May 1 2013

How do I analyze historical consumption for 13,000 items?

Supply chain consultant Hadas Gur asked the following question:

I have data of demands for 13000 SKUs (consumptions from the last 5 years). 6000 of the observations are zeros.  I can’t recognize the distribution of the data . I have tried the q-q plot to find a match to any known distribution. What can I do in this case if I want to find the reorder point? Is it ok to use the reorder point formula which is in your post “Safety Stocks : Beware of Formulas” even though the distribution is not normal?

You do not give a context. Are those SKUs components supplied to a manufacturing company or retail items on supermarket shelves? The demand patterns may be radically different. In retail, for example, the demand for milk is the sum of the quantities bought by a large number of individual consumers acting independently, and the normal distribution is a likely fit. On the other hand, if you are supplying a model-specific part to a car manufacturer, it is unlikely to fit.

Do not try to apply the same approach to all 13,000 SKUs! For example, reorder point makes no senses for the 6,000 items that have had no demand in the past 5 years. You would want to investigate whether they should still be in the catalog and, if so, they are strangers and you need to organize to make or buy them when an order arrives.

For the others, I would suggest you explore the data rather than focus on fitting a distribution, starting with a Runner/Repeater/Stranger analysis. Then, starting with runners, investigate trends and seasonal variations. For repeaters, I would investigate ways to group them into families that make sense for what you are trying to do.

Do not use only the data. In order to understand what is possible, you need to visit the warehouses or distribution centers and understand how physical distribution distribution is organized, and the people involved.

Then consider a range of approaches for different items and item families, including just-in-sequence, Kanban, two-bin, reorder point, vendor-managed inventory, consignment, etc.  Examine how these approaches would have performed with the consumption pattern of the last 5 years. You can also simulate future demand.

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Apr 26 2013

Improving operations: How far can you go with common sense?

In the Lean Six Sigma discussion group on LinkedIn, Brian P. Sheets argues that ” the alphabet soup of acronyms describing the multitude of process improvement & management methodologies that have come and gone over the last 50 years […]  is just plain, old, common sense.”  The list he targets in this statement is Six Sigma, TQM, BPR, BPM, TOC, MBO, Kaizen, and Gemba Kaizen, and overlap the one I discussed earlier in this blog. To support his argument, he invokes not only the great work done in US manufacturing during World War II without these acronyms, but goes back all the way to Egypt’s pyramids.

I see things differently. The old days were not so great and we have learned a few new tricks in the 68 years since the end of World War II, as a result of which we are not only able to make better products, but we also use fewer people to make them, at a higher quality. There definitely is something to some of the ideas that have been packaged under various brands in that time, and it is definitely more than common sense.

What is common sense anyway? The common sense approach to a problem is the solution that would be chosen by an intelligent person without any specialized knowledge. It is what you resort to when faced with a new situation you are unprepared for, like the businessman played by Anthony Hopkins in The Edge, who is stranded in the Alaskan wilderness by a plane crash and has to kill a grizzly.

Once you have been working on something for a few years, however, you are supposed to have acquired specialized knowledge of it, and apply solutions that are beyond common sense. And these solutions are counter-intuitive to anyone without this experience. Lean manufacturing concepts like one-piece flow or heijunka are bewildering to beginners, because they have nothing to go by beyond their common sense.

“Common sense,” Descartes said, “is the most fairly distributed thing in the world, for each one thinks he is so well-endowed with it that even those who are hardest to satisfy in all other matters are not in the habit of desiring more of it than they already have.” After that, he proceeds to explain a method “to seek truth in science” and presents three applications of this method, the best known being analytic geometry. All of this is far beyond common sense.

For all these reasons, I am not too fond of invoking common sense in support of any new concept. What you really need is a rationale, and experimental proof through a small scale implementation.

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Apr 25 2013

Achieving one-piece flow | Darren Dolcemascolo

See on Scoop.it – lean manufacturing

“Sometimes referred to as “single-piece flow” or “continuous flow,” one-piece flow is a key concept within the Toyota Production System. Achieving one-piece flow helps manufacturers achieve true just-in-time manufacturing. That is, the right parts can be made available when they are needed in the quantity they are needed. In the simplest of terms, one-piece flow means that parts are moved through operations from step to step with no work-in-process (WIP) in between either one piece at a time or a small batch at a time. This system works best in combination with a cellular layout in which all necessary equipment is located within a cell in the sequence in which it is used.”

Michel Baudin‘s insight:

In the current issue of Reliable Plant, Darren Dolcemascolo explains the concept and the value of one-piece flow in simple terms, including the prerequisites for it to work.

See on www.reliableplant.com

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Apr 23 2013

Forget Excel: This Was Reinhart and Rogoff’s Biggest Mistake | The Atlantic

See on Scoop.it – lean manufacturing

“For an economist, the five most terrifying words in the English language are: I can’t replicate your results. But for economists Carmen Reinhart and Ken Rogoff of Harvard, there are seven even more terrifying ones: I think you made an Excel error.”

Michel Baudin‘s insight:

While not a story about manufacturing, it is a cautionary tale that manufacturing professionals who use Excel should ponder.

It is about two economists from a prestigious institution whose sweeping conclusions have been leading foreign governments to adopt disastrous policies and fueled the argument in favor of the same policies in the US.

Reviewing the Harvard paper, researchers Thomas Herndon, Michael Ash and Robert Pollin have discovered that Reinhart and Rogoff had selectively excluded data, calculated averages in “unusual ways,” and made a mistake in an Excel calculation.

On the face of it, the general sloppiness of the work would be forgivable in a summer intern, but the Excel error should give us pause. When inputting the range of a sum, they didn’t drag the cursor down far enough and left five rows out.

With Excel, this kind of error is easy to make and difficult to detect. In spreadsheets generated by others, I have found products with no sales showing positive revenues, and formulas with exponents applied to the wrong parameters. And I suspect others may have found errors in my own.

Following are a few recommendations that may protect you from egg on your face:

  1. Use meaningful names for cells and ranges. Refer to cells as “GDP” or “Viscosity” rather than “A3” or “RR1.” It will be easier to validate formulas, as they will more closely resemble their mathematical forms and errors will stand out.
  2. Break down complex formulas into simple ones, with additional cells or columns for intermediate results.
  3. Include comments explaining your calculations.
  4. Whenever possible, use built-in functions or pre-existing templates from a trusted source.
  5. Explain the innards of your spreadsheet to a colleague for validation, and return the favor for his or her spreadsheets.
  6. Use Excel for calculations and graphic displays, but NOT as a database management system (DBMS). Use a real DBMS for data storage and retrieval.

See on www.theatlantic.com

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