“Smart” Part Numbers Strike Again: Wrong Part Shipped

I own two dishwashers in two homes, different models from the same brand, bought in the same store, and both on a service contract. For the first one, the model number  is SHE55R56UC; for the second one, SHE65T55UC. Today, we needed help on the first one, but customer service shipped us parts for the second one, which the repair technician discovered when unpacking them.

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Manufacturing Data Cleaning: Thankless But Necessary

Whether you manage operations with paper and pencil as in 1920 or use the state of the art in information technology (IT), you need clean data. If you don’t have it, you will suffer all sorts of dysfunctions. You will order materials you already have or don’t need, and be surprised by shortages. You will make delivery promises you can’t keep, and ship wrong or defective products. And you will have no idea what works and what doesn’t in your plant.

I have never seen a factory with perfect data, and perhaps none exists. Dirty data is the norm, not the exception, and the reason most factories are able to ship anything at all is that their people find ways to work around the defects in their data, from using expediters to find parts that aren’t where the system thought they were, to engineers who work directly with production to make sure a technical change is implemented. Mei-chen Lo, of Kainan University in Taiwan, proposed a useful classification of the issues with data quality. What I would like to propose here is pointers on addressing them.

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Is Vendor Selection Really The First Step in ERP Implementation?

A free guide that you can download from ERP Focus makes vendor selection the first of an 11-step implementation process, while defining success is the last.  In other words, they have you choose who you buy from before having a clear idea of what you are trying to accomplish.

It reminds me of a meeting at a client site where ERP implementation was about to begin. “This train has left the station,” I was told. The purpose of the meeting was to draw a “Value Stream Map” for the whole plant, in preparation for ERP, and the participants included managers from Manufacturing, Quality, Production Control, Maintenance, Purchasing, Sales, and Engineering.

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Industry 4.0: Without Stable Processes, Nothing Works | Wiegand’s Watch

This is a translation of Bodo Wiegand’s latest newsletter, about Lean in Germany, followed by my comments:

This week I was with a company that is on its way to implement industry 4.0. All machines were networked. The manager could see from his desk which machines were running and which were not. All data were collected centrally and also shown locally to the machine operator. The trend was easy to see. One third of the machines had a malfunction. With an average OEE of 62%, the machines do not always run.

“As long as we buy new machines, we have to live with this,” was his answer to my question.

But, it was not only the newest, but also the older machines that don’t need to be smeared with oil and dirty, even even while generating chips. Provided on request, the Fire-Fighting-factor reported to us by the maintenance technicians was above 75%. The chief knew exactly: 76.6%. An OEE of 62% and 76.6% Firefighting means in plain language: In this business, there is no stable processes.

But what drives intelligent managers then to link his whole company, only to find that the processes are unstable? With some thought they could have discovered this without networking and invested first in stabilizing the processes. Introducing Industry 4.0 For industry on unstable processes will fail. The crucial question: how I manage to stabilize the processes and avoid unplanned shutdowns?

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More Recommendations on Part Numbering

Three years ago, a previous post made the case for the key approach to nomenclature, as opposed to the obsolete “smart” numbering systems. In the key approach, the only job of a part number is to be a unique item identifier, through which all relevant information can be retrieved from a database. But you still need to think what items you want to have unique IDs for.

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Preventing Errors in Food Delivery by Natural Mapping at Benihana

Restaurant waiters who deliver food to tables of five or more customers rarely remember who ordered what, and have to ask.

Generic restaurant order form

Generic restaurant order form

Most restaurants still use paper order forms, and the most common are not much help, because they tell the waiter what was ordered at each table, but not which customer ordered it. The row of titles on the top, with “APPT- SOUP/SAL-…” is intended as a series of column headers to record each customer’s choice in each category.

 

 

Order form with table layouts and numbered positionsSome form suppliers, like National Checking with their WaitRpad, have addressed this problem by providing table maps at the top of the form. These sketches include the following:

  1. The shape of the table.
  2. Where the waiter is to stand when delivering food.
  3. A clockwise numbered position for each customer.

The waiter arrives with a tray carrying the dishes laid out clockwise to match the customer positions.  National Checking posted the following video to highlight the advantages of this form:

Benihana table

Benihana table

Benihana, however, goes one step further and takes advantage of the special characteristics of their service. It is a chain of Japanese restaurants in the US, with a single 8-seat table layout and a chef at each table cooking on a hot plate in front of the customers, from ingredients in a cart. The work done away from the table is limited to kitting the ingredients to match the customers’ orders.

 

 

Benihana order form

Benihana order form

The order form is a map of the table, which is possible only because the tables are all identical, and the form can be filled out with abbreviations because the orders are all for full-course meals: “DIA” for “Diablo,” “SM” for “Splash-and-Meadow,” etc., with a few options, such as fried rice versus steamed rice.

While this is effective at ensuring that customers receive exactly what they ordered, it is not mistake-proofing/poka-yoke. It does not physically prevent mistakes, nor does it have a mechanism to signal any error that may happen. A disorganized chef could still get it wrong, and customers could confuse any chef by switching seats.

It is instead an application of the usability engineering principle of natural mapping. An order form that is a map of the table makes it easy for the chef to know which dishes to give to which customers and thereby reduces the likelihood of errors. Mistake-proofing would be better, if someone could find a way to do it.

Orbit charts, and why you should use them

What I propose to call “Orbit Chart” is rarely used in manufacturing today, and I think it should be. This chart tracks the path followed by a system or an object in a plane where the coordinates are two of its characteristics. In earlier applications, there were geographical coordinates; in current ones, they may be the numerator and denominator of a financial ratio like Return On Net Assets (RONA), metrics of productivity and quality, or physical characteristics, like the depth and diameter of a drilled hole.

Most data visualizations in manufacturing are limited to simple charts, that show how one parameter varies by category, in  bar graphs or continuously as a function of time in line plots. Charts that show more dimensions require more effort both to generate and to the read, but reveal information that you would not otherwise find.

For details, click on the following:

What is an “orbit chart”?

When you have an object moving in a plane, you can separately plot each coordinate against time, but the juxtaposition of the two plots would not show you the path followed in two dimensions. When you want to study a possible relationship between two parameters from a table of values where both are measured on the same objects, you usually start part generating a scatter plot, from which you try to infer some form of correlation between the two. When you do this, however, you lose the sequence information. When you consider two neighboring points on your scatter plot, you have no way to tell whether they are measurements on two units of product made consecutively or with many others in between.

On an orbit chart, you connect the points that succeed each other in the sequence, and label each point with its sequence number. As a result, what you plot is no longer a cloud of points, but a path followed by your object in the plane of your two parameters. On a road trip, the sequence of your locations at the end of each day are not independent: where you are tonight is where you were last night plus today’s increment. In machining with a tool that wears out, its condition after the 50th workpiece is what it was after the 49th, plus the effect of the 50th. The orbit chart is a visualization tool for this kind of phenomenon.

Let us assume that you are plotting the quality performance of a production line, as represented by its first-pass yield, against its productivity in terms of units/operator/shift. If you are practicing management whack-a-mole, you improve quality at the expense of productivity, by adding inspections and rework, or productivity at the expense of quality, by pressuring operators to cut corners. In this case, you can expect the orbit of your production line in the Quality versus Productivity plane to be a cycle, looking somewhat like a figure eight, and showing no real improvement.

Orbit chart of Quality versus Productivity Whack-a-Mole

Orbit chart of Quality versus Productivity Whack-a-Mole

On the other hand, if you are practicing Lean, quality and productivity improve together, resulting in a chart that does not loop.

Plotting an orbit, of course, is not always meaningful. In the following example, we show a scatterplot of two parameters measured on a sequence of independent events. The scatterplot lends itself to correlation and regression analysis of the two parameters, but tracing the path of the values in the sequence of points as on the right is meaningless.

The orbit chart is meaningless if the points are independent

The orbit chart is meaningless if the points are independent

Examples of orbit charts

The examples below are not from manufacturing but from military history, nuclear power plant maintenance, macro-economics, and ecosystems analysis. 25 years ago, radar charts were used in magazine like Britain’s The Economist, to compare parameters like inflation, unemployment and growth in multiple countries, but were unknown in factories, where they now are commonplace. Such may be the fate of orbit charts as well. The caveat is that, like radar charts, they are richer and more sophisticated than the usual charts  you find on performance boards, and may be difficult for operators to relate to. As a result, they may be more useful as an analytical tools for engineers and managers than as a communication tool on the shop floor. The only way to find out is to try.

Minard’s Russia campaign chart

Perhaps the best known example of an orbit chart is Minard’s map showing the path of Napoleon’s army in 1812 Russia, on the offensive in brown, and retreating in black, with the thickness of the line showing the size of the army. It is annotated with dates, and with a temperature chart below. It was drawn in 1869, and Edward Tufte brought to the attention of readers in The Visual Display of Quantitative Information, as the most eloquent summary ever written about this disastrous campaign, and the mother of all infographics. Please click on the picture if you would like to enlarge it and read the text, knowing that it is in French.Minard's Russia campaign chart Tufte calls it a “narrative of space and time.” I prefer to call it “orbit chart” because (1) it is a shorter name and (2) the x and y coordinates do not necessarily represent space, nor is the index of the points always time. In manufacturing, for example, x and y could be quality characteristics,  indexed by the serial numbers rather than time.

Unplanned versus planned downtime in nuclear power plants

I used the orbit chart below on p. 282 of Working with Machines, to compare the maintenance performance of the Japanese and French nuclear industries in the 1980s:

France versus Japan nuclear plant downtime stats 1979-1989

Jean-Pierre Mercier, from the French electrical utility EDF, published this chart to compare the evolution of nuclear reactor downtime in Japan and France. Each node on each of the orbits indicates performance in a given year, in terms of planned unavailability on the y-axis, and unplanned unavailability on the x-axis, and the sum of the two gives the total unavailability, so that diagonal lines indicate a constant total value. The orbits enable us to track year-by-year progress, and improvement is marked on the chart by movement towards the origin. The Japanese and the French orbits both show improvement over the years but are so different that they do not even intersect, which begs the question of why. What did the Japanese and French industries do so differently that it produced such radically different results? Once the chart prompts you to ask the question, it is easily answered:

  • France has one national utility company, with reactors of just two designs all made by the same supplier. This company reduced downtime by redesigning components or subsystems that failed in one reactor, and retrofitting the changes to all sites of the same design.
  • Japan has nine private utilities and reactors with many different designs, which made the French approach impossible. Instead, they overdid preventive maintenance in the beginning, and gradually improved it, eventually achieving almost the same performance as the French utility.

GINI index versus GDP in Brazil and the US, 1980-2011

This was back in 1990. Fast forward to 2013, and I am reading a new book by Alberto Cairo, called The Functional Art, about the design of information graphics and visualization. On p. XIX of the Introduction, I find the following chart of the orbit of Brazil’s economy in terms of GDP on the x-axis and the GINI index of inequality on the y-axis through five presidential administrations: Brazil GINI versus GDP chart As explained on Wikipedia, the GINI index is a cleverly defined ratio, which is 0 if every member of a society has an equal share of its wealth, and 100 if it is all in the hands of a single individual. Worldwide, among the countries for which data is available, Sweden’s GINI index of 23 is the lowest, while South Africa and Lesotho have the highest, at 62. The US has gone from a low of 38 in the late 1940 to 47.7  in 2010. The GDP is a better known metric, and is here shown as evaluated in constant US$ by institutions external to Brazil, like the World Bank and the IMF.

Assuming the underlying Brazilian economic statistics are credible, this chart tells quite a story, from the peak of inequality with low growth during hyperinflation under Sarney to sustained growth with steady reductions in inequality under Lula, the only pause in growth coming with the financial crisis of 2008. Would we see anything similar from plotting the same chart for the US economy? I tried, and the result is as follows: US-Gini-index-versus-GDP-1980-2011What does this chart tell us? The first obvious conclusion are:

  1. Regardless of economic circumstances or the political affiliation of the president, inequality has steadily increased in the US for over 30 years.
  2. We must always be wary of highly aggregated numbers. The  US census bureau warns us that the formula for calculating the GINI index was changed for 1993, and that before-and-after comparisons are therefore not meaningful. On the face of the charts, it appears that even the high point of 47.7 for the US is lower than the low point of 53.8 for Brazil, but we would have to assume that the numbers are calculated the same way, which is doubtful. The formula was changed in the US; it may be different in Brazil and, unbeknownst to us, it may have been changed as well along the way.
  3. We can see, at the Bush(43)/Obama juncture, that the crisis of 2008 had a bigger impact on the US than on Brazil.
  4. While still roughly one sixth  the size of the US economy, Brazil’s is growing faster. Back in 1980, it was less than one tenth.

Populations of predators and preys

For a long time, orbit charts have been used in population dynamics, with the x-axis being a prey population and the y-axis a predator population. Orbit charts can represent both theoretical models and actual data, when these are available. The following example, from a course taught at Portland State University,  shows a simulation starting at the bottom right-hand side with a large prey population and few predators. This stimulates population growth for the predator, which depletes the prey population. This leads to a food shortage for the predators, causing their population to collapse, which in turn gives preys the opportunity to multiply again…

Orbit chart - Predator-prey limit cycle

Predator-prey limit cycle

Eventually, the two populations spiral down not to a stable point, but to a repeating loop called limit cycle.

Parker Hannifin’s “North-by-Northwest” chart

Parker Hannifin is a diversified manufacturing company that included the following orbit chart is in its 2012 annual report:

Orbit chart -- Parker Hannifin North-by-Northwest chart

Given the type of publication, the axes are unlabeled. Internally, this chart is also generated by division and monitored by General Managers. The black straight line represent constant RONA, and the desired movement is upwards and orthogonal to it, hence the nickname of “North-by-Northwest chart” given to it by managers. When it was first introduced a decade ago, it was not immediately understood, but it has taken root in the organization. I would connect the dots and annotate it as follows:

Orbit chart -- Parker Hannifin North-by-Northwest chart annotated

We can also see on this chart that, while the RONA improvements of 2010 and 2011 involved movement along both axes, in 2012, it was only a reduction in Net Assets/Sales, which is no doubt meaningful to someone familiar with the company’s operations.

Orbit charts for spacecraft

The term “orbit” of course is from astronomy, and I found a great example of an orbit chart drawn by NASA’s George Resteck of the path of the Pioneer 10 and 11 probes sent to explore the solar system in the early 1970s. As we can see on the chart, Pioneer 10 “only” managed to fly by Jupiter; Pioneer 11 flew by both Jupiter and Saturn, but crossed the other planets’ orbits far from where they were.
Pioneer 10 and 11 flight path
Only Voyager 2, launched 5 years later, managed to get close to all big four planets: Jupiter, Saturn, Uranus, and Neptune. The closest I could find to a similar chart for Voyagers 1 and 2 is as follows:

Voyager 1 and 2 flight paths

It is the same general idea, but with less details particularly on planet positions over time, and less information on scale. You can also find animations of their paths, but Resteck’s chart for Pioneer 10 and 11 still gives you the most information at a glance.

Recovery from crisis at Toyota versus GM

The following orbits of Toyota and GM profitability as a function of number of vehicles produced in recovery from crisis were included in a previous post:

GM sales and profits chart

Toyota sales and profits chart 2001-2011

The first shows GM through the growth of the twenties and the great depression; the second, Toyota through its 2001-2008 boom, followed by the financial crisis, the mass recalls of 2010, and the Fukushima earthquake and Thailand floods of 2011. It also shows how the economics of the auto industry changed in 80 years. In good times, today’s mature automobile industry yields profit margins that are barely 1/3 of what they used to be, on volumes that are many times higher. In the worst year of the great depression, 1932, GM made only 28% as many vehicles as in 1929. If the worst of the current crisis was in 2009-2010, Toyota’s drop in volume, while similar in absolute terms to GM’s in the great depression, was much smaller in relative terms, at barely 15% off from the 2008 peak.

The Toyota chart further shows three distinct periods:

  1. From 2001 to 2004, profit margins and volume rose together, suggesting that Toyota was enjoying some form of economies of scale.
  2. From 2004 to 2008, volume kept rising rapidly, but profit margins were flat. Toyota was criticized during that time for pursuing faster growth than it could manage.
  3. From 2008, the dominant effect is the financial crisis and recovery. with the 2010 recalls further reducing volume.

Janet Yellen’s orbit chart of inflation versus unemployment

David J (Joe) Armstrong pointed out to me an article from the New York Times on 10/9/2013 about Janet Yellen’s analysis of inflation and unemployment in the US since the Great Depression, using orbit charts and animations. The overall chart is as follows:

Inflation versus unemployment in US 19690-2013

See the articles for animations and explanations of the different segments.

Lora Cecere’s orbit charts of  inventory turns versus operating margins for Colgate, P&G, Walmart, and Target

David J (Joe) Armstrong also pointed out to me an article from Forbes Magazine on 12/13/2013 about supply chain collaboration, using orbit charts to show lack of progress. The charts are as follows:

Colgate and P&G inventory turns versus operatings margins

walmart versus target inventory turns versus operating margins

Read the article for Lora Cecere’s analysis of the significance of these charts. My first question about them is whether these companies compute the plotted parameters in the same way. Only if we can be sure that they do, can we venture some conclusions from the charts. No units are given on the operating margin axis. Given that it is a ratio, I assume “0.06” means 6%.

The key point is that, in 12 years, none of these companies moved towards the best scenario of a high number of inventory turns and a high operating margin. In the case of Walmart versus Target, they seemed to have had “orthogonal” strategies, with Walmart moving up and down in inventory turns without any large gains in margin, while Target was moving back and forth in margin without major changes in inventory turns.

I think there are many factors that these charts don’t show, such as the impact of the competitive environment and the financial crisis. While neither manufacturers like P&G and Colgate nor retailers like Walmart or Target have shown much progress, the impact of inventory on their operations is quite different. Overall, the manufacturing companies buy materials and generate their margins from selling finished goods; they may have some items on consignment, but only a minority.

By contrast, if I understand how very large retailers work, their income does not come from markups on products but from the float in accounts payable. They pay suppliers a month or two after collecting revenues from selling to consumers, which adds up to  a very large float, and the companies’ profits come from the income generated by this float in financial markets. In essence, their inventory is on consignment and their holding costs are nothing like the manufacturers’. They still have the costs of operating warehouses, but their own money is not tied up in materials paid upfront. On the other hand, their margins are sensitive to the health of the financial markets.

How to generate orbit charts in Excel

No matter how great orbit charts may be, not many people in manufacturing will use them unless they are easy to generate with Excel. Generating the orbit itself is not a problem. With Excel 2007 or 2010, all you have to do is, under the Insert tab, select Scatter and click one of the two options for Scatter with lines and markers.  Then you can use the various formatting options to refine the axes, gridlines, etc. Most of the charts in this post were generated this way.

Selecting scatter chart with straight lines and markers on Excel

Selecting scatter chart with straight lines and markers in Excel

As you can see in the following example, Excel does not mind the curve looping and spiraling:

Looping and spiraling chart generated with Excel

Looping and spiraling chart generated with Excel

Labeling the points on the charts is trickier. If there are few enough, you can manually add text boxes on the chart, which is what I did, but it would not work for thousands of points. In fact, for any large number of points, your only options are:

  1. Labeling every n-th point. 
  2. Making labels pop up next to a point when you hover on it or click on it.

The labeling option in Excel charts will display the numeric values for x and y or the name of the data series next to each point, which doesn’t tell you anything you don’t already know. You want to label each point with the value of its index in the data table so that, if it is time, you can know when each point was generated. Excel won’t do it, but Microsoft Support provides a Macro with which you can, with the result as follows:

Orbit chart with date labels affixed by Excel macro

Orbit chart with date labels affixed by Excel macro

In  Alberto Cairo’s chart, the successive presidencies of Brazil were marked by a different color. In Excel, you could achieve this effect by have having a separate data series for each presidency, which you could then color as you wish.

Orbit charts for multidimensional data

Visualizing two dimensions of the evolution over time of a group of machines or the output of a production line is an improvement over plotting just one. But what if, instead of two, you have fifty or even ten characteristics of interest?

You cannot see a point with 10 coordinates, but you can use dimensionality reduction techniques to work around this problem. Principal Component Analysis, for example, projects these multidimensional points onto a plane so that the projections contain most of the variability of the full multidimensional cloud. Linda E. Kavraki provides the following illustration of the concept:

a) A data set given as 3-dimensional points. b) The three orthogonal Principal Components (PCs) for the data, ordered by variance. c) The projection of the data set into the first two PCs, discarding the third one.

a) A data set given as 3-dimensional points. b) The three orthogonal Principal Components for the data, ordered by variance. c) The projection of the data set into the first two Principal Components

The coordinates on this plane are two uncorrelated linear combinations of the full set of coordinates called first and second principal components. Then you can plot the orbit of your population in this plane. Technically, it is straightforward, because you will easily find software packages to perform Principal Component Analysis. Minitab does it, and so does the XLSTAT add-in to Excel.

The challenge is making sense of the orbit chart. When you just plot the projection of your cloud of points onto the first two principal components, you may notice a small clump of points off to the side and identify them as outliers. But, when the points are generated over time, following the orbit may not tell you much because the coordinates are linear combinations of the original coordinates with no obvious meaning. The first principal component could well be three times the length of the ship minus half the captain’s age.

ERP and Lean

The discussion Pat Moody started in the Blue Heron Journal is in the form of advice to a production planner in a heavy equipment plant who has been put in charge of implementing a new ERP system to replace a collection of legacy systems. The call for help is signed “Hopeful in the Midwest.”

What would we say if, instead, this person had been tasked with throwing out all the machine tools of multiple vintages that make up the plant’s machine shop and replace them with one single, integrated Flexible Manufacturing System (FMS)?

My recommendation to this person would be to find another job. Unless the company has gone through preparation steps that Hopeful does not mention, the ERP project is likewise headed for disaster and Hopeful should run from it.

ERP boosters take it for granted that one single integrated system to handle all information processing for a plant is an improvement over having multiple systems. From a marketing standpoint, it is a powerful message, well received by decision makers, as evidenced by the size of the ERP industry.

Yet most plants do have multiple systems, and it is worth asking why. It is not just because organizational silos are uncoordinated. It is also because the best systems for each function are made by specialized suppliers. The best systems for production planning and scheduling, supply chain management, maintenance, quality, human resources, etc. are developed by organizations led by experts in each of these domains.

ERP systems are built by companies that grew based on expertise in one of these domains and then expanded to the others, in which they had no expertise. One major ERP supplier got its start in multi-currency accounting; another by dominating the market for Database Management Systems; yet another by focusing on HR management. Unsurprisingly, the software they provided in all other areas has frustrated practitioners by its mediocrity.

Perhaps, the reason you hardly ever meet any manufacturer who is happy with an ERP implementation is that the idea of an all-in-one integrated system is not that great to begin with.

What is the alternative?

First, management should respect the need for departments to have the systems that support them best, requiring only that they should be able to share information with other departments.

For example, Marketing, Engineering, and Accounting should not be mandated to use modules from a single all-in-one system, but they should be required to use the same product IDs and product families, for management to be able to view sales, production, and financial results accordingly.

To make this possible, the company needs a consistent information model of its activities, including the objects that need to be represented, the states these objects can be in, the information they need to exchange, and a structure for all the retained information.

The development of such a model is beyond the capabilities of a production planner, and often beyond the capability of anyone in the IT department of a manufacturing company. It requires high-level know-how in systems analysis and database design, and should be done by a consultant who is independent of any ERP supplier, in cooperation with the operating department and the IT group.

The first phase should focus on improving the performance of the legacy systems in targeted areas, and introducing middleware to facilitate the integration of data from multiple legacy systems. This involves work in Master Data Management for specs and nomenclature, Data Warehousing for history, and real-time databases for status.

The replacement of legacy systems should be considered based on the lessons learned through improvement, in particular with a realistic, internally developed view of costs and benefits. As in the case with new production equipment, the introduction of new IT systems may best be coordinated with the development of new production lines or plants.