wave start and end time, trailer load duration, number of floor spaces occupied, number of time floor space turned over, number of workers needed

Once the model ‘behaved’ like the current process the team began introducing rule changes which uncovered obstacles.  One of the first changes was reducing the wave batch size from 90 minutes to 30.  Next came changes to packaging and trailer loading.  By the end of the workshop new procedures were debugged and ready for full scale dry runs leading to a live implementation next month.  Stay tuned …

The post Table top simulation – dock operations appeared first on Lean Sigma Supply Chain.

Surveys indicate that even world-class companies have average forecast accuracy in only the high 70%’s, especially at the SKU or component level that makes or breaks service levels and financial performance. Contributors to forecast inaccuracy include factors that can be addressed through best practices, and factors that will always be unpredictable:

• Chronic bias, which can be minimized with effective Sales/Inventory/Operations Planning, or SIOP. (It’s a safe bet that world-class businesses are already doing this, and they are still achieving only high 70%’s.)

• Unforeseen and unforeseeable special causes, such as natural disasters.

The reality is that forecasting can be only so accurate, in part because it predicts the timing and magnitude of only three types of variation:

1. Trend
2. Seasonality
3. Certain foreseeable special causes, such as promotions

A forecast can predict the timing and magnitude of these variations only so well. Realistically, then, many businesses – and even world-class companies – are left with a significant forecast-accuracy gap that, if not bridged, compromises target fill rates, inventory performance and financial goals.

So first, how do you bridge this gap today? Obviously, you strive continuously to improve your forecasting accuracy, and you always will. That said, though, here are some bridging “techniques” that I’ve observed and even been part of, and likely you can think of some that I’ve missed:

• Mind-set: Complain about, but begrudgingly resign yourself to, suboptimal inventory and fill-rate performance, and related costs and risks
• P&L: Increase the budget for airfreight, expediters or other expediting costs and expenses
• Balance Sheet: Increase safety-stock levels
• Obsolete/Slow-moving: Increase efforts to return excess materials to suppliers

Some of these techniques can and do help achieve service-level targets. But at a minimum, they rarely help, and may in fact hurt, financial performance. Fortunately, a financially-beneficial service-level bridge from forecast to reality is right there in your data! How so?

Well, there is still variation that’s left over after bias, unforeseeable special causes and the three variations with predictable timing: Common-cause random variation in demand (or usage, for components) and replenishment lead time.

The good news is that it is possible to quantify the magnitude of random variation by analyzing historical demand and/or lead-time data. Of course, the timing of random variation is unpredictable (that’s what makes it random), and this means that common-cause random variation must be addressed with properly-determined safety stock.

At this point, you may be saying, “I’m already doing that. I use a statistical technique to determine my safety stock levels.” Forgive the blunt and clichéd reply, but – how’s that working for you? Honestly, is your technique consistently achieving your fill-rate targets on an item-by-item basis? Or does it often seem to put too much inventory in place? And in many cases, does it put too little inventory in place, and you have to subjectively override or increase the calculated safety-stock level?

The fact that your safety-stock calculation is unreliable does not mean that there is no statistical solution. What it does mean, however, is that you must

1. Properly identify and isolate the common-cause random variation from all the other variations, and
2. Use a correct, comprehensive statistical safety-stock approach that includes not just common-cause random variations in demand and lead time, but all six factors that affect safety stock, service levels, inventory performance and expediting.

We aren’t talking about a spreadsheet, or perhaps an unused ERP feature. We’re talking about outside expertise in safety-stock analysis.

A recent CSCO Insights executive brief (from Supply Chain Digest and Cognizant) entitled “Five Strategies for Improving Inventory Management Across Complex Supply Chain Networks” recommends this as the first of five strategies: “Get Much More Granular with Safety Stock Management.” (http://www.scdigest.com/assets/reps/exec_brief_network_inventories.pdf.)

This brief advises using “many more attributes associated with each SKU.” The result of this expanded effort is compelling: “The greater the [safety-stock] precision a company will have.” The CSCO Insights authors advise that to do this “obviously requires a lot more work,” and that this increased analysis offers “rich dividends.” The brief concludes that pursuing the strategy of a “higher level of safety stock management” may require a “relatively uncommon skill set” that may best be provided by outside expertise.

The post Safety Stock to Bridge the Forecast-Accuracy Gap appeared first on Lean Sigma Supply Chain.

Euclides A. Coimbra and his associates at the Kaizen Institute have created a wonderful and detailed work on the application of continuous improvement to supply chains.  Here is a full exploration and application of lean from end to end of the extended value stream.  Two thumbs up!

Some of the graphics look to once have been powerpoint and when reproduced are to small and grainy to be able to read.  There isn’t an index so finding topics is limited to the table of contents.  The book is hard bound, and printed on good paper.

Some of the vocabulary is odd; “border of line” might be better said as” interface” or “borderline”.

Economic Order Quantity, or as referred to in this book, Wilson’s Formula, is treated in a refreshing way.

We can say that Wilson’s formula still applies today.  The only problem is when people assume that changeover time (or, generally speaking, ordering cost) is rigid and cannot be reduced.  Many people don’t think to do Wilson’s calculations because they are still misled by two strong paradigms: flow at any cost and efficiency at any cost.

The ‘flow at any cost’ paradigm is a rising paradigm that is currently gaining in popularity.  People hear about the wonderful Toyota Production System (TPS) and start to increase the flow by reducing the batch sizes blindly, without looking at Wilson’s formula.  What happens is that the CAPEX requirements explode, because the small batch sizes together with big changeover times decrease efficiency.  The result is that flow is indeed achieved – but at the expense of capital expenditure, not by internally reducing the changeover time and increasing equipment flexibility.  You can see this effect in many rich companies that are implementing Lean manufacturing and the TPS.

For a more in depth review check out Jon Miller’s posting on Gemba Panta Rei,
Review of Total Flow Management by Euclides Coimbra.

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Type 1 – Rate-base or Just-in-time

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